From 280d851e469976847a92b9bf2c63bbe498b83013 Mon Sep 17 00:00:00 2001 From: Ruoyao Shi Date: Mon, 12 Aug 2019 14:57:13 -0700 Subject: [PATCH 1/6] Update demo_exactVC.ipynb --- VCTest/VC test_new.ipynb | 1540 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 1540 insertions(+) create mode 100644 VCTest/VC test_new.ipynb diff --git a/VCTest/VC test_new.ipynb b/VCTest/VC test_new.ipynb new file mode 100644 index 0000000..73ca541 --- /dev/null +++ b/VCTest/VC test_new.ipynb @@ -0,0 +1,1540 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# New variance component test for SNP set analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the oringinal tutorial, this one changes a new dataset for SNP 24 and focuses on genes with 3 or more SNPs. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check version " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial has been checked with Julia version 1.1.1" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Julia Version 1.1.0\n", + "Commit 80516ca202 (2019-01-21 21:24 UTC)\n", + "Platform Info:\n", + " OS: macOS (x86_64-apple-darwin14.5.0)\n", + " CPU: Intel(R) Core(TM) i7-6567U CPU @ 3.30GHz\n", + " WORD_SIZE: 64\n", + " LIBM: libopenlibm\n", + " LLVM: libLLVM-6.0.1 (ORCJIT, skylake)\n" + ] + } + ], + "source": [ + "versioninfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install packages when first use" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You need to have the statistial software R and R package \"pracma\" installed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "using Pkg\n", + "Pkg.add(PackageSpec(url=\"https://github.com/OpenMendel/SnpArrays.jl.git\"))\n", + "Pkg.add(PackageSpec(url=\"https://github.com/jinjinzhou/VarianceComponentTest.jl.git\"))\n", + "Pkg.add(PackageSpec(url=\"https://github.com/OpenMendel/MendelPlots.jl.git\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/Query/wGX7b.ji for Query [1a8c2f83-1ff3-5112-b086-8aa67b057ba1]\n", + "└ @ Base loading.jl:1184\n", + "┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/VarianceComponentTest/QfNDj.ji for VarianceComponentTest [1b8b39c6-5c46-50f5-9c36-ac18d7f28ae0]\n", + "└ @ Base loading.jl:1184\n", + "┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/TraitSimulation/VikWX.ji for TraitSimulation [dec3038e-29bc-11e9-2207-9f3d5855a202]\n", + "└ @ Base loading.jl:1184\n" + ] + } + ], + "source": [ + "using SnpArrays\n", + "using CSV\n", + "using DataFrames, Query\n", + "using Distributions, DelimitedFiles, LinearAlgebra\n", + "using VarianceComponentTest\n", + "using StatsModels, Random, StatsBase, TraitSimulation\n", + "using Random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reproducibility" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set a random seed using the Random.jl package for reproducibility. If you wish to end up with different data, you will need to comment out these commands or use another value in Random.seed!()." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Random.seed!(100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Enter the folder path" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/janets/Documents/lap_top_janet/janet/genetics/variance_components/Ruoyao_notebooksAugust2019/newvctest/VCtest_24_input_file\n" + ] + } + ], + "source": [ + ";pwd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can change the folder path according to your own computer. For example: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + ";cd /Users/apple/Desktop/Julia/VCtest_24 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load and filter missing value of the definition file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The gene annotation is contained in the definition file in the last (sixth) column. This data is from Option 24 of the Mendel data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "df= (CSV.read(\"SNP_def24a.in\",header=false))[:,[1,2,3,6]]\n", + "chromosomes = df[:, 2]\n", + "AdjBasepairs = df[:,3]\n", + "genes = df[:, end];" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set missing value as -1 and count the number of unique genes because we are only interested in genes with 3 or more snps." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4824" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genes[ismissing.(genes)] .= -1 # missing value as -1\n", + "chromosomes[ismissing.(genes)] .= -1\n", + "uniquegenes = unique(genes)\n", + "genes_number = length(uniquegenes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in plink format genotype files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read three files containing information about the names of snps, locations and phenotype messages. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "plinkFile = \"SNP_24\"\n", + "## parse data from files\n", + "plinkBedfile = string(plinkFile, \".bed\");\n", + "plinkBimfile = string(plinkFile, \".bim\");\n", + "plinkFamfile = string(plinkFile, \".fam\");\n", + "SNP_24 = SnpData(\"SNP_24\");" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# BIM file: chr, rs#, morgan, bp position, allele1, allele2\n", + "bimdata = readdlm(plinkBimfile, String);\n", + "chrID = bimdata[:, 1];\n", + "snpID = bimdata[:, 2];\n", + "snpPos = bimdata[:, 4];\n", + "nSNP = length(snpID);" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& FamID & PID\\\\\n", + "\t\\hline\n", + "\t& Any & Any\\\\\n", + "\t\\hline\n", + "\t1 & 1 & 1 \\\\\n", + "\t2 & 2 & 1 \\\\\n", + "\t3 & 3 & 1 \\\\\n", + "\t4 & 4 & 1 \\\\\n", + "\t5 & 5 & 1 \\\\\n", + "\t6 & 6 & 1 \\\\\n", + "\t7 & 7 & 1 \\\\\n", + "\t8 & 8 & 1 \\\\\n", + "\t9 & 9 & 1 \\\\\n", + "\t10 & 10 & 1 \\\\\n", + "\t11 & 11 & 1 \\\\\n", + "\t12 & 12 & 1 \\\\\n", + "\t13 & 13 & 1 \\\\\n", + "\t14 & 14 & 1 \\\\\n", + "\t15 & 15 & 1 \\\\\n", + "\t16 & 16 & 1 \\\\\n", + "\t17 & 17 & 1 \\\\\n", + "\t18 & 18 & 1 \\\\\n", + "\t19 & 19 & 1 \\\\\n", + "\t20 & 20 & 1 \\\\\n", + "\t21 & 21 & 1 \\\\\n", + "\t22 & 22 & 1 \\\\\n", + "\t23 & 23 & 1 \\\\\n", + "\t24 & 24 & 1 \\\\\n", + "\t25 & 25 & 1 \\\\\n", + "\t26 & 26 & 1 \\\\\n", + "\t27 & 27 & 1 \\\\\n", + "\t28 & 28 & 1 \\\\\n", + "\t29 & 29 & 1 \\\\\n", + "\t30 & 30 & 1 \\\\\n", + "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "2200×2 DataFrame\n", + "│ Row │ FamID │ PID │\n", + "│ │ \u001b[90mAny\u001b[39m │ \u001b[90mAny\u001b[39m │\n", + "├──────┼───────┼─────┤\n", + "│ 1 │ 1 │ 1 │\n", + "│ 2 │ 2 │ 1 │\n", + "│ 3 │ 3 │ 1 │\n", + "│ 4 │ 4 │ 1 │\n", + "│ 5 │ 5 │ 1 │\n", + "│ 6 │ 6 │ 1 │\n", + "│ 7 │ 7 │ 1 │\n", + "│ 8 │ 8 │ 1 │\n", + "│ 9 │ 9 │ 1 │\n", + "│ 10 │ 10 │ 1 │\n", + "⋮\n", + "│ 2190 │ 2190 │ 1 │\n", + "│ 2191 │ 2191 │ 1 │\n", + "│ 2192 │ 2192 │ 1 │\n", + "│ 2193 │ 2193 │ 1 │\n", + "│ 2194 │ 2194 │ 1 │\n", + "│ 2195 │ 2195 │ 1 │\n", + "│ 2196 │ 2196 │ 1 │\n", + "│ 2197 │ 2197 │ 1 │\n", + "│ 2198 │ 2198 │ 1 │\n", + "│ 2199 │ 2199 │ 1 │\n", + "│ 2200 │ 2200 │ 1 │" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# FAM file: fam ID, ind ID, father ID, mother ID, sex, phenotype\n", + "famdata = readdlm(plinkFamfile,',')\n", + "nPer = length(famdata[:, 1])\n", + "Fam_Person_id = DataFrame(FamID = famdata[:,1],PID =famdata[:,2]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Trait Simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use TraitSimulation.jl package to simulate traits from genotype data for unrelateds by Generalized Linear Models (GLMs). We will simulate our continuous trait treating sex and three snps as fixed effects in the model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2200×10000 SnpArray:\n", + " 0x02 0x03 0x03 0x00 0x02 0x03 … 0x02 0x03 0x03 0x03 0x02 0x03\n", + " 0x02 0x02 0x03 0x02 0x02 0x03 0x03 0x02 0x02 0x03 0x02 0x02\n", + " 0x03 0x02 0x03 0x02 0x02 0x03 0x03 0x02 0x02 0x02 0x02 0x03\n", + " 0x03 0x02 0x03 0x02 0x03 0x03 0x03 0x00 0x03 0x03 0x03 0x02\n", + " 0x03 0x03 0x03 0x00 0x02 0x03 0x03 0x03 0x03 0x03 0x02 0x03\n", + " 0x03 0x02 0x03 0x02 0x03 0x03 … 0x03 0x03 0x03 0x03 0x02 0x03\n", + " 0x03 0x02 0x02 0x02 0x03 0x03 0x02 0x00 0x02 0x03 0x02 0x02\n", + " 0x03 0x00 0x02 0x03 0x00 0x03 0x03 0x02 0x02 0x03 0x02 0x03\n", + " 0x03 0x02 0x00 0x02 0x02 0x03 0x03 0x02 0x03 0x03 0x02 0x03\n", + " 0x03 0x02 0x03 0x02 0x03 0x03 0x02 0x03 0x03 0x03 0x03 0x03\n", + " 0x02 0x02 0x02 0x00 0x03 0x03 … 0x03 0x03 0x03 0x03 0x02 0x02\n", + " 0x03 0x03 0x03 0x00 0x03 0x03 0x02 0x00 0x00 0x02 0x03 0x02\n", + " 0x02 0x02 0x03 0x02 0x02 0x03 0x03 0x03 0x02 0x03 0x03 0x03\n", + " ⋮ ⋮ ⋱ ⋮ \n", + " 0x03 0x03 0x02 0x03 0x03 0x03 0x02 0x03 0x02 0x03 0x02 0x02\n", + " 0x03 0x02 0x03 0x03 0x03 0x03 0x02 0x02 0x02 0x03 0x02 0x02\n", + " 0x03 0x02 0x00 0x02 0x02 0x03 … 0x03 0x02 0x03 0x03 0x03 0x02\n", + " 0x03 0x02 0x03 0x00 0x02 0x03 0x03 0x03 0x03 0x03 0x03 0x02\n", + " 0x00 0x03 0x02 0x03 0x03 0x03 0x03 0x03 0x03 0x03 0x03 0x03\n", + " 0x02 0x03 0x03 0x02 0x02 0x03 0x02 0x00 0x00 0x02 0x02 0x03\n", + " 0x02 0x02 0x03 0x00 0x03 0x03 0x02 0x03 0x03 0x03 0x02 0x02\n", + " 0x02 0x02 0x02 0x02 0x02 0x03 … 0x00 0x03 0x03 0x03 0x02 0x03\n", + " 0x03 0x02 0x03 0x03 0x03 0x03 0x03 0x00 0x00 0x03 0x02 0x02\n", + " 0x03 0x02 0x03 0x03 0x03 0x02 0x03 0x02 0x02 0x03 0x03 0x02\n", + " 0x03 0x03 0x02 0x00 0x00 0x02 0x02 0x03 0x03 0x03 0x00 0x03\n", + " 0x03 0x03 0x03 0x00 0x03 0x03 0x03 0x03 0x00 0x03 0x03 0x02" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snpdata = SnpArray(\"Snp_24.bed\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10000-element Array{SubString{String},1}:\n", + " \"rs3934834\" \n", + " \"rs6603793\" \n", + " \"rs2843153\" \n", + " \"rs7535528\" \n", + " \"rs12046158\"\n", + " \"rs2817134\" \n", + " \"rs4494089\" \n", + " \"rs4648505\" \n", + " \"rs1181883\" \n", + " \"rs10915433\"\n", + " \"rs4609397\" \n", + " \"rs600933\" \n", + " \"rs350187\" \n", + " ⋮ \n", + " \"rs135720\" \n", + " \"rs138407\" \n", + " \"rs2413552\" \n", + " \"rs139426\" \n", + " \"rs137636\" \n", + " \"rs132572\" \n", + " \"rs137948\" \n", + " \"rs739182\" \n", + " \"rs12159200\"\n", + " \"rs9611502\" \n", + " \"rs6519287\" \n", + " \"rs11090076\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snpdef24 = readdlm(\"SNP_24.bim\";header = false)\n", + "snpid = map(x -> strip(string(x)), snpdef24[:, 2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We focus on genes with 3 or more snps. Although it can be altered, we use rs649296, rs11122146, rs1875238 and rs12489977 in this tutorial. \n", + "These four snps are from four different genes and chromosomes. Among them, rs649296 is from gene 24175 (on chromosome 8) which has 20 snps; rs11122146 is from gene 158 (on chromosome 1) which has 5 snps; rs1875238 is from gene 25064 (on chromosome 9); rs12489977 is from gene 17442 (on chromosome 3). " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1834" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ind_rs649296= findall(x -> x == \"rs649296\",snpid)[1]\n", + "ind_rs11122146= findall(x -> x == \"rs11122146\",snpid)[1]\n", + "ind_rs1875238=findall(x -> x == \"rs1875238\",snpid)[1]\n", + "ind_rs12489977 = findall(x -> x == \"rs12489977\",snpid)[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/latex": [ + "\\begin{tabular}{r|ccccc}\n", + "\t& sex & snp1 & snp2 & snp3 & snp4\\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64 & Float64 & Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t2 & 0.0 & 1.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t3 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t4 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t5 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n", + "\t6 & 0.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t7 & 0.0 & 1.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t8 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t9 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t10 & 0.0 & 2.0 & 1.0 & 2.0 & 0.0 \\\\\n", + "\t11 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n", + "\t12 & 0.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t13 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n", + "\t14 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t15 & 0.0 & 2.0 & 2.0 & 2.0 & 0.0 \\\\\n", + "\t16 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t17 & 0.0 & 2.0 & 2.0 & 1.0 & 0.0 \\\\\n", + "\t18 & 1.0 & 2.0 & 0.0 & 2.0 & 1.0 \\\\\n", + "\t19 & 1.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n", + "\t20 & 1.0 & 2.0 & 0.0 & 2.0 & 1.0 \\\\\n", + "\t21 & 1.0 & 1.0 & 2.0 & 2.0 & 0.0 \\\\\n", + "\t22 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t23 & 0.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n", + "\t24 & 0.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t25 & 0.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n", + "\t26 & 0.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t27 & 0.0 & 2.0 & 1.0 & 2.0 & 2.0 \\\\\n", + "\t28 & 0.0 & 2.0 & 1.0 & 2.0 & 0.0 \\\\\n", + "\t29 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t30 & 1.0 & 2.0 & 1.0 & 2.0 & 2.0 \\\\\n", + "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "2200×5 DataFrame\n", + "│ Row │ sex │ snp1 │ snp2 │ snp3 │ snp4 │\n", + "│ │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n", + "├──────┼─────────┼─────────┼─────────┼─────────┼─────────┤\n", + "│ 1 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2 │ 0.0 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 3 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 4 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 5 │ 1.0 │ 2.0 │ 2.0 │ 1.0 │ 2.0 │\n", + "│ 6 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 7 │ 0.0 │ 1.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 8 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 9 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 10 │ 0.0 │ 2.0 │ 1.0 │ 2.0 │ 0.0 │\n", + "⋮\n", + "│ 2190 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 2191 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 2192 │ 0.0 │ 1.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 2193 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2194 │ 0.0 │ 2.0 │ 1.0 │ 2.0 │ 2.0 │\n", + "│ 2195 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │ 0.0 │\n", + "│ 2196 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 2197 │ 1.0 │ 2.0 │ 1.0 │ 1.0 │ 1.0 │\n", + "│ 2198 │ 1.0 │ 2.0 │ 2.0 │ 1.0 │ 2.0 │\n", + "│ 2199 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 2200 │ 1.0 │ 1.0 │ 2.0 │ 2.0 │ 0.0 │" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snp1 = convert(Vector{Float64}, @view(snpdata[:, ind_rs649296]))\n", + "snp2 = convert(Vector{Float64}, @view(snpdata[:, ind_rs11122146]))\n", + "snp3 = convert(Vector{Float64}, @view(snpdata[:, ind_rs1875238]))\n", + "snp4 = convert(Vector{Float64}, @view(snpdata[:, ind_rs12489977]))\n", + "sex = Float64.(famdata[:, 5])\n", + "X = DataFrame(sex = sex, snp1 = snp1, snp2 = snp2,snp3 = snp3,snp4 = snp4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generalized Linear Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The snp parameters can be changed by the user. Note make the value large enough or the final pvalue will not be significant. The following ones just are examples and should be modified according to the sample size and application and may not reflect a user's actual data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "mean_formula = \"20 + 3(sex) - 0.36(snp1) + 0.45(snp2) - 0.44(snp3) + 0.46(snp4)\" # 4diff \n", + " = \"20 + 3(sex) + 0.35(snp1) - 0.56(snp2) + 0.60(snp3)\" # 3diff \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

2,200 rows × 1 columns

trait1
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121.9037
221.3475
321.5572
422.337
526.8175
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720.9121
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1123.0516
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1325.4339
1426.3005
1519.031
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2025.3199
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⋮⋮
" + ], + "text/latex": [ + "\\begin{tabular}{r|c}\n", + "\t& trait1\\\\\n", + "\t\\hline\n", + "\t& Float64\\\\\n", + "\t\\hline\n", + "\t1 & 21.9037 \\\\\n", + "\t2 & 21.3475 \\\\\n", + "\t3 & 21.5572 \\\\\n", + "\t4 & 22.337 \\\\\n", + "\t5 & 26.8175 \\\\\n", + "\t6 & 23.8251 \\\\\n", + "\t7 & 20.9121 \\\\\n", + "\t8 & 21.0186 \\\\\n", + "\t9 & 21.3486 \\\\\n", + "\t10 & 22.1254 \\\\\n", + "\t11 & 23.0516 \\\\\n", + "\t12 & 19.332 \\\\\n", + "\t13 & 25.4339 \\\\\n", + "\t14 & 26.3005 \\\\\n", + "\t15 & 19.031 \\\\\n", + "\t16 & 23.0451 \\\\\n", + "\t17 & 18.9116 \\\\\n", + "\t18 & 21.0699 \\\\\n", + "\t19 & 25.8202 \\\\\n", + "\t20 & 25.3199 \\\\\n", + "\t21 & 22.2284 \\\\\n", + "\t22 & 21.7282 \\\\\n", + "\t23 & 20.5106 \\\\\n", + "\t24 & 18.9595 \\\\\n", + "\t25 & 19.4494 \\\\\n", + "\t26 & 20.2138 \\\\\n", + "\t27 & 22.0342 \\\\\n", + "\t28 & 18.706 \\\\\n", + "\t29 & 23.5814 \\\\\n", + "\t30 & 23.8188 \\\\\n", + "\t$\\dots$ & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "2200×1 DataFrame\n", + "│ Row │ trait1 │\n", + "│ │ \u001b[90mFloat64\u001b[39m │\n", + "├──────┼─────────┤\n", + "│ 1 │ 21.9037 │\n", + "│ 2 │ 21.3475 │\n", + "│ 3 │ 21.5572 │\n", + "│ 4 │ 22.337 │\n", + "│ 5 │ 26.8175 │\n", + "│ 6 │ 23.8251 │\n", + "│ 7 │ 20.9121 │\n", + "│ 8 │ 21.0186 │\n", + "│ 9 │ 21.3486 │\n", + "│ 10 │ 22.1254 │\n", + "⋮\n", + "│ 2190 │ 20.19 │\n", + "│ 2191 │ 20.2657 │\n", + "│ 2192 │ 17.6796 │\n", + "│ 2193 │ 20.6588 │\n", + "│ 2194 │ 21.8511 │\n", + "│ 2195 │ 22.2285 │\n", + "│ 2196 │ 16.6548 │\n", + "│ 2197 │ 16.908 │\n", + "│ 2198 │ 24.6326 │\n", + "│ 2199 │ 24.3004 │\n", + "│ 2200 │ 20.6471 │" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_formula = \"20 + 3(sex) - 0.36(snp1) + 0.45(snp2) - 0.44(snp3) + 0.46(snp4)\"\n", + "GLM_trait_model = GLMTrait(mean_formula, X, NormalResponse(2), IdentityLink())\n", + "Simulated_GLM_trait = simulate(GLM_trait_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Warning: The `stats` keyword argument has been deprecated. Use describe(df, stats...) instead.\n", + "│ caller = ip:0x0\n", + "└ @ Core :-1\n" + ] + }, + { + "data": { + "text/html": [ + "

1 rows × 9 columns

variablemeanstdminq25medianq75maxeltype
SymbolFloat64Float64Float64Float64Float64Float64Float64DataType
1trait121.53682.550113.771319.715321.636223.355529.6356Float64
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2,200 rows × 8 columns

FamIDPIDtrait1sexsnp1snp2snp3snp4
AnyAnyFloat64Float64Float64Float64Float64Float64
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33121.55721.02.02.02.01.0
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1616123.04511.02.02.02.02.0
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1818121.06991.02.00.02.01.0
1919125.82021.02.02.01.01.0
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2525119.44940.02.02.01.01.0
2626120.21380.02.02.02.02.0
2727122.03420.02.01.02.02.0
2828118.7060.02.01.02.00.0
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3030123.81881.02.01.02.02.0
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" + ], + "text/latex": [ + "\\begin{tabular}{r|cccccccc}\n", + "\t& FamID & PID & trait1 & sex & snp1 & snp2 & snp3 & snp4\\\\\n", + "\t\\hline\n", + "\t& Any & Any & Float64 & Float64 & Float64 & Float64 & Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 1 & 1 & 21.9037 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t2 & 2 & 1 & 21.3475 & 0.0 & 1.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t3 & 3 & 1 & 21.5572 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t4 & 4 & 1 & 22.337 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t5 & 5 & 1 & 26.8175 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n", + "\t6 & 6 & 1 & 23.8251 & 0.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t7 & 7 & 1 & 20.9121 & 0.0 & 1.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t8 & 8 & 1 & 21.0186 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t9 & 9 & 1 & 21.3486 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t10 & 10 & 1 & 22.1254 & 0.0 & 2.0 & 1.0 & 2.0 & 0.0 \\\\\n", + "\t11 & 11 & 1 & 23.0516 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n", + "\t12 & 12 & 1 & 19.332 & 0.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t13 & 13 & 1 & 25.4339 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n", + "\t14 & 14 & 1 & 26.3005 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t15 & 15 & 1 & 19.031 & 0.0 & 2.0 & 2.0 & 2.0 & 0.0 \\\\\n", + "\t16 & 16 & 1 & 23.0451 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t17 & 17 & 1 & 18.9116 & 0.0 & 2.0 & 2.0 & 1.0 & 0.0 \\\\\n", + "\t18 & 18 & 1 & 21.0699 & 1.0 & 2.0 & 0.0 & 2.0 & 1.0 \\\\\n", + "\t19 & 19 & 1 & 25.8202 & 1.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n", + "\t20 & 20 & 1 & 25.3199 & 1.0 & 2.0 & 0.0 & 2.0 & 1.0 \\\\\n", + "\t21 & 21 & 1 & 22.2284 & 1.0 & 1.0 & 2.0 & 2.0 & 0.0 \\\\\n", + "\t22 & 22 & 1 & 21.7282 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n", + "\t23 & 23 & 1 & 20.5106 & 0.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n", + "\t24 & 24 & 1 & 18.9595 & 0.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t25 & 25 & 1 & 19.4494 & 0.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n", + "\t26 & 26 & 1 & 20.2138 & 0.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t27 & 27 & 1 & 22.0342 & 0.0 & 2.0 & 1.0 & 2.0 & 2.0 \\\\\n", + "\t28 & 28 & 1 & 18.706 & 0.0 & 2.0 & 1.0 & 2.0 & 0.0 \\\\\n", + "\t29 & 29 & 1 & 23.5814 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n", + "\t30 & 30 & 1 & 23.8188 & 1.0 & 2.0 & 1.0 & 2.0 & 2.0 \\\\\n", + "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "2200×8 DataFrame. Omitted printing of 1 columns\n", + "│ Row │ FamID │ PID │ trait1 │ sex │ snp1 │ snp2 │ snp3 │\n", + "│ │ \u001b[90mAny\u001b[39m │ \u001b[90mAny\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n", + "├──────┼───────┼─────┼─────────┼─────────┼─────────┼─────────┼─────────┤\n", + "│ 1 │ 1 │ 1 │ 21.9037 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2 │ 2 │ 1 │ 21.3475 │ 0.0 │ 1.0 │ 2.0 │ 2.0 │\n", + "│ 3 │ 3 │ 1 │ 21.5572 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 4 │ 4 │ 1 │ 22.337 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 5 │ 5 │ 1 │ 26.8175 │ 1.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 6 │ 6 │ 1 │ 23.8251 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 7 │ 7 │ 1 │ 20.9121 │ 0.0 │ 1.0 │ 2.0 │ 2.0 │\n", + "│ 8 │ 8 │ 1 │ 21.0186 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 9 │ 9 │ 1 │ 21.3486 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 10 │ 10 │ 1 │ 22.1254 │ 0.0 │ 2.0 │ 1.0 │ 2.0 │\n", + "⋮\n", + "│ 2190 │ 2190 │ 1 │ 20.19 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2191 │ 2191 │ 1 │ 20.2657 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2192 │ 2192 │ 1 │ 17.6796 │ 0.0 │ 1.0 │ 2.0 │ 2.0 │\n", + "│ 2193 │ 2193 │ 1 │ 20.6588 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2194 │ 2194 │ 1 │ 21.8511 │ 0.0 │ 2.0 │ 1.0 │ 2.0 │\n", + "│ 2195 │ 2195 │ 1 │ 22.2285 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2196 │ 2196 │ 1 │ 16.6548 │ 0.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2197 │ 2197 │ 1 │ 16.908 │ 1.0 │ 2.0 │ 1.0 │ 1.0 │\n", + "│ 2198 │ 2198 │ 1 │ 24.6326 │ 1.0 │ 2.0 │ 2.0 │ 1.0 │\n", + "│ 2199 │ 2199 │ 1 │ 24.3004 │ 1.0 │ 2.0 │ 2.0 │ 2.0 │\n", + "│ 2200 │ 2200 │ 1 │ 20.6471 │ 1.0 │ 1.0 │ 2.0 │ 2.0 │" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Trait1_GLM = hcat(Fam_Person_id, Simulated_GLM_trait, X)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Trait1_GLM.csv\"" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CSV.write(\"Trait1_GLM.csv\", Trait1_GLM)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract phenotype and covariates" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2200-element Array{Float64,1}:\n", + " 21.903727897598223\n", + " 21.347506361432973\n", + " 21.557158012566536\n", + " 22.33696667367645 \n", + " 26.81747848096493 \n", + " 23.825127838651703\n", + " 20.91214104662801 \n", + " 21.018594298261547\n", + " 21.348637996132272\n", + " 22.125421882328816\n", + " 23.05157370228284 \n", + " 19.33201631688663 \n", + " 25.43389985511292 \n", + " ⋮ \n", + " 21.266162824991035\n", + " 20.1899787106751 \n", + " 20.265726565300394\n", + " 17.679625219688123\n", + " 20.658765295147006\n", + " 21.851078808252776\n", + " 22.22852457488234 \n", + " 16.65477571667752 \n", + " 16.908007881309874\n", + " 24.632551836965924\n", + " 24.300409661278852\n", + " 20.64711104441882 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ytrait = readdlm(\"Trait1_GLM.csv\", ',';header = false, skipstart=1)\n", + "y = convert(Vector{Float64}, ytrait[:, 3])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2200×2 Array{Float64,2}:\n", + " 1.0 1.0\n", + " 1.0 0.0\n", + " 1.0 1.0\n", + " 1.0 1.0\n", + " 1.0 1.0\n", + " 1.0 0.0\n", + " 1.0 0.0\n", + " 1.0 1.0\n", + " 1.0 1.0\n", + " 1.0 0.0\n", + " 1.0 1.0\n", + " 1.0 0.0\n", + " 1.0 1.0\n", + " ⋮ \n", + " 1.0 0.0\n", + " 1.0 0.0\n", + " 1.0 0.0\n", + " 1.0 0.0\n", + " 1.0 0.0\n", + " 1.0 0.0\n", + " 1.0 0.0\n", + " 1.0 0.0\n", + " 1.0 1.0\n", + " 1.0 1.0\n", + " 1.0 1.0\n", + " 1.0 1.0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# covariates\n", + "#2 change to 1.0; 1 change to 0.0\n", + "X = [ones(size(sex,1)) Float64.(sex)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loop over genes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will conduct gene set analysis for valid genes with 3 or more snps. In this example the time reasonably short because we have only a small number of genes with 3 or more snps but depending on the user's computer it may take around 2 - 10 minutes. To track the process we write out the progress as we go along." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Warning: RCall.jl: Loading required package: pracma\n", + "└ @ RCall /Users/janets/.julia/packages/RCall/ffM0W/src/io.jl:113\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finished 15\n", + "finished 111\n", + "finished 128\n", + "finished 195\n", + "finished 359\n", + "finished 370\n", + "finished 412\n", + "finished 416\n", + "finished 418\n", + "finished 423\n", + "finished 424\n", + "finished 450\n", + "finished 480\n", + "finished 490\n", + "finished 512\n", + "finished 522\n", + "finished 565\n", + "finished 568\n", + "finished 621\n", + "finished 630\n", + "finished 635\n", + "finished 637\n", + "finished 682\n", + "finished 702\n", + "finished 736\n", + "finished 748\n", + "finished 761\n", + "finished 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pvalELRT[i] = NaN\n", + " pvalERLRT[i] = NaN\n", + " pvalEscore[i] = NaN\n", + " continue\n", + " end\n", + " snpset_index = genes.== uniquegenes[i]\n", + " if (sum((snpset_index)) < 3 )\n", + " pvalELRT[i] = NaN\n", + " pvalERLRT[i] = NaN\n", + " pvalEscore[i] = NaN\n", + " continue\n", + " else\n", + " G = convert(Matrix{Float64}, SNP_24.snparray[:, snpset_index])\n", + " # get rid of mono-snp\n", + " maf = sum(G,dims=1)/(2*nPer);\n", + " G = G[:, map(x -> maf[x] > 0, 1:size(G,2))];\n", + " windowSize = size(G,2);\n", + " \n", + " if(windowSize > 2 && windowSize < 50)\n", + " (b, vc0, vc1, pvalELRT[i]) =\n", + " vctest(y, X, G, \n", + " tests = \"eLRT\", \n", + " Vform = \"half\", \n", + " nPtsChi2 = nPtsChi2,\n", + " windowSize = windowSize,\n", + " pvalueComputings = \"chi2\",\n", + "# pvalueComputings = \"MonteCarlo\", \n", + " nNullSimPts = nNullSimPts); \n", + " (b, vc0, vc1, pvalERLRT[i]) =\n", + " vctest(y, X, G, \n", + " tests = \"eRLRT\", \n", + " Vform = \"half\",\n", + " tolX = 1e-6,\n", + " nPtsChi2 = nPtsChi2,\n", + " windowSize = windowSize,\n", + " pvalueComputings = \"chi2\",\n", + "# pvalueComputings = \"MonteCarlo\", \n", + " nNullSimPts = nNullSimPts);\n", + "### \n", + "## eScore uses integration to calculate p-value -currently Julia's package not updated used R package \n", + "## used R package pracma, need to first instll: install.packages(\"pracma\")\n", + " (b, vc0, vc1, pvalEscore[i]) =\n", + " vctest(y, X, G, tests = \"eScore\", \n", + " Vform = \"half\",\n", + " windowSize = windowSize)\n", + " println(\"finished $i\")\n", + " \n", + " else\n", + " pvalELRT[i] = NaN\n", + " pvalERLRT[i] = NaN\n", + " pvalEscore[i] = NaN\n", + " end\n", + " end \n", + "end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Output results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because of a problem in the CSV package, the number format of very small pvalues in .txt files is not convenient to read when you read them in Julia or other languages. You can remove the commented code to read the files." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "chromosomes = Array{Int64}(undef, genes_number)\n", + "for i in 1:length(uniquegenes)\n", + " chromosomes[i] = df[findfirst(genes .== uniquegenes[i]), 2]\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "AdjBasepairs = Array{Int64}(undef, genes_number)\n", + "for j in 1:length(uniquegenes)\n", + " AdjBasepairs[j] = df[findfirst(genes .== uniquegenes[j]),3]\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"exactVC_eLRT_pVal_4diffsnps.txt\"" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = .!isnan.(pvalELRT)\n", + "plot_ELRT = DataFrame(GeneID = uniquegenes[index],\n", + " Chromosome = chromosomes[index], \n", + " AdjBasepair = AdjBasepairs[index],\n", + " Pvalue = pvalELRT[index])\n", + "CSV.write(\"exactVC_eLRT_pVal_4diffsnps.txt\", plot_ELRT)\n", + "\n", + "#= read all the pvalues\n", + "df1 = CSV.read(\"exactVC_eLRT_pVal_3diffsnps.txt\")[4]\n", + "for i in 1:length(df1)\n", + " println(df1[i])\n", + "end\n", + "=#\n", + "\n", + "#= read pvalues which are not equal to 1\n", + "df1 = CSV.read(\"exactVC_eLRT_pVal_4diffsnps.txt\")[4]\n", + "for i in 1:length(df1)\n", + " if df1[i] != 1\n", + " println(df1[i])\n", + " end\n", + "end\n", + "=#" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"exactVC_eRLRT_pVal_4diffsnps.txt\"" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = .!isnan.(pvalERLRT)\n", + "plot_ERLRT = DataFrame(GeneID = uniquegenes[index],\n", + " Chromosome = chromosomes[index], \n", + " AdjBasepair = AdjBasepairs[index],\n", + " Pvalue = pvalERLRT[index])\n", + "CSV.write(\"exactVC_eRLRT_pVal_4diffsnps.txt\", plot_ERLRT)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"exactVC_Escore_pVal_4diffsnps.txt\"" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = .!isnan.(pvalEscore)\n", + "plot_Escore = DataFrame(GeneID = uniquegenes[index],\n", + " Chromosome = chromosomes[index], \n", + " AdjBasepair = AdjBasepairs[index],\n", + " Pvalue = pvalEscore[index])\n", + "CSV.write(\"exactVC_Escore_pVal_4diffsnps.txt\", plot_Escore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Manhattan Plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code produces the Manhattan plots for the three different methods and saves them as `.png` files." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/MendelPlots/5R3iq.ji for MendelPlots [7ab2ea78-72dd-4c03-85d8-fc2b0db252af]\n", + "└ @ Base loading.jl:1184\n", + "┌ Info: Loading DataFrames support into Gadfly.jl\n", + "└ @ Gadfly /Users/janets/.julia/packages/Gadfly/09PWZ/src/mapping.jl:228\n" + ] + } + ], + "source": [ + "using MendelPlots\n", + "\n", + "manhattan(plot_ELRT; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n", + " posvar = \"AdjBasepair\", outfile = \"exactVC_eLRT_manhattan.png\", \n", + " fontsize = 18pt, linecolor = \"red\")\n", + "\n", + "manhattan(plot_ERLRT; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n", + " posvar = \"AdjBasepair\", outfile = \"exactVC_eRLRT_manhattan.png\", \n", + " fontsize = 18pt, linecolor = \"red\")\n", + "\n", + "manhattan(plot_Escore; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n", + " posvar = \"AdjBasepair\", outfile = \"exactVC_eScore_manhattan.png\", \n", + " fontsize = 18pt, linecolor = \"red\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### eRLRT" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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snNp+VldhtX68163sn9oHlv58pCY/3eNCj9/ev31O7+z+ofUnrf9vSb0qyUE9yT+w7riUel2SB/Ui/z40UutbhwaG/rxXBcrWel5KTulR+p8sW8sbmyc0j03q5UkO6FGdpNY3NQebPzubFIt5jpsDzbOT+gdz1VRXSn3N+oHmH84mxWIe86k4r3bXPKH5Sw8sWpnt4yW6dVBN2dQcaK6fp3oAACwBFjQAAACwYGrKM7sI+2ZrvHV7z5vZL5Wh1PzS/NSq/7052HzBnKYsOahOtjcnOWxO8z6gOdj82VrKlUkOnsZhNcm3k9yUZEtNrqslNyf5/rQbKPWcntxtXMrvJOUVc553N/X1pZF/To8eAbKLlSU5b6YHL+Y5Xju49lGl5E+ncUhN8p2afCbJVTX5SJKPJfnXJO3p1K4l/2Pt4NoZPd5iMY/51HmdV7vasGbds0sjs1m0Mq3vyV2UUvKBDSeuWz3D4wEAWGZ68uw7AAAA6EZJfrFTTL3/Ahl7982kXp3auHayf/ILK7PyjpT0TUxMHNUo9adTy4tqcmK6XFDQSE5P8tG5aq7UvD5z84z6HzMwMHBASS5K93dBfz6lfHCiPfGhy8Yv++beAtYOrn1UX+k7o9S8LMmju0laU87fsGbdLZds2fx3XfYxFz6blCtK2p9Ju+/W0je5ol0bD0/az0/Kqen+butOc7MtyedL8rma8rkk3yqlfUyteVxSjk9ybJd1ntk8sfmI1tWtb3UZn2Txz3Ff+t6R7nYm+ZeU8va723dfMj4+vn1vASeffPKhB+9Y+fRaylAtOSOdx2Rlf/rflOSM6fS82Md8lpbFeZUkL17z4sMn6s4Pp/udc+5MykdSc2VjsnHT5IMmb221WjubA82HNvoax9R2PTa1bkjJmiQru8i3st0uV5x2/KnPuvjaS2+ebv8AACwvPX92HwAAAOxNc3XzkHJw7u4UV0v+oLWl9ab56Gm+DQ2uG0nK0AwO/WpJfcuDbj3ig5tu2rRzqsDTTjrtoZOTkxcktdlF3u/ddu/tR91www0T3TTRHGz+Vkne3lXHezdZk79rJJ+qpdxWa72jkVpq8ojJRh259OpLv7yvA4cG1r3t/juuO9pWS17T2tLaNI2+yvo169bXWjYlObSL+Fv7y4onfHjLh7/XTfJZzPunaju/27qm9cl9BZx+3OmrdvTd945acuYM8v9QO6nvua9/x+uvvPLKvZ6jw8PDjVs+fcuLS62/X5Of6pyynD06Nnr+dJpYzHPcPKF5bGnkCx3CJpNy+ujY6MXd5Pyh004+7eiJiYkLSrK2Q2i7lMZTRraMfL7b3It5zJ1X3ZvGWG1Pre9akQP++EPjH/pBp+D7F0pMvDqpv5ekr4v8Hxsda63uIg4AgGXMDg0AAAAsjENzRCa7irRDw27qX9RV5bdGW5u3dRN98VUX35FkaGhw6H8l9XUdwg8/8uAjn5s53KVhH+5IyvCOlTs+dMUVV9w13YM3rFn37HYtv9UpriRfbte8qDXWumWaJerIls2XrF+z/rO1ti9P5zumj56oO34zyR9Os0632qn19aPjmzs+vuCi6y7amuTlQ4PrdiblVTOo9flSG78xMj7yqamChoeH20k+2Dy+OVYa+ZeUHDN12ro2SdcXXhf9HDdySscGas5pjU9vMUOSXHzlxbcmWTc0uO5dSfnNqbqodfINSV7cTd5FP+bTt+zOqyRZP7DuV2t3ixn+vZTG4MhY9wtiHlh8Mtw8oXl9aeTDSX6iwyG/vOGEdb9yyTWb/2+3NQAAWH4aC90AAAAAy9Rkl88Yn8xXetzJolGTl42ObX5lq9XqajHDbseuqm9I8vFOcY3S+ULs7JQP1B153OjY6PkzWcyQJO1a3pxOv9MouWeir31ya3zaF11/ZGTLyL/WHXlGkq91ji6/2VzdPGSmtaawM+1yUjcXXXdVV5XfSjLNrehL68G3Hv7fOl103VXr2tadaZQzktQOoc85/bjTV3WbdwnM8YumerOW/N/WeOs9Xebaa4oH33rE75Rkn7uY3K88rduES2DMp2NZnlcnn3zyobWUP+8i9PON/r5fmM7uHrtqXdP6ZG3kF5Pc3im2XcofzaQGAADLhwUNAAAALIhSyuFdBTby/R63skiUP2yNtS6c6dGtVmsytbyzU1y75udnWqOTkrx7dGx0Y+v61ozndP2J65+S5Dmd4mo7L7v0qks7bfnfUev61vdrO7+apN0h9IiyqrxitvX2VEp93eg1o2PTPa7Vau1I6lXTOOSz2/q2bez0CJO9Gd0yen1KLu0Q1r+jb8fDusm3FOa4JP91yvfb5WPd9rcvm27atLPW0mnXlceuXr264w6tS2HMp2M5nldJctDkyl9P8qAOYXc22n0nXXLlJf/Rbd69aV3d+lap9aXpNMclvzi0Zuj5s6kFAMDSZkEDAAAAC6N2t0PDfX33dXxu91JXa24YHRsdnnWeQ+q1Se6dKqYkD51tnb3WTsZHxlqvSec7jqfO065ndxF2RWu8tXk2dXbVuqb1ydT6to6B7fraJGWu6ibZMrJl87tmfHS70fWF10baL7/qqqum/N6YUq2f7SLqyK5SLfI5bj6/+aAkB00VUxt1VheLf+iJz3jilcmUi776jzzwyJ/slGexj/k0Lcvzqtls9tU65SNKkiSl1pdecs0lX+8mZycj45uvS+q7Owa2c/Jc1AMAYGmyoAEAAICF0d2CholZXQxaIkotnS/6deGBR1Vc1yGsq4tj07Qz7bx2tkmaxzePSOpLOsU1avtNs621px0HTLw1yY4pg0qOaQ42f2buqpZpbYe/p74VfTd2Gfq3l4xd+g+zqZU0vtQpot1oH9UpZknMcX863jFfavmFaTW3D8PDw+0kfz9lrZQpFzQsiTGfluV3XiVJ2VpelOQxHcKuGhnfPN5Nvm5NpP3udNylodqhAQCAfbKgAQAAgAXSPqyLoGW/O0NJvjB6zejcXWCq9YsdIrp+HnvXSvnz1jWtTnU7688p6XDne5KrLhm/9J9mXWsPV1xxxV1JPtoxsOaX56JeLbl5dGz047PJMXHgxPfSzY4Ypfyf2dRJkpra8cJrulkssxTmuNGx/yT1uG4eBdGN2sibasnL9/lnov7tlAmWwph3admeV/dn6/jojkZt/0F3ubp32dhl36jJtR3CntA8sfmIua4NAMDSMCcfnAAAAGC6SiPbaudLQst+QUMttZVZPqZhD3fOYa5u3LaivWJO7uwuqc/suPN8LX81F7X23kBppdY1U4eU5yQ5bw6qXTzbBK1Wa3JosHl3kikXD3W84N2FiYmJ/1jR3+HXTLXzhdelMMddjUXyyKMOPrI1MDCwYXx8fPsMOv2R1tWtv0+HXRqmshTGfBqW5XnVbDZXZmue1SHP3/di0UqSlFo2pdSBDjHPT/L+XtQHAGBxs0MDAAAAC6XjhfXSaZvqZaDUdLu9eVdqo3x3LvN1UfFDHxr/0JwsTCm1PLNDyNZ6SO10J/CM7Vix4yPptD1+6nPmolajXT89F3nS+Ty7o3Vt6yuzLdLf199x0U2jdt65YCnM8eXXXX5bkvu6KPeiQ8shVzVXNw/pusEeWApj3q3lel5lW56WTrtwlDrWdWPTtK1/23Xp8O95Sf25XtUHAGBxs6ABAACABdGunS+s15oHzUcv+7MdE5NzuqChtOd5QUNtfH4u0jSPbx5Rk2OnDCoZb7Va2+ai3t48sD3+xzqEHb32+LUPn22tRv+Kf5xtjiSpJVs7hMz+USBzZInNcbfn7QvKQfn60MDQG0855ZSHdHnMnFliY97RcjyvkiTt/FKnkNLu69milauuuurelPzbVDG15om9qg8AwOJmQQMAAAALo3Tx6IOyzBc01PzHA3d7z51G7pnTfB3UvjonCxpKKb+QDvvil9SPz0WtqRupX+8UsqJ/xWwvTH/r4qsuvmOWObp11zzV6WgpzXGpdVP39fKQlPqmFdv7bx0abH5saM3Q76xfs/5n0vE5ELO3lMa8C8vyvEqSkjy7U0x/6e/tIoyaTv8W/Jee1gcAYNHq+EA/AAAA6IlG7sxkx6j+0487fdVF113U6W7YJamWzNfFt16puSe3zEmmRoe7yJOUkn+Zk1pTqY1bk6l3ga+1HjGrEsncLmKZSin7z4XXJTTHP8jWzYflkLfU5FHTqNyX5Dmp9Tk19W1DA83vpuQTpdSPpdH38faB7c+2Wq3Of2tOxxIa844llut5db8ndwrY0b/jIc2Tmj3bJaRM5u4Oz894WK9qAwCwuFnQAAAAwIJYObHyuztLh8emJ9l54M4HJR23916qvr/QDczSN1s3tOZmR4jSPjx16hvWJ3eWz81JranaKO3baoc+UjOrC68l+cFsjp+WOr87dkxpCc3x+Pj49g2Dp66raXwyyQEzayQPSfKiWsuLMtlO2Zq7hgaaf1OTj6adj7aubX1lRnl3q7F0xrxjjeV6Xt2v40KFMjn1IyFmq8NihiQ5rJf1AQBYvDxyAgAAgAXxofEP/SDJRKe49s72Y+ahnf3VYl/QcO9cJSoph08ZUPPd1rWtzo8xmaVaG7d2ETarC6+ZxwuvpdT2fNXqZKnN8SVjl36mJr+RZOfsu0qSPDglp5SSC0pfvjw02Pza+jXNczesWffMzPDxFEttzDtYlufVAw5d6AYAAGCmLGgAAABgIX2vU0At9efmo5H9UWM+7yjez9VOF17L/DyzvtFod9y2vpb64NnUKMndszl+sVqKc9waa12UWp6b5NuzbuzHPbrWnN2u5W/XDza/tn6w+SfNE5uPmE6CpTjm+7JczysAAFjsPHICAACAhfSdJEdOFVCy/y1o2HDchp9or5h8x1QxtebG1ljrbbOpU1M67mCxfNQpL7zWeVr80a6N0mnz9MYs785evvO+NOd4dHz0b087+bSnTk7u/KPU8mtJ+mbb455q8qgk/6O089qhgaFNtdQ/bo21vtPFkUtyzPdm+Z5XAACwuNmhAQAAgAVU/q5jRMp+t6Bhsm/yxNSsm+pPKVm90H0uJbVmyruzS8nW+emjPrxjTBoddx7hxy3lOb74yotvHd2y+cySxhNL8u4kvXqMwwEp9ZyS/HPzxObTOwUv5TEHAACWBgsaAAAAWDCltG/oFFOTY18y8JLD5qGd7pUMdoyp5bPz0MmyUZKdUwbUrJynVjpu6V9qe1626V9qlsMcj4yNfGlkrPWag+9d9YjUckpK/T9JvjWTXB0cXdq5Yf3AuuOmCloOYw4AACxuHjkBAADAgtk50f5Yf1/H3dfLjrJjbZILe99RZwMDAweU5Hmd4mqtFjTMrU53Zz9oPppoJI+YemP8pF2LC68zs2zm+MIbLrwvyRUP/ElzsPmzJXUgKc9N8qwkB88m/wMOqqV8XkknVQAAEoZJREFU4JRTTnnS5Zdf/t19xCybMWdqpeS8haxfa516cQ0AAMuWBQ0AAAAsmMuuvezbQ4PNLyY5tkPor2U/WdBwWFb9cu3iYmO7v21Bw5wq30umvOQ5Lxdea8kjpm4jafQ1XHidkeU7x62x1j8n+eckf3Lm085ccddRdz09pf3cWsrqUvPMJAfNMPXRK7aveEeSM/b+9vIdc3bzpZEtrXMWugkAANgbj5wAAABgQZWaj3WMSZ7dPL75uPnop6NSOj9uItnaOLDx5Z73spyU3Nkh4qjTjzt9Va/bqDXHdIrZuXPnbb3uY0kyx0mSTTdt2jk6Pvq3o2Ob39za0nr+3fWew1PK81LqH9fkc9PPWE970Yte9OC9vmXMl4tOux88ZvXq1W58AwBgv2RBAwAAAAuqlnJDN3Glv76sx6109JKBlxxWk9M7BtZyVavVmpyHlpaNmtppa/zGfSvv+6+97qMkj+kQ8p3Lrr3s273uYykyx3s3Pj6+fXTL6N+Mbtn8P1tjrac02n2PqSW/V5JvdJlixcqdK1+4tzeM+bLRaeHKiiMPOPLR89EIAABMlwUNAAAALKiJyYkbugqs5ZWnHHfKUb3tZmo7y87fSnJEp7jaqBfPQzvLSqNd/71j0GR+tpc9bBg89RlJHj5lUM2NvexhKTPH3bnkmku+3trSesut997+uFJyXlcH1Txvby8b8+WhJh3nuZTy+PnoBQAApsuCBgAAABbUZdde9u3UXNlF6OEr+le8recN7UPz+OYRSX1tF6F35eBc0/OGlpl2u/xdp5hSynN72UNN47SOMQ0XXmfKHE/PDTfcMDGypXVOSbr4e7Hu9ZEOxnx5KLV8smNMX9uCBgAA9kuejQYAAMCCK/2NP6yT7ZM7R9ZfHRoceu/o2OjHe9/V7kqjvi4ph3WMSy4dbbV2zEdPy0nr2tZXhgabtyWZYpeOemJzdfOQ1g2te+a6/vDwcOOWG29e3ymu0a6fnuvay8VSmOOhNUMvTa1/sa/3S8n7Rra0zplpj3szMtZ63dCa5impedwUYQ/b24tLYczprDTan6i1vGaqmNouz0nyrl71MDTY/EySn97X+yX1NSNjm/+yV/UBAFi87NAAAADAghu5auQfa3J1N7G11AuazeZBve5pV+vXrP+ZlNLNRch2o93+8543tHx1upv8oMZB9YW9KPyFT33+udnHReFdbNu2Ysff96L+MrKo57jW2p9k1b7+1JpnzFG7exQuN3SIePAU7y3qMaezHTsnO+7QkJIXrl+z/gm9qD80MPSsJE/LlOdG445e1AYAYPGzoAEAAID9Ql/af9hNXKl5UrknlzebzZW97ilJ1h6/9uG1trckObhjcK0XXXzNpZ/rfVfLVCl/2ymklvLaJGWuS9dG6bgtfkk2X3nllXfPde1lZZHPcSnlGx1SPHn16tW92DH1nzu8v3Wf7yzyMaezy6+7/LYkX+wQ1khtv74nDZT2KzpE1J0H7vxET2oDALDoWdAAAADAfuGSsUs/k5KxroJLji/3pNWjC4M/cvpxp6/q7+u7Kskjuwi/rzHZ/8Ze9rPc1VIvTjLRIexpQ4NDG+ay7unHnb6qJqd2iqsptkufpcU+x5OZ7LSg4cAjDzny56fVXBdqqVPtwJDUfH2KYxf1mNOt+sGOEclpzZOaj5nLqvfvztBx4co/XH755d+dy7oAACwdFjQAAACw36glXe3SkCQpOfmog4/68EknndR554QZePGaFx++ve++S3P/NtndNPTnl1x3yb/3ohfu17q69a3UsrlzZH3LySeffOhc1d2x4r63Zeot+1OSL4+OjX58rmouV4t9ji/dcunXktw6ZbF23jDtBjsoyQlTB9Sv7eutxT7mdKdOlgsy1U4d9+tvTOR/zFXNlwy85LCUelGSvqniSqnvmKuaAAAsPRY0AAAAsN9oXd26MbX+WfdH1ObBkwfesn5w3Zw+37052Dxhor3z8yk5vstD/uWAiQPeNJc9sHcl5d1dhD3mgIkDRprN5pQX0brRHGy+oNa8slNcrfV/z7YW91vkc1yTMuVOMyU5sXli8+ldN9jB+oH1v5CaX5qyZs3fT93Toh5zutC6tnVnUt/bKa6WvKw52HzBbOs1m82VO7PjwiRT7vhQkn9rH1wum209AACWLgsaAAAA2K+Mjm9+XU26vrhRk0fVlCuGBptXN09oHjub2s3B5sPWDzT/d0nGU3JMl4fdVSez9qLrLup05ytzYGR85FNJbuwUV5KBcm99z2wuvjZPaP5SSTpu057kS3dn63kzrcPuFvscl9K+umNMO61TTzz1p7rJN5XmYPNhtbRHOoRtb+8sH5kqYLGPOd2ZSPsd6fx4kRUluaJ5QnPKRTJTecnASw4rWzOeklM6xbaTP2q1WpMzrQUAwNJnQQMAAAD7m5pVeWm6uLi2hzWlkS8MDTY/NzQ49EfNNc2nJSmdDmo+v/mg9YPrXjY02PxoSb5ZS86cTq8l9fTWta2vTLNXZqH0NV6ZZGfHwFp+I1vzqeZg88nTrdEcbL66NPI3SY7q3FB51fj4+Pbp1mDfFvMctw8uVyf5fx3CHtnXbny8OdB8Xjc596Y52DyhJP+U5JEdQq9uXd/6fqd8i3nM6c5lY5d9o5b8URehB5dGPjo0ODS8cfXGA6dTY/2adWt3NnbclKSb7+2/aY21LpxOfgAAlp/+hW4AAAAA9tRqtbaddvJpJ09OTHwqyaOnefiTk/rkUvN7Q4PN22ry/0pya1JuS9p3J1lZUla1S44pNcckeWxNOWAmfdbkDaNjmzvejc3cGrlq5B+bg83fL8mfdIotyc8l+cz6weZIu2TzPe17rt3XRdLmic1HlFpemlrPSPKErpqp5ZLRsdHrp/UF0NFinuNWq7VjaGDozSl1U4fQh5WS/zs02Pzrmrz1nnrPxztdwH/xmhcfPlEnTk/qbyT5mS7a2VlK443d9L2Yx5zuPennn/Tmm2+8+VklOa5D6IFJ/YNtB289fWhg6MLSV64cuXrks3sLXDu49lErSmNNbZeX1ppf7LKV+yYb7ZdPr3sAAJYjCxoAAADYL1185cW3Ngeaa0rJ3yV50AzTHFV+dCdwzQ83bKhJSp1VexO15BWtLa2OzyOnN5709Cf96c033jxYkmd3Eb6yJqeXmtMPLYfcPTTY/FKS79SkUVLuSakPLzXH1HYendTp7Gb5zYn2xG/N7Cugk8U8xw++7cEXfv/o7/1uTbp5rMTzSvK8Q8sh9zUHmp8qpf5TLeW7jdTv1dooSR6Rkkem1p+YqDufnqTrO+ZLcu7IlpF/7TZ+MY853RkeHm43B5ovTck/JXlEp/iaPDalvqm265uGBpu3J/lWTW4rJROpeWiShyV5ZP3Pf2K7U8orLr360i/P7KsAAGA5saABAACA/VZrvHVLc6D5zFIyku7uRp4Pd5da142Obb5uoRtZzoaHh9sbjtvwkvaKyb9OzeOmceihSZ6W7LK85f7/pus7JY1fueza1renfyjdWMxzvOmmTTvXn7R+Qybbf5ek2x1gDiwlq5OyutSkpuRHXc9sAdZNKycO/P3pHLCYx5zutcZbtzdPbK4t7VyT5PBpHHpkkiNLMtPvySRJLfm91pbR9888AwAAy8l0VkcDAADAvGuNt26pq/L0UtNp+/b58M2+dvuXRsYtZtgfXHLdJf/e19f/S0n2ug16D91ea35lZGzkS/Ncd9lZzHM8ctXIP9aS35zLpqbh6339/Wsuuu6irdM9cDGPOd1rXd26cbKv/cwkX5vfyuVPWltab5nfmgAALGYWNAAAALDfa7Va20bGWy9Pyvokdy1ACztK8u6+vv6nXnzNpZ9bgPrsw8VXXnxr3ZFfTskn56nk10uj8YLWeOuWeaq37C3mOW5taW1K6hvnoqlu1ZKb62RecPGVF9860xyLeczp3qVXXfqFnRMTv5Dkxnkot70mvzo6NvqGeagFAMASYkEDAAAAi8bo2OjojpU7H1NLfi/J7fNQsl2SixrtvmNHxlqvufiqi++Yh5pMU+v61vfvbt/z/KS+IcndPSpzX1L+sK7KE0euHpnvO9eXvcU8x6Njm99can1pSu6Zq5z7Vv4yB+fnW9e2vjLbTIt5zOne5dddfltdldW15A+STHtHjy5d0zfZflprrHVRj/IDALCE9S90AwAAADAdV1xxxV1J3nLSSSe98+D2Ab9ea/mdJI+c4zJ3luSadvLW0bHWv8xx7v9Uck+H55DP+QXQ0i731DKLh5/vlmz++9+X8fHx7Un+pDnYfF8p9S2pZWPm5kaOdmo+Uvvz262rRudma/b5HLc6da7arnNzAXNndmRldiRZue9WZldrUc3xHkbGN3/otJNPu35yYvIPk/rrmfvfyf11qfXPRsZb43OZdFGNufNqxlqt1rYkb1p7/Nr/09/fePMczvNNpdY3eEwTAACzURa6AQAAAJil0hxsPiXJ80ryvCTPSXLoNHO0k3ym1FyTNMbbh7Q/3Wq1Jue8U+bNhhM2PLr2TZxUazkpyS9niguCe/GDpFxbavvqdsp4a7w1H7uBME2LdY43nLzhmDo5cWq7lmZJnpWZXTiuSb6QlL8ppbxnZMvI5+e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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\"image/png\", read(\"exactVC_eRLRT_manhattan.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### eLRT" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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lDTjoq+LAPQ58R1Q9o/2YD1JZXlfJZDLhPuASJZIkc//k8rXLn45TZ5QVHSvvkfzayMBQZ+aiPQAAAJQmBjQAAAAAAAoj3oCG7mHdDCoR5hZ90y+GnUtV3BMRFuvm2CB1KdTFw60keVpyP8nPi4oLPLxsuG311jmm+xuSOgcMMh2arEm+N3et2qCmw+8tUZl4IGboT5a33/aL4bQlBU9GRYRBeFBUTEmc4wpFPjFvbv8wqOT6kUqlQkk/G7At2YADGkrimA9K+V1XkmSb7WxJR0SErV7RsbIjTn1xdSu8VpGzNDgzNAAAAKBfDGgAAAAAABRIuFeMoLKfncGkx9vWtuXuBpP7ExERsddjj83suvTadFS70So0RRFPvktavbzjtl8Nu61e7rzzzlckfT8y0PXRXLTnpkfb2tt+NJw6uvfofllxZsQw++/htCNJLo+88ao4g2VK4RwHkflL8lPjLAURhwe6zE2f7vdft/9kwApK4ZjHVLbX1Y7aIpfuCDz8ary64ru9/fZnXLo7IuzdycnJw3LdNgAAAEpDTjpOAAAAAAAMlgXa6tG3hMp+QIObpzXMZRp6eSmHdcWxoTKszMmT3SY/KXLmebcbctFW3wlYWu6TBg6xkyU15qC1ZcOtIJ1OZ2prkpskDTh4KPKGdwzd3d3PVVZE/Mzk0TdeS+EcxzoW0lsPGndgurq6emZHR8f2IWT6uvRd6Z8pYpaGgZTCMR+EsryukslklTbrQxH1/Cwfg1YkydxaZF4dEfNxSTfmo30AAACMbszQAAAAAAAolMgb6xY1TXUZMFfc6c1j8cBezGV9MVq85ZaOW3IyMMXcTooI2ewTPOpJ4CHrrOz8nqKmx5efnIu2gtB/mYt6FH2dbUzfnf7DcBupSFREDroJPHrmglI4x3fcc8cGSdtiNHf2njZhdXJickLsBPOgFI55XOV6XWmrTlDULBzm7bETG6StFVvvUcT/z03+9/lqHwAAAKMbAxoAAAAAAAURevSNdXftPRK5FLPO7kxOBzRYOMIDGjz4bS6qSZ6W3M+lowcMMnWk0+mtuWivLzunx78vIuzgqadNPWS4bQUVlf873DokyU2bI0KGvxRIjpTYOY573X7Cxurp2uraL0+ZMmX/mNvkTIkd80jleF1JkkJ9OCrEwkTeBq2sXr16i0x/GijGXcfkq30AAACMbgxoAAAAAAAUhsVY+sDKfECD67mdT3vnTqDXclpfBE94TgY0mNk/KGJefJP/KBdtDZyIPx0VUllROdwb039ZtnrZxmHWEdcrI9ROpFI6x+beEr897S/zyyq3V6yvrUneVzup9nMzJs14ryLXgRi+UjrmMZTldSVJJn0kKqbCKvI7CMMV9f+CN+e1fQAAAIxakQv6AQAAAACQF4FeUiYyqmLWqbPG33zPzVFPw5YkN43Uzbd8cb2mx3JSUxDxFLkkM/0mJ20NxIP10sCzwLv7fsNqQsrtIJaBmBXPjdcSOsd/0+aVe2nCFS4dPoiWE5JOlvvJLr+qtjr5okz3m/l9ChI/CvcIH06n09F/NQejhI55ZBPlel3t8HdRAZ0Vnfsnz0jmbZYQy2hTxPoZb8pX2wAAABjdGNAAAAAAACiIqu6qF7ssYtl0SV17dO0tRU7vXapeLXQCw/Rsel06NzNCWLivfOAH1jNd9khO2hooDQs3eEQecg3rxqtJfxvO9oPiIztjx4BK6Bx3dHRsn1kzbbor+LGkMUNLRPtLOtvdzlYmlG3WK7XVyR+69H2F+n767vQfhlRvjzZK55hHtlGu19UOkQMVLDPwkhDDFTGYQZL2ymf7AAAAGL1YcgIAAAAAUBC3dNzyN0ndUXFhV3jECKRTrEb7gIYtuarIZPsOGOB6MX13OnoZk2FyD9bHCBvWjVeN4I1XMw9Hqq0opXaOl7ff9qBL/yypa/hZSZL2kWmKmZotod/X1iSfmjEpuXjmpOknaYjLU5TaMY9QltfVTnsWOgEAAABgqBjQAAAAAAAopJejAtz870cikWIUjOQTxUXOo2682sisWR8EYeS09W6+z3DaMGnTcLYfrUrxHKfb0zfL7RRJfx12Ym/0Nnc1hG4/mVGTfGpGTfLrycnJwwZTQSke8/6U63UFAAAAjHYsOQEAAAAAKKTnJR04UICp+AY0zDx15lvCysy3Bopx1wPp9vRVw2nHZZEzWJQPH/DGq4/Q4I/QA4uaPD0Y5tPZ5XveS/Mct3W0/eScM885PpPp+ne5XSApMdwce3PpcElfsFAX11bXtrj5f6Tb08/H2LIkj3lfyve6AgAAAEY3ZmgAAAAAABSQ/TQyQlZ0AxoyicxkuaYP9M9MEwudZylx14BPZ5tp88jk4YdExiiInHkEb1TK53jZqmXr29asnGsKjjHpWkn5WsZhjMwXmPTr5OTkiVHBpXzMAQAAAJQGBjQAAAAAAArGLFwXFePS0edVn7fXCKQTn6kmMsbt4RHIpGyY1DVggKtqhFKJnNLfPByRafpLTTmc4xXtK55c0Z5eOG7L+MPkNkXm/y3pL0OpK8LBFmrdjOrppw4UVA7HHAAAAMDoxpITAAAAAICC6eoO76tIRM6+bp3WOVVSa/4zilZdXT3GpI9Fxbk7AxpyK+rp7L1HIolAOmzgifGl0I0br0NTNue4dV3rNkl37vynZE3yOJNXS3aKpA9JGjec+nca62Y3TZky5T133HHHi/3ElM0xx8DM1FjI9t194ME1AAAAKFsMaAAAAAAAFMztd9/+19qa5BOSjo4IvUBFMqBhL43/qMe42RhWhAxoyCl7WRrwlueI3Hh102EDpyEFiYAbr0NSvuc43Z7+taRfS/r63BPmVr5y0CsnysJT3GyiuU6SNHaIVR9cub3yW5Jm9/12+R5z9PDkijXpBYVOAgAAAOgLS04AAAAAAArKXPdFxkgfSZ6WPHIk8olkFr3chLQ52CP4fd5zKSemlyIiDpp16qzx+U7DXYdGxXR1dW3Idx4liXMsSWp5qKWrraPtJ23tKy9Pr0l/fJO/tq/MPibz/3DpkcHX6OecffbZ+/T5Fse8XETNfnDExIkTefANAAAARYkBDQAAAACAgnKzdXHirMLn5DmVSOdVn7eXS7MiA91Wp9PpzAikVDZcHjU1frCtatv78p2HSUdEhDx/+923/zXfeZQiznHfOjo6tretafth25qV/5ZuTx8bhIkj3PQlk56JWUVlVVfVWX29wTEvG1EDVyoPHHPg20YiEQAAAGCwGNAAAAAAACio7kz3uliBbvOmnDrloPxmM7Au6/qspP2i4jzwZSOQTlkJQv9zZFBGx+Uzh5k10z4g6ZABg1wP5DOHUsY5jmf52uVPp9ekr1i/5YUjzdQYayPXx/oq5piXB5ciz7OZHTUSuQAAAACDxYAGAAAAAEBB3X737X+Va1WM0H0rKyqvyntC/UieltxP8otjhL6icVqb94TKTBjaT6NizOyUfObgCs6JjAm48TpUnOPBWbduXfeKNekFJsX4u+h9LunAMS8P5vbjyJhEyIAGAAAAFCXWRgMAAAAAFJxVBF/zTHhmdKSfX1tT+5229rYf5T+rnizwz0u2V2ScdFtbOt05EjmVk/Td6T/U1iQ3SBpglg6fnJyYnJBel34t1+2nUqngsQcenREVF4T+y1y3XS5K4RzXTqr9pNyv7+99My1dsSa9YKg59mVFe/rztZOSU+Q6coCwN/VVWArHHNEsCO93t4UDxXhoJ0u6Jl851NYkH5T0rv7eN/nCFe0rv52v9gEAADB6MUMDAAAAAKDgVqxe8b8u3RUn1s2bk8nk2HznlG3GpBnvlVmcm5BhEIbX5T2h8hX1NPnYYKyflY+GH//5b09RPzeFs2zdWtn5s3y0X0b+f3v3HqRnVd8B/HveDQETLjIKAk4RnbaOpUyng1IvWDMokmxInAaeTbik1XGsOp3pvZ2W8YIIo9M6dnqZOtJ2SietYfdJCEKyC0iVsa2lVKReoFadesNREkWQUCC7+5z+wVQrQ/Z9N9nd5CWfzyR/vb9zfr/3nN3JTM55fs9Q73GtdVmSlfv7W2t+YYHKfUrickefiGfP8dlQrzn97Zue7duhISVv2Lh240sWI//YmrFXJTk7c/5u9L67GLkBABh+LjQAAABwWBhJ955B4krNmWVvdjRNs3yxa0qSDRdsOLXWbleSFX2Da92y9Zbtn1v8qo5QpfxLv5Baym8lKQuduvZK37b4Jdl20003PbLQuY8oQ77HpZSv95nirFWrVi1Gx9T/6PP5o/v9ZMjXnP523LZjd5L/6hPWS+3+cFEKKN3b+kTU6WOm/2lRcgMAMPRcaAAAAOCwcP3k9k+nZHKg4JILyt60i3Qw+EObX7955bKRkZuTnD5A+OO92WXvXMx6jnS11K1JZvqEnT02OrZpIfNufv3mlTW5qF9cTdEu/SAN+x7PZrbfhYZjTjr2pJfNq7gB1FLn6sCQ1HxtjrFDveYMqv5934jkkmZd88KFzPpkd4a+F1f+bceOHd9byLwAADxzuNAAAADAYaOWDNSlIUlSsv7kFSd/ZN26df07JxyAS9deeuITI49vz5Ntsgcp6M+uv+36by5GLTyp3dl+K7Vs6x9Zr1m/fv1xC5V331GP/3Hmbtmfknx5YnLikwuV80g17Hu8fdf2ryZ5YM5kXa6Yd4F9lGT13AH1q/v7aNjXnMHU2fKXmatTx5OW9WbyBwuV87I1lx2fUrckGZkrrpT6wYXKCQDAM48LDQAAABw22p3tXan1A4OPqM2K2WPu2zh68YK+370ZbVbPdNNfSMkFAw75/NEzR1+1kDXw9ErKnw4Q9sKjZ44eb5pmzkO0QTSjzfm15u394mqtHz7YXDxpyPe4JmXOTjMlubC5sDln4AL72Lhm48tTc+6cOWv+de6ahnrNGUB7a/tgUv+mX1wteVMz2px/sPmaplk+nX3XJZmz40NJ/rtbUW442HwAADxzudAAAADAYWViatvv12Tgw42avKCm3Dg22ux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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\"image/png\", read(\"exactVC_eScore_manhattan.png\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "Julia 1.1.0", + "language": "julia", + "name": "julia-1.1" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.1.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0b42e230a5885042defb51125342cff4a507b3a6 Mon Sep 17 00:00:00 2001 From: Ruoyao Shi Date: Mon, 12 Aug 2019 14:59:19 -0700 Subject: [PATCH 2/6] Delete original files --- VCTest/demo_exactVC.html | 13511 ------------------------------------ VCTest/demo_exactVC.ipynb | 1673 ----- 2 files changed, 15184 deletions(-) delete mode 100644 VCTest/demo_exactVC.html delete mode 100644 VCTest/demo_exactVC.ipynb diff --git a/VCTest/demo_exactVC.html b/VCTest/demo_exactVC.html deleted file mode 100644 index ac04979..0000000 --- a/VCTest/demo_exactVC.html +++ /dev/null @@ -1,13511 +0,0 @@ - - - -demo_exactVC02062019 - - - - - - - - - - - - - - - - - - - -
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Exact variance component test for SNP set anlaysis

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Check version of Julia

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In [1]:
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versioninfo()
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Julia Version 1.0.3
-Commit 099e826241 (2018-12-18 01:34 UTC)
-Platform Info:
-  OS: macOS (x86_64-apple-darwin14.5.0)
-  CPU: Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz
-  WORD_SIZE: 64
-  LIBM: libopenlibm
-  LLVM: libLLVM-6.0.0 (ORCJIT, skylake)
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Install packages if first time using them

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In [2]:
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#(v0.7) pkg> add https://github.com/OpenMendel/SnpArrays.jl.git
-#Pkg.clone("git@github.com:jinjinzhou/VarianceComponentTest.jl.git")
-#(v0.7) pkg> add Query
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Load required packages

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using SnpArrays
-using CSV
-using DataFrames, Query
-using Distributions, DelimitedFiles, LinearAlgebra
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-#using StatPlots
-# using VarianceComponentTest
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In [4]:
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include("../VarianceComponentTest.jl-master/src/vctest.jl")
-include("../VarianceComponentTest.jl-master/src/vctestnullsim.jl")
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vctestnullsim (generic function with 1 method)
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Annotation file

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In [5]:
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gene = CSV.read("chr19_chr20_gene_start_end.txt")
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Out[5]:
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3,792 rows × 4 columns

Gene nameGene end (bp)Gene start (bp)Chromosome/scaffold name
String⍰Int64⍰Int64⍰Int64⍰
1WASH5P716266095119
2AC008993.1691786840319
3AC008993.2699726916719
4AC008993.3727187177819
5MIR1302-11721107197319
6FAM138F776867616319
7OR4G3P949749406219
8OR4G1P1060779917519
9OR4F1711710210710419
10WBP1LP1114581214548519
11OR4F8P15721515627919
12AC092192.117791317689619
13SEPT14P1919569618637319
14CICP1919559519323919
15AC092299.119806619731019
16AC010507.120077519796119
17LINC0100223944019826419
18AC010507.220117420049919
19RNU6-1076P22326122315819
20AC016588.225157124855119
21PLPP229150428104019
22MIER234481530144419
23AC016588.130646730557319
24THEG37667036174719
25AC010641.139917339758919
26C2CD4C40917040543819
27SHC246099641658319
28RNA5SP46245324545313419
29ODF3L247498346334619
30MADCAM150534248917619
⋮⋮⋮⋮⋮
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In [6]:
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## Number of genes on chromosome 19
-gene = gene[gene[:,4].==19,:]
-genes = size(gene,1)
-#geneNames = unique(gene[gene[:,4].==19,1])
-#size(geneNames,1)
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In [7]:
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plinkFile = "SNP_29a"
-## parse data from files
-plinkBedfile = string(plinkFile, ".bed");
-plinkBimfile = string(plinkFile, ".bim");
-plinkFamfile = string(plinkFile, ".fam");
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-SNP_29a = SnpData("SNP_29a");
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In [8]:
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# BIM file: chr, rs#, morgan, bp position, allele1, allele2
-bimdata = readdlm(plinkBimfile, String);
-chrID = bimdata[:, 1];
-snpID = bimdata[:, 2];
-nSNP = length(snpID);
-snpPos = bimdata[:, 4];
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-# FAM file: fam ID, ind ID, father ID, mother ID, sex, phenotype
-famdata = readdlm(plinkFamfile);
-nPer = length(famdata[:, 1]);
-nSNP
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253141
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Extract phenotype and covariates from .fam file

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fam = readdlm(plinkFamfile, ','; header = false)
-y = convert(Vector{Float64}, fam[:, 7])
-#Trait2 = convert(Vector{Float64}, pedLMM[:, 8])
-#Y = [Trait1 Trait2]
-sex = map(x -> strip(x) == "F" ? 1.0 : 0.0,  fam[:, 5])
-# covariates
-X = [ones(size(sex,1)) sex]
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212×2 Array{Float64,2}:
- 1.0  1.0
- 1.0  1.0
- 1.0  0.0
- 1.0  0.0
- 1.0  1.0
- 1.0  1.0
- 1.0  0.0
- 1.0  0.0
- 1.0  1.0
- 1.0  0.0
- 1.0  1.0
- 1.0  0.0
- 1.0  1.0
- ⋮       
- 1.0  0.0
- 1.0  0.0
- 1.0  0.0
- 1.0  1.0
- 1.0  0.0
- 1.0  0.0
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Option pvalueComputing

Chi squared approximation is recommended (default).

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Note: Option pvalueComputing is only for eLRT and eRLRT. For eScore, it employs the method of inverting characteristics function to compute the p-value.

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Option nNullSimPts

If Monte Carlo method is chosen, generate 10,000 (nNullSimPts) replicate for calculation of p-values.

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If Chi squared approximation method is chosen, you can generate more replicates (default value is 1,000,000) to have a better estimator of point mass at 0.

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Loop over genes

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In [10]:
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# genes = 100;
-nNullSimPts = 1000000;
-nPtsChi2 = 500;
-pvalELRT = Array{Float64}(undef, genes)
-pvalERLRT = Array{Float64}(undef, genes)
-pvalEscore = Array{Float64}(undef, genes)
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-basepairs = SNP_29a.snp_info[:, 4]
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-@time for i = 1:genes
-    snpset_index = (basepairs .< gene[i,2]) .&  (basepairs .> gene[i,3])
-    if(sum(snpset_index)==0) 
-        pvalELRT[i] = NaN
-        pvalERLRT[i] = NaN
-        pvalEscore[i] = NaN
-        continue
-    else
-        G = convert(Matrix{Float64}, SNP_29a.snparray[:, snpset_index])
-        # get rid of mono-snp
-        maf = sum(G,dims=1)/(2*nPer);
-        G = G[:, map(x -> maf[x] > 0, 1:size(G,2))];
-        windowSize = size(G,2);
-    
-        if(windowSize > 5 && windowSize < 50)
-            (b, vc0, vc1, pvalELRT[i]) =
-                vctest(y, X, G, 
-                   tests = "eLRT", 
-                   Vform = "half", 
-                    nPtsChi2 = nPtsChi2,
-                    windowSize = windowSize,
-                    pvalueComputings = "chi2",
-#                     pvalueComputings = "MonteCarlo", 
-                    nNullSimPts = nNullSimPts);  
-            
-            (b, vc0, vc1, pvalERLRT[i]) =
-                vctest(y, X, G, 
-                tests = "eRLRT", 
-                Vform = "half",
-                tolX = 1e-6,
-                nPtsChi2 = nPtsChi2,
-                windowSize = windowSize,
-                pvalueComputings = "chi2",
-#                 pvalueComputings = "MonteCarlo", 
-                nNullSimPts = nNullSimPts);
-            
-            (b, vc0, vc1, pvalEscore[i]) =
-                vctest(y, X, G, tests = "eScore", 
-                    Vform = "half",
-                    windowSize = windowSize)
-            println("finished $i")
-            
-        else
-            pvalELRT[i] = NaN
-            pvalERLRT[i] = NaN
-            pvalEscore[i] = NaN
-        end
-    end    
-end
-
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┌ Warning: RCall.jl: Loading required package: pracma
-└ @ RCall /Users/jingzhai/.julia/packages/RCall/RPlFw/src/io.jl:110
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-2562.119901 seconds (4.13 G allocations: 2.855 TiB, 70.74% gc time)
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Output results to file

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In [11]:
-
-
-
index = .!isnan.(pvalELRT)
-
-plot_ELRT = DataFrame(AdjBasepairs = floor.((gene[index,2].+gene[index,3])/2), 
-    Chromosome = gene[index,4], 
-    Pvalue = pvalELRT[index])
-CSV.write("exactVC_eLRT_pVal.txt", plot_ELRT)
-
- -
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Out[11]:
- - - - -
-
"exactVC_eLRT_pVal.txt"
-
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-
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-
-
In [12]:
-
-
-
index = .!isnan.(pvalERLRT)
-
-plot_ERLRT = DataFrame(AdjBasepairs = floor.((gene[index,2].+gene[index,3])/2), 
-    Chromosome = gene[index,4], 
-    Pvalue = pvalERLRT[index])
-CSV.write("exactVC_eRLRT_pVal.txt", plot_ERLRT)
-
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Out[12]:
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"exactVC_eRLRT_pVal.txt"
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In [13]:
-
-
-
index = .!isnan.(pvalEscore)
-
-plot_Escore = DataFrame(AdjBasepairs = floor.((gene[index,2].+gene[index,3])/2), 
-    Chromosome = gene[index,4], 
-    Pvalue = pvalEscore[index])
-CSV.write("exactVC_Escore_pVal.txt", plot_Escore)
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Out[13]:
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"exactVC_Escore_pVal.txt"
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Manhattan Plot

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In [15]:
-
-
-
using MendelPlots
-
-manhattan(plot_ELRT; pvalvar = "Pvalue", chrvar = "Chromosome", 
-    posvar = "AdjBasepairs", outfile = "exactVC_eLRT_manhattan.pdf", 
-    fontsize = 18pt, linecolor = "red")
-
-manhattan(plot_ERLRT; pvalvar = "Pvalue", chrvar = "Chromosome", 
-    posvar = "AdjBasepairs", outfile = "exactVC_eRLRT_manhattan.pdf", 
-    fontsize = 18pt, linecolor = "red")
-
-manhattan(plot_Escore; pvalvar = "Pvalue", chrvar = "Chromosome", 
-    posvar = "AdjBasepairs", outfile = "exactVC_eScore_manhattan.pdf", 
-    fontsize = 18pt, linecolor = "red")
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eRLRT

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In [18]:
-
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-
display("image/png", read("exactVC_eRLRT_manhattan.png"))
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eLRT

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In [19]:
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-
display("image/png", read("exactVC_eLRT_manhattan.png"))
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eScore

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In [20]:
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display("image/png", read("exactVC_eScore_manhattan.png"))
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- - - - - - diff --git a/VCTest/demo_exactVC.ipynb b/VCTest/demo_exactVC.ipynb deleted file mode 100644 index 6202c80..0000000 --- a/VCTest/demo_exactVC.ipynb +++ /dev/null @@ -1,1673 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exact variance component test for SNP set analysis\n", - "\n", - "The purpose of this tutorial is to show how one can conduct a gene set analysis in Open Mendel. In gene set analysis, variants are grouped and analyzed together. This analysis can be far more power over analyzing the variants separately, particularly when the variants are rare. In this example we use a variance component approach which is equivalent to a sequence kernel association test (SKAT) approach in order to conduct the gene set analysis. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check version of Julia\n", - "This tutorial has been checked with Julia version 1.1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Julia Version 1.1.0\n", - "Commit 80516ca202 (2019-01-21 21:24 UTC)\n", - "Platform Info:\n", - " OS: macOS (x86_64-apple-darwin14.5.0)\n", - " CPU: Intel(R) Core(TM) i7-6567U CPU @ 3.30GHz\n", - " WORD_SIZE: 64\n", - " LIBM: libopenlibm\n", - " LLVM: libLLVM-6.0.1 (ORCJIT, skylake)\n", - "Environment:\n", - " JULIA_NUM_THREADS = 8\n" - ] - } - ], - "source": [ - "versioninfo()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Install packages if this is the first time you are using them\n", - "\n", - "Note: For this tutorial you will need to have the statistical software R and the R package \"pracma\" installed ahead of time." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/OpenMendel/SnpArrays.jl.git`\n", - "\u001b[?25l\u001b[2K\u001b[?25h\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.1/Project.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.1/Manifest.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/jinjinzhou/VarianceComponentTest.jl.git`\n", - "\u001b[?25l\u001b[2K\u001b[?25h\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.1/Project.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.1/Manifest.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/OpenMendel/MendelPlots.jl.git`\n", - "\u001b[?25l\u001b[2K\u001b[?25h\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.1/Project.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.1/Manifest.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n", - "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.1/Project.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.1/Manifest.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n" - ] - } - ], - "source": [ - "using Pkg\n", - "Pkg.add(PackageSpec(url=\"https://github.com/OpenMendel/SnpArrays.jl.git\"))\n", - "Pkg.add(PackageSpec(url=\"https://github.com/jinjinzhou/VarianceComponentTest.jl.git\"))\n", - "Pkg.add(PackageSpec(url=\"https://github.com/OpenMendel/MendelPlots.jl.git\"))\n", - "Pkg.add(\"Query\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load required packages" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/SnpArrays/iEYce.ji for SnpArrays [4e780e97-f5bf-4111-9dc4-b70aaf691b06]\n", - "└ @ Base loading.jl:1184\n", - "┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/VarianceComponentTest/QfNDj.ji for VarianceComponentTest [1b8b39c6-5c46-50f5-9c36-ac18d7f28ae0]\n", - "└ @ Base loading.jl:1184\n" - ] - } - ], - "source": [ - "using SnpArrays\n", - "using CSV\n", - "using DataFrames, Query\n", - "using Distributions, DelimitedFiles, LinearAlgebra\n", - "\n", - "#using StatPlots\n", - "using VarianceComponentTest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Annotation file\n", - "In order to conduct gene set analyses we need to determine which variants are within and near a gene. There are several data bases that can assist you.\n", - "- Gene location annotation from http://uswest.ensembl.org/biomart/martview/e292ba28e6325bf11d6b1449b56ba690\n", - "- SNP annotation can be done by [SnpEff](http://snpeff.sourceforge.net/download.html), [Manual](http://snpeff.sourceforge.net/SnpEff_manual.html)\n", - "\n", - "For your convenience we have created a list of the genes and their locations on chromosomes 19 and 21 in csv format. We need that information into a dataframe called gene." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

3,792 rows × 4 columns

Gene nameGene end (bp)Gene start (bp)Chromosome/scaffold name
String⍰Int64⍰Int64⍰Int64⍰
1WASH5P716266095119
2AC008993.1691786840319
3AC008993.2699726916719
4AC008993.3727187177819
5MIR1302-11721107197319
6FAM138F776867616319
7OR4G3P949749406219
8OR4G1P1060779917519
9OR4F1711710210710419
10WBP1LP1114581214548519
11OR4F8P15721515627919
12AC092192.117791317689619
13SEPT14P1919569618637319
14CICP1919559519323919
15AC092299.119806619731019
16AC010507.120077519796119
17LINC0100223944019826419
18AC010507.220117420049919
19RNU6-1076P22326122315819
20AC016588.225157124855119
21PLPP229150428104019
22MIER234481530144419
23AC016588.130646730557319
24THEG37667036174719
25AC010641.139917339758919
26C2CD4C40917040543819
27SHC246099641658319
28RNA5SP46245324545313419
29ODF3L247498346334619
30MADCAM150534248917619
⋮⋮⋮⋮⋮
" - ], - "text/latex": [ - "\\begin{tabular}{r|cccc}\n", - "\t& Gene name & Gene end (bp) & Gene start (bp) & Chromosome/scaffold name\\\\\n", - "\t\\hline\n", - "\t& String⍰ & Int64⍰ & Int64⍰ & Int64⍰\\\\\n", - "\t\\hline\n", - "\t1 & WASH5P & 71626 & 60951 & 19 \\\\\n", - "\t2 & AC008993.1 & 69178 & 68403 & 19 \\\\\n", - "\t3 & AC008993.2 & 69972 & 69167 & 19 \\\\\n", - "\t4 & AC008993.3 & 72718 & 71778 & 19 \\\\\n", - "\t5 & MIR1302-11 & 72110 & 71973 & 19 \\\\\n", - "\t6 & FAM138F & 77686 & 76163 & 19 \\\\\n", - "\t7 & OR4G3P & 94974 & 94062 & 19 \\\\\n", - "\t8 & OR4G1P & 106077 & 99175 & 19 \\\\\n", - "\t9 & OR4F17 & 117102 & 107104 & 19 \\\\\n", - "\t10 & WBP1LP11 & 145812 & 145485 & 19 \\\\\n", - "\t11 & OR4F8P & 157215 & 156279 & 19 \\\\\n", - "\t12 & AC092192.1 & 177913 & 176896 & 19 \\\\\n", - "\t13 & SEPT14P19 & 195696 & 186373 & 19 \\\\\n", - "\t14 & CICP19 & 195595 & 193239 & 19 \\\\\n", - "\t15 & AC092299.1 & 198066 & 197310 & 19 \\\\\n", - "\t16 & AC010507.1 & 200775 & 197961 & 19 \\\\\n", - "\t17 & LINC01002 & 239440 & 198264 & 19 \\\\\n", - "\t18 & AC010507.2 & 201174 & 200499 & 19 \\\\\n", - "\t19 & RNU6-1076P & 223261 & 223158 & 19 \\\\\n", - "\t20 & AC016588.2 & 251571 & 248551 & 19 \\\\\n", - "\t21 & PLPP2 & 291504 & 281040 & 19 \\\\\n", - "\t22 & MIER2 & 344815 & 301444 & 19 \\\\\n", - "\t23 & AC016588.1 & 306467 & 305573 & 19 \\\\\n", - "\t24 & THEG & 376670 & 361747 & 19 \\\\\n", - "\t25 & AC010641.1 & 399173 & 397589 & 19 \\\\\n", - "\t26 & C2CD4C & 409170 & 405438 & 19 \\\\\n", - "\t27 & SHC2 & 460996 & 416583 & 19 \\\\\n", - "\t28 & RNA5SP462 & 453245 & 453134 & 19 \\\\\n", - "\t29 & ODF3L2 & 474983 & 463346 & 19 \\\\\n", - "\t30 & MADCAM1 & 505342 & 489176 & 19 \\\\\n", - "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "3792×4 DataFrame. Omitted printing of 1 columns\n", - "│ Row │ Gene name │ Gene end (bp) │ Gene start (bp) │\n", - "│ │ \u001b[90mString⍰\u001b[39m │ \u001b[90mInt64⍰\u001b[39m │ \u001b[90mInt64⍰\u001b[39m │\n", - "├──────┼────────────┼───────────────┼─────────────────┤\n", - "│ 1 │ WASH5P │ 71626 │ 60951 │\n", - "│ 2 │ AC008993.1 │ 69178 │ 68403 │\n", - "│ 3 │ AC008993.2 │ 69972 │ 69167 │\n", - "│ 4 │ AC008993.3 │ 72718 │ 71778 │\n", - "│ 5 │ MIR1302-11 │ 72110 │ 71973 │\n", - "│ 6 │ FAM138F │ 77686 │ 76163 │\n", - "│ 7 │ OR4G3P │ 94974 │ 94062 │\n", - "│ 8 │ OR4G1P │ 106077 │ 99175 │\n", - "│ 9 │ OR4F17 │ 117102 │ 107104 │\n", - "│ 10 │ WBP1LP11 │ 145812 │ 145485 │\n", - "⋮\n", - "│ 3782 │ YBEY │ 46297751 │ 46286337 │\n", - "│ 3783 │ C21orf58 │ 46323875 │ 46300181 │\n", - "│ 3784 │ PCNT │ 46445769 │ 46324122 │\n", - "│ 3785 │ RPL18AP2 │ 46421034 │ 46420553 │\n", - "│ 3786 │ DIP2A │ 46569852 │ 46458899 │\n", - "│ 3787 │ DIP2A-IT1 │ 46469306 │ 46462471 │\n", - "│ 3788 │ RNU6-396P │ 46525722 │ 46525618 │\n", - "│ 3789 │ S100B │ 46605208 │ 46598962 │\n", - "│ 3790 │ PRMT2 │ 46665124 │ 46635167 │\n", - "│ 3791 │ DSTNP1 │ 46654022 │ 46653558 │\n", - "│ 3792 │ RPL23AP4 │ 46691226 │ 46690764 │" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gene = CSV.read(\"chr19_chr20_gene_start_end.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2959" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Number of genes on chromosome 19\n", - "gene = gene[gene[:,4].==19,:]\n", - "genes = size(gene,1)\n", - "#geneNames = unique(gene[gene[:,4].==19,1])\n", - "#size(geneNames,1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read in plink format genotype files\n", - "We have three files that provide us with the snps, their names and locations, and the information on the individuals who have been genotyped. We read them in and then extract the relevant information. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "plinkFile = \"SNP_29a\"\n", - "## parse data from files\n", - "plinkBedfile = string(plinkFile, \".bed\");\n", - "plinkBimfile = string(plinkFile, \".bim\");\n", - "plinkFamfile = string(plinkFile, \".fam\");\n", - "\n", - "SNP_29a = SnpData(\"SNP_29a\");" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "212" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# BIM file: chr, rs#, morgan, bp position, allele1, allele2\n", - "bimdata = readdlm(plinkBimfile, String);\n", - "chrID = bimdata[:, 1];\n", - "snpID = bimdata[:, 2];\n", - "nSNP = length(snpID);\n", - "snpPos = bimdata[:, 4];\n", - "\n", - "# FAM file: fam ID, ind ID, father ID, mother ID, sex, phenotype\n", - "famdata = readdlm(plinkFamfile);\n", - "nPer = length(famdata[:, 1]);\n", - "nSNP\n", - "nPer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract phenotype and covariates from `.fam` file\n", - "- Note that one can load phenotype file in `plink` format. X contains the covariates, in this case an indicator for sex (male = 0 and female 1) and a vector of ones so the grand mean is estimated." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "212×2 Array{Float64,2}:\n", - " 1.0 1.0\n", - " 1.0 1.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 1.0\n", - " 1.0 1.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 1.0\n", - " 1.0 0.0\n", - " 1.0 1.0\n", - " 1.0 0.0\n", - " 1.0 1.0\n", - " ⋮ \n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 1.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 0.0\n", - " 1.0 0.0" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fam = readdlm(plinkFamfile, ','; header = false)\n", - "y = convert(Vector{Float64}, fam[:, 7])\n", - "#Trait2 = convert(Vector{Float64}, pedLMM[:, 8])\n", - "#Y = [Trait1 Trait2]\n", - "sex = map(x -> strip(x) == \"F\" ? 1.0 : 0.0, fam[:, 5])\n", - "# covariates\n", - "X = [ones(size(sex,1)) sex]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Option `pvalueComputing`\n", - "\n", - "VarianceComponentTest allows several options for determining the pvalue. Chi squared approximation is recommended (default). \n", - "\n", - "**Note**: Option `pvalueComputing` is only for eLRT and eRLRT. For eScore, it employs the method of inverting characteristics function to compute the p-value.\n", - "\n", - "## Option `nNullSimPts`\n", - "\n", - "If Monte Carlo method is chosen, generate 10,000 (`nNullSimPts`) replicate for calculation of p-values.\n", - "\n", - "If Chi squared approximation method is chosen, you can generate more replicates (default value is 1,000,000) to have a better estimator of point mass at 0." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loop over genes\n", - "Now we conduct the gene set analysis for all the genes on chromosome 19. This analysis will take a little while because there are ~3000 genes on chromosome 19. If you want to work on a smaller problem change the value of genes so only a subset, say 100, genes are tested. 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"finished 2952\n", - "finished 2953\n", - "finished 2955\n", - "finished 2959\n", - "2753.656282 seconds (2.33 G allocations: 2.828 TiB, 72.34% gc time)\n" - ] - } - ], - "source": [ - "# if decided to run smaller number of genes uncomment the following two lines.\n", - "#genes = 100;\n", - "#gene = gene[1:genes,: ]\n", - "\n", - "nNullSimPts = 1000000;\n", - "nPtsChi2 = 500;\n", - "pvalELRT = Array{Float64}(undef, genes)\n", - "pvalERLRT = Array{Float64}(undef, genes)\n", - "pvalEscore = Array{Float64}(undef, genes)\n", - "\n", - "basepairs = SNP_29a.snp_info[:, 4]\n", - " \n", - "@time for i = 1:genes\n", - " snpset_index = (basepairs .< gene[i,2]) .& (basepairs .> gene[i,3])\n", - " if(sum(snpset_index)==0) \n", - " pvalELRT[i] = NaN\n", - " pvalERLRT[i] = NaN\n", - " pvalEscore[i] = NaN\n", - " continue\n", - " else\n", - " G = convert(Matrix{Float64}, SNP_29a.snparray[:, snpset_index])\n", - " # get rid of mono-snp\n", - " maf = sum(G,dims=1)/(2*nPer);\n", - " G = G[:, map(x -> maf[x] > 0, 1:size(G,2))];\n", - " windowSize = size(G,2);\n", - " \n", - " if(windowSize > 5 && windowSize < 50)\n", - " (b, vc0, vc1, pvalELRT[i]) =\n", - " vctest(y, X, G, \n", - " tests = \"eLRT\", \n", - " Vform = \"half\", \n", - " nPtsChi2 = nPtsChi2,\n", - " windowSize = windowSize,\n", - " pvalueComputings = \"chi2\",\n", - "# pvalueComputings = \"MonteCarlo\", \n", - " nNullSimPts = nNullSimPts); \n", - " \n", - " (b, vc0, vc1, pvalERLRT[i]) =\n", - " vctest(y, X, G, \n", - " tests = \"eRLRT\", \n", - " Vform = \"half\",\n", - " tolX = 1e-6,\n", - " nPtsChi2 = nPtsChi2,\n", - " windowSize = windowSize,\n", - " pvalueComputings = \"chi2\",\n", - "# pvalueComputings = \"MonteCarlo\", \n", - " nNullSimPts = nNullSimPts);\n", - "### \n", - "## eScore uses integration to calculate p-value -currently Julia's package not updated used R package \n", - "## used R package pracma, need to first instll: install.packages(\"pracma\")\n", - " (b, vc0, vc1, pvalEscore[i]) =\n", - " vctest(y, X, G, tests = \"eScore\", \n", - " Vform = \"half\",\n", - " windowSize = windowSize)\n", - " println(\"finished $i\")\n", - " \n", - " else\n", - " pvalELRT[i] = NaN\n", - " pvalERLRT[i] = NaN\n", - " pvalEscore[i] = NaN\n", - " end\n", - " end \n", - "end" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Output results to file" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\"exactVC_eLRT_pVal.txt\"" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "index = .!isnan.(pvalELRT)\n", - "\n", - "plot_ELRT = DataFrame(AdjBasepairs = floor.((gene[index,2].+gene[index,3])/2), \n", - " Chromosome = gene[index,4], \n", - " Pvalue = pvalELRT[index])\n", - "CSV.write(\"exactVC_eLRT_pVal.txt\", plot_ELRT)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"exactVC_eRLRT_pVal.txt\"" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "index = .!isnan.(pvalERLRT)\n", - "\n", - "plot_ERLRT = DataFrame(AdjBasepairs = floor.((gene[index,2].+gene[index,3])/2), \n", - " Chromosome = gene[index,4], \n", - " Pvalue = pvalERLRT[index])\n", - "CSV.write(\"exactVC_eRLRT_pVal.txt\", plot_ERLRT)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"exactVC_Escore_pVal.txt\"" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "index = .!isnan.(pvalEscore)\n", - "\n", - "plot_Escore = DataFrame(AdjBasepairs = floor.((gene[index,2].+gene[index,3])/2), \n", - " Chromosome = gene[index,4], \n", - " Pvalue = pvalEscore[index])\n", - "CSV.write(\"exactVC_Escore_pVal.txt\", plot_Escore)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Manhattan Plot\n", - "As with single snp analysis, an excellent way to display the data is to plot the log of the pvalue versus chromosomal position. We make pdfs that are saved to your computer. Each of the three different ways of calculating the test statistic pvalue are displayed in a separate plot. We then display the results in our notebook. Note that in general the escore statistic pvalue is more conservative than the eRLRT or the eLRT pvalue. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "┌ Info: Loading Cairo backend into Compose.jl\n", - "└ @ Compose /Users/janets/.julia/packages/Compose/wlPCt/src/Compose.jl:164\n", - "┌ Info: Loading DataFrames support into Gadfly.jl\n", - "└ @ Gadfly /Users/janets/.julia/packages/Gadfly/09PWZ/src/mapping.jl:228\n" - ] - } - ], - "source": [ - "using MendelPlots\n", - "\n", - "manhattan(plot_ELRT; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n", - " posvar = \"AdjBasepairs\", outfile = \"exactVC_eLRT_manhattan.pdf\", \n", - " fontsize = 18pt, linecolor = \"red\")\n", - "\n", - "manhattan(plot_ERLRT; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n", - " posvar = \"AdjBasepairs\", outfile = \"exactVC_eRLRT_manhattan.pdf\", \n", - " fontsize = 18pt, linecolor = \"red\")\n", - "\n", - "manhattan(plot_Escore; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n", - " posvar = \"AdjBasepairs\", outfile = \"exactVC_eScore_manhattan.pdf\", \n", - " fontsize = 18pt, linecolor = \"red\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### eRLRT" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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0IgAAAACjEbRjTocC5PcXf1tZsobbVc/yAssGAAAAADsV29NiGgcy8exrAAAAAAAYzEftAwAAAADoj8WU0JrTtXK5ff9eexLG8NP13fleAAAAAFIUmGQzjweDUakBACC3KsshrMEAAIBGSC8CAAAAMCARWeaRkj/i8UOe5VjEbGF0SSEFQ3MKAAAAAAAAAAxjnrUrlnwAAMAz6UUAAAAAgF4FrgPInWFkvU60PqMFucsGAAAAAHtknRMz4QYAAAAAAAD7fdY+AAAAAACAM8KTPiy3bzYkbP8l8bvscyisTNkI/PDXlB8AOrWzR9bTAQAMb71myd9qJAkAAAAAAACHLOu61j4GAAAY3LIs97+NwAHGkGMldA5WVzOwfNXwRcU58aWqYXlVysYJihMAozrXF+vmAACGFztpYwAJxZQMiqnaAADMpsoitCoD71rL7Wa7y5inRMGEPN4DuNieRpDP2gcAAAAAAHBM1lj4cvsxxrb99z3fLkpXS62ycfRzUt6odAHQrJSOWDcHADC8/XNrez4HAAAABrBeu3nMVY/MIQBjONRTiLwD8Jb0IgAAAACM7HVkRRCFb73OIqHYNKvAmpvEDCMhRxiV5QQAYunmGMBzMVYgASBc4r4pvTOUV2y7owoOAAAA8MhTrADIQXoRAAAAAAa0M6xyf5k4Skc8toVOBRZdW68BaI1ujn69Lr2P/1fJBIAoW696dAypLwYAAIB0te6viyUuBBhD1OM9LmZWAXiyrOta+xgAAGBwy7Lc/zYCBxhDL8HOOaMCHn05tpK1T3noS+NlI9PhKaUAtCBHN6ePo4BzRVfhBIAcfuqX9bzQjtwTsOr75HYWMOUEABhV4wseopRfcTfhALLKssYJzzPkFl6X1VMYhu1phPisfQAAAAAAECYxrCJZO7DHcjvWUPSSlAoATsjUzR3tbeGQlHLrthEActC3QvuyPmlcIzCto4XKHRkAALyW9d4NKCNHLRZ/B+DRIjkNAADkJj1kyzwMDTinlyDcVK1Z7EWZ6tT1pXDtUxJ60fIjbjzVE4CB6eboUWC5VUQBAOjFi2Fw9UzKxtXTSixOSg4AMJgyyx6qD6JKru6o/mNrsbYKupa1CquwMADb0wjxWfsAAAAAKng79fb4AlNpAO0Lj6lI1t6mXjL7MIN2Wol2jgSABr0ePiX2IAXGZro5wsWWW0UUAIDG7RkA31+zZ3Ab/hxsI+o5hZQid2QAAABwiFtpADaL5DQAAJCb9JBNOb1OxWwa8EUXaQ4mabvyXYtJTmAvqlQ6ZaAXzRaPYgemrALw6GgHdK4fmeQxeowkU6FVSgEAaFDK6PftEDdqaG0sPafwWzMFCQAYRu7ISwsDJ4soynCeoVMFKq9qC72zPY0Q0osAAEB27t8akT7jZkINeCS9SDuyXotJzmHjKlY3BaALtUqI9CIANCjr/rGoLzpKN0cId44A0KY9fbSuFg4JGfrmnvxUr6cl7SMAwGszPGNJ/voCLFaBHqm5wB62pxFCehEAAMjO/VsLAmfczKkBd+1nGJmhyZKvfXjVK5oC0L5mE9AUPjBlFYBiW8iivmsnfRzpZnjkIwD05VzvrM+FtwqHxdVlDplhr2xdr8+wswQAvcgxamptJGDSvgBr6qA70osAe9ieRojP2gcAAACQV/hc23IzrQbQiuqJJ8jK9QUA2Clw4LRn3qPwOM1UDIkKlFilFAAOOd07b2/U7cJPYoe+e2rc/f/u+WqVd3JZb80mvynbeW7vL5v5XAFAF9Zr8NhJ7w/QvpIh+MlvogG4SC8CAACMLdNcm2k1oAtaqiia/SrkFqF3yjAAxcisCi1QcQBgj5Cxq24XvlU3Mq5WUt2cvcO5ii9dFwC0LzDDSJudfngKlS8fziXzSb44zwAAPfuofQAAAABdsl8U0A5U5xIMzMUFANgp3/4xGIPCDADtCOyXdfHwRdZKocaRrkwpmqqsLrfU35v+CQBAVum5G9Zr0wkgMh1byz+5vHxnw3kGAOia9CIAAMCwrIQAIIo+pSRnG85RdwCIpWcBACBQ+PDSeBXuClQHNY4Uyk84GbsAYBIp+UG6yP4QfpBd/OrCctex3/UAACAASURBVJwT5xnClb81czMIMDnpRQAAgDFZRAVk1X4LMHwYr/1LAAAAueUeFbcz6m7nSOhL4ZKjoALATzL1kjpfACYkYxcAzGZLMrJzJdihF7cg6mj7+tWFxZ4Z5xkAYACftQ8AAACgY8stfq78xdIN8/LATpoL+mUJIwDATrUGTgZsAAAcknUAmSNUB30pdo+mutGF4QtqvoxdY583ABjDwP31ek0a5wx8ZqJspyhxMOk8AwAM46P2AQAAAMTrcaPLcvv7b89revyBMBJ1cE6u+5yExrvgMgFAPobBAACkKDCeNGSFYlQ3TlBsAuXO2AUAUNF6PbP249y7ppVyrpxnAICRfNY+AAAAgL6lP8Xl3CqN7V2m7IFnWgb6ZeUiAMBOBk7QJg98BgCgJPeGQCC3tABAdY+jkZ/ud4xYUtzP3s7bSWcbAGBI0osAAACj6WgRVfqhWt4BfKFNAAAAYpl8AADgnGIxO0NWKEZ1o32jltKOVsIAAIQYclDXCGNLAIDJfdQ+AAAAgElFTdAvN3P9UFTLNU5Ula61XLkAAAAAAACgipIZuwAAGNjR9cbWJwMADEl6EQAAgArCJ9zN4ANyi0A41aojVS6WEgJALXUnAXS79MJ0GQDUVbgv1vUzIcUeAACAnVIShUgyAgAwGOlFAAAAUh2dN880z276Hqa1Xu21AwAAMjLnAAAAwMYdIoxNHQcAGE/IGM9AEQBgGNKLAAAAFGWGHYglsQhkonJ1xyVzBgAAHhkdAQAAMACLTAAAOG25RY4nYz8NAIBaPmsfAADAXP775989L/v153fuIwGqyD2xvtxsnIBZqOwAz9ZruXUMb9vhkgcDAAAAAABsLJwAAIBLthXLxtsQzjIzAAqTXgQAILudKUV+ekvXqUZO/PZEXZ8uCGHivkcvJoVdTTZKAsNrLTym0gEAvFB4eZOxGQAAe4iRAQAAAAC8ZR4VYHLSiwAAZBSSXOP+Id0lziifWwQa19rGaarbUyTurzGTC9WphvNwrbtWZrdzg4WkwUMCAJiQURkAbATFAKhLzqkUzh4AwACyTs4YMQIAdE16EQCALHJk1tg+s7skI0B5Ju7bdyJyI88IQBma2QHkzjCyv5CUyXUCAFXo5gAAAID27Zy+EB4CAGAkBaJ4FipDLPF3AEqSXgQAIFiOxCJfPl+GEeiRKT826SVBVAY2oinkoIEdxmxNhKILwMB0cwAAAMAJh8IE24sbnIWYKtgBAABAAQ3e/Fa35+7beQNG8lH7AAAAhpI7t0jJbwG6ZolJm6Kui+tblwniObnuubVwhls4BgJluqBHP1a5AmBgBbo5PSmJChchJRYAAOCLvm6UlptYPAAA3Ss2pjV4hlhl7qD7uk/PbZsH2NmamTQARiK9CABAmJJZP2QYgaaYaGOP2ClFc5QAgdar3nxM4Zf13AfmLl1KLwAV6YYAAABqcUcGmYjFAwAAwPDO3fubNADGIL0IAECM8vk+ZBgB6EimmUQTlJOwPPQnxc6MSzAwiUWGF3V9my0qbR4VAOOp0uPo5gihIAFAFbpgAHrXSCxelwoAwCGFx7GNDJthGJ5iVUZ6ihBJRoDefdY+AACAERzN9PHrz++Qj/rvn39ffBRMa702N1/T2vFQmAIwngbbmWm5FqQQLZtEekORXlQyNVbKMAAt0M3BRqEFgOp0xzCYb2821fQe9RvOW26KHAAAAEXlu4l2h7sJPL3mDYB+SS8CAFDOnlQg99fszDPSbIaRoylXoF9mhXgt90opU5PDc32rcwmK6Xd1Ke3bKvKJAhbYAoSXcK0TwMxaGzjp5mhcgSqj0AIAMI+so9+3Q/fHFxiHU8Cc4fgJfzIAAEA7cgQ33ehtwk/snPMGwACkFwEASLUzj8bRJCC//vweMkNHm8lQALpmapLJZd0npnINzMWd0P2iv200MhWP01lOfvooAChjT78T1c3p48iktaQ8AABd2DOCMoava6SB7okfsr1FISQ34XgAAAAKi53zcVe7yTSNZt4A6NGyrmvtYwAA6NieDCCJCTUKfEUOPx12g4dawLIs97+NwIsps45q52RQlUVdJqpaUOzSu9xVFLi+rux+OS6H819eyR7T9aWilKKu6AJw1+Ytp26OlmWqNYouAPykcIBMpxzlxIVz8isqH4kOv9wyMs+g9zw4dQvYANUcAIAyag28DSAhE3MmgXK3kM4zxdieRoiP2gcAADC49Gwaez5hTwoSAAbW+3qsTpkLbkr45XB9qyh22l1f6lqvZwrhuXcBQKKjvc/WYZ17F+SWo5gpugDQCJ1yiOV2MuR0+o2kK1z428wtEvg58K26BUwfBwDMbLvfdNcJzCkxjC4Kf6cTAfjis/YBAAB07G1Sj/TcIsBp61WWWSozFzmDrE2NRuaowMvh5AMF3Jua122XFgmAnxSY+kjxtqfTx1FFbMVRjAHgtcaHrHyRfrG2TzBGKq9YXWs2t8j90xS/ZukROqIeAQB1vRg3fvlfxi3AJLbm7tBttRayPBNTQF+kFwEA6MCvP7/fpjIBntn2z1TMSw7G1TznRBzlpw+hFgnCmJAyCUCzQjqpYj3di2Gk3pZHITcdChUANEXXnC5wVlbEqooeczfkOGDFj3yULgCA3I7eI8hxCUzlsbnzhI9Dik2amToAOvJR+wAAAIb168/vkp8m/wh8yxwNkFuOdkbblWi9njyHp99IrKxXwSUGAEZi4HS5XJbb33+Jr2EqKXd/7hwB4JAC/aauOV34UNnYe0ixdS1fIVH8GFKxzk6vCgBUkRLEEQACJrTFK5//AcBO0osAAJwknccLP52c2JQrsFMLO/9N2E1IyGoqsfPyWowo+6+L4EqDMl0OVxkAGM/MA6cTC0atMeXR0TtBd44AwJAyjZANvMvrKAFl7uKh+LXJ/VQiJxAAGFXIAN5dAADf0kEAfOuz9gEAAIxJHg1oynoNmxuyYgP4SXpTo4XJwVntV2D3ff9AAIAhTThwSvy929vb/5mU8VgSnouWcgIA6cLHq18+nBRZl9cvNxeotO2Eh1/WvnKLQD7DN2tj/zoAoEGxdwfDj9b2yDoJ8+JLAdjojIBefNQ+AAAA9nqbsuS/f/4tcyTQo5CZmo6mezo6VBjJ6ecYewAyfCuwXqhiAMDYpho4RS2LtKWNZ9vt+eM/ACBEpl5VZ53IkHhUsVWj04qmeLep0+LUjqwn0NUBAArLMWh3IwAAAHtILwIAAMwiZUtA+nYCSzFgHof2INmtBK+F1BG1DACYwSQDp/AH2QEAUEb4ULP9sSsXQ+56oipIeEVTJCCRjF0AwBjy3Rq46QAAgLc+ax8AAABAUfdVETujCFZRAKdpQCDKej0Z/lcNAYDZbOOfE2OnLgZOmR5k18VvBwAYwOnB6refQ6JiG64MuWtJrHEDXDVlr02nIz7chZ9DNQUAGIx7AQA2piAAfiK9CADAGf/98++L//vrz+9iRwKcVj5+UGyhjNAIAOM5lCBMVwgATG7/2KmjgVPWB9l1dB4AAHqXGC8zcoNDTiQZUcvIysaeEIGLT1R5AKC8AmPCmUM/hTP6TXueAQC6Jr0IAADAUEzWAzA2PR0AwH7DjJ1yr4OceZkpAEB5J/IdXAYa3LbA9v7Z7MlBWaCKKXgoA4HOdabPnwAAUJgxIQAAVCe9CAAAwf77599v//uvP78LHwk0qHBecAAAAIDemUsBABjS467muikPyE06v3bMdiGUPYYnYxcAwE9mvh0otlB52jMMANA76UUAAACKyjpxb7IeAAAA4ISZl5kCAFRnJAYwPMlD87l3o29Psg4XAAAAAJBeBAAAoLRMGUYsBGlKsQTwAAAAMLCSN9cyjAAAAAD9Mq0BADSu8IrKmeM+BdavTntuAQAG8FH7AAAAAGYUPrFuph4AAAAAAAB4TX58oDwtDwAAFJZ1UbEVywAAXfusfQAAAF369ed3+S/9759/y39pAW9/V5WzDQUEZgc3U89FMQAAAAAAAAAAAAAAYHqBmzUABiO9CADAOPpKw7E/W8rjK/v6jfBW+qSVjBItMykJAAAAKcrfVi83ky0AAAAQaZiguRkDAIAUwwwLO5JpCauBMQBA76QXAQDow/5kHI1L/CHb2yUZYSTbPPuJGXwT9AAAAAAAAAAAAADAs9NLlF98GgAAvZNeBACASK+zh0QlSZFkhPHc59z3TOKboO9Ipuzv334RAAAAAAAAABBORB4AgH6FLGQ1JN7j+Tw7bzAVVR7ohfQiAAAd2JOVo/1cG1G5RR4/sP1fDUeZVOIExQYAYAxfFpoY5gEAAAAAAAAAibblB+eSjFi68Nrrs/r4f51JKK/Yg0IB+iK9CABA68KzcoxkOzmSjAAtMy8JAMBPrDIBAAAAAGic6VkAAMZwNMmIkfALJxYGb29xViHFt1WvkWrVyGEA7CG9CABA03bmFpk8v8Z///w7+RkAGpc1w4i5SACAHh0dH1plAgAAAISQGZ95mEwDAAB49niv9DxF4E5qj5SpFcs/4ISUBziZDgV4Jr0IAEC7duYW4SLDCDArAQYAgO5YZQIAAADAhOzloFPmYwEAGJjh7lFRN7bLzcmH907UuFprq9RooC/SiwAANGp/bpGu02q8OPij2VVkGAFalmO1nIlIAIDuhIwJJRkBAAAAACjANCwAAHAXuwxYhhF4LfABTpLeAnzxUfsAAAD4xgy5RX79+f364N++AKAv6zUyEiCoAADQnfCFJgAAAADtE9XirmRhUPBIpxQBAAQyuKJ3ORZpWPgB31pukQ9w2mTthvRxQHeWdV1rHwMAAP+zP7HIpb3cIjsP/sRh955vZVmW+99G4EDijKcpSACAHmVaFGJwCIytyoo6TSsAADMoOdg2xuZRsbKn4LWj0/1yihAAQLjyI0ODOqJkLb0KKjwKr26PVSxHXVaFKcz2NEJ81D4AAAD+p+vcInv8+vP73GHvf9ehcwhQxXo9P5NoChIAoEf5Fpp0ujQfAAAAmITYFl+UKRIKHokUIQAAoBgLP+AuR3V4/Mzw+30TCECnPmsfAAAAl8vxpBid5hZJf7vsIcAw7vOJe2ZCTT4CAPTLQhCA09Zr6VbUDTgAAJMoP9iGYtzZkdLEKT8AAPkUvhU1tCNKgXK73JRYyPv0pnsVi+qM1Fmga9KLAADUJ7dIrP/++bfHUwRMy/QiAMDArDIBAAAApmXKgm/JbjObKld8a38Ofa8mCwAAeOYGFsbwJcPIJa12m0MAeie9CABATUcTi1ymzy3y68/vEycNAAAABibDCAAAAHCUFA9UlK/4mSXj0b08vChvygzQlD39o4YLYA+tJd2x8IPJlX+A07kkI+opMIZlXdfaxwAAMKNJEotksufsNXW6lmW5/20EDgAAMImSW3REr4GBFWtOtaUAAEwo33jbAJu3woufUtcsM6UAr51rJ7V4QKfKDA41koQonJhVuWVaLSwJeH0MqiftsD2NEJ+1DwAAYEZyiwAAAAAAQOO+LCOzbgwAGM96zbJ838CJPWKLn1LXskxNDcAAUprHL09fB+hFgcGh5hGAb70YQus7gKksktMAAJQksUiUt2eyqfMmPSQAAFN5uxBENI5JeI4NQJQCLapWlLud5U2ZAQBGEjvkNlLiqMQSqMh1wTPqAZ5FtY1aP6A7WQeHWkUClc+TqAAzIcur4BDb0wjxWfsAAABmcSKxyKWxHBkAAAAv7A/13V8pXMfAPI0TIFDuB9kZk3A53ncb0wIAI4kachsacc7pEqjIdaTAM+oB+hLYKr54BjtAm/INDrWHAADw1iI5DQBAASdyi0gs8tqeU9rOOZQeEgCAsXm2JDzzEJuuvbh8zjPUIr0IWaUXMKUIABhDyrjIiIgQOwuh8tYpd/cAmxztoWYQ6E54Y6glJFaVDImKMROywgoOsT2NENKLAADkJbFIPm/PbTtn0v0bAAAD81xT+JaFJj06dNWcbSjPg+zIIbBcKUsAwDCOjpEMhLhky9Z6/1jFbBju7gHyhZA0hkB3TNHTMqs+oAAVDY6yPY0Qn7UPAABgZHKLAAAAAwsM720fJXQH1HKiQdNwQXnr1YPsCBZbopabEgUADOI+qnk9XjL44bJvUJ2SIkQxG4+7e2ByWfdPmp4CurO1Wulto9YPAAD2k14EACALiUUAAICx5Vj6ZsUbUF5iaybJCBQWuAdJzcWAFgDgLWMbfnJuOG0ihU1shhElCuhIgWezm54CepQyPtToAQDAUdKLAADEO5pbRGIRAACgL/mWvlnxBpQU1Zppu6CkkAfZNVhnPRa+MANaAAA4TbZW0kVlGFGQAADGcB/X7R8lGgoCAMA50osAAAQ7lFtEYhEAAACAKmI3lttMDoWdTjLSWlXd+RPuL2vt+DuV+zmxOgUAAAYmWytREvOHKj9Ad3JPST1+kUYS6NdjC/bccmrfAAAgnfQiAACR5BYBAACGZzcmMIAcTZnmC8o7tBOptRp6riHylG8AAKCW8OkUcylcTiUZUWwAACZh4Ae89eJ2UhsCAD+RXgQAIMz+3CISiwAAAJ0q81gty8qBrPI1ZZovqKK7epfeCmltUhjQAgDACZkG0kbObO7FwMYwYFRlpqQev06zCQAMbM/g6v4a4yIA+EJ6EQCAGHKLAAAAALQv9xJea3aB16Jaoe1zNDgAAEDvzKXwSGEAAAAasV5LJwhjjxMXRVwVAL6QXgQAIMDO3CISiwAt+3a+1VwqAPCoZNTcmnI4SpVphxYM+En4aEqDc5QBLQAAnCBbKwAAAFBX4uyEJCMAcCe9CABAKrlFgH69nWm9v8B0KgAAMAAPFwIqytQE2YYHAABkZToFAAAAChDyeyFqdkJoFQAul/9j716ym+eNRYFaXh6Yp5J+Mppz+mcqnplug7mKogfFBx5VwN7LjT/5ZJkECw8WQPDru/cBAABMwd4ixX3c1UWZw7rL375M6/J5S8cAAAC2cPcEPNAszMl1BwCAjUxGAzAwfRwAwBllR1NSEKHY7QWgi5/eBwAAkNvHTS6+Rt/nwjYfkM7JlKhtmwFgWu0nVg08SO36azlCOK4I0Evt9seoaSMdAQAA7NV4FL38uV03OOtH6F4JgGnJGQLAeRZ+ROASUJZBMsCX7UUAAGqzuQa8szHZJ4NTVpEc64FFXQAAQFXG5wCT87QAAAAwjC3z0Rsnvm8fc8cEAABAZO5bX6q3t4jZVQBm9t37AAAAEvvXP/65/gF7i1Si5LO7/O1I9i0ftvHwecWL0UUBAIB1FiJMzk0TsNAaAAAASfW9nXk3wX1s4tuqAwAAAA6w8KOj2jfyEgVBqGUA7dleBACAUz5u5PFxKxCmcmbJjhTeGZVKz0UBAIAIEk2033aQfP5p8KcBxqahAwAAxnN/p3M+iWSTEQAAAPZqsCQj0aqPwcgSzEZdA1j89D4AAICsPu6a8XHfDY6xX0le5xNwyzdI64Ry+XNFAADgretv9bUIKQbkWwrh9pkUZwSwkRVpAAAAJy1T0gVvr8xxAzADnR0AZKHXfsk0ay/rJV8pXBssr/pS1wDufPc+AAAAxtd4QxAbuwRU9hVA0oV7KTEAAOho8snpA/eD3iILAAAAwL3iySLZJwAAALart/Bj8iUlEUgRLJa1Oh9LY+PHDlAXAFqyvQgAQBV2uKik8U4lFFEjfySRt12DsnI5AABg3ZwLTU6uJ7DJCMABWk4AAICN3EAB0Ezk2RwAYKMaHbpBwjvu2Vs6tj4n3aoe1Q3g3k/vAwAAoIOPm3Ts2h7lf/7vfz9+4b/+8c/zW65s2VvExi7R1EsbXf5keQJxOQAAYN31t/z9UeRBeKmTda8BpJZrQRUAi4+ttwEqAAxD6gkAAIDtyi78cENKBCdDevn1UsFcY23V7ZsBuPfd+wAAAFJa3+fCDhcvbdkcpN6vMyRPaHykiAAAII6yc9WRZ77L3om4rwEAoIHlPXtbBp/bPwkAAAAAwEiuv2VWa0Re8jGhORP+BWc6gm+7o7oBPLO9CAAABWzcUeXwFiEbf9HGLtHMmWsDAAB4p9Qqk8gz3zXuBN1dAgBQz+EVtDYZAYAB6M0BGFLkiSQAGMCZZRvBl3xE4Fa9geKFHHaHEdUN4KWf3gcAADCgw5tonBd/f42lcHYdp71FkmqT2rv8Sfq81Ti76loAAMAWy7D58HA9+Ki73m2IOw4Azrv+Wo8IPDrfLBipAkB2enMAGpCYAoDx7F3+4d6TICqNSwsmWE6urXr4HgCe2V4EAIAy/uf//nf7viq3T67sCdJxlxZysdYHAKCN9oveDPMY2JCrTGo3Ee7+AEhHzwWRFRy+GqkCAAAQh1tUAGjpvud9Tjvrl5lK2emSM4sVVT2AdbYXAQDYzbYX7+zaYWRRqjBXtimhCy9bAAAA+Mgqk708twkAQBHFZzGMVAEAAFjX/l0OAEBjssTEl+vVQbevGuwFTgDd2V4EAICSDuwwUuSPNv6LwEvWTwPAwKx1I4WPgRp5vBr52LaI30pYtgvAV9vuIHvnDgOr1A7IkANAXvpxANpokJvSowEA8E7eZTMrL3AyAAY4wPYiAAAU1niHEXuL8GWtDwBAZV1mFg3w2G57iN4+KcDySnQDmOU4Aajk3RBFBwEDSzRYBQAAADjP870AwIPacyXGGwDn2V4EAIDyli0/am8yYmMRAABoIO9bC5jB4fhcftF8cykaCgASafCS2HX2O4O+arcAdhgBYCTdB88AMJ6q3asbUtpYieH7fxKQABCKJA8AN9+9DwAAgGFV3f7D3iKRyT1F4CoAAEX0GlRYacRHl78C8WnYPJuWbYt2DPjSFPBJkfEMsEubSqdqQy5Lj3z7AQCA2iqlDWUjqW3vfZP7LACYlgEAQHA/vQ8AAICRLZuA/Osf/yz+nQAAQG32FiGsgsHp1eIZuWoAnBTqHezLkejaAKClja/a/tJHAwBQR/H0lIErtZ2JWFN7AFDDfe/8sauNMzcKQASX6/Xa+xgAAJjFmX1GUu8qcrlcbv89wwi8S/rJ/NMzFwIAOMPeIoRVIzgF3hnpbj0aHLCIAm4aN5Lan12iraJz+aA2bTJwuB1Qo5lctJFzPSo7AO2d72f1X9RWcDQoXAHYIt0qlGa2l8zz6bQv1RRFChnN9ngaldheBACAPrZsNZJ6S5F7s92/SeoF4UIAAId1XC9uOMG6esEp9g7LeOtR+5iFE3CvZTup/dkr2nOSriBUZXsRmJznNuGMaCPnSlRzALo408/qvKit+DhQ0AKwhb0wHhwrkPuTyri2B3hptsfTqOSn9wEAADCpYbYOAQAAijChyEdVp7ovf4KQMgQS0Iv254Drb6znJA1IAKCGUt29nhoAgBqWQebeUauhKQ3UyJ26sQIgoMh905nuePndyGcHQC/fvQ8AAACAQcg/AsAYvKyAaYV6upiq6rU5WjPgmZYhuGgXyIAEKmlfuVRnCKJsZVS1mVa0YXMNM5wjAJFdf//9s+Uzui0aqHf748YKALa4/JXpNPW8ADz76X0AAAAAAABMzQI4tjDbTVnX3/JBpTUDOtIEnXHsDbH1eIEnAJTiVdtQUI1cCgDwzGiTCGoP/NxYAbCuZRYiZpdk02QAqrK9CAAAAAAA3cSco2ValrJNpex6FJED61aq2wzVx2N4KUTbZAQAOKnqq7ZnGMTCM7c2AAADM9IDgCB0ygDUZnsRAACAMTVe3WUZJQCMwfQkMYlMKinyJLm7IVixpX7dPjN2baqaqBm76Bp7KMxegxAPLQNAcDprpmWHEQCAwUiBAhBTmxREwM5I4gWABmwvAgAAFGZFEQCQyMO4JeCU4fAsGyIaMTmhw5uMCBVYcaBOLb8ycM2qlDQbuMQiCLLbCACwl14b6imyWysAAN0Z0QEQnEcSAKAS24sAAADpeYjinWZ5VZcAgFxW+sf7f9LBQRDWCtDGrmdj9BGw4mS7PfYmI8VzNaMWFF+2PAOAE9pkEnTWTG6wJ3xUZwBgKkEGcu6qgFAsFZhQwKsZpI8GYHi2FwEAAAAAprB3+u32+YBTiQBUos2Hk0oteBp4VXHBZ/BGLSIAgCwGHrXCFrf49+gLAEAiBm8AD3Y1jGO/KSGgetubBryI+mgAmrlcr9fexwAAAIO7XC63/55nBN4ywxUwwRdK7Wuh/AFI4WSHOE9/12Wecp7i5RhhGV/ja+TqQFjFW4OB67vRaUbtxyQuNBTktgLm4RaVk9ZDyBVfkfoZGFcWAJhEwDGbkRjQkQm7LCaZhg7YTZ8Rs5BhAHM+nkZxP70PAAAA4BS5p4/q7dz8pfwByKBIP+jVEwAA62okHwZ+G/xyXgcKbdQC4aWBqwAAQDQbB+e3jxmnjcTVBAAmMdhDywzpZZQasVPJ+VbRcrJmDk+trnxbNLppAFqyvQgAAFBF1S0t2MvlAGBaZXtAT/cBALxUL+0w9gDsdmpbCnDgcgAAyGvs8eokjt3OeILoWdIpaRcRAJhExqEak/gYnPcfMICnCMvJkjqfeXClAGBhexEAACAxab7taiznUv4ABFdjfYwpYYBnLZ8e0QhDQLVbgBkGYMOfIABAG56XY5ciryk2mL+XdIcRAIDhGaQR04HItNUj51lOltpSzgcuogvUmAIHCO679wEAAADDqp0Yknjaq2CJXX+VPwDR1VsfY+UNAAAA5NI+oS2FDhBcqTTv5U/G+L/k6gFzHS0AAAzj5J2UGzECEpMtLYvYN97UW/EOAM9+eh8AAAAwsnqvJ5LpO6bIFVH4AMRXe8rWSycAHrR5Oa22FwJqs1TO6AsAACio+I2Me5Z7ZdNEh99LvPGbAQCG54l3oim426NRPXtVbRLFZHvDFPhgnfUw1wVgYN+9DwAAABhcjQyRrNMZZ7ZhtoUzAFCc0QWMYbC1DsBG6j4AAJBOpRsZ90f3SmV9b99TPI0sLw0AAF2UvXVyI8YuAgYAYGF7EQAAoLqyS3Ms9Cli70YhNhYBhqLIxAAAIABJREFUIJE2k8GmnAEAWjL6AuCklilu6XSAsNxZNHNyfvn51wt2r3pqAGAeBsCEUiMgBTkbWU4Gbci6AKTw0/sAAACAKVx/y+RMpZzKeijP+2ukqAFgTqWGbcBUmrUblz+3KgAAAMBBtTMYEhfPlgLZVfIrZXjg27Z/OQAAUFW9OzL3YgBBaI0BsrC9CAAA0MjJtT7yTQ0oZAAG0HJrDAsUYFSqNhBfhB1C229JZvQFwEltttTUWwHMzG3LS/dl8q4v3l5uxxYeuC4AANCR3R7py3IyaEDkAyRiexEAAKCpsq8nAgAgtTgjvY0D1DgHDNOyFodprXRVD/+kjgDAito7jOiIAcJqv0MiL5XqK7cvPNA7AwDTMgYGANqQfgHIxfYiAABAB2VfTwQAQEFtXuYcx66TXT5spNrYbDGZjqsDte2tZXorqKT9mERFhnRUWwC+bI3alqIGAID42qTW3YsRh2gkhYJTnwIeIB3biwAAAJ3JKAEApbR/xN188Bndi+5wwHhse2yuLBDHmbGN3goA3rGDIcCEtPwAAO+sj5QkmalKgAFAR0tHfDJvpjcHyOi79wEAAAAAABBLg2m/vjOLl78CTxR4JqElU9HcU/uYRJFQV18A4KXitxjXX7ctEIKaSBDuxQCA+JYJ04/jlo0fA3inZQOiseIlgUF8fbOahyc4zIwA5GV7EQAAAAAAHg08+Vdw3YC1dOMZOPKBXMr2VkApLYcKhiVQW8FlryoszEwLAABAOsemOE2MUpz7KQCIY/ukyfJJ/ThAaj+9DwAAAAAAgIiuv7WWiHWcX6xxRpc/M6Yt1AtIgGiKN3e6KgB45/yNhk4WID45JQCAm5NDo+XX3QsDG7kdA4jp3XDOMA9gErYXAQAAAADgtRobOgy2t8jtm02vDsBFBCKo1FvpqqCUNlueqbDQ0lLjDlRtVRVisj8pY9sY3jopAOClUkNlCWfOE0JUooECAICPbC8CAAAAAMBbZR/JGHJvEZqp+oCQNUZABFV7K+spIQtVFbq4Vb2P3bFKCtxoEGhp1w3j7cOiFABYFE8+SzgDABRk02QAWrK9CAAAAAAAa4rMXw6/vMwSujYqzaa7dkAEDVYL6a2gCMv7YGz6ShhAm85ac0EzZ+LZbSAA8FUt+WykwWEiBwA60hED8N37AAAAAAAAiO76e3xm8czvltLm+U9PmbZRPJy6xycAkE698YORCQAA986nHC9/8pYAQC2GGRwgBQoAL+kiAWjG9iIAAMAUllVT1k4BAJyxbBSyfS4zwsYiDKlgaAlRIIhm+YqCf6h9E6rRJpQaASnIAaCU2r2qXps2Ct7BmSUHgGnVHgYYZkQW8M4l4CEBQBwNOkp9MQBfX18/vQ8AAACgipWZy4d/kiYDANjrYQR1G1/tGlltXGp2frTWck3b5c/wsp3r76mL60rl5doBEMTJ0cjDVwEAZRXsqZ+/GWqrEb1SlwAwIXt/EIrhKAD0pS8GYPHd+wAAAAAKu/ztmxnd+3kAgBtTbovr779/Nto1AFs+bMDGS7sC7+RvAdTTuJvTq0JBRcYVRiYAUEmNTlbHTWruBwGAGowx2MLNFNCL9odc6kWsugDAje1FAACAcZx57tQzqwAwAHNg8RmwUcP2DW5sLAJQVstGVQNOZIfHGAYnAFBb2a5Wx00bVbOgUqwAMA/9Pl8x7mJkQYHZaPQ4yabJANT20/sAAAAAyigyIXr5kz4DAKily4Ct/bI5Q8qOlHwX11/rUwHg6+v/D0W2dIsGLQDQ0vY+esv3kFGu3EWDQ5XABACKM8DgJVEBAMeUTWfpkQF4YHsRAABgBAUzaMtXyaMBQFItV4obMGxX9qJYnQYzU/2329j2KlJOajP6EqgkIlwBIKYzA1f9OwAAkFfj7fbcQAEB5dp4FBY2TQagHtuLAAAA6dXI+XpmFQCgFKM1GJ61ONHsuhw22exO9flIfMK6j82ISgQAi1ufuH0QrhvlmMOR0+wOUX4VAIYn88y92nN5xpbEIRrpThBSlk2TAajhcr1eex8DAAAM7nK53P7bCLw4814AwLMGi6UyjhO6PDjRfbTWZeVcxvCAk9rUNZXro5MXQgl30WuRd9nL3b3HhwkdqHdqEwA8e+5S9ZijSvHC9hQHCRDHxmZTi8ecTJLyrF5UuPSsaNwciUZWWNVAdjZNBr48nkYhP70PAAAA4DivWQAA2GLvqGn5fPyJRu/YhDhqv/TsK0Oj1N35S5Cl/Segeo2AgIRnh6ubdh4AnukZKS7F3iIAqY067QWQlAYW4EaTSFW3AHt5TyT8ANjuYnMaAACozfaQldhGGgBYUXWokGiEcL4czpxsgwHbx8PzYi5oSdvbS/GSV9ot9Xp+rMZVForQQJGKpnIBAHMKPr/c/vbQsBBIp++0F+RikpQHtgino2YtkoDkI0saAMjO42kU8d37AAAAAELzkigASKrelG2iyeAiI5mTr0avzWgNJpGo7W2vRkuodeWYslVVxYdnpdrny5+mHgCgCjcyAPX0nfYCSK1e66ddhWaWxP7DD3tZTgYA8GV7EQAAIClpcQDgoxoTt4kmgwuOl4IPvYIfHkylUiOZqO1tz3pQorn+FqizRb4ExlO8ZdbUAwCziXyjYWwGsG6eaS+A4mq3e9pVPmpzLxb5ju+wjzuJ2GrkgCFDBQBgF9uLAAAAfCDtDgB5lZ0STjTB3P3JQyMomFbxTQEStb3j0Zi30SXIa//Rw02BjUXgnUptsqYeAJhN1TsOtzMAlXSf9gIAaOzAjiE2Gdmu+JIGCQEAIJef3gcAAAAAAFBLwYnzRDPB9Z48/FgIVioAi+tvgQYhUcPbi1aX4JZavDFQVXlYocEHgDkZS1dSJGvx8msBqKHjtBfAANqkFjWqfFTpRuz++4dxsqCWXx+pQCopFZOKGgDI6HK9XnsfAwAADO5yudz+2wi8iPbr6eV/ASCdOTcW+ao/UnpXGt2feIxzYLkCBmo7XAdVpY+atW+uRQN6K2BFgyZCmwAA0RwbAOjTdyk7yjpf+F3yq2IGiK/XtBcMwOiCr7Zh4OqzRaWYHCn8pl3s1JElDQDk4vE0ivjufQAAAAAAAIV13+qilwYn/vJPRC5w0/nQ1/V3dzU88CsAwBmRx/MAMJvL3/GuWZ++S8HkgzwGQCW9pr0AOECLyhY17p5GuiMrW4/Uyo0saQAAJvTT+wAAAAASuPzJBQNAGsUnyI0EVliOAGxxa0XfNRqa2b1aNr/6wQauv14hCLxmvA0AUznf9S/fYMy/0fl7MUUNAACwS9lJsZFuympMB5jn3e7jkoavseINAJic7UUAAAAAgHFUevouxYx7sycPb6XhWcdn8eME+lJHACCmFLc8ADCw4i8o1rNvdDjPqYQBams/7QWDabyr9ZcBUjxm8wmryIKTwdqcehXWUGcvxQUAzMD2IgAAAADAIKqujzHj/iDRaqT2i+eggfWo1l5Rlk6wgWa9lUsJiRjEAsAkvKC4uy3vKH7+MAAAcM9tCLucmRobLNJqzwWomwAAPLC9CAAAAAAwggaP3kWecW/85KEHHV8KGx6MZGPtu31MWA5JIwwAAMBHkZOZYXUsMVskA9xrP+2l0wSA9nZt9fhl7hto5WWjpAkCYCS2FwEAAIBTVia3ZJMBIAir8xnAsRhefsu4FFJo0FtpDYAVHqYCgC6q3gXo3wEAZtZyhtSwEzjjvg15aLiGb17aNNTyA/DRx8p4/wEVChr4WCvVRDjD9iIAAACnbEnuS14Macul99J4gGaarYsy455X7fVzAoN6zoeutgv40lVBNnbHA4AZ6PEBAADg3lTzWdICEMGBmuhNP1DP9iqpJsIZthcBAAA4Ylcy0R4Tg5FKBoCk6u0wopennlJBa0S6xcvSVmi01PJlkgAAMLMIbz70gmL6EhgAMIM2OWfjCoD45AfgpTMjJetwoKxj9VFNhGNsLwIAALDb4WSi/EV2J2fcBQDAGALOuHsOebsaS+iixQMjKR6uAVuwCNbL+f5flR4NVFrtLXoBAOBr/5sPv4YYS8sGZJFxx8kIO/UAFKG7ZGC1xxjqDiyMjQFyKfiyHy08nHS+PnpGA/ayvQgAAMBnt1xDkWSiTGJSUskAYaVbck1fZZfQ6dapp1LjZjh6c6CEzUbThq4KAACK8/IAuHcypOfcqQdoxsQfJKKLB2NjDjPmgY7KVkDrcOCMgvVRZYTtbC8CAACwleTFzKSSbzYWRd4TBGAGpR7b1t9RT9W1RKmHo6WcKWGPltHAEmDnmwKBCgAAX4Veflh2dO0hIl4qu91kDXbqAYCM4o8xICNjY9KxTgAWNcZF6hccU7w+qoyw0XfvAwAAANityz1/jeSFidssKqWSc1kidtf7FtKdIwB5HRgfXn9PjSpP/jrQUamRquEuDeiqAADgpIKzFaknPvIe+YQa3Mod/hNFduoBALooPsaQgmZyxsYASdVrfjXssFelWqMywhY/vQ8AAAAgAVmGmVVNJWeZaPeyBRjDQ11WN89I1IazbrmOu3o6l54GGtyATNuOlS3baYuRlm4xtj16hSUAACy8iRSeddxb5PY9KhEAdHFgYnT9q2BOBe80179KRQMorvZqHEkP2K5qfVQZ4SPbiwAAAPQkeRGcVHLB97oHP1MY0noVvv/X1DXULmCct+XJ7dTVhFyaNWvxx6LFea6M1D72VkIRAADaOH8nKKXJuutvrCApfjCmDqGIjXUzY12L1gzCYM5XsYwNy5zaN6czxEbLIr39rRkKFgCgLAu6YJ3tRQAAgJ4OPxIz0mICyQvC8mp3yGtv/bUoIbuRhkYHFIxbVQAGVq+dbD/QnbzZR28FAABbuG8itUr3/u4omcdDDRos+He1DyYBgWdLg3BgsKElYXK9bjNtzwdQRJtm3Epp2EL2HrqzvQgAANDBx4yA9Q1EMHkq2avdIamTlVc9BYADhu9Axz47gJhsJgUAZ9TuRoe/DSSCsgPCwxE70oatDG8lXO//KXXgnamSnkxep2QIYmM1LxKxty/5+EdVEIhAV97A4dclZrfeEQx/+lCcjAesazYJrjLCisv1eu19DAAAMLjL5XL778lH4GdyAS/v7UdaYS95EVDLAIsWAFXPPdrJwkgiLDVur9d4IFoRjTQu2iXahYCT2tflSSpRm4JtWZiNQ2WSOAGIRmsPAMc060MD7tewQl+f1/mAiRyrIpPzDgdquvArVSVTnLhUObM5FvPili1mXv9WVpwlGWOXc3u7rmzZwu+eHNh7AEPG3rR7ykzIpBvEYYB6ksfTKOK79wEAAACzOJkIePnrQ97wE0ScGcH2Zj53SK1s5dUUkILRILDFkJ2aBhAAAAAmcTIJEHlvkWZ/hYGdCaFc4VfwaHOdOAzv8ne8Vp75Xahh4IAMdWqhDia1A63o8isDXIJjZzHGuS8+XsrbB4Y5ZYA5acbhnYvNaQAAoDbbQ5a9LX9e/1Tptv/6a6/iqc189b2IDDKq1xum0P2FHkHMNhcS8BLAeV7JWMOob72I/zpuAM4btRcDgHpSzO/IZ3JM47dqyzwQXMEQDR6E004CuiNmBqXiXAyzLsVdUnABF2MMWc4tnb+mie65iksdfoeLPfVZ82U1DpSwXo82xrzKeJ7H0yjiu/cBAAAAgyueAnj+whr3/O33FiGU9lc/Trx5ERlkVK9Oqa3vjDflkI5LAGw0cF+mJQSgIN0KAEB819///Hz8zEktMyoDZ2+op2zYzBmEc541hFKwGl7+VGqoKGb9inlUKZRqM2due5Oe+8nDTnrWLFw7OGNpAD/Wo40fA4L46X0AAADAyColCC5/jyuiyu4GYjE9XbTZOgcYj3Q8N/Psj6aLZFRdqvCWP6rSbfd8u5rdYKcDkM48g3wAAHZxw87MatwlhU3rVb0lDHvWC3fEjG2qpgyoRK0/oMbrEqe9CrnOvdSlz3XWACcdazyX39JaQnDfvQ8AAADgiEobMRR5ldNhFkbw4LaPb7PY8CIy4JnaSjRmnqA975cIq3aTqMkFmIQGH4BhtL91dbMM0Fi9hjdgk97gkCR+v9wU04N6x9hEOGHVe13iMQMMQrLU97LHmeWsAc44ny6Ik3AIchgQje1FAACAWtrfip/cHGSAZD0D80gn8FKbZiF+46MTvxm7KPruBAd8RZr65aZew6jJBQjCZlIAMB79L8HZqYewaofKtKHY/sUnGzXoMXXKtFe1okWrxTCA4NUq+OGFoqymVePSCydgbAVbOQ0mhGV7EQAAoIo2L1F5ae9zp8vnrRggi6lexwSsU217MWxoz2gNQknUASU61DNqtJBaXYBQNMsAwEmGEwDp9MptRttkRBfGYDouqAOgnsNt7wBDneD9jtW2AHsVb980mBCT7UUAAIDEVtINt01D3uXf7SpCXtEW9Bw2xlnAJFTYRKoOb9qPnQzYIKZhRqTDKNtUangBAqrUOGvzAQCALNokJKU9F5NkgN0UAzOYoT2fnEu8hd2dqgp77rUPLOyJAxxWqWW7fa2WE+KwvQgAAFBetDv/+61G7CrCMKJVNIC+mnXuKUYRVZ88bFkCKUobZmZEGkqpNlPbCxBW8SZamw8AHemIAVgRJPUa5DBsuMkwmtWpIJUXIIjgraIxSSXBrztAQFVbzuXL9XoQh+1FAAAAICtTIDAzLcCzBnMPiaY3PHkIGWWsaPqjUE5upmkvToD4Cm4mpc0HgHno94EBtMxDynneC1Iapr0AcgnSfcxDgQcx84WY9tynPXFgPBo0mI3tRQAAgMIaJxcq/TkpErIQq0AzGpx0Sj0x+Pw9bdZcWtkJcMyB9t9D5gCJnG+xtfkAEIROGYAUgkwR1pv2giEFqbkA3aVoDw1Oiktx3QFmo3GGUGwvAgAAAAAMouqMe9Lp/JOH/e7Xa5dG0tKGOZn9jWlZJb/enG75DAABHW69NfsAMCG9P8ABcp4P4hRIpWkvaCBOPQKgnsOt/QCjlGl7umlPHBhJs6asS5s5QCcLNfz0PgAAAADglMufzBfAf1x/q0xCpG5pbwe/vWT6nm/q0oY5GZFG5tIAjGr7OF9fAABhVUpm3n8/wABme2Av5vnGSQIfmPb60icCtKXVhQNqpwggso3Bf7h/Ub8A4IHtRQAAAOBRulRynKU8ABGka8ab+bjgcldvYicXqEELBgDsZQgNAKlJBQDAMWWnvWA8lhIBpCNFUIQyTGTvxVo+n2KEk+IgYaHZhDnZXgQAACipS35hy2zoywNb+S1pegBIrXhXPtjqq1LnUrycRypkmM1g7SQAAAA0YxtfAFIImwSOeVQAQVgGycwyBv8ysDl55Cp+Y2HHyZGdCdFEm4wAQFi2FwEAAIb1Mfl4/4EIecYIx0BepigAHthhpI0iKxvuvwqw1qcUxQgAADCb9vfUMbfxlWdjO/kTAACAaM5kCeQEiK9ILmLvQr6WaUPVEOJQH+Gd794HAAAAUN7lb3cS8MCvMDbpJIDsavTsRgvvnO839bzAGLRmAAAAHFNwpxLYItT8uLiFLOK0GwDQhpEqvVx///1T6fPQS8F7CrcnAHDYT+8DAAAAKOlkrnD5dRl2aENdA+qpN32499UH81iK5UDJK0941v5ly0VoIQEAAOgu6T314nCG7f7X4aO8dQQAgPjcmEBZD3XqdkOnrpFR8YzErmUqbdKGuermywLJdQqc1CVP2CyHL5hhhe1FAACAcZRKNHgojkXqFagHzHa+wMBqt2aGCituJfPxKihDWGdsBgAAAMFVSnAd2GREqo3tZJwABrPSsBshAAvTjjAS/Tt5VeqMLOTb6+OFuP+AsqWGpdoaoEJfthcBAABK6nirX/bvtk9bSMDFlCt7JUsOQBy6JDgv11gUAAAAghjjhvo+vfbudKTg2CVsvRDJ8CB4R2ZZQhBbguT2GZcMaGbIBid410wKtgODqg3p9puU2k168Bp94NyXXwl+XvCSuIV1thcBAADSq5TsMycEVUnbQTpZqm2bHtzSSaANi9UAACAsb/CDybWs+BoZzpNiAhiGZwKBA8w5jk0LH5btwEjhY6CeD84GfVCEHUYi1+KTp+yGghqqDlCFK3xkexEAACC9Aaa+pDAim2p6daqThexUWO49B4PRBQxDgw8AAEGsjMwf/sldOXQ3+ZtIIRG1CWA7zwSyi2tNewNHnRlb9rIdGPFtj9LxNsGp0apHLpxSJ+t9bBRXaYglUGEL24sAAADAByYIi5O5Aypp2Vx3nzBbP1mvUIaRGI4CHLCl5TRMAmCjvQNyDwNABHO+iRReCptZUpvYaMIE6YSnzEeeCQToS++cUZerFmQ7MBHLisOxcWwk2SwUdx1e2ToSeYBdtvzdTVBc8Q5LiMJGthcBAADoTBYjhXmmW9qc6bs/oTpAWKpnKMceavpyHSGzpf5OMiIFOGNXU+nZbwA+OjMI19FAd/NM7sAKtQAOMIQjGs8EAic1GBPO0LDEvMecoeQnpLPOJdHFOt+IjZT0LrUOJ3Jp1Oi2NFCc9xBCBYdYghO2u1yv197HAAAAg7tcLrf/nmQEHnASJSxZjHSCh/fhiAp1XuoFbNes8qaomO2bsvbFMvaMJrBdqMHbvWiNzAxdA3DvTK1XfwF4ZjkjjGGSN5Eyjy0hfR+rYfNID9Qv1jWO5AgBGbnyRiifqVQKBtexvQmbMoKwt0hZ0froqQr/gGjXa5eTF3fafscigXfKlszGs05xOQ4fZPBLX7Xwg587u8Spp9Z7bDTh42nUYHsRAACobsL7t9QZ+ZfqbTw/VS5jJGGDvGVmvDa1A7ZoU4Wz1McuDVrLwvEgBLDL8K3iFtMumYIJefwbgOK6LLYGKilSo1Vk+joQxjFfrr6F6sZLE+65H7YKxymiGXgmcCQtK7WLy429RWqI00dPWPgP4lyLShLtMBIqGq0TeFajTLaceJxtC7bYeLTTXvEHKcqBLaLV073HM2EoTvh4GjX89D4AAABgQHkXKr1jbxEeLNcue5wHP/7LnzoCn43X5/JO2QutjYVSnuumygXQXsGRkmESAIvi+RZdDPR1flpHFaajw6Gbd/pgOXL1DswDUjsA3Kc0plLDMFTnCCa5BFk66xQHObNK9SVLfG53fzoPhZbrTCdpIRnVrbp9jORcFROiudicBgAAaptze0iZqS0kNcYWbTffe7lqqJoCH3lp1aJL49amfOx0BqFsr5Lda1nkEWkz076RCebR611bAAys3hhSFwMRePkhueSa1KtBHeTehK9fjtkIRCulgbUJABe0JXMWNNYs5OaMt+7d9JzF/hWg5Ns7c63nHE7ocO/VLo31Eui7aiX1FiFnGACwS+N6KmzOm/PxNIr77n0AAADAmNz5bzHhPAcRpAu8dAcM7dXrdnXoEdRrBjWwsNflb1/FWT4/T12bvNeY/PShl3rv2gJgWlV7AV0MRHD9/fdPkY9BPVOllVYoBCanJ5qZBnBILSu1sQTU1veecc5BgpYtpoDRGPCQBhatVt4W6jwf2Mo/jWTss6MGbSbMyfYiAABALXINEFDSxHHSw4aWanS7uvIINIAQxMnlBepyR7ozGJjHvwEorkH7r4uBOG4biLz8gb70F/eUBje12+eY7X+0o4p2PJynmW2scSVyfWfW8urPHGld7iLn7I5nDrMz5z5ntHy1OvH4xdu34kSutsNvMtKGMgTIy/YiAABARZUyp/ETstvJrA0s4KyheIOxlW12Rupt8/JQEwRRpKZ0WZqgMW9GUUN7RkoAAAABVUqSuEHjpl4iLnKKL/KxUYl2j4KEE7TRbAvLOQcGmrIzqsbMnAG5iH/uzSpO3hpqkxFYNGvQ4recMA/biwAAAHUVzwKMl1aQmqSN7JGW/fihjSK9pJdzzkYDCyuKryQYuMZF7jvmfKkpAMNYBiQrP0ApFlsDEETqnqLewacuFsqqkY6Ln+IzfwcjaV+ddaMTctGjKdKVTzseEM9fpwthzldFBj884hhpum2YE2FImmUI5XK9XnsfAwAADO5yudz+e9oReKl01S2tMFj+S7pkYM1idT2KhqkyKgtsdLjWt6xl7w7y8DF0aevqlVjL09G6wkv1qmHjStemPYnfklQqh/gnDkMyUmIGB+JcuMJJ+hcAIhhmUq8Sfehs2teI+DHWt5WIXz7DEPyTcB9KPYMtnxiM3O8ubpFuzodB2cLMEpbTrhOIMJ5MV3/jX9aPDAA4o3b8CJVSPJ5GEd+9DwAAAJhCkXSAnAK8o3YAD/a+sWT5fJvG5OMbtr2Cuz2lDY2NV+lSDEfnfKkpABkdviFyJwVnNK4+aisAL+kg4KbXDc5tlirsHVazGT06ihl71BDh5R9Ae7t6c10/BRWMpURhaZ1AR+kKyniJyVWts+kaBBjexeY0AABQm+0h7x1LvQ2wpfFHkiYD676b72D1RWWBY56bgsa16UxbtPFQB9t9v/HpaF3hQfchXFlVTydXA1KwKHKdOIwkwru2oJ4iES5o4QD9CwARDDapV4k+dHgBK0LwqGtWYsHLYSSDTXqyhYpMcVoSxhBwZLi4/iauZSePPGlNL3W9spx+nPgMW4tXZLnKL8W59ORVI4oESVkeT6OI794HAAAAzGXvZuo2X2cAYhiIYOlS739aOjnlEHaicZi9RYAHDeqgLYR6KVUUihSAGkqNEMK+ZBsAgBWGcPAVtSIEv8myrgayi9zCAPAs+9Dr8Ogx9bCzyMHnPf02DGlgDMXbOo0nxPTT+wAAAIAZSRMwm3rb1X+sTVL2QF8Fn477+tTo9Xo3CEAKlRrJjDd3yzEfLo2MpwxAfDW66cufbgsAgAEZ6I4q/hTPlrmqjmpPk4U9cQBgVNHGh4MNh26ns6Wchzn3w2PmXCUQre6kI+0ABTMMahOEZXsRAAAAaKHGah5JNyC44u2e2bt69pbtu4vrAjGAZist2rdpxUekqav8gU1GUp8vAHNyDwUbdVlvrYYCANxUfVcZQEpZAAAgAElEQVRH2S+PPIrr+NYT4LDGN6SRGzGAvqZtHmc78V37qnzNVz4sDJngfEZFJYLgbC8CAAAQglzkDOzmW4TKAllUWgi13gjUfjPbw9+a0MfivX1gzvKB+Eq1k8PU8fsTGWbjpPsTSXfwAHPyHjkAAGBm9e6JusxV9eWtJwAAsIVRbhst1/JBEesRO1vTcWyTkdlKCZKyvQgAAAC0UyRXLu8GxFd1XjDyqs1RHbig9hkhqRneEef9Eu/kPa+Vq/nwT3nPEW66rD8z/qSq2lEtgAEAUvCwzS5GuXQXOQjLPr8X9jQBgLG1v0WKPMAD2tMmzGZjvzPnksjbydp7BUZiexEAACAl+xmT15nnOfem3lQTYEJtBglTzYWcLE+zrRCT90uMYe8VXD7vOgLE0SZ1Y0wOAACEZUa7hvN7TH/JIsK4ZIoAoIh342397AML/gnuWHzOufxmtvOFsdleBAAAADrYu6BHSg5IpMGMoDVPbZS6lHNOp0EK3i+R15kmWrMMAAAAQASpnzGLP1d15im+4KcGAABdbBlg3z5jUA3BnU9KxM8MALxjexEAACAr+xkzgIes4n1ISzgCHFZ7kDBJE128DE2nQWSqZy5FmmjNMkB3LXObmn0AAIDi4t9qbdxg+vnzAGTXfmmlTgQaiD/+HNiBRtVrP26SLvhX47bIW0TeuwZMzvYiAAAAEIX0IjCAZnOB6xN49WYlJ2mrK5WeaVd4pl6wS9n2WfgBACy6LG42EgMAJpfx6bK8vPgkF5sCzENLCMBet75D993Myf56nq0H1s8x6Q4j6Sjnjbx3DeC79wEAAAAcN1IiZqRzAYAIavStk/TXZhmZluAnuBohKuzJpVfETjIIBAAAAPZKml67/v7nBwCAjJIORNMpVc6uF8RR771rAInYXgQAAAAAKKPxHMnHP1d2TeSyu/+Wn4yarR9NWj4A3dVrP7XMZCFWGUz7kFaJAAAAAEKxxQ9VtQwwwcyEIoR93kVKWZQtXhcrQq2ZgQHAOjURYGF7EQAAILeMmSkAoJkiL15bvmH73FLq+fsGR563cABGpWUmuNSDKwAAAADYyzOBAMBGJtEqqVGwLpaRJ33VroPqOJCI7UUAAID0ZBvhnSFrx5AnBdR2suk4NvFz5jnYXm2dKS6AmOz9xMwEJwBtNL4Tl+QEACY30v3+SOcCwGDa5B9kOZhWnOA3Ii2uXpGOerG2V4cirwpjnQFAR6PWcWA8thcBAABGkD1Flf34ASC+ZW5y11xmEWc2GWmm/VAkfpkwJ8NyYtJmMrMI8a93AAAAGnDrAcADzwRSiYsOMKQIc2rDqF2Ytb8/RV+f4iBZkfEKaicB7tleBAAAGETZRFXGtBcA9NVlAubAH73tM/K828j9/1/2dMxOAe+49SAOvRXRiEkAGms2OHcXAAAn6UyBSWjugPNqtyRaKoZxLJhDVQEza/RyuPrselUYuyjYjrTGQAo/vQ8AAACgmCUXdjIpc0uoFX+u+ONfBD5SX4DiXjYsNcYAl799jVizochN+z+nVYdhfGxA1HdgL2tuAAAAAKAqqXuYRL21B5oR6mm/ZmYMluKc1ybwXKl7t6J4V/hFng6YkAHAPfED8OC79wEAAAAUdiZplTHhBetENcBe9SaTYk5T6SmAwy5///4p+EkeKDToy0gJYDYNWn6dCwDv6CMAeFavd9DvBNH4Qrju06px6YUTLNQFjqm6EqBlWBb8W9ff1z8cZgAAwDu2FwEAAAa0N6X47vNWEkMo6gswgF1zw4YiQFiH9wqxyUhwrg5BBAlFIyWAOVVt/3UuAHCe/hSYjWcCgSLKVnzNCIM5GdKhakSQWbaklN5eoYK/uDHOzgCgC40JEN9P7wMAAACo6CGNdUvW7Np5pF6KR5aNNqqGMRDcffXX72wRrcHUhm+3XlDin40aV7qkkXm+iC5/Wc8dAAAAoB4JYQBeKttByM9HYwBAM0v1Pxlv2hB4SWNONOPF5Hhn1IwBgMgBeGZ7EQAAYCLHcluVMpKpE23wrHbuXpWBjVZq4sM/qVbP2swk7X2ovl4De38YXWbRiuwvsPHID2wzBzwr2FbYYWQLSxyYU5DI10YBzMykQF7vLpzCBxiGJh2YlmcCOU8AsDiT9xBFNBNktmgXex+wV+1VE9b0cu9wPLjQAEO6XK/X3scAAACDu1wut/82As+rbI5Vro32as9dFVnLsv7lwLoI0z/tp8mTHv+Bwy5+bA/H0GuJw5kreOaY9Sysa1Mj0sWhByzb69I4uyh0F2HlpYpAAxp5SKFUVVX7qtp1mVwLIIXaD/9EuO06TEs+jNRx+Exk0tiBGiRKD1gv5+JF2mZVDzzYGHjih/ZSL0CKMNZVbQ8YdeJmpDW9qVuGUGYbAIxau5mWx9MowvYiAABQnfu3YVhJTHb1MqT3UV37AXjg2fl6V6qipZ7Da3zwfXcYef7rubYXKXK0+hdW2F7kWZuRJA8scWBO3ddcqgU0E/8eBPiKlHXhmY1HgYHVfvin+53XMVrvwSSNw5cEJ13M9kxgM3tbpywT1iKBLW5BKGDoLvUCpEXf4a5afMDAs/MjrekdoHGIaSnYUU924NrNnDyeRhE/vQ8AAAAgjSKvcpItoqNKryN7iOqyf0WVgY+K1Lix54eGYSiyKNXLCHtWNHiJq9i7ufwpDeA/uj9ZpEUC4MHSNRzroXQrVZ0cNrgTAYKrnZ5qkP4qTrtNWIKTXsReccc6x4JzjvU6aNHCRkIFCjqTV4SyrOk9Y5JM8gznCMC9794HAAAAkMn193gG7czvbnH5+68feKl4EL78wlJ/RcIaPirb4Os+4os8FGmjeJQKe96pWl/SVcbaNUVNBCIYY7AEQCV7uwndSlWlJkFMpgDBle1KnvumXF1VrqMFIJ3zdwel7i9qdHm6USCdkTI2GmGCGGZNb5cDGKlRAoDF5Xq99j4GAAAY3OVyuf23EfhgtmQMa28pslH3lC6hlEp2f4yrw39IxMIWMd9c1HI6rWBb0WUWsFlRb/lDKUqg3kHqd3gnZkvbWJv2IVeZNJOicYayhD2zaRbz4hyKe66/KlobNVpO1w6IrMGDyvEfktFQDyx++G0hRCG7sm1RkTah2aoegJh6jRIrNZsmv7KY5EqNsaZ3kotFKQKGwXg8jSJ+eh8AAABAYh1TP3tTXcvnlwO27pl3kbD3G2r8IdHIVF7Wjo21oN6cx+VPTczh4TI9hMTei3j9jb6Kt+rhCXveqVE1BNtLqiEAALkYvnZRKTngfgSIrMGkXuTksPaZ+EQpZFe8Eyxyf1Gkd9ZAAcxMvot3xljT2yWVoVpBTOutgWoL79heBAAAIJkzKdF3v3v//0ujzOPYesQDEXL7lXd/S9QxlY+VbkubHHal71fD2TtNx4OxCyRyzDO8gs1axnqq9gFAVW1uoDIOQgCeuT1JZ8sl00nBRrUf/vk4l9eFJmIGkXe3AWYQeQfDM1uM6UMBgHVb8gDBRxRuJ2FyG1uA28eCt2nQnu1FAAAAMmmQDF3+hBzKPDauFywSEuKKyR1ow1+2yW36AhWW2Qh7VnhHXBuq4TMLYgBmULu1170CbOF+pKztXZulvbDLAA//bDHAKTAP4QqpVU3IlLrF2LvJiHapqo0XwlUAIJHU3Vb7BRXSyElZezOYY1fTAzLwwPYiAAAAObTPgX7JoUzG5YaqzjTjXdrkM5NhDeZjNFlTMb1HBN4RBwDUY00bwEf2283i8JUyLRXK83V0aaKpdEX6DkqF2bTcEAFd5LrFWH8fiT60gV0B4+4GiEArxCTcUcJsTlZ5M0FwY3sRAACABHplP+VQAM4r1YYvbXKW+bCqh6pvqqRxgAW8jkY+fDTbO+KydDoAwDvZRyMAC/cmKRS5TJIzHa1fwft/dY2GpKWlr0TzXw/0XMBhG9u9l42MlqclWyjCPFRYSKfLjaTbQLYQJDUUXIn95RrB19d37wMAAAAgtKTreACCKNuKNm6TT/65Svl3af3ZGIoQ0PX3v342/hNbqPLdiVuAXooPHoxGAA5wS3JYwaJzFdq7/O1+GbvLNBgXlFG1uS1SgyCjZjX35R/aNZoy9OqoSOG7gnDMYMntwU5nYO2vlNg4Qw/LLqpbdsWrvDYEbC8CAAAQXff8RfcDAEhK+1l8VsY0T23NSjjspVRtOcB+IlQlrihoWcf88gcIolSzr/sARmKsEp91vXmduR1wKwEUVKM9ud0W2WEEiOPwCMrQq73UL5IBgOH17Vv17KwzUVtcpUqnLjO5n94HAAAAwJogmYvLn2wXwD5BGvDurr/FikJPBABV6WpHtWUwdv+ZvpFQcPQISZ2vBdpzgDPMhuxVb12vC1FbkWvnSg3ALRjdNQjCBtkG7SHbrUSjKBrS0j4YeuVSo9dw+WBObrigBjULplK1yhulMzPbiwAAALBJswTKxjSQbA5AFkXWS2n2m2mwytbVBNjFbgscdvhNmF8z9dfznCmJLGF5oAqLZwAas643r4LXbrY7iMFIONBdvSDUjxBNri2Ax9a++zP0ykXfBHMqXj373m1pbQ5rOTXvMh0jlcFhbSq4qg1kcbler72PAQAABne5XG7/bQTOLgHToFXTXnGemvh4JNJ/wLqADfhhBVu8Y8XSrMltf9Ui9yb1SmPXWXepSpGvCzSmDta2pYRvBWLvJ3YpEjDbo6LsO1cbNz6Cn/jkqYDJydhEZjV2UpUunIuVUZy5DPEzp2b9iOcD6etMBAqqGuJ0f2eIjUrc40AoLVvs7oujytLUnOH2IbII9Wvh8uVl7U0ubWp9uqvm8TSK+Ol9AAAAAHA8+1P8vRwbj+T2sXQZJaCBOPNY0exdxDl2Gxv87Crt1h/8rAGa2d7GtnkVofZ5MKU68Y/ht+udq9vDzGvB4IFABWYmzxaZq5OUl7FzoxYzA3FOdyeDUPfKO2IDIKYI408dxEltZipdpgMi1C8GULWOq9plNav1bq+Yk+1FAAAAgoqZCS2eQClymkWO6tiRtHnYD2Ak2swsis+lZbn0pouAqs7cd1Si0RtM8Wh52TMe+CtunwEARmJdb1IxJx9BNZ/TkC1StG7ruZBDHd7wmm0BDJTSpm+K1llAZM32oy9VK4cc4s6p5bsQgPa8dw3gy/YiAAAAdFQwPXdmRcX5w7CeA7gxuZiXueFnBctELwkQs5fRPg+mUpjdL3c+/8LVrw2B57VgAABQXIMxtkclE4mZpgDOW6/d9/+qxa6qzRbAHDBSDygqgKS2N8VtWrkifyVO/6JrSMFlOiBOLWMM0753DeDmu/cBAAAAkEzZV6yUdfj9yb3+OgBzSjSfVORQE50vMLPL39ufIl8ekPZ5MG2eFQz1PQAAzMDoMRfXi71kJ+Y0cFvR8dT2ZjJLZT55Vm8LYHggKgpqWZguHNM6MFyBXurdq7oLhiAKVkb1uobGwwCjDiZkexEAAAA6qJeF6TsFJbsEkJqZnpeuv8dL5szv9pLugIGTtuwhcnKfkZi3CZq7wdQOs+IPnHz8ttohqgoAABwW8x6Hda4awMBOpi71EWVVLU8XC4Ckwg45iswWxTk1k19l1ShP1+iYOLWMwZyvkhnXRgIsfnofAAAAAC+MnQwNcnb13hgjVwjAiqTdxHLY23vPpKc5ti2Xz4Xj3vW39dC9cQQeOLvbr2w/1CC3P/fUdIL4ePtcrxVSCwAYz0OnqbMDAJhEkeTJ8iXGkJCRRUpAfAFnSxf2FuGjspOVrhEEtHdJ5MMvAuRlexEAAABGs2XuvPYbY+QNYU5x5ow5rPaz9Nk7iOfjr7rktP3WBkPaVYYHtk6AjM63LRvH/LUbMXs/scjbXXbZYURFAGAYK73k/T/p+xiAaZeTGt8yuF7xhbqLFDATChWB2ZUtTPXxvAbh7TJBDe37JnWZeYQd+w1WBwc7nVAObz3w/CVAWLtquhoNjMH2IgAAADTVZsZofRY27KwVNLAe/xLfPJgzJOptaTFkeQ55Ur28DLwzJXwmkr2rkIGVauQjLP1cjuH+MJ7PrvtBwrrGO4yoEQCMwT6SMBL3cQAcU2MyK0LOM69mC1FcpjO82wCgpchNbpH+NMIJGhW0cXiTERfovAgV7YHL+ix1hnNvjFlT10CXiu9em9nYXgQAAADKk2Mimo3JVg8bwKLGwjLVipc+RtrhN04XiWFDGloutG0TbMXf5/n1/si7zHarsxMKuKCquFJtkQoCwABm20fSs3+Maj2wD6eDIClJSDig3hhJlYR0VFsgpvgpnQHaz+zHn86uTUZcHYY3QIZztgkXgHu2FwEAAKCdlpNG76Z/4k9cQVnHYl7uG8o+vqI2ZdHySh0IsO2Nc8HoHWBJDdxUuhd4WU28qRK22xLGh98Jdv/rAJBaqRGmAST0dexlmF/ZxrSaGoCqrHmAw1QfgDZmaG/7nqOb7o4UPhzLcEarO17cBUzuu/cBAAAAwJhmmCQjuMvf2Tg8/w2QWqm5H3NIZ4xaeifffrD9/Q9F6Asm16YaNvgrIhki2/6ar73NxYFfAYCAyo5mjY1XGDlQz8l8u5oLwKJBj6DTOaBxoblGxyg3gDYStbeJDnWxTHvJX0FHk1fAMxnOUKuRy764K855AWxnexEAAICIhsw/RkifRTgGaKNs+ps5DdkZ7XVyVt6kPs9KzSm++5JKjba+YHK1m7IB9hbpW0fU0JnNefVvSyfftR4fPwAAudTo8RONInTojKFUOihR5YVdxDZEo1YyHlENQHayZEBfw2Q4J59zAVjYXgQAAIBhdc/WdT8AplU89gQzkzvwbKrHWQtqVpLpNjh4/raqzbW+gEqGaS3v64j6As3c7yRiSxEAhlRvbGnUCs3Uzgi1/OuMwX0T5KVVBwCCSzdcSXfAQEczZ1T6ZjjH+NMEN3MFZ04/vQ8AAAAAgJIqpb8vf5Kncyl7ud+FZa6guh3tSi3LdUbcS7e3yO07t0RmjT/HbK6/VWKsTURZHAAp6GUAoL0s/W+l+5GXfwiKq50RAgDgoxmmCV6eo0EjQCUz9CwAN8NkOGu/uKvSGY2x+HaXZrNCMDPbiwAAAACMI2n6exjDJLWLXOgtRXH7TK7QynW0YxigclV93bSYpI3iNXG80FUfAQAoK/u9MJSS91YrQkZogMQaQCJV+6zG7bls53ZduloXiHvvgvD+/xcwQANuP4GBTTuaipDhLPXnGvyJUmc08OJbIIjv3gcAAADAa3I9xZm+Ynht0t/w0eVvd6gc+BVmU3VolH3c1bIGqaqTu/4Wqy/N6t0kQTvJaQIATKXNGC/LSLLBHUT25AABZalfAABZLDNi9z8bf4svM/IA+WnGoaNps8danr3Ol5jFt0AbP70PAAAAgGRyJUnlywD2OtPOn2x1l1/P1dHQUqVXrTYIOQMSBnO+Mg7c1KvvAACkk+W155XSArcvh7JyvQyTCVVtVGGd8AN2WW8x7v/V0GgLM/IAz9xfQ2QRbiGnbSJGynB2j6ItLL4FWvrufQAAAAC8JcUDbNcs/Z0iz95R9qa7494ixb+HIRWvYvYWOWC8M+KA6+/B6nP4FwEAYCruvF6qdDfhJmUvJRZH2LZCkMQXNnggtfFaP20Ffe19BffyeXG7hVJ60L4BH6/LABrQdEAXqh5tWHz7oHHVU9OZkO1FAAAAACCrskntCHuL1Pg2BlMw7E0LwUnLXiFbqtL2T5alQwEAgMFk3HiUkzJeo1B3o6EOhiBEBQDBndwl5P539XrvKBmgOA0LMJjJX5/jfYeHHTgji2+B9mwvAgAAEFq01GS04wG6kH0OYmmTi7TMJyfDaoSEMGPF+bBvNgEskpnEbfeQdz8AAEBkue5ebTwagaKLY73+5qrdtCEqiGDIfmTIkyKygUOuSFd1coOSSSiiey3r1MD1FwBq6NJ1ztxfjzeWDn46Ft++06wazlzfmZntRQAAAKKTswA+GiMXPIz27fbJJ7dPHnC98BPYrDgc9nY6AGAkOjUAGJKUyEel9tslPpfpjF6NiasGTEjTNzkBUJC3djemiACO0fvDqCZ/iU6vjUVmHpRafAv08tP7AAAAAEhj2oQpwF7X3+rZ+ec2efl/tv/dIq167dO8/Ol9WHMLjy2hKJYKUjdhRYNhQGMfT0eDAABAe3tTYc+/y0nj3fuUEqRYghwG0YQNDC3zhPQjwDPNQhdm/W7a9E1KGwAOcAvZgBIuaOMY2+Lbj7qsxIZJ2F4EAACgp+eUx8skRYTEqOwJA/NQOjVUbbpXAvL+n14eQLpgHmCSgwYECTC5Gs3g9pHM7ZNaYwAAGtu1yYjxanG1Z69cMiir+3QzPIiwCqIUk1lw3jANwtdY7RvADAzkgMXMrYHh66iWK5s6tnutxIbh2V4EAACgqY8JjvsP3OcszryI77wi2RPT5wTkmUky2h6EtcNVqw7r1BEgqcPN1wDrEjJyrw0AYAg6JJd1u+dn2jveI7hwwLTsMAJnyHD2pQW7sYUiEJYGBHTT9UQYjU84Im1Z7NkX81Sq/nkLBIr47n0AAAAAs7j87U5tPP9Kr0TGciTPP5DX4RgOGPldDilgOQRUo9GeM6Mt3gCgmfPdro6bY+Yc6AIAZFdpFGdwmJQLF1nku3WRM7PBrn7kikZtjYNZ3Ykv3TUa8iocU+/apYsK2Gu2IJ/tfCEC9a4G48B5pH70o3j1156A7UUAAABaOJOOecjmXH8DZTTsM7IuzpXi3vmgFfZsV7bRDtWqqAUA8E6oLnuXUv27AXNjeUMOAIABWNcbjdsxIJfBmv3BGuHBrg40I0OenRfJANFoQ+DGZsdlGbX20rHk896tjLoSG3r56X0AAAAAgyv4dNZ9LmP57zj5neVIPmZbrr+Bjpk5FYzAh1oJK4q0fuINAHip1CCh+M2aATMAQF7rg0PDPB4UnLQSXXm5dhwmgUC0xQ8nCelpNVsPM1iADVP3GUzZGj1YtWW7LVEkPLo7cwmshoUuilc9TTETelmJ4teF8xmk+OcIzdheBAAAoKKyGcznZSj3//P5b7VfhbNxk5FJKIeAPDBJR2faZGH2pbpBMOojWVjRtV2lgtKDN5M92sUJAASxcURx+5hOnHsnB6XCKTWXL7jUN4zM43xyI842JXJiQEbargelsu5KdU7bg0eOJbsGM3RiA57V66Y3fu0YFTPC7TOhZBmWHG4Bgp8XNHa5Xq+9jwEAAAZ3uVxu/20EPpVKebdjqY3GScD1g5wkIykJFU29wOt+rXvVqe4nntcAs1Bdoi5ygcBLA4951EcSaV8TbxUk0ZsqLXobRt6uR5AAwL10fbqunAd7Y3iwEOqbO5W55aUUPYtA4uZAxK6/l6WXslHdMc/JLnKte8Wps3yNGGBFHI5S5TmnItulpZarYY8/0xozJLaccswjZzBlm9wJdyuI1mLXK8xoKdNoJf9Slties0vyeBpF/PQ+AAAAgDHVS/0ce1nEynqaGu/wWT/I7C9V3mK8VFR2VUOu+ytcutQpQX7Gu23vlSoAZHffm2e58UlxkIzNMBgAspPa4sHGZ7zFDAAv7doyaZLeRAYvkapp4fECXmyTwoGVdePVVrYo0qZ1X4TGAVkmhc/bdZq3Dwtp6jm8+r3IxiK3300a5JM0XL0kjYp7WeZ94h8hhGV7EQAAgPJqJ91OpiPfPdZelh1GmEreSQKCED8AwAFZhhBGy83U2D/U/TsAtJe0/zXq4yVRAcBhxzqROEOpUqOjIKfDdpWC0LAK+tqy+5V6OrOCLb8ESzMFy3n43n+kzReezyXU4XHA9h0qX17r85U3yy4MwSnAmKK14UBBthcBAACYXb3FKNPuMCKVFs2okQYAXRjqwDvPtSP+myoNlYdUMPAatPm6FQAYiZWm0IV6B/Bg4KUIzehcTioehK4IxKE+8qz4wCN1giXLSKx4CZc98VABMMDmC+uncP+voUqevfZevrKNVeqmG1aIbRjVd+8DAAAAGE2b2ZFSf6X20a5//5D5piFPKrVmE5YpZkYBmrn8vf4BaClCs1PpBiHdfUeEazGV8xFy/f3Pl9SLt3SRDAB8ZOAHwDF6EHipS9WQsSmiYDG6IgCRVeqsUw+P4/dc9SZwi3xzqAIsGIpdonrvUiVLm+ZR40ILnrBCtasZiW0Yku1FAAAAqGu2HUY8RE0X41UlIKMtPaAuEmimcVOzMh6r8fKrIrTGYzu8hPHlL9a443AXAwArUneUxplMLnX9BRhGkNb45LjIsCq78R4wpoElPeu6QxZVO2sjgUpqt7Fnvj9aF1A8CFtG9Zm1SdY1Da/e9c0SOVmOM6lQLXkpYgbGY3sRAACAkhqnv7t/QxHRJkXK8hD1VOa50APXWeCYA52d/jEpXQAc402VXzONlkPZtRh9/ZNlYy9vJAMAAAApSD4co9zKOrMeZuy1NABskXdyLWwX1ubADnTiAfv9SuHXJqqL/JW8FZC+RM4BDRrAaG1sRmIbBvPT+wAAAAAY3+Xvc2Ju+cDAuafbqR3OUa4Xzv9j786S3daVRIFun/DAPHTPzO9DN/RUaig2aDITa8X5qKi7LYFAIgECFGjp863CEQUQx5Vke/u3RjGgucHzwD157Potj2zJFQ3flXqxf4lkANjp35/Ey5t7NgWAJvQ1gA2pJ1RTGFY6uVfsnoDUCou73UzJXZCCrrotYDYbPMg+ft2nqgg77ndtu64rh21LbpGzpGipiWIkDSALx4sAAAAkdmXxOuYK6aGHKpI62mo7q+L68SVkN2xTVowBd63Sjv14YB3nTmfokSQL33PR2+lDRgz3ALAUN/usLOBvqEjqMZAkVTgn72tOxpdZnhlAJQOUMWykTr3A0vzu+PQHTq/D6QU4ZEB4dwrsHiVP3Qd5NSZ7C5tDhtXVgDXb8u0utqESx4sAAAA0k/GRlLBmPXk55umi2+d/XWI7V4ydH05JHlkGRvLGj6+kZeCTnecq1kuMVHLo9lkwA8A57iuBbWba9XS+7f0AACAASURBVGyk/af/6VDrG1BALwASka+ASlo9jXm/Azr6gW6cw0p0nmbJJ5pgTRYHAO5+/fv3b3YZAACguF+/ft3/bzPw2iYeh3FC/HfdRKvPHuX59HWtvsuuRrQoGsDp2gwTfxyhq04BUK+V621J1msjSjJI7bHgVBkIYiP/yBIAb+W9tZTYWdys5Xo3xUn1fgV3itFELNFbrgWxXKWFc2bFeYphcQpJAPYbnEkKdM8rNXboLW4F6mquiaPkxbbz2CRfSd13cebDU2opwnOPcZrghMixvQg/T6OJ37MLAAAAwAQplqVC7eh3Ksnbc80bfpdz0xfUteMIJyYSfqH0yzP1Rq5Q8xmgn3rpC6C5PZOi+99IqgCP8t5amiezuLyd9xDd/LorcXL7t1oB4Mmn1CphcouBReZpQD9yyAn3UXh/7e0fuA3xrcyN7StriQNKbqkTmpvVp3rcDsgPQDqOFwEAAIAvRu6aNP8uuxoL6vQojEACbnoPi0auyDQNAHDCiQmkH0kCPPHjN0iqX+c1UyqjSYTsWVNNN5Q4GgA4ysGmHHKLgbCDY7qBG2C/r+eMGKZniTD02B6iksiPwJlttq2BsA0NsMHxIgAAAPA/Uxb4HleQOy3XRl6kppPmq/9CiFcjN5lE4Gp2jlxfIzBI5NiRBQCWdXEW5ClSgEfBf/wGfDL+ZZhWbrNo20wFdgN3hq6jAYBXpw82/emfTLbLJpVN30QLe581pUgCEiIrMOF/Ve+KUgs1Gh4N+GGFL9kT1xEqyPkJMAo0uRk5dxWiEYjA8SIAAADENf1Jgp8hq3i3XYeuX2RjY0ENg0rwAHfTx+WfI2XwoHlbqhEAOKTV1NGaBsCjK4t+EdbbYU1ehsmrHgn568w5+EBwtGyWf4Gb62eb9kgjO0sllQXR+51AfJLlZRIA5QUcAe0NQWFxevfF0waXPVtk1n10nMiBGhwvAgAAJGPtgJEKrOKxsutv2pFR2TbmWWRxuKa3u1Cn4236e++DP7gPANCcN7ED9HNi0W/iG7nlcLgZ/DJMK7fLWjPrrnnVwE/Tg01/2o1r50o1fSdrEV9r+NMf2OZry8skAEIJO8ztvNcbXH53oHBRwB50es/lhLApdyJ3BzCL40UAAIA0vNyDruYGzIAVQxsbyzr34wHRwk4OTVjKxLZu8tVzh8LsncW4AADsN+VN7ACrecyKbxOvtAnRXDno50SPzr4YVVvXptmeOVcNDD/LZ48pwS8s+2neoE8feKLtrhdpzWxWdWzirbwvkwAAKGb8PDz4XK73nou7nlfuDmAux4sAAAAJeLnHyjxJ0JCf4gwQtobvBdvuUGHLz7LEJD9Nd9cMheeoNCjJrRZ0tdG/yg+s/XKLuRwQwacsNzdBSY+QyNFDRsJ28LAFi2/6KwcKLwi4ZYCJBq+EjMmld3suwWZWfOlqdfCQPaB+mhzBk64dAYILfnso80MZufpyrtIm5e4ApnO8CAAAEJqXe/AzZMv8bYQE3zuBc+RDmuuXpYXr4m47QD3ePie0AIBO9kxd7n9Tck7Sey3FXA6YQnoHmnvKFU95plUmsXLLJ04YYUFVY366Q1Pln0YjyPjW/Prckc2si2Y9FMQwbc/f+dGgACtZbV4EIw1bINKLLUo8aVUh7g7giv9mFwAAAOCj5puLvVme6Kdr3S7VcCPXKH/9ffMfUFKPRLpUcuaTTgOH8egQnRFoRT6hthP3vG6TAeI7nd5leOCQf3/+z39tP7k5N3dXDBsgvn6RdoQB0nW0QwU+N+lNPVX+VHibWU2k6y9jDKuWrl/UI5hX6yAAnRRIpwUuAcoz1ZepHvVYFlDDcI7jRQAAgKCsHexh0e06ddjW15NEBh81on1hmLYPvuu8ARWbShW7nH50RtgpaWdJWmyI5uIdburf1TwZcyFlqguI72LCCZivTP9gTfHPK2GWqq0ZcAiGcyJ30olT5el9/KkAXcsz/WIH6xfzkXtTef3CeLUOArCyaDk/WnlYzdv3Pp5+E6Spcm8yxhjqGU5wvAgAABCRl3v0lnFB0AvWgvOi5h8RBS16QfP3c8Inxcag5nRGUhO9AWkUqmo1ozAzAQil1bplvfXPE8wDIYJW6zx69EWDB4U9X1d1DdD4y5MpIVGyc/3MnioH6d1BilFSzOeC5uaQAcmkar6CYnRVgMUdPTdk/x93HWKMXyWdblZ30xCK40UAAIBwUr/cY+RCWK5Ft1aPLTa86u2Psoa1X/AXNefqKVDAlVytw+538c0DsE1nhHXo73BF29lX9rncyPJnrysguOZJRtYCgrhyA1j1EAputC/ws2+YMFW+u5V8QPnzVtFpbYekGgNc3h989g7gBTsIQEOyKGRx5ZnAnf+205ywxmz8Cpn2kbsDiMbxIgAAQCzu7fnKC9YuivnzpwJ9f+WggleHHke+/bFOtMfXY0ScM3KIinqiMwKdSCyU1GMiYXICMF2nVCzDA0EcXfyxWLSUYs1t8KWGUF3SVPnJsJLnraLTQr2+KEj9Z/zB55iqC9JArEbgASSS/VG6JiWfcsJIqPtZpnN3AAH9nl0AAACA0X797f5ihwHLE9cvYUw579/V4wPPld+SZUPNTyrp90iERUOYQspt5UQSu/8TrbC4xwD4FEiChJJS3+8MZrYMJ/TrNb2XrToZn0aSVhQQXNdsJnEBoWwsGUlWWCiABX1N/hGmylJTJTt/37jsQ0FvL6H5AF2gogBYR6jF1Tgl4au5NxHXQ6Vh+W8ftV2khhNO3QQgPseLAAAAgYx8uYelqwKOHjKi0dvq9KLmpM2UtNhAChfzbd7UOsZS9bPOlQJHdf3hkORDPb3Xr5aanwAw2OBfjBvRIAVddQy/ih/PvRU1OPGHSg4F84nIb572o/W+RD/4HFl1RnwGm5IZBHknO1tT/UdmtswnBQJjyhPaV979+fgJ/JQIwrdONLG7A4jJ8SIAAADt9V6zfrvw8fUbX//VmLX13ss0T5//eEVWiPpJ96JmP5gEMmqVuPa8fwCgnhr3O8N0qq4y9QODeeoFYLwBU0fpHYDBDD1Qw5TnTx6ZKk9XoH4uRtHj5T99VPaaeTXglfL1Kg2Gqfo74QWdOPFK8oRECqTruZdw7pAReRIgF8eLAAAAURRYznvU7wmPjdM0tt3/8vET6p3ebYFygKQvavaDSSCX5imrwKOHAEdNf/I+l+bVVax+4KbYKgoAN0ul92GbAmaDAACtDHv+5NVSU2U6uR5Fj7/rdqNx5ZXyag8y0nPbOj0qOWQEsghyC3MlXXS9hP0PEO6cdkqMS9HcUInjRQAAgEUN+IVtjyc8Nt7Isd/IrY68C0n1Dl7hxw8mgTw6jUFX5j+FR8bCB69UvS7Yz9kiR7XK9iUrJ4U9zad1sig8RQFYmfQOQAqFV4Mhr97Pn0y3PVWWlPJq2HaD76emRN3+C7z/ZcA14fFV516bMQxGBbQ98QoIqECuHnAJh+ZOMh4XTbk7uBG9sM3xIgAAAB21fcKjydkij59wf7PHrBfdkNGYlb5+e/9XXuTy9qMAmiuw0wkQgXR6zvUJs3nyeIfay7MUV0gsANQw4EfjZhoAAM31e/4EOml+3+EUiVcqBGrTx1tpOyQZj0IZfD6mpmcPcUJJ6QLbeA3b/ptdAAAAgOL+/WmzNtH2bJH753Q9T92vbmKyWPZzuRJa9WuA8YzOExk7YKSu6W6F2eC5a1yhZqK539cP/rcAUMngATHI+Nt12mZOCBDElYRcI5kHGXaZrkY837R6+GTn56w5VY4pY+V0KnPGqgA4p9IcZq4eY4fxCAIK0jGvZO9hlxCkrkgk6bTEUzGwwfEiAABACFNu3bMcWf34bEePZY6u9WBRppiRDTrg/Zl+MAkENCDTGp0XYcBiZb3PFlnHztnv7c+WqpkgmoS6iUFkWgeArjrN30wLAQC6urgQZ7bGGJ5EAoqRefLq13aiIo5hU1xz6ciCdElBQknZAztIfoBofs8uAAAAwCoejwg5+k96672vn3Rd6d8fK0r17eyYSWMYoC0j43XqEAbQy5ozGY6pYajnvW0fTHoBoJ7md6kmFQCfZFwYzFhmCGLApOj2FYc6qakaw4x5m4KQBmC61ONRrju+vPXMUnIFauoMxkg14kTAwyvHiwAAAIz2uDzxtEC/vXKRaDWfkmpHoHVDIIJhmfbrF8mK2WlBluWpZRbRPNQFNgAsq+EPCUwnAICwcv14cr+v79K4MkMrWWMAwIZ6b+cyn1nHgAl/xi7ASNcjRMriuo2fqFz/wOw8FQNPHC8CAAAsbfpKwf5vT71uOL2eT6v3mFGuhsgbOQDZ3Ya/1yRcb2QcTx0CcFGnccT9FwBrmnKDFm3YvRXmSlWEuhwAGsq+mGmEYoopgVc+2rOno6WMfJtC+ciHYjbyg+48187Uff+zLO01ZkgyHsXRdcaolYObfrMgQgjoYlYsGdVGbXjkeBEAAIAEpi99AgC9BRzu3x4y4gnO63rXoW0wluWpZVbQNc6HxXa9198xfoooTgCaO53M5WSAnQZPm1vlZwvClDEsmC0eXqH2PlEtQFJVz60o49zs6NPrakgny+3e9GCbXgAiSx0e7r94q3ZUCHu4c7wIAAAAI+RdjhmwiTJsnyZpEwAwV95BfE0aC6CwAXeOXcd9jxEDQHD3wXfPqG2khqpeM4D+TpafnAEwnfECuHNuRXDXM3bwxho5JHmoprf9W4c97l41Lp+IDbbNfT/Hia8W0rAax4sAAABEZ/d9uq5rfNbjAIjv6bmQfiPjlOfUe4zFn66idx2aV7CywdnDU2JwiMeIASAX4y+sZnvG/vi/yg9ZNG+p2wfatia7YXsQFg9hjK59zelacMXF7mN3oLeG+c20J7tc492e5ND27lV484nYIKzTCXCdCZjZC9w4XgQAAAgh1yI156Rejun9G+ABXSBv5QMQxONQ7o0fr/ZUyNOPMdrWYfYKBGDbsGWTtjfv5V9/x93IxT3xAADQxIn5m/n5Rdn3xLOXH36cMPLz8/Mu/4ct6p3kA5CFcyviaz6qaqmfkJWws6GjFXuPnYeMXA/1jJWzJjcLcNOkLwQc0YBO/ptdAAAAAMih02u++n3+sA+H2n79/d9/0FWWGHs6HaPh+PJ4cEk657JE2x9jZKw3AMpr+xgxAJThDg6I4Moc25r5FQNGgd57jic+//avBo+ABlzeWjZ93Xc839bA9v860tueO71URFYpPMqMXOMvpEzVFdDj3Ara6lSl0VoqWnkGOzSpy1tXXy/zyk3o+BtYTpsVw3n7DiMlzSTelwaL+D27AAAAAGyxBBlKw3cZvV0v6/SuJGtzcMinbvj0/9ez4Cb7Gz+uf/XFy78d+X/lQ6QjgBUMXhxo8kYar79b0JhXQAsDoK1hr68HiKNV3jNFj2lMo9y/5euvuYDpjqb9+98HmSpHKMN0WdLpxN92dqqiIL0Asuh3bkWWNBhf15ympSJYcNj6Gni3/3V/zQjjXBaMeRij7VvTAjJvgR/HiwAAAMAhvX9E3fzpBOtfsN+h3vf4aB0s5XVz5ehm/Ou/ff1/ptgAbvhLjJ9TVy3/wFw2m2GDx4iX1XsiJwAAAC5qO1szRT8ny/rnHkcDYNi1i0zeynh+68UCTPznTUQoAylM727XZS8/dBWhj1eaw/cToaWWtXJ87gm8xz94rStxC/RjCvGjEiAqx4sAAABRWDsgi+Y/on79m1Z9wc4H7HSl09kdZ0Fvw/7Q+Bik11wpRvOJ61Otbnx+kNqDUNxLEt9ST6pVff3d+JWrwkECAMAUPSa0VsjP6XR/oS2AO0umZcjtc3mY7ZyR9aaPBNG7xRe872h+vbJZYRr3UIpYLZkUJvLJIu89xYITMFiK40UAAAAYp9JK09FDRg5d+JUTTE58HaysycL97UP0O/hp1xGC76t1KtvjTElKAShge7x4/F+l/UMqrS2U1G8ip90BAK7ot9pmin5O25lzoiYYsPabqDagk8g7LPvVuAqGST0hyVtyuJGx4xvWRqmzcVI64I3YY7Aa8VbjKuiqVXYN/iworOm/2QUAAAD4/yxUkc6/P3v/O/3h5/5hD+N7qJxAb20XrC1/s5QBAd97FDj9+V2vXSYBqOHX32Mpffvvp4wOp7/UWMZPn4mcJQKgn8EZRkIDphjwLnFOOL0V+Po5uXQtcLraoLZcqzrTPfbfvFfRlpwWgVY4Z0y9aZ2lTB8axBsBTe8XMIvgJxezCCCg37MLAAAAAGy5rSruWQ1/XX/cuYZu4ZI19dhk8iYETnNA+2C6KgCdXBnQb/829SBV/vV3I+dsqSPhZ+E3sQMAwFFXJs95p8qWxKETPauSvEl+ln5rhl2HrcIN3Xu4L1x1uRh6ekgd3h6dqmR/Jo9wTpzYAyLLuxTWKrvmrQGoyvEiAADA0qwmD6bCTztadUdflH3iKyC1fuvUtiqhoU67Slc66YBdrqc08vUb5RyAOJoME27Q4JX7LKC3YQ81ymbAFGNSnDnbFfeq299YBWq70/grFPlkhR+xpL7GvD034GYWPWjoc/rdbpevOt6aPtMbs4LU/BpTzxDYMGU98/6lG4F64g63lelZgjGqpjWHL5S3/22jez4qY7QkLTZU5XgRAAAAKOX00tue37B5UTPsYauSRYwJ9eZDT4rueeKYsJ8klwadTNmB1ul41DwCTSm/mlVFSZ8eHk+nAABYkDnbdV9/hTWlhvdM788VzAkj0FCln+gkupZbtllzM+tV+Z+KaehzegTGIlWXQu1eD5HNOkj0UAaeeM4I8JUJ1SzXp8fj267hUlv520ZI5L/ZBQAAAPg/LFfBFdcX3X79tXJHfYIccvn3p+UG1RXBs4dBHGCWTuk3XVZPV+DTLF59pVMAGY05QBNgPJOopG6Loq//jXRbb9wZQucWJ/vFp8iHXJJOlR+L3fASktZGEL3zv4Y+p+3FLlV1BNQ7AkU49bg7o55+udoosIjTq3z3f5g6tYpzCMLxIgAAwLosT1BJ218Ub3zUmI6je9LPsLc3wFFS37aL9TP+4ftZ5B8AyC77pKXrbMRUB+iqawbOnt4BWMrpbcdD/9D0HhpK3aGSTpVfi319K2qdzazUNPQ5QV4mAU34VflX02cmZWryk5E1PL01j0pXYJiifJ5MYf9pwlPOHe6q0rVAXr9nFwAAAODZvz8jVnizLEyMqQ2y6xEkv/6m6SYALOLc6futhrNEUzKDOMBIvU9SuD0mMn4YMpRs69coah5grk4ZXnoH1mFhqoDrQ+HtE7YjYcB9rmiEFJL2041iz93MYphzN48a+iIVyE/POd7XTv34vT1WkER4apoP1tG7vw94NkDKiiZFizSfg527dwYa+m92AQAAAN5IsVDCUZq1k36La58+ecD6OHTi7Q0EJwHusfM8/nrH9h9y+v2ikNfg/r5seuHJmF9D8cncyumRBwrkFp0CKKB5Ni6Q3oG8zJ04qmHMRAi/CGVgcaaC25LWz/6XS+/8tKT18Fala/lq5ZeNH9Vk43LlCqSrW3zuCdGnv+zxC9seTImZSwTCCaY9rEO0w0S/ZxcAAABgAosRsNOn84a9qDm4Pa2jqgfo9FQBcKNnAUAcaz4d2PbWuMDcZlgYeAk50FvDDC9fAb2tORWnk+bh9GnqHiRuX4th4C4sSNR1kvTqkva4Q8VOeo0cpaG3tcpRrZYEt8ujNZdyOjhv//B2bFCTCBd4PfR7uHG68ddlUwbuCvSFApcAQBOOFwEAAIJyeMFd4a0OrpsYGz0iM133DGh/ozj5YqT7swVtP/AtDZqdcf+Tr9Ui+F95yAOgq5EnKRDWbai92EbGa4CAmtyey/BAP24TaK5TUE1fonwqwPZlPv6vxnHoJ2n/Slrs8ex18qhtMFyZV+wsSeQHad5eQsByZnE9OG8BeXGPoGsLysbD6ImwiJGdvdOkWr4imlnTFX0BfhwvAgAARObwgkrUfA9jltW2TzHwouY4rr9Vo7zpG+cXq7rA8y7s5Km7J4L/iumP78MwIzOnbkV5gvyo0w8Qq2qAyK78PkSGB/qxckgPXeMqyBLl0WtcZ/sMzjm9mZWxW2Us81z2OrnpEQYn5hXnihFkJvC18A5HO6dVcN4D8sQiUu/2kod/jEdAU+PH2SZvuXj9QAC4cbwIAAAQWtvVsbxLY7Y6iEnfDKJJQwR5uHMFqz3vwjmG/ps4wZ+6OWR4aEuH4ib10EAnh1ax6iWTwZ3CDAcYZvH0DkTjTiSsPU0TdpgYP5kf7Mo32mqpwbtwO7lfYOoc+Fau0kJM/XLv/oXB62WYuAh5ovDmLXs0j8zHan+s/E9fNKaB3DmOpNPBk3p3B3M1eYZQhcMjPQJuHC8CAAAkcH11zEIA9ZTZCNQ9L2oYCX6dNcyhqr7YxJ4gyWvxE0aaPOz1I/hhGSsnTGjO6HmRCgQoSXoHInDzG9P+dll5xTL7DtTKbQd7FOsdxS5nlsU3Ound+numFq3KMH4a4BGRGiY2QYT0GycCe49Hca4UIji6QvKjE+1z8UWtKhmAt/6bXQAAAIBd/v05v8JVY2msxlXA3a1TC+yLOr1VgwH2VPWvv40feSGdZZNk24OT+FEPVDcywpfNzPCV3gEAAP00XCumoXPtEq01hxUm1FWfU+ASGMlSCUzpBbpeBGNGzO1vyfssjUdEuupXLXEqPE5J4ug3NBh04O70Wke0RZJPIvT3E49bN3lC+9ZGr/8BkN3v2QUAAAA44NARvBGW89rycg+yuPe+p4it1yvn6pQQsr9BLpHtqu7xvIuWzSjy6N8pogQ/EJZkwgpyxXmu0gIAAGVcX8P0VvmkrDbDCvR0yG78szRfv3FnVmlb8qrZ7PRF9X7uomqFF9Cp6TU33DTpYlLofp+ezX79gyt2vjmv4TdySNinSYFcHC8CAADks/JSVOTfGDNS8DC4L/ev3Ft76xoDNmyG+VTVzo7h0aHj1bLrF/w/10YlczAIa2TfNJJyZ1CAJ1M6hbQMACzCDUg0DVtk+pxWdJ0wvdUAErG/tqDpuzaDC3D0L0e+gebHvOXBmMCYXuFS7pN+FaJn7WcyUNusFRJx9RPprWCO0B1sVvBrYqjnv9kFAAAA4JiMCzQZy8xFlu+z04JVadm8/v2JNZ72KMyA9wWtbPHLB2CPUJMNAICMfv198x9cIYSiad4iSzXxUhcLXGGRinNEDiO3m89916ebxH4l710n+l0ccSbbQaLC2SLQ2zorJIv0+otL2VbCx1DJQEO/ZxcAAAAAqGn6OxkKs0ZcyWtPGfDIi76Z18b7l27/U978MOx9QT/L7PsCi3ibPyU6TrgeNt4TBQCs6esU6PEPzNUhtU63PBbt09Fk9LAnwwi8YXTzhqwZUkyTeH5KMh4R2e/chYzMQpVqO7VOja5x4c4KSTGtGrRrC34q5Dox48YKaMvxIgAAAEAvlvtT03xT2APgqCn9NHtykN+gjPHjZpAE4ueLNJcxTjKWGQAo5sQtiZNPOSTjcnHh8O7aHFMWHDIGWBxB1ogo4FBPfDpxvrbpZ1Lo5ulorwjm7trkmtu4N6SqCFGdKxvQXIQgLC/CCsmw+4UVIqptTbad5Owp2yJ3ynNHt9p1C8tyvAgAAEAy9j/IxWM3zUkC9YzvJjpmYdOftjxn2QMCAI7y80WaExsAAOdcWc24OEt/+9XmddDVgAVMK5ZQVY8Ecs8Ys7aEFslXMnMrSbcvYYBhXaNrQvMD7wik2bsIpx70Nn5gvXjVZgIcFe2EkdrC1qFnYx6FbaaMSkYInPPf7AIAAABQnIUYyKvYqnT8dFSswpmld6jH70qdLHvhQBC//l79+aKZBq+aj27DhkvjMj/CAIB5Ws2uj37I7Xs//av7/2rmX4amBMiu67i8wqAf4ca/fCUPY/uS3vTWuSL3wSlvOlnZ3MtfvPL5iZ2Oyliqo5WPqK53rFf+rWdjHk2/nPIdAZbleBEAAIBMpi8SwQnitiGVSStiqbaurx5qblY06gVAIq1SltTXSoHnJ/796XUVBSqHE7Q7AOtoO6ne82knDg2p9wg1iZSMvZFvlScR7ZXIlJtWEdKEBQf2ECewR/aBSU9PJHuwbQtydel6RLoCM93OvtY1tMrHbe+Edu7zPRsDMMzv2QUAAACgshMLrBuLeuWXa4Fl2c+guX9/2sdVvYH4199jF9WjVgG+6vTzxXpZnZ0KNH2BSwAAkuqxLLC9OnHxPY0/5k4AQGurzS6O7iUN9mm6GLDMnXbZAl4prKnrTrqens6UFQnPcnSSqAMmKmpeATuaSWZkR2+mmj8bU6Adp3e63nXoaUyYyPEiAADAWvySKqyd7+i704IwRo1F9juL0QNEeHsAN20DXpMB3Ax+Hq7f1MXt1bIGzPA9RgwAlNR1cv46yWn4nkYzqIxSr2MPiLqR7yoY3BYr9Fk7NQAXHXq6pu2w8var939F8yGg/KDJTqYWO/WeavqB9yxhu0DVY0+D3LWNubFqeKW9CxyhUWhrf18zyTwh4Ngx/mhvvqpXe/WuCK5wvAgAAFDcp/Wmp/9/ivWCgMt523bW6rnrqrr/VJVVWijvaDKXxsdotYFauKWOjlCefQduPg1k2ymi6y3SFcZlmvMYMQBQTO9Z+tMChfc0wlveVUA0Emwi1vZbEfNznQjj62u/X7/00OB7+wPbl1CVH3jzasyc2UwvBXPy7CI3X8PoMvS0tXMUGHy0dxZzO92wejM6wCyOFwEAAMo6tNbQ6cUdfHVxSSj1wt8VVtPgNN3nrmEKPV2lkcffnRcVsORPLj6lN/FFo2FJI8DdfTDdmRa+Dnxz04tDRg4xHHzlMeLVDO4U4gEghTKrK4N5TyO8WupdBTosENaU7BRhFS5CZr7+aM3P8Rbsd5rJxWad3hzABj/w5lWEkbStelf0Vo9r9H4C+mlyjN0isTT9DuvJ4KO92aNkjZW8KLjiv9kFAAAAaO/X30u/c462I2Q/zQAAIABJREFUcJbU11WYVlWtyQAmKpnMDxUmVMk3/PtzeIPkxD9J6kQLLlIzwB7nBoLXfxVnQAlSDGpoOGIafAEglxOrK9EmovdSDSvb7Vu6vqcRhmkYb9f7YMbgH1PmifdZbvGYQuCRV6txYdgG6J5/fm4jcp3tS0itSVfV2YvJeF8W2YDVqn59sPknSxeFnXug61xImGf2E2EIiFCGRMb3Bb0PpnC8CAAAUE2rHzlzxZ6zRdrSZMB+FqNb6ZHM5+bz0wWYXvKdbluh211gz9/wkzCTpCswrOA+fEQbRKKVJyypdY/r8wozk0SGtZSQAIjs4vnvEWRZ5zmh6nVR2PhfU8eRscxAbW7GZ2k7Iuz5tJHj731f0vYl1HO6z+rvVbnHaWVATfbug60+X7rgk/2xYZ45ndHhkyk1U7g7VL0uuOL37AIAAAA003xP3VLCOePPFrl/7FJN9u+PdVU4Tw+6ruubVMfn81bHk2UZibKUMziZBKar0Q1jXkKiQY0UbuF0NNoFIQCkc31ye/uEWdOA6ZPzAQUw1SeRHr+mPhr/04/DLtxha6zqkI7AO21uOlq54Xpc+Pb4MvHJq8KjHizr3q93njfEOYkGyk73OFkuv4kCZ4s8fsuVy5E0+EqQ8Kj2OlsBXeczmh7ecrwIAABQxPg9dV7tqa6uOxyaDNgv0f56QL2rbnA+b3g5c38Dw3gyCVCY26s9DASH7HySWOClNqBTiBCAmAqc/25eB6HokjfF7s2frsU9NVMIvBMqJaJcxr/swZNXXDQ+x4quLLQUd8aFK8qcLfL0dd5PAL2FvQsOWzCm6HQ3YdSATxwvAgAAVDB+T50namkKDz/BRTrROWMqbdgQ7Dk5Lrr+TpUBigXka20Xu0CIw4i2hynlCeKqNm8WAlhQ9tWV1aZz5vnEZ+d3mOm3tNMLwJoE3n5y5kTjX/YQefx9KpvIBIDmJg6ve95PYPSHo9z2klHzFRvDB2xwvAgAAMAXnjPbdqhyBqxXai/gkCBPEAYpRjQDUnrk5+TIZc/jDuf+mJvtunr8X3W91RhDiUAcwhNvFgJYSvbVFRM5iGb8r6mDS1fgTz5dRep76hpNs6bUgTdMnAjXWJ08DjEBx9+NItkVAvYbP+jLS1+VuccZLOBg3dz0AkANbqA+iZDo+Krhm940N2xzvAgAAJCeEyu6ervHdq42LFn24Mknelsh+zVcj17B4IrqOgRr9AjqDWSHInbM5afO5Cfq5/ZPUl81hLLy7fB+xcYyaMKbhQBoovd01ESOsOotmoXiVvdRhGCLUAYWJPA+iZYh12ymNa/65ui13/8+Wugua2R2vTe6lA56QXlj2tfNMhSQaDhIVFTGuzi3MZzBHv/NLgAAAMAlVpcG+Pfn+b/gVouK+C0CKaTIb22duN4pCTZvVs9bckbqnXlSZ7YrnejXX31wIanjnAJkG/ikYX6W6gHCMhdKSsMRVrTgDDIRjVYtJ3ytySBVzWoW3Bzco0DOYb9bcw/7xXKTv+nxbwEYrGHSlv8B7qTEr1RRFqdb6rbUc3tu0NODsM3xIgAAALtEWF8Y/2iLh2my0FLr0NbQSpzn5IBXrbb39EFoQlfakLFy3FMw0vV481svgMiyr65knMsBrcgAj4KcgHxl8u/GgStOxI+QG2bNdL3sVV+/cD8eC2JMhpSH2W9ktIhMehg5uhlJISkz4f1UVGTXzwR5/eePR41ofXjkeBEAACAxN/mJDG6sOLHxtCzVb5Uq+PZk8OL1k31FctmGG+xWz2p7Q95OBHv06/5JE0vbLi+BLCJptAOs4OJPBAHgp8+dnbtF4ltwLqRjThQn3o7eQTz+/eCriFNpNHGLpY0I/PoHkFSEXyzbGKpn/OFlkvNOOkhhegFQj8y2k/H9KDUWzcgn7VM/0g9t/Z5dAAAAACho59rT/c+arIP/+2PNa76dD8RcaXENTRMngrBS4A1+Ts5m5zZpjUedfjOmG65AMmE8IcceDW8GU7tf+J6Os2wtAeRiLgRsMKPLqNPSyrlgePpXbqwYTIzxREh0ZWMIGGDMNuL0zGO3lCYMo8AK4ue6dYb1KZd5+9LgMQC9OV4EAABgrwhrSSNXi6ZfbFLnGqjVQtXtExZZUozmULW3PVmmk8hlA6ik38AdYfp6iKrgonU218fTicrQjmNs5CI/ilvwkgEIyI0DWbjP7Wrnre5SrdD8YlvN//d/zrD2cmsDA6yTfh+Vv+qn8dfGUGH9BuVPLbvUtI0TekeInHNUk0S9Qq9f4RrJwlAbk0Y5zS3Dq8F1Mj16xQCLc7wIAAAAtHF9navVQlW0Q0bKr75dqepzJ8vYqqnKY68bGi7l6z4BSWsMYEdwEfIJMNfRFOStOACwk3s6KM/tfBCtllYkbaCejfQo6Z3Te/R3EzFdjy0bbcpSvvag1x5hq3SM8rnIGEoEgnAPOf8i6W6iINHrgQ1W9t/sAgAAAJwUZFlhvDFLGG2/ZUpjDf7SVl/362+zj/r353//0VWT9jrxIb1bVuTMouZLWnbSksXcfjfg0cksBhQ1UW1whcGUMTKmFL2jqyu38w2XAgAAKCbjND5jmeMbectwfXtxbgwM+HZBDqv5unRz+wPLO/Cq4WNLez7KGM22fhHS/EnLncPK27/UEWjiHl2P/4EMwwnBwyZycptSdWMqJFq1RysPjOF4EQAAgHyCr7UtqPm6UtsPvJ8z8vrfAIXDte22WajVycKtFtD42ta+xLRmZK65IfdWikKSiFP24JVO0U+re0OjIQCJpB62UheeNZnMT7dmE5xbXQmyJtO1DBEuEAjLL2/3s0e2lOuj5/5PMFKzrUeENPzM0+PI0z/UEejEVIdh5LE99EeSEroQhONFAAAAeGZl9pBO61xjls96t3XhWOrRQEc/s1P1Fm61LDz2yrKmxKdOsRo7lEsJ8oMWoLa2I8vi45T34AEAfJLo9jZRUQ+Zfl2zCrDnjQWDX2ywU6jCQC66z3WWNeDV6anCiX8oj7Gt4cS17Rz4+tjhhJEx1K2pzuJ0AeqJnNP0uGEihwF04ngRAACAlPotGFmKOqTrcpK1qrD6Nc30E0ZkgCA1EPDsGBmJMYL0wTFGditdmJUtlVjgEx2hkwjnTma3fZiIo0YAAO7M6pnr8QyRp//CCv5qeqA8qxkb7JEt69D84cpMw5DNV9eDpO3BIq2S1eNHBZ+uk50RdmVeash+G62podcUefiIXDbowfEiAAAAWXkiaroaC0mOqonmxAkjDd+qQRzOjmFZI2NVvwhi8JyqxhQOYD/jXSdxzp1M6ugD0w4ZAQCIP7ePX8Iral9dVQ1bzY8zeSUk+MpSBnyycXJZqyPMZGm+Oh1pbWeGvc8xN4+lHxs39CBl7ZSi92nN61I09H7FLgeyc7wIAABAYm23f4ot5NW4nDFLaY6qOSTm+ubFCreX/CjO0QZxMnzMsKcwGQkAWjGqJlV7Bn7leVPPqgIAi7u+mO/Q+StWuMajblP07f/matJqmh44bXoahJU1OaPENKC8/SfatDr75tGwc8x7FP6iUIXhCrOdNXXqwjIDs0hlj1aujZWvnQX9nl0AAAAArvr35+pyRr812cLrLMUu7RYDTS6q9hL/mHb/9fdMNZ5rxNrtVUCTvqmVIzjXr1fWcGDa/opZxk8kBCHAgmT+fgYM5dfH7p2FHBwnTaru9iEiHABOM4xmd3E74PrG4saHl9ej9jI6VAnTJ/BXVpvXiW24EfM92KB5Yo+MkU5PAx5jxgxwEYskigFPYoyke8JEzTvgInl4HTuP7pLGAWb5b3YBAAAAaOD06fI9ztSP8zaqGkZWY5MXd9DE6Xbf+bqJgG+lCGVMzez/ltPl0cqhGBlP6BfDukYc+gVAP3JsJ/Er9tDMc+Q0te0XxW8IAICu9qzz3//m6c/aLo6tttS22vW+OjcVn75CfnS12Q4LewgSdrKIAXMdekLm7V+WSfhlLqSG3qPDxueLhCsM66/UybIa3jjLS8VoUF5lGSyylBOu+z27AAAAACc5s/bVfT1uT800X7xbqjkKX2yTF3dUlavdV2iR1I420MQMnyvy4/N2rBO87wK4yM3jdTJnXtPfEc0VJ6aOp9PdgFDpkYrNrgEAfs7O4lrdLK85Hxu81BCnkq9f9fS71KevfrqiOFUNU+gCEVjNhq6uJDrdk7bGhNPGErqQHmDKQ7yzNN+v+VR7ZWqskov5RJvWc/R0V+MRwBSOFwEAACho8Hqrpb3exv9aZu5RNdDP/udEu+5bXHxmhez8BvKEhl1S5QOwINOPhiKvgUT+pV+/ehPeANQw8ilqQyd3p0+cf/znrYz/LYG+cFTDBoozjQ9SDFLzUyh2ipP6gNN6vBXDIMJEEyOw/Ji4v2Lvf1ngIJgms52vF/v4B+UDKZFzS0xasKQTzZoo0S1C34RFOF4EAACAS2Iu6lnbakVNTuQBo1Y20tT2aSP2LSCU611SUgUAqmp75zLsFXMA5GXdrK3byKtWmeXEL0Css40UpLabJyi7YBBQtI5pdgQE1Co13fOtsybXNHKA2x7fC4y2Uy5ho1ZPFybaTGy8E1XX79B8ztn5UkNNVpWWBcjl179//2aXAQAAivv169f9/zYDb2vK3ob1r0dht5e6NpPAW9b4ptfuV7R642Lzdk/XrEFSfb3fN6aLhDhONF/A2jaduFMV/Xhvz5MIyT8p8fNJuqDSlE0EvDHsVKRWATOmxoQ3wHiDx8R6S0N3vVcjt78OXm0EYe/4SZdY1pnr9rvSCFcHTYSaXVwUqmNWqtibndU78qyBepX8VqjApqFZe2Ft34rheJEFRbv3iVaeo4I8crDIkPrVifZte2wTLCJazsmyzrYhbBoJ+HzCCdPb96iw8XDj52k08Xt2AQAAAM6LdvT4asKu9Wggeggb8Lx1pb2e3mzQcKyRnXhkRnHazpdd/Oh0rGejUzz9T3oHrMn0g0NyBUyu0gLA3eDxy3DJHhPjJN2b2wcUOEK37XqNZvKUsc4ZDYOp2AFUMrlM2Qtr2EfelmpMNzTpYsMtPPKenxhhLJtegLxaVZ27S5iiYb+LkMwBluJ4EQAAAM6wigfE1GPTsck+ui3MK9QebwmM7MwnGzpamU9naS3CswjnrBYnsC1gGgn+S7+ANQZAKybY1w3+XZm5PaQTodtK9bBfmdmRX2ZG4KwBeDV+L6z3qSJPf1BjEGGPsK/TG3nISDEq7bS2VWceC7317mJmRAAj/Te7AAAAAJdMWQ62ehW5BuwQwMqabzo++vfnZIY5/Q/pJ0iLRB5Poatff8V/M1cqc8GGCJL/E1Fj2zLWz2q9vjwNCsAims+7pk/kthcMO73IF1IYE6ttX6zKRe5rqERO6GHZWh154ctWMllM2Qu7MkW53fE9/jddhDKQRddo6fThU+4p7l/qjuatW/r99N/9b3p8L9DJsFU785YngytE/cM6HC8CAACkZyFjsMhL8IIBVjZm0/HQBobdjibUIVSy4HkWXTWpTC0CkNSYBH7lWwYPMUY0gPEs2pywc8Gwbd1qKXJJF7GdChyhHobNsU3mqSRC571Or+wnTt3WiFVWMH4v7Ppu5rlP6Ncr9XeOKnyP05aHH97aUy1dq06jAADs4XgRAACAM6xBB1RvCwrYb/Cm4+vbZt7+R2QaCMYzhW6rYX0u9eST/L+futojYy2t098BgBqy/6bl9IJhqwXGjFNW6KpHp2h+JJCeC6npxc0tW58jzxooX8kWRZMavxfW9huPij9TZR3NI0coMpJxnxXUzqvjr652fe6nHmApjhcBAAAqsJwxTNiVdzEA9BM29a2ga3qPMHaIrmUtuA8q2tvqUZ/rtNH07pCCWlrH7aHqjf+IaWTTCAMANvSeN2afl14v/5XfJ/ttM3llfHN7vSOB3AjARafTglcavKUSGvpUmSqZaMbvhTX/xuknjOjXXNHwHkcoAmQnk89VoP5zXUKu0sJpjhcBAAAgPes4sDgPuXY1MccO+GojCIwRKlEX6Pih6jMpz7FtUzmHJK2u/aeHOGeERAQqwBQZTwFI9/lHb2Hc8lBA0je3X/mKxXuuyTxVnTgfxFLMJ6slyU5hsFo1kle/NPjpkzt947kTRuYeVQmPLgaSOGQW02lWIMcuJfjCJpDR79kFAAAAyOrX3+VWUmKuua/WCsCTMalpwZw/3bAK//cn6ABHeevE3iKXOUzX+lxtvFunG+63VAA0lC6WzpX29q8EyZpWGyAAOKrHdGjM0NNvIjfgZITHkhupKekW2K066cgF55v9JdeFobbTeWzuclPMpYB0q3CnTZmjLlK3pDA+GgPuvl2ZDAdM4KR24h7nZ2wcrjNDAFjHyNxu7gSsyfEiAAAAZDVrRc+O1Jq0OzGfoqtqcFXr4JQ3MX1F61zZM/mA+lxtvDv9dGbDsaPtz6WuWKrpm1tnOuGQEXZapEcAcNd2OpT9JyjDzkaBFVzvpBP3E28+lV8vhhW4O24uzmpqRhvr/ytU6WrbH3mN3wuLHP+Hkp4Ipzf3OORi6GcFY55S0JU+KTYgSpuwGseLAAAAnGclZQp1DtxFfs6jksE/l52S59f5STChlA+82ldHJftfuvU4SDV5kP3+gR6LL6B8Vn+07HpIkFaOUIZt8UsIQA+tBsrx04y8Z6PAIq6cDRpBkGIcYkoP1+lHXdVeTe16XW+X9arWJGwYvMR98esyziepTUwCrGMj5/c+AS3m4Sl7inT/m3QjZtdJcpBnLb5K12pwmuNFAAAACM0yzY9KWJV2Z00TIz/LBgYcZUC5y14Vw3JU3lMD9lTR9qWduPC2v3Ga+Fh80kaPpvYPG57kzRX0tkgXAOCti9OhAutCJkjQz/6zQX90xpzcZlKJW+MBqlZy1euCncZ3AZ0OAEit6xOPbxdqjn7d7e/Prfn0fp7zUKlOlOTKtV//doBtjhcBAAC4xHNOa/ITdFjTyjlf3oNOQu2DthUtaSybwFdwKNgabt4/2v8ryj1f/fQ3Ad/HwrZ1DhlZeXo8jBoGIKMT06EIQ17es1FgNbobENwKi0LTpa7kjTXqMdf1tKaXujIhEcvpAOPJvSxi5IOdp7+o07M6w1yp4VzX3jVzxn8IOUszQROOFwEAALhqkTXo4As6ha0QXVnEX9lcjeaoR8ZjTf3GF33qrkBVDB71stzjRHtwoVOlHXo/85XPp61FDhnJki647jWYJ/4MBoAU9vxkMeBEIunZKACtzJ3S379dagU21Fh8eLyK8UnPml4/b+NTbcdUI5mQ0fjHz1bIQh7qAxbXPA2+jh1NPv/Encj0x+paffuVuzBjHNCD40UAAAAasPW+IJtSdCWlEFDvvCfsWVmP/qVP3amKqq73mlxvCPmRK3LaU8NuLXOxGnA392cwAOSSbqTYc8ZfuosC+CrOzY5zRsgrTj+6KGzvK1PDjyauVJesz/G+VqNltJ3KB6THCwGAfhruYnc6W+Txow5NiiY+KtP2e7M8pNR11hr5cYv4TQNtOV4EAAAA4rJWFU3klU0YYPph8FBMvzFlep+KM1xOrwp6aL5/nyhO2o7FiS6cyHJ1olwSVWyQoT9RjQGQhcEFWESQKf0rt5zkErYrkYL4yehEq2X5SSH87I5w8UwQHuoDuA3KF5Nh17NFHj/z6AkjDUsy5WyRx49dfPoUc8hevFFYk+NFAAAA2qi63BNwBSeOmCtc9Dag3UsmE8qYeBj8ABI7w3SNtDh9ajpVUVKEBxdqWPCSoRWTRgAAoJLgNzh+hg3cBc9Xuay5Kt7QxWg0uhHcoQi//3GNkN649oYXOHKXoUa7ALDf6ZM43g4Z/QasE7ck1wfQ/d/o9rMrT1xABI4XAQAAaGbM9tIwFm726LrClTFsuC5+u2/HfPzyc13b1CdmWI2DRcZQFdfFfLY41IMLUwx+GQvslKUHNeTcSQAAoIYsO8IL3niSTpbeBHerBW3DcaRV1RndCOhKeKcO6T0XnvQglU+XlusqdvJbZYC7/YeMbIwIAZNq28NTZjk0awrYCk2EGrVDhQcM43gRAACAEdJtL8VZsomv0wpXllBZU79lzcjtvvOS06U7zmnVC8SJGliNU8kGKFkVJudjBH/qsW0YTLxYZ9XBHvrCCSoNAACOyrXoFHzpBirR12BDmbV6eHU9vG+fkCiqz11yk8uc/pPaqs93Ta9YgFCCJ/nTk+FDh4yc+IoBQ4kbgSC0Aiv79e/fv9llAACA4n79+nX/v83Au8qyMRB8JSJUNQavq0dt6y3Rha+seWcJ2+5XrrTrRU3JV2GbaZbTrRC5JkeGVuR6oLmlziOTotuaNUWPVqUD6iHaJd8VONvu6CWEbYtOQt2Jn7Zaq/1M7Zs1YqatBSMQAAAuynhnYeZPWBk71CcxO1qlGmaKJoFdYK0+shW6edi2XucJqEcR3qkTJOxTtNchQSqWCOqFNwwzLJdG66fRLrzwM3jTx+tosbefn6fRxH+zCwAAALCcX3/nL4h8ErZg8TVcY8q7XLWa5i0VsANez1eRMx5N/PtzuC+c+CdVqYelSIZw0ZhOFLOr9ivVsFo98UWmkaTQYzpniggAAIzhvhsa0qHgqPGLYLc150//7fyEfmWDiXpEYPyoblXCi58TZEeg3q5ckIoFgA3FBt9cPMMMPz8/v2cXAAAAoJl/fzIttfz6G25hIlHtxdQkAqNFBdtu7dWw79w/KkIktL2uCFdEP/f23QibRDGQa0ZBCl0fN0zUuYCAuqaR69nv9gkrJDrTj7zatt0K0d6JqgMAgEVYEYXedDHW9PVRjd7rtwY46okc1W179NcrzbIBFLnJTrD1xo+ZLe98zQzC5mZkCi02ALHfxMFa1MGP40UAAAAmsjaxLWPlXD9sItTpEuzUY4lz+g8pe1yRqF6BVt5PXa3D44Zw3coPLgy49k6X7Kw61tHq3MlDce652EdSBAAAHJX6hsJCAfSjczHGLdJiLnC9fVQjYDmhla7hHXPa1uOSP11puuwRs8lOa/7aMCCv/amgySPcT19XKbWWN3jUKDbyHmWMhrkcLwIAAJQSc+95Q5yFoVz1Fty9TS+eMxIkNtij037krDDolBCaX066nE8iootcTBvKm5KUBFUEeQejMhPjkUw/srvSguXDGwAAAABuUiyFTXkfzArL4Oy0vdTcME4W3JXod8lPXThv3dbLRQ4ZgcWd7v6HJoTb3/L4v6bIsXImwAocLwIAADBZhC0ZS4HNNanSkc8rDNuZrq3HPvH4FLHgm0ngra4/8dUR1hFtlrWnPOITFtR2kpblrLqAnDCS3YnDRq9EtYC5KZ8ZAACoZ2Mm329+W+z2YYVVAhhPt2KApzALvsB1G24il5Bidgbb/c8u5u0xsR1q2tb7ksskjVCt1opDRhZUL4w54Xqv/5oSj35Fq3EcAC5yvAgAAFBNjU0aUq+cto3Arjt2g3emF+FV7bVpjqV0mlQIIXpouJ9t3Cea8fd3hvsrnFUHP9+OnhTGDalMAAAS2XPL3PZ9tjbNy/MCCRoSMMwS/CmvwWWzBr6sc5E28rVVVwQJ7GHHqdQQpNWac8gIrKNhT/804F78iqqZ9pwgtRFzgJhyx5Rlnglc4XgRAACgoOB7z6/mrosFrKvUC1I96rNHhNTema5nWJYYkBDaXsuspXO9YB3NY0zwLCXILOt0MXKN+1nKCfsFySHRlJ+JpVvQ4KuuEStgAAAghcG7cm4TyvMCCZoTJMxljYuVXQ/+8vsmAHBU16e4BxxcAkEIUajtv9kFAAAA6CLdWoad8hr6tWPbT75+bLaI/SRLDMz6isFf1E+BS2C/tgfiwEhNhuxznyDamxtcpVpwQU2mN+ZITfz7ow9ygGgBAIDIri/QHf3n7s1rOxdRtfd2b1e38R9fWVsgAnHIzwJh8HqBrcapE0PeakNk6uu9bRs9/jdG6kqjuXTbl7lKS1tdn+DtdHAJK4sfAFZXoCrHiwAAABCLlf1PWv3er+HmNE9614k6f2tW0tAcS7keZuk22snoKS+1fV2GpAdsG5koFslIZg7sJ1oAACCmwb8XXW0Rb6mLvfECiSeHukaxa2/IqgIjbcebaLyRr4aZvoPfvK0jB0/kssXX7zGA05zvtojxh9q0kq7AIHOyLUJaE6VQz+/ZBQAAAGCmaMs9EZbATotWma967EynbrK2xgRAvzqPH8Aw163rnegp8uSy5ubVTq/LOBTP//6Mq4RFOtqwKl2kPoNLNDdLVNR0Tk8/ZnGHONHIYRcAANhj8K6cO4La2p4lXeDm/VyF3P5VgcuHwqxxUTgGngagTpdZY6Dn1ayWffre/XF7/8uYAVk41XDCzmCIGcy8ytu7DeLEJ0qhGMeLAAAAZaXbBrDskvry4x8tYWeaUNpGzqyE3zv+v16U3jfevc73hJwGYpZ+KdG4DzyZddMtHcGrdOtg18kDAACENXhXbrV7gdV4gcSj67WR+vIXt+DSRzE7u166M5dJ6h6QU3JL12/cM9Kt1sUqXe/Ejbkr/9Dsi5H2x9uhwG4bzBtfrb9cUSnhw5Mgd8RlFlVqXAVc5HgRAAAA5rNMk1qZ5cIrRq7bqvC3Kp0wUuZ1H7Wpc8KKsJV4NyY5L9UfB1TpUvXJRaESTlUqmUP8+gIAACIYPCd3C1CbF0g8alUbPX7mGuR3PlCD3rS4knthj98owjln4vyt5PluJm9fvTZZihrbGWmzTszZ872eBlxZwGzJAOkavWGgGo5hLseLAAAAMFm6pbFX8Y+W6F1C69pEUGCt+crmpT4IjBEt4YQqzBgFxjtqEIcDqGTOccgIAD87BoIFb6YAxhi8JWfmz2nRVnq/ah7t6WqgBzXAMHuCzaCW3c4W3BMMg/fC6m29GeNoKPL5bvBof3Q1OTHn0Dee+1K95qgao7lBfK7g08I4xROoUIPjRQAAgMrirKTwytLSMHoBX5UJkrxpf8rOJVDPmBx4aI8wb2Zek3EHa7+GAAAgAElEQVSEnfTrAfJW8mvJ5ZYp7tWeN5YAOGF/2vcaTIC8PMG/Di+QuOtUFYlqgBs7DlVp1uwOteCnBxuGhcHbzN81vTgb7sawe12B+AkVBuYVhwSvq51x1fYqdsbzxS/1QCDwyODVSqg5CczieBEAAABGsyJzUcxVrZilGmP8cm2N2u50FeMX0K9fSMMC14gNoJh+mXnZjNepSpetT44K8rBC7WlPkEo+4W3JH/+fhVstrIlP6ncllgCenE7vtadVAION356gMO17l6Uq0v3IxySQYbaDLVfH4cmVW9EpNqKx2O5bzJt9mwX8PATnRo8THuy3P1p6JPmvybbVl8bM6nSyZnMveMmnBVl8aBKocy9kzb4Gj/6bXQAAAIC+3PnTW4R1ug3Biwc076R6PTDA0VTT6Typlf3707gGItdn5LJBD7Wnc7/+Fr/AFORVgGKuD68GaIDTbin0/t/I74Um4sdS7xLGrwGeWNZIZ6PJ3ImklrH5viaQ3hkmXY11lTGEaOVr6w+7yerd681bujr0yES/WNr45LZfKmduUz9tSV/B1WigCN02Qhlgot+zCwAAANCLe34YzFG+8MnrkPT1XRwXv05nBKJp+PIEKe6uSa2qz1fqZIMb7d4WqeHbZeprnCZ4AO4aTh4sKAEcMv32bXoBpqs9bGnfkdacBaW+5CDvamaP7bNFyCtd8+1Pes0zzMS6SjHA2Szgq95B0m9ecSvzp5LnSqRTSpsoM7zNtz0qLUVih+wG329ud+rgg8WVpBTn0qRWVuZ4EQAAoKA4iw7UFj/S4pcQajixTTjgZWtWvYFompysJLk9uVKrKjMyj8izAlNWALio+YzR6Aywhxt2iok8AcjV3RKVNmyLU4yzRapK13xHk17XV8XwVuTZCEF0DZIe27JfSyvVjDS+nvt9Y5mE+amKalzddWUaeqelLnaP8qND+QuELBwvAgAAVGPRgTFEGsQ0vW9OL8DdavtMwGCnk8zpB2XktA1HazVdZTprIwit0NuCNezNhIfsiZAVKnOFawTYo9PMwYISwIYF79qYQqSNt9QUqMaVWjGOz9kipwXvpIma72JN1v7lf8AsutRYzDldd5Tador9hbz/ZbQuWcmYun1MYl45tuFr5Tz+Qd7LrCHgbGGYYdf+FOSLVHjAy0ydV+EKx4sAAAB1BFxx4K0CCzGCDa7LngfekhyAu5X3Wfc49ExeySGjh8eKeq1b1biTioLxCqwS9LZ/UnH/y6pVWvW6AI7qer9paAZ4y1pfKIaqFeh0PVTqO3ZhwqoUZjxJ0enaRqBf/o9kOYI9+sVJq6nF0eJJL8WMP80kixM14zUVi9DEP8YCYBLHiwAAAEVYWzln1iMXGRe47wTbhtQtuzJPX10XswJ1SSA4OaqfenVrutJbvZhJR4Tz1unA2L4XSJpUZSqAYawpATzJOH+G/Qz9P2tUQr0LPHSOOQPsiTHtRVvDMptzRsZYYTjmuq4njPxc6OYOFgloZCXLYJ9cbAUVO1HSDeUmBlx7jfvZoz007PVKNazpv9kFAAAAaCDscgP1CDYAAKgq1G5xqML8xLsVilY/XBQtwCL49bfB83aVKlavB7irlN4BUpB4YRG3++jH/8r496fybXXhS2NBkeM5ZlYcXKqYlVBMj0qO3LMI6MTE6eg/KTbV5G7waSYpNCmnLjNR7zE08hjdtWw1zhYBsnO8CAAAkJ7llaQyNlzGMlOVaGQnoQITRd4EhYxWfnCht5WvnThMXB81rI1PH5Wr4+cqLUBXw0ZMQzPAjXwYkBuEFQTpegWOGql9sMjdIpdZQN6uRGRuk8+RNkltQH+8zS425hhf/+CTFMlEiuC65rdRKfpOSf0SQvxU06mEzhYBgnC8CAAAQBSzVsrir9AFYS0PGlp51wEAuDnxOtCuA33ACb+JzToChh8TNY+H7AEmGQIAAI9Sn/VAXrkC79yPXbNb86o/UQ8ZRW61RAmwEzUwktreEDlRDDYyTh5PErk43xDeI6ntiTpVvjadpcfok2VEa17Olc8WCX7VwYsHPTheBAAAyM3NPEATWdbrAXKRXeHJ9mEie44accJIxq+AnQL2wfFGPm8Xv/v7URDAXIZmAJkwslxnPVBG/MBzK/3z8htgggjed6YTq1f0jq6q0btU1C11sYRVNZkwy5oRteZVR9B2JM01Ljcs7cpniwABOV4EAAAghLmLZbO+PdEaWaKiMl6uxW4WJIPBXIYJuDn65P3GOSO6VUNJKzNpseGrrlP3dBk1ctkAZrHIAzCSrJtC/LMeKEnUAUBXhtpP1MyjXLWRqLSvuzP2azjEsV9VtUoFGVNKkzI7WwSIxvEiAABAYhZZGsq4YLcsjUUnw0JLDAPAUi7+zOP1347/+f1c/eZOZmUEFLAPVpLohJGYpQIAAHYaPKV3L8l4oo74BqdiizlXBK+9xTNe7csPHnttLXWxRFM7k8CjMdH++giKJD/Gvz+XqvriP5/rSuFTXzhQmONFAAAA5rNsBABQmMkeK2vy+Mjj0yEDnkcJ+IBXjzSSNzXlLTlsm5h8QnUrz1cBAEANThihPFGXhXUGGrJWH9ZtI+nxP3YKG4Ea8VXYxppInDQXJMyCFIPgCo/4wbvAic3cMvu/hy7k9sePf18vYms0Kyzr9+wCAAAAnFRvkYWwBNt+1gqz+/ene8APCJIBVwFwVL3UZNBnj7Zh/+vv0oHXNo2sXJMQ07B5wqdc2jDJXPko2QkgmsUn4cDKii3lDXYfOwYvit6+y8jFSI+TpXq7AOQ1PhMulXut1Wdxb6YrlbxIYl9nCMt+pakLv7IaDRe5+7wtmBF2iilBYgV7lnu1f2r3wu1S+NL6CTuIwMocLwIAAACZWJfkqNdl2e0o6rodKICBld1yoN0y1tEj2qf//H6uVmkk4KXtl7rwEFyTm8ErnVQHBwCAesYvisZc1QlOjV0h5Igs8i+BM3o8Tujn8ugmdQyz7AFkhy55nXSxzpXCCUFS5Z5ifO3IevpSbjdlMvwsQVJHfOITiMbxIgAAAFCWFfNlbbf74//a+7XVTx87jOCH/XZ2FnuBrUhQGW03md7xljjv50oaufiz/+nNqrstws9yetuo4Ss/jbj+s4plH/QHAIBKPm08/Sx/biyFCTmehDpufsC67rLxf7pul62xueTqryJsA73qsUsb80q/SlpsErXa1w4VJAgjlIG35jZNpYG+zIUAEJzjRQAAACCN4AvHwYu3iKP7NPe/f2q+5juCwgMCOtTNP6ULTgj1XOk5i4TBzjbSO16lDu+7yA+g3Au2v6rDXgvwEy9tHk0ybec2kdMvAABwxch10dR3FkF+tjdX0kpIWuwVTGmaxyxUODDyJtujNs7P+hn4Ogfvjbgi9fRgjGi72F9LcnqXNt2QLXTpLU6MbZQkV7dlvNtAL05gmENjh+4JATleBAAAAHKIs4tDTBfXXl8fpGi4njsleq1Hw4aLb5U3JDUhTUV2rmlu/0oHYaTtZ5frRWPhMcigQEz7e1zbAC7c2QEAoLavM3k3vwG5/2rCnSxxvIZiv7XHdcL+65U+/UGPg+lPvDdinQbab52auXKN0Q4Z2eNEy9qXgbv9fWdix9Fh2Sl7el9hlgIx6X0s6L/ZBQAAAAC+O71uNWzBa/GVtfGX/+nxlCt+/X3+nH9/rl7a9U84LfVGEXR1vXe8pguWUnvQvx7eeocamOI26Xr6r8e3TCfAIKAeHVNnBwCAdCKsGzxKfVsRrTLZT9uFtfiDE1WXi8c4caVtV+lPb13Z0r0ob+01ib2u+02dHG2yVpfWu5YSNQFPUqSRowE2JdpT1CQRZA8VCb+w7MH5SrhCAY4XAQAAgOiun+9Abc1fE/3kXAjlesigNg3Bo4YZo96+F7SK6pWf11z2whlJmMF1KeaEOjsAALOYix61c0tofMVqyq8ibyFFLtu2vCXnuk+tH+Fwk7ZlWCfO515pk/0mo+ErdXJUoi5/tNc0eevS0//RVqLKJ53T8T842iVtDhEwALCT40UAAAAgtBTbhCkKWcljhY95TfShd5JEOFjERhG86nHegb520fRseULGMu+kg/BKI74KkgRKNk2QuoVQSnZ2gFxMUaAT85zgZL9DVFcnKjYduT2+xbtVwxBdpyanny3S8KPkqCeFK2SdHrrhaPuee8zp9V81r3ytSScND9ZpxdkiJ0gRxWhQEqkXrvWuCPb4PbsAAAAARPHvj+X4cFqtWHVtXMtqN1N6UNfXRL9tWc0NPPmULiipcFt3GlJ1EOhH/+rnqWLH3+as1rKpl2JSFx4AYJi3s6a3x3xDLimC1gLChvg1k/cBhlklj9+m0018cGJAVGwUIEJXytujp+j0mhlZ4lHJCql3RaedaN/73290wK+f2TDXaU2aaxtUraI9+AQGxpDzSeR0uLolhGgcLwIAADDZysuCK1/7eJ0W5jTi3ch1z9suuJXWT9TMI52Um679ouSjV7wq3MoGjlbU5CLiTETrDUAR6rZYlbLhKdhONP2YcK3X0wGApRyaMt3/2PyH+ETpGF4gkdf0FR42THxwYlanbv6l6yzX3Kpu/MWOf83MtggL11Oku/BFOuZ+p5PVxZq8/fMrwaMpuWJk/FzPk5WiPd2oQRyVOgLllQzXkhcFezheBAAAAFbRfA/DmtpEA7aj1nkqCMorvIE966m+hrI8YZC6krf1rn/jKSVlyV3AfpGnVQZTgDvTMEjkSm81/yGC10HnYlgaws7xAgnznyw+NVPMYJv44ES/kB7cU5aargxeN5P0RqoUyb0vxIh81JVDRhw9SSKnQ/1reGfMOSNTpfxQg3Ykl+sRa0oJofw3uwAAAAAEMvjw8ixmFbXH9zb8zEQtOEDVFc+q1wU0Nyxd/Pr7f/57+/+Uu5oz6F+0SEwucpncBckM9QIvSMUyUqhG3z+Vqtf7AHgVapCCRJosT1njYq7bEPDvz//5L6/sval55adrzXQFnhVyUyrq675M2L2biQ9OpAvpT3r8kDiyaDF82rkLSd125WWfqnU1vedeP6Hv8RI0NGHtv20scIP5Vr0rYgBhQy5VI7bqdcEev2cXAAAAYGlWJRjvyusRnj4EgPhGHvzRb3Q4ehWDXyN2UeSD+bPU4Wlhaz4X1bisIOmr0tsFb4JU7N3g8hRrzaSeplKhAhKAaFMF4EnbHlrvfocURF1ADScASdvXFCigEy0Sbe/melydvpYyIb3aXGXA9Y4JjNUarjDtGF+TTq3PblAz0bRqkVwzpcerHjPNE/mpab41Zb8HbHtAZ5yq0B9Z3H+zCwAAAHCSW/pOxlSs5vuqdxWdPga+5PnxbIizkrtTugJ3pbdSwJU32gV8G14iRvy2Codi4Uv7MZLuEKSKwr4E9bQgFcviQnWrIMUAWIoJCZzQY9JiIjTLmmnQkmBkTVondftmKfysvD2yfi4uF8RZbfi51rMu1vmyKTf7VceJ3vGyt90n0dp0Zz1PySFJY2BiEzf86miBCty95sYBz/2S1LK3AERw5c63edwG6QhBigET/Z5dAAAAgHWFXZgIdTRsEFXr5BaEey4tbLgCsCH74LXOy3wGzDQeK2Hju+LXVUPZO0gE6pCfePdK0V6Celqoih1WmAINBwADhJonAHf9OmaK1S1qEGzx7d/bffsPszML+mTw2SKtPidOWN5LMv7BiQJRfaIps191v+gdWS2h+mBkg8P11iiapobmkXPrttlTaFs6C9MJwrCiZUuhwlxPk0w/GfhZ4AJhD8eLAAAAzLHywsTK177T4CrSIlDSua5t84Ar2j6I1vxlPsFDt+ve9tO1B6+Kquo9qRnqaQzmOv3Lln7q9TgAgJFMpSAa9zhTRPstCtzt/0FIvdShY87VtvIDjm5TynPoJ14wlyRM0hgYP+J0qqWAQyfbxneZGhGSJc9s1Ha/po/cxKHGiCAlidxerCxCZM7NGBFqACJwvAgAAABvrLnAvS3U8jdAW4fyW4qTGsiux5gb/2mbTpON4FcNlBHtOXgzlrYG3BGv3FgWHAA4auQJlcAepnOUEX8RlUdrNlbAg24XsebGzUhvqyJFqJ9ox+yrYSuHbva2O2HYJS8bVPV0DZhb/lmtG76lyzDX1wjs0VWF/X7DbhvHN8qnixIe8RnBn0ypED0FHv03uwAAAAAnpV5kSbE80aOQKS58w5jyZ68l6kmdb5d1KJOca+Jff8VGdHkbqF/JI9dJpz5lXvFJ5GBIYYUK1H1qWCFWG9oO+66dQo8DgKM6jZ4GZThhzH2Hu5sp1syKgo0U/v3533+MsebGzXS1K0f/fTK+uU9/o7ZbnADYNqAvr3zCEUSw/zak4Q2Le59zelfasEa5PVG2/VzZ1z9gWZGzx8iySaTw6vfsAgAAAJyRegks0fJE26NhE134RGoJuG5/Jrme5G+fIHfRUOppHixCPyUXz1k25I06ABBK86HZrAmAftxR0snbCYxga6t3fTZfvttT4PhT3xXCeNhb5XtYfNl5tWF9wPXmCqfVAiCmxVshUZcZ2VKJqiWpczV8PQYStWzA1NSvSGPa5UThPcwZ2fg+Ej8SBtwVxq8EmOW/2QUAAAA4LNr6434Zjz5tUuaMF/5JvwupVEtACg3H07xDMwuKGa7e+wfRmJkXIxk21KN36HE/KgGAsxqOIAYjOGfk7YZbmymkR8hoSs+tmi5yjT7739ftzd5tXanMqn2nvNUaruv1ZqzMjGUeYNjIsvgQJvwY7PZY74nAu004L06T0gV8wALn3d2+Ejzud8jlSrq7Z+lP/wGf/J5dAAAAgFWkXqG4cjps6gt/q8f5wfVqCWYJeAr+YDvzSfNaWvwFTbQypv9GC9d07/1jcYuPs9ueKkfX46KwM9uGBdNNHoVtcQCCu/5uNyMywLYF5+pWFIHBrqedc4k67Ju9Vxt3Brywmh403OIWnCRHs2YfDDhqfzWms2SsmVDaVuD1FtegbbXthgNap1VpNz5HjM0ycgaVrpUPzW3SXR0E5HgRAAAgh9QbIWWWMCzc3KVbbgZWsD+ZdBpVPXDMRanne6eteaIKBHeov2z0YqeNhCITfnWofprcFGsRAGjo3E9KDMeQjlubWfx4MjW9Bk5LkfquFzLsISOruTfBdpvGGZRNzG7WOeCgU+yljqJ1Wn+PwfVwT0FxsuIAqfsL67CFGjMvJdrdHvYg2U/+YEvKsVPb8pYc0nG8CAAAEFe0FcbHBYuVj9h4vag11xmbbJGuVmlAP9PPFrl/uMxGCmIV+GRncjgxnq553xSK5N/WlZtiDfFJzOftAEhk/+/xgCZM3pbix5PXueVhpMHxZn513bm1u4atbPEwjq8N8fYPDDFzfX2m7qnVkrZX2wlhmZxjnjzdCtPs7P2ldxtlr58yWrVygecK5ualT1V3ccCqdLbI09elDrakoo0LdpRgTY4XAQAAIgqy4bGxIGKt5NHKtXF6xXnlSitphZ1awjqUTwYEqmfvYL+RY4e+CQ1d6byez+CQYTcap2Py6E2x4P/K3SUATRhzAToxYwdemXrN0jwhB9lJMdCQ3Z5+lPpAiiYTwgjZpq3UbZrU47B15QnG4K1WprP0q+oyVZRd1anpaWHTS+Td7Vk15iGWKSKMCzsLcP8zQQLFOF4EAACIJc56okUQ9ju04iy0oLewu1PNySdUski3hezKd9WvY2vDNw4Zx6dQ851sv5pSnR+1zh0NAABk5JeTV4y/33FPurJF7q/7BXnw2utUPOuHMNL2wvL4Mhz9V+fKXDvJ7Lm64ONLavf6/1rJ+/9yonqdpcfs9FMt7fmiejU8kanpW1PuyHbW2NdJSOqaPyd7vGXUvI80P1jk7b8SJ1CG40UAAIBAIm9XwFeWzKhKbB9yetH/tZ4/fc7pFhk2ztptCmKRB2cBmhj/vKPhkp0GDOhtQ1FgN2Ei90RcAQAQTYrf413Rb+HC/Q7FPPWU8RFeaZlx/7V0reQBVSoNNqdKs0vagkcPGSmTrn++XXWlK00qwni6X96AOXHgftu54v4nzd66/3G0Jkh3KmX2qWlXKVYAgtRwhIrKHm8ZteojvQ8WefoEcQI1OF4EAACIIsLS2KM9yx+2yoC7FHsh65jbHK0eIjGOQFfT97rGp6nplxyKgZtHs96lpleyU9eUJQjD8kb0i3ZWnS4AAMBFV14WTW8m/PReBw4SY82XGYNnsODF25C35GGp0hqyt+NjBj5x1kAuOxtr+8yCkru09a6IT7bb+vF/3X4Y7GLMvP3w0595+4fF8hVxjNzxzBvGccYRD7GMd6WPHG2sVpFm4IAaHC8CAACEEGdpbI8mW2VAPWO2wEtutJf0lPz3bCF35VVpAPBq+su7So6Y5qs9dKrVeuFXj0NGTqjxdj4AgInK/yqSPbo2uqUDRhJvh6irm9VObElKrV4UZII3tx2bV0KQWu3hXEv56WlksuhRR2tsI/6bny3SpDVLblsPMKArtWqauW/TtOOZiGwwxb3O93STcw3UvAMKFcjO8SIAAMB8iVYMbZUBq8mbu6I9LJi3JmE/cQ4bdJBo9rdI7xmFHffB8lZ48/ltq+N1ktZnLoceJ6pnTMbOmxwAAJq4/gZmHq05dT9kwCaOWOVu8LGtU/Yor9/VZklcw8o55mecZQxOuSVrNdrjDTT31L65JioXg3P6k5O5ansMCeeQi1sPP013eTqdLXL/qCD9ZeSweOWSU0xNQ71Ns2vLBoneE+RkHnWK5E5hFmfgAE5wvAgAADBZonWx7FtlwAC9N7fkkEM8ggN6AWzQQWp7276mUsPoX500rNiv3WH/F4152oybx0rWy55crxBrdwDAgk5MosyavjJX38nPihhp1rGtI53+VY+s1Y+67UGtVjK3NQdk8o0LzHJ+X6djC+wikUiTWG3blX56nkrvh+KVxHyb5uDDHzlHKijGvAt4y/EiAADATFkWLEqe8A100m8X3EY7EJkZzlcRUre56CyqfYpE75bUN9nvFioXI3M73k5/uB8ZnrZR5+pzTw20zdVyMgCwjoZvYOYuwhpgIk1ucj99LDxpu8G6nQYTbeZmKeeN0qJWWzFWdq2Bo4Ea9gjvGscWRKtVcgk77tx6U6fiBdklGTOjjnClPbQ6kf+nTxWtcPgjxNE7lwYZNYAT/ptdAAAAgOh6bJUBtXXaVhnPsi+89evv9//i0JHnihkV3OkgVWVs2XpZImMrJHK6ev/96XW2SMNPWMeeScLr3yw1tRh/tki/zwQACKXVlNLE6YkKOaftGoIVCTY0D49PN+mzssHpH7Tz1vVzuBYxLPFGq9XmF77aEDaxQftV9cVpdqgg73dswU2xgM91OaEiLXLVhaqoV12LF/zagxhcS/u/rvmh/D2utFXf/7r3zWnyAPuJFkjq9+wCAAAA60qxmlD7hG+gn7aHrL9mjAEn9EtT58TM8FPG3JhVccWhaqz6WMwni1zmfikmugxTqYMkeutmE0tdLOncc8ueQB18RoPXmH91orYXzEizzha5f7IYBgC+Snqb3Px3Jj/m/z8/P0tO2htq1ZuEIl/dgqTTO2Y6ffgh+9OyrNWPuu1BrTa0+HDZ9WyRVh9Su41GLr1++qJWGysUZtyJsEvSe9Vl+gX20PVwqB5ng14pcI0WlG3oSoABGxwvAgAA8FHvE75rLG4CnzTZ4tpIFL230AqkKYvjtHL9FUNze5Md98Ekn1yS/hAIUtC/xmgyEPdoqQI3FD3oFDtFCB4xDAAEdH1+4s0KnRSb6k9pzYu/LFo8Ajnq0LGt+8VJBRE2p5YVJwzGEGZtLbKoPvEag58t8vhpcztX7za6XeD4gD/xRhlZbk0rpOIs+iWKkr17TPZu69xSQMnmg9SmT1+BExwvAgAAMI3FFCiv96OQThiBAVq9Zaj2CSMZ9WiRFJU8PRoD6tpB1PZ4eeu8ZPc0AKXQr41KRvUVusNOO8NGfQIAHGUGRUBdT+qHrzqdMxLExspMxuvNWGZaCdj6/Uaf2u+NmNuUWc4WuX/mrMYa2UzDdmlPf4tDRv4fe/eS5LrNJAy06oYX5q14bq/Gnnsr3ln9g+pfrVZJFB94ZCbOiW/QEX2tAhIPgiCYXFDAi84sQR7z9ZgoItSruTFdt1Ov2HlfVrLhIgsyCXBagSvaziroqHCO9CIAAMAcBfYsAHY6kWRk/3anDCPQVcPxNX00dZouTBE31repGSD8ZFC3Ei3DiIGZVIHDZKEGQmShcotMX8MDAPFFu+WZaOW1U7E+MKAdpRQhmmKj+N7Tl6LD1jfg0D56dQsb204CNlkN/VaYK+cW+ei2XpW/+5xb7QY8pb3++3uSjLg3PKFwD6ettuOrScebMt6DZA/senkyLcT3tLNpOJo7NK3JBwfnSC8CAAAwU+0HgcC9t4+uT88GAzKMfNh7ZUnNR9b0637AJ+73v5b6tE3qwvOteSeseukMPlqrhj2vyL1loldhmdKBe7fRxfXPzuLd/lnYScBY2CNm801fwwMA3FxclgxYlFo78dbpfqhr0c8K9+zmZ3oY1qkCDtIBde/xLGbx3CKdBN9gP/cXx+v6lLbSF2UehCpMGVUnq9PidPsTX3d79SNcF6djMMbboXf/D/SNZbWaGU5P9Q66w1HSiwAAAEBHP/e5bF3xkbMb2HtNbcqr4Nl7S6eITX/C2qozNK9F6lMaqQvPvYazZfY5EJqIOT3OWtbuicb4HBlj2ujc+udc2WLetsQcC6FEazIAgEOCJ+L8Fr+EeRWLbb/F+cVAxbzdo4BiQ3jDbX9mnSqPt1RsTcgDRP5uxFFLjQ7263R24vabPTredpndGx7iUsIJV0ZZ4S43ZVxPP/+WQpwp97QTVbCBE0TG7tekzGYn2E96EQAAgMlsZNSzvcMlTzPNjdwIzjVlyanBaV17zvRxdP38aNvyz/oCEjx1/dqxQu8KezZuheAnErOT3Iw81OLMzQmV3joLPhYmutJAorO4sw0AACAASURBVAoALCjI+vat6ZufxNRqDa+D0Za7y1CM7kRGNlbAcTqy+mG/G3FIwEZsJXL+7tN/a7x0aXSCrEgjlKGewvNVJUdTCBks/QSZDwMymXwEe15PCg0Hju4HO0kvAgAAAM0c3d4a/21qBnjbDbI3tydDYT3texrrhAEP+SKMoxNJRnqUOfUj1aSFn9734judgmep2IbNMEIQWbrHgFMFV0LRu3gx0xS2PTUyfWbOMhYGmN4WV0ToSwBAcL1vk61GGKNTT2s7OqzP4TTDhyYW70Xjq3/xuxHT28sGKdta9ZCfXX3WF2XiP0INUsJZs1OEuqcTdg35UKplD+Lq1dFokXthJxCi6TFwdD94S3oRAAAAaOD6p5VtY2W3vw+0fZo16+Mhemwc233AIyu23Y/lV72lxlvN967XyMhawaGP3rgsRqAVOK3T4taXmU9ofoVdKnoAAMzV7x2tdHtZS63DK+0TpsgtcvvNdfoY/VQav/uFrXXYQb2/YGFj21DYZhpmYgSCfDfiqNrjImb+7mU9hKh23/uIMcCvGF/+8l2itxSZOyKXrZWAdXSFumeqeUon4a1+Y0f3g22/ZhcAAAAAAAAAAAAAAAAAAAAAAOjlt9kFAAAAQHrU9Jqkzv3+ET0ho9MdIHWjm7imC57wPm/3GBbYgINocHlm9eGL1Qw+9HbaqEW0bhmBmLzS77PMJ2imUOJ0jP2aX5fbBiH1jcN+nXrOxEVXxrHQSeTxBQDQUI875fI3AgTRqaf5+iiwx87hHGpHejWm3I8YQbiVwZO+cwSnttv6cMzFYmM56oK1bZFzKVU9xHPledVI54H+sOHKHs7OwK48HWVn7MBE0osAAADAJc1fG7PRmcv1DjBg97wT3XUiu+owRY2ht12L2//XDM8eQQ7N6K400XBxmyJNxvjBOzfNh4kCAIAx2t4pW8cyQL9u1vvG070eV0TY1YS59ryEb5q9iRaKaOV5qvZMW7t2ecVZH3Z9hBqkjufILVKM4yV8RJp7JzLbvHWinxyKqux7vGKOgg3SiwAAAMB5PfaFbWYl0qoD5G30vCXvp3dMPI7qanB4Vx5Bs3rylYCvNvocBGFDqOGgi0YTqnsc1eTSnDoCE3nrrDCRBwBW0+pFMusott16yJX+ppsB05mIpriFXfzfEiKCsMW908hnNNuN0inDSOpuILdIYeaoWfTzCLTCTvsniuaf/LwxU0UzZvi4SMEr0osAAAD8H0G+v00K/bpKhM2sPbWbXsi5euxiLx7S5mbN5/2GcJYrlJ7MNrlFcomwLCGIgANB56S54JNe8OKdFnB6ua5kpU4o2WMBAN76XgWdXhO2XURZmpZ030nO9bfea3WvBwB7BB/CwYt3WtV6NSdQV1iCwkeHY6it5qXx52PHz6hmofEcfWRNZpvmuobUTAVw79fsAgAAAARiz4j9Cu8Lf/63t3b7/2U9y1b8J6EYJkuoXUyJSW6RK1a+4vMtZh/ofcVxRTshYD8ZbEAEBPk0oRvPRAoALO7r98MrohP/SUyW31097STfnedt/9n5z6A2c1QE5+Yi09dFLgE7CVRJ2pQe3i4qGiYEyduH5RZZylLBH9y3804CcLM9RQw7mLTUTBXZyIbQ6PDUb7MLAAAAEIXtV/Yb885YlqdrC2Z07tcBfGatjOZNaYMbTrs4GI2+GxepZXUdBee+jlW1K1atVzqnpzuXjHPErSpzGgDAt/t10dPVr4UTbUXoUYNfD4hQZeAQw3YKYQeY4nv6vbJC7jGBn3tEm0LVeiXiHo11mHAaGhzMFQ69F77WA61ILwIAAMwRbdui9iYRSY181nJ9PK6w3/qt99yV8RlbzDKHusqsI2BPCC7m8Okq19jMVdoBFuyxNB8FP7vQ/kN1uh98MxufJnTbrp9yfvgpAAAeWCYBsLjrl8JhZ40qXbUr1QUgqXPb7wUm8MFVcMAjiHWex1maflunxbnuaW+ZNXvruhO5ZEME0osAAADYHuKYertaDWtUfr91TOuXDyNHZZl2svfbLHEGGGbkxBjzIhItLSbDnFiQ6yrniFtA16e+mFM6AABQlcdqkEKucZqltBvbOFmqQEkBd31rjAhrngJ2JhkZ09Aeg9KVKYvyTKE1mKwaCh5JbQ0/SS8CAAAAcQ3Yz2q+zW0PDr61GgtZnkUZ+NSTZfQN5kK/jk5DQBd6RVg45OJQco1j27nvKH6YygAA+P+sDFfmlhN40PyiEPN16Fmlcs1tTkjrqdSm9/NMpXqtZp22G1zTgMsDVjBgEbjOpMGaps/ehU8xxbx3BuL4NbsAAADAuqpux5wgFIkU22vr99ZoSSPrlS6G6QpMWy5ksBRz/gq6tnKuLjTs+2DAGLmmoJF+TkRfv//P//b8tzv/ZRPmTACAQyyfuhofXg0K8NF5MjTThqI54JXP/2x3c5XrKV2tM0dNHEoG2izrdO8e7qMnkmWYjiCj32YXAAAAAJij91ujtgvhuvhPUIx0DpnSpV2S4Kj4Vx/gCmOcoyylRAAAAADO6XdP3ek7zIk2ARIVFSYqP1LaPgr3ifsFlbme6rrMVWYowZoKny20ugM2/JpdAAAAYGkxt2NilgraGrBjaFMSahv5fXIYz1Vsm/hwUa4u1Pt653oa06FemqtLAwAAAEABzXdWL/6gnV6IZpFR+fmfhxRcEu16SiVLzU4r9/zV6r5Ux+4qVCRDFSad1SYBKEN6EQAAYDJ7CiLANruWQYxviLd/Ud+gh7dXpe+sIiUvXlMqVTKSQGoWGE91/cYmjDRxjOvtNKQ7AQAQx8jVqZUwkMuAWavhn0g3x66zmT+4adYJbHnpBvVFrbruanHjW8NTQLoQNGRZEpBGIR2XZuAV6UUAAID55u5cPP3rNlOobdget830fsSWfm4JRJ7+D+CbKxEXpetCPS6CLqwA8ZmrAYACBi9prKCA4ExTxVx/gtnqGWi6TW+oasDBhpiXErMQFwW5np6g8xNBp3749mdjXpKqMtvUVrh9u04UZiHIS3oRAAAghGU3F5atOABvFX5iAUBMLj3b2t6+uRlkPGN8JGO8NxEGACCOMatTa2CglZKz1rlXmn1cgafso14U8DNj62jSexeP4eJcT2lukatq12ouEkOgq05XagsASE16EQAAIIpoWwzRygNJebwBwFwWdRBWxoViq+NxpiZWo88Hp4FeERkAoIxhCxsrKAAm+t6/fXsx2vnPgFwGj2tzCIW5nsIhA449bP8Jib3GyHi+JTLxHK/5aF1n+ENVv80uAAAAwP/6+n30hpF9VRZkW7YAjbjH+GvKT5//meqTGdxtVuse04ckQCtXrherTf4A/fRevZuxAQDYz+oUyKX8rDW9AGN4GA03xsJNk5khwnmbFMp3vPIVzM5QhWGMNWpoeOGwSIACfs0uAAAAwP8RKqN515LEqSYAQG+es0J8xukhJ77HFeput5PyFeScZTtGlopnKedT/QqfOiwAkMLnf+//R1sDVjiLL6KsTiGRlYfVre5mLaCMWdNO2Omuyc1U2NoB9wzV6YbtX23/IZdCaGKFHekmeegyjv2MZYbepBcBAAAiGrP1sOdPdCqGTQqgGNPaKys8cihGZ+7BQAAKu+UZ+XkFuf9/ub6ko8laiRbJaOXhuh5tqp8AQD+HUodIMtKcLyv0ZnX6kbDAsDKzVkMWLXBv2amgGO0Ikd3njDNa+TBp92SpTzGnLxwNrzimLIhAehEAAGBR+zcmmm9h2BNhNbbXmcV8S2Tr9E9XAWAd8ol8rHSBu7dmrdkjft+IX8I92taiRkwAIKDTuUIkGUnBIurG6nQ8UeKcNXvO0xTJXX8frpuyFLT+TGTuzBN23mvVh8NWMALBYZZX371gvMELhrd/bnA30OsKmNKIek4Q+482LX4ICgqTXgQAAIir307E0V9uWBLbKxzVts84AwHw1oCLtfXAMEINAJGtcKWOXMfIZTuq1ZGmSjEBgFCuP5uQZKSVHgsei6gHTVanTu3DAKuNslf19fFhoId15oTyNW21uIWVNXwxe+NHvP7Nx8D5Vk8bLM77FNTz8CGln/8roEYtoDnpRQAAgNDiHHHzqAwIIuAR6siTW4SyBWwyGEb/B1hQhAVYeYJ8jrjdKxmNK5UqczoKAAJquEFkr6mJtisfi6hXLq5OUxtZ/uyxYjpd6NvFS4N7amCWIJNPkGJ0dXqqv/2HxaJUrDr08zB2rqya9v+3lV4I5wRN31ac3ci2LWuWYDo9EKaTXgQAAIiu+Y7Yxf98YnYSItCOkE7eYZu35AV0Db6WHUaoATjnxBXERee0FUIXsI4Bi9TKiS04u3YA0FXzI/hxzvRn1/uzyXwcf6fLO2AwxSKDbk813VNfIQ7w01LjYpHKym7wrWq98orZIhtD4OjouHho+f5/LKJ3W+tLs7SK/MPvyBJLbXodvPLb7AIAAAC8931jf/28YNudtT3lsSXBRbpQHF+/jz61nKX1s5QTjuo06pcaMqlf9hg/7QNUYhYloLnLsFCDYoUV6X0dX0V+hTgAwHSdlkCf/7mUt3HlCawmOOQhXLeYFw7jmJugwgFksFC37T0cfX/120ZMjL4gNATxdZ1gow2BaFeTfvdNp392WIhanTWFV171sXOLrus/xRTBJ5l+M6HOOd59zK9fTLUgcURbP8NqpBcBAADSuLKJ0GM7zBYbQBa2oXdyaXvQvOeI8EiiDTBX3gVYlnOxleTtLYcEOdBcr/+8tWCVASCIrisfGUYaOrpSFfnrxBACKrw7cWWzCyKYMjz1/4aW+qpHkE1gvi3V95iiYWco3K8KV22Koxtie1IHnvg15jq93thowfFZYgvfg3NI755g4oINv2YXAAAA4ICv3//nf0f/E2godY+aUvjUEQsoVDxDFWZboqLeS1rsSlotJCxIBmvVagBcYSIlTh+oVJIrLwXFiQMAUJ7T4encHsL+XDdu/L9gQ+/eojfSXL1OZd4Ggmg+FwWf3Ey/GwZE5uHV5ba/rGXD0jQ7CdSyrs9gOs8sryK/f6cuyJ7e9ALskaKQ9fQLuwaFbb/NLgAAAMAZ2zmVbQdQg54czciE2SlaP0Uh70l5zmkXO0+6wdLExOGWOuCJZqrUceapRN2PRNL1q+vHqnrX90oJIzdH17JFu2Zd/6KUz8sDAHwc/14rh4gtTfS719NF6eT0PXtAhsl4kTffYLpWAyTR5HZfVJPDvcGz5YJ9j950BvLaPni//Z8QVqs2cjvDRD26n+kL3pJeBAAAyM3NP1MEf2cMBkjaRTM+BRlW5qRtOsy5E6WiOl7zDxBJLAVwUaK3MppMxUulyWirU+jCBm3/0Nj4JtUDb9gCAEGkWP8DY3g9gIz2v3QX88mjMbIIDU061x8W6Pa89bST6HuLiLkwC8gxGD523PJouyAGN8TgLLHmbe617Q8mMdhDehEAAAA4w87mmsa0e/ydzfglhE72fGLIAJkoY/B/ltkag3VkHLOcFj/JSPwOGb+ED04UuPlFMH7QmmdGAwBYivRqkILXA8grTn97+3wqTlGhAAOqK1/14GNStnF9jyZ0CeZq3gN16cimtM7KWWKzlLOwVt1PU8JOn19fX7PLAAAAxX1+ft7+bytwKGZkquaGBr/UV2+rrmsAd4Zr4puZ2Rt0SuiuB613sbM3KwElHWtPTZn2I7+B/2HSKG1k39ORVnb9G3HBk1BEPnMT/4tkrUpokgEAmMIDCOCVi/OD8U5Yvk7BvTFroQW7hIcXVW23bKW2qPT0vKGJSeg2/nT8uLEhwqG+FKxgmxg/t68Q1WhmHd+a29YjL9BxDsgZX3Gc7hXrNKLX02jit9kFAAAAgMRWTtW8U7HqLE5rTtTp4y23Hwc29BuAG6Ov66i/yKRBEzrS4h46wJ4Z7+E/aThP9uiNbXOgZBwvV8rcJHoZgwYAwAmf/1n7QRpX7uWNdFam/wOFmeL6SRHbJs96ztU0RXw4IfJBC4Cdpl+kFnxen6u05Z3ogVoQTviUnAYAAHqTHhLKS/fOmO+6XDf+PfMxBXilUjvm/W7A9F4Hh9T7/tKUhGIxD76YN8rzcUVyudJjB/TDWedityX6ItmJoppeAADmyrsBC4y0c64wwMnCRwK413s5tGyv8PCC1NwobZOEjrZ6jLh6na3rvFQvXE+Z21eweCufq/7RKkQ4HRcq7DzY6CHLNpzX02hCehEAAOjO/RusIOY7YxscvLhuynvmXQvwVMlGzJucZXqvg/3qpRf5mJRQLMIz1HvmjRUkyjsAN0f7bYr7r1xps+41L/me0ppYAAAiKLkjBAxwmz2MaPLyFiv3ZJzpROoW8lr85eSd9kcpY+0YrO2gq9rlfOnqInP7IvKeNW2od5bYuafjYsYcNng9jSakFwEAgO7cv8E6Ar4ztsHBiyaahDHyrnrJdsz+yGdKggM4qvDLJBerdq6QQZKMmDfWkSvvANx723sn9sBoX2Z2ZBAAgOYK7wgBwFveYuWejDM9OOVCXl5BP+ppxLJXisHmHurLwpeuLho5va8W2ziynzXtoUeW2FlH41IEHB54PY0mpBcBAIDu3L/BarJ8S8HBi1amvGfe5E+/VbURCzzYu16Fqo1LHOVfJjlRwevF860GRvJxRViBI4MAALRVfkcIAN7yIis3Ms704OEFeRU4qQIZXRl6iwylhrPTIhG7Z25fgVYext4y7OT1NJqQXgQAALpz/waLe9jxDLUX6eBFQ1PeMz/3d3eq3YjDHkV0DeO5WtRuWeJY5IHfzmq2LdieP3r7i60awtSxJh9XhBU4MggAQEOL7AgBwLaJX6cgGo9pevDwgqS8nAwTHR2Aqw0iCfJOq3EEkbc09DCDt5cFnKS8nkYT0osAAEB37t+AyBy8aO5tSJvHRyOeMOY5xJgw7q9L+WYlGg/8gnCOmSt8XBFW4MggAACtSC8CADezvk5BQJ7UNOfhBUlVOqkCSW0Pw8WHz+k5StwGWDzI02nlwQYEXLRJzetpNCG9CAAAdOf+DQjOwYsCNOIJVR9C3NdrkaYkLOlFQjnXHKLKh7wDsAxHBgEAuE56EQD4afzXKQjIY5rmWq08BZmRvJwMxHdopjLhfOs9vYtzBFXPmkbWL+ZCTXZeT6MJ6UUAAKA7929AfA5eFKART+j61GepSMJT0ovEtKddBJOfroxoPQoScWQQAIArpBcBANjmMU1bHl6QjlfQgUSeTlnmmZ8cQVyEhp6lYeTFmRq8nkYT0osAAEB37t+ALBy8KEAjHtXpqc+awYSfhr1SYtDBGEcHtbEJqTkyCADAUdK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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(\"image/png\", read(\"exactVC_eScore_manhattan.png\")) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusions\n", - "Using VarianceComponentTest.jl we can conduct a gene set analysis in a reasonable amount of time. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Julia 1.0.1", - "language": "julia", - "name": "julia-1.0" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.0.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From ea79b6bed031a4177ec13ac4dd3abf7c06eb016a Mon Sep 17 00:00:00 2001 From: Ruoyao Shi Date: Mon, 12 Aug 2019 15:15:42 -0700 Subject: [PATCH 3/6] Delete original files --- VCTest/SNP_29a.bed | Bin 13416476 -> 0 bytes VCTest/SNP_29a.bim | 253141 ------------------------------------------ VCTest/SNP_29a.fam | 212 - VCTest/SNP_29a.log | 21 - 4 files changed, 253374 deletions(-) delete mode 100644 VCTest/SNP_29a.bed delete mode 100644 VCTest/SNP_29a.bim delete mode 100644 VCTest/SNP_29a.fam delete mode 100644 VCTest/SNP_29a.log diff --git a/VCTest/SNP_29a.bed b/VCTest/SNP_29a.bed 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a/VCTest/SNP_29a.fam +++ /dev/null @@ -1,212 +0,0 @@ - 1 , 16 , , , F , , 29.20564, - 1 , 8228 , , , F , , 31.80179, - 1 , 17008 , , , M , , 37.82143, - 1 , 9218 , 17008 , 16 , M , , 35.08036, - 1 , 3226 , 9218 , 8228 , F , , 28.32902, - 2 , 29 , , , F , , 36.17929, - 2 , 2294 , , , M , , 42.88099, - 2 , 3416 , , , M , , 40.98316, - 2 , 17893 , 2294 , 29 , F , , 35.55038, - 2 , 6952 , 3416 , 17893 , M , , 48.06048, - 2 , 14695 , 2294 , 29 , F , , 37.60566, - 2 , 6790 , 2294 , 29 , M , , 46.36752, - 2 , 3916 , 2294 , 29 , F , , 35.05782, - 3 , 39 , , , F , , 34.28877, - 3 , 4521 , , , F , , 38.13171, - 3 , 8366 , , , M , , 40.98539, - 3 , 16693 , , , F , , 34.21628, - 3 , 21688 , 8366 , 16693 , M , , 36.63124, - 3 , 25532 , 21688 , 39 , F , , 31.88658, - 3 , 26294 , 21688 , 39 , M , , 39.75311, - 3 , 16795 , 21688 , 39 , F , , 33.99074, - 3 , 17445 , 8366 , 16693 , M , , 38.53802, - 3 , 2039 , 17445 , 4521 , M , , 39.46585, - 3 , 2831 , 8366 , 16693 , M , , 42.91433, - 4 , 54 , , , F , , 32.13501, - 4 , 5072 , , , M , , 37.75151, - 4 , 17240 , 5072 , 54 , F , , 33.84349, - 5 , 70 , , , F , , 34.59888, - 5 , 24010 , , , M , , 40.09751, - 5 , 21999 , 24010 , 70 , M , , 42.99402, - 8 , 109 , , , M , , 37.41603, - 8 , 7395 , , , F , , 33.77752, - 8 , 19099 , 109 , 7395 , M , , 38.80441, - 8 , 13234 , 109 , 7395 , F , , 34.40569, - 8 , 12043 , 109 , 7395 , F , , 29.52345, - 8 , 5226 , 109 , 7395 , M , , 42.36854, - 10 , 141 , , , F , , 32.21419, - 10 , 16886 , , , M , , 43.51632, - 10 , 6825 , 16886 , 141 , F , , 35.97481, - 10 , 480 , 16886 , 141 , F , , 35.34510, - 11 , 182 , , , M , , 35.39880, - 11 , 4117 , , , F , , 35.54144, - 11 , 13854 , , , F , , 29.53534, - 11 , 19440 , , , M , , 39.17309, - 11 , 20318 , , , F , , 30.56993, - 11 , 25910 , 19440 , 13854 , M , , 36.36442, - 11 , 21515 , 19440 , 13854 , F , , 31.16509, - 11 , 11280 , 182 , 21515 , F , , 30.11250, - 11 , 7670 , 182 , 21515 , M , , 37.51577, - 11 , 12003 , 19440 , 13854 , M , , 33.01421, - 11 , 14143 , 12003 , 4117 , M , , 37.07798, - 11 , 15898 , 14143 , 20318 , M , , 34.79655, - 11 , 10579 , 14143 , 20318 , F , , 31.99262, - 11 , 18979 , 12003 , 4117 , F , , 34.10469, - 11 , 6244 , 14143 , 20318 , F , , 34.25889, - 14 , 264 , , , F , , 36.84310, - 14 , 16636 , , , M , , 39.25868, - 14 , 26732 , , , M , , 42.63249, - 14 , 12511 , 16636 , 264 , F , , 34.95879, - 14 , 4078 , 16636 , 264 , F , , 37.09359, - 14 , 25732 , 26732 , 4078 , M , , 42.52580, - 17 , 309 , , , F , , 37.10268, - 17 , 6637 , , , M , , 40.16181, - 17 , 11879 , , , F , , 31.45488, - 17 , 19275 , , , M , , 39.41931, - 17 , 22601 , , , M , , 38.09502, - 17 , 24818 , , , M , , 37.04647, - 17 , 26856 , , , M , , 38.61527, - 17 , 26857 , , , M , , 44.61365, - 17 , 19113 , 6637 , 309 , F , , 35.17484, - 17 , 19235 , 22601 , 19113 , F , , 30.99862, - 17 , 6932 , 22601 , 19113 , M , , 39.35415, - 17 , 11361 , 22601 , 19113 , F , , 31.91075, - 17 , 12004 , 6637 , 309 , F , , 35.94537, - 17 , 18645 , 26856 , 12004 , M , , 35.58701, - 17 , 22301 , 26857 , 12004 , M , , 41.88404, - 17 , 7098 , 6637 , 309 , M , , 44.08404, - 17 , 12785 , 7098 , 11879 , M , , 45.44023, - 17 , 19176 , 7098 , 11879 , F , , 37.60122, - 17 , 10418 , 7098 , 11879 , M , , 43.64714, - 17 , 16612 , 7098 , 11879 , M , , 42.98597, - 17 , 908 , 7098 , 11879 , M , , 43.09925, - 17 , 1649 , 24818 , 309 , F , , 32.62620, - 17 , 20334 , 19275 , 1649 , M , , 40.68269, - 19 , 352 , , , F , , 33.63291, - 19 , 720 , , , M , , 35.84852, - 19 , 16617 , , , F , , 36.19565, - 19 , 18679 , , , F , , 33.21179, - 19 , 25222 , , , M , , 44.06816, - 19 , 26591 , 720 , 352 , F , , 30.74533, - 19 , 1706 , 25222 , 26591 , M , , 40.27193, - 19 , 13367 , 25222 , 26591 , F , , 37.19222, - 19 , 19856 , 720 , 352 , M , , 40.76144, - 19 , 3593 , 720 , 352 , M , , 38.34054, - 19 , 8344 , 3593 , 18679 , F , , 32.32320, - 19 , 23699 , 3593 , 18679 , M , , 38.96241, - 19 , 2500 , 3593 , 18679 , F , , 37.99763, - 19 , 9513 , 3593 , 18679 , M , , 37.62798, - 19 , 2145 , 720 , 352 , M , , 36.78084, - 19 , 14432 , 2145 , 16617 , M , , 36.11487, - 19 , 22375 , 2145 , 16617 , M , , 39.58272, - 19 , 10272 , 2145 , 16617 , M , , 39.69039, - 22 , 384 , , , F , , 35.58803, - 22 , 2381 , , , M , , 44.43538, - 22 , 6894 , , , M , , 39.06898, - 22 , 14412 , , , F , , 36.84326, - 22 , 22190 , , , F , , 36.93727, - 22 , 24468 , 2381 , 384 , M , , 43.46473, - 22 , 23690 , 6894 , 14412 , F , , 34.99459, - 22 , 6208 , 6894 , 14412 , F , , 34.26172, - 22 , 18921 , 24468 , 6208 , F , , 38.37541, - 22 , 10130 , 24468 , 6208 , M , , 40.25900, - 23 , 392 , , , M , , 43.74924, - 23 , 743 , , , F , , 37.01867, - 23 , 7079 , , , F , , 38.70568, - 23 , 25566 , , , F , , 29.82125, - 23 , 25997 , , , M , , 42.56723, - 23 , 26274 , 392 , 743 , F , , 32.53380, - 23 , 18694 , 25997 , 26274 , M , , 40.82751, - 23 , 5586 , 25997 , 26274 , F , , 33.00677, - 23 , 25256 , 392 , 743 , M , , 40.66506, - 23 , 19279 , 392 , 743 , M , , 40.52716, - 23 , 3760 , 392 , 743 , M , , 43.26506, - 23 , 17654 , 3760 , 7079 , M , , 45.51807, - 23 , 25271 , 3760 , 7079 , F , , 36.88655, - 23 , 22743 , 3760 , 7079 , F , , 37.04680, - 23 , 3121 , 392 , 743 , M , , 41.44442, - 23 , 9943 , 3121 , 25566 , F , , 35.38159, - 23 , 18496 , 3121 , 25566 , F , , 34.27991, - 23 , 283 , 392 , 743 , F , , 33.97507, - 24 , 394 , , , F , , 32.06582, - 24 , 22156 , , , M , , 40.90737, - 24 , 19621 , , , F , , 32.54329, - 24 , 13575 , 22156 , 394 , M , , 38.23117, - 24 , 10817 , 13575 , 19621 , F , , 30.34157, - 24 , 15061 , 13575 , 19621 , M , , 40.18703, - 24 , 1018 , 22156 , 394 , F , , 30.62705, - 25 , 398 , , , F , , 33.71504, - 25 , 3016 , , , M , , 41.24736, - 25 , 22041 , , , M , , 38.04592, - 25 , 23404 , , , M , , 39.04070, - 25 , 22711 , 3016 , 398 , F , , 32.94377, - 25 , 3012 , 22041 , 22711 , F , , 30.24151, - 25 , 15940 , 22041 , 22711 , M , , 37.81352, - 25 , 5229 , 22041 , 22711 , F , , 30.80268, - 25 , 5299 , 3016 , 398 , F , , 32.42869, - 25 , 11822 , 23404 , 5299 , M , , 39.92737, - 25 , 24192 , 23404 , 5299 , M , , 37.62434, - 25 , 1269 , 23404 , 5299 , M , , 38.78483, - 25 , 3094 , 3016 , 398 , M , , 39.91245, - 27 , 437 , , , F , , 33.14137, - 27 , 10069 , , , M , , 43.83929, - 27 , 11621 , , , F , , 31.83510, - 27 , 14123 , , , F , , 31.87239, - 27 , 23649 , 10069 , 437 , M , , 40.30189, - 27 , 17951 , 23649 , 11621 , M , , 37.32415, - 27 , 17917 , 17951 , 14123 , F , , 30.87942, - 28 , 443 , , , F , , 35.51953, - 28 , 9782 , , , M , , 41.42338, - 28 , 21962 , 9782 , 443 , M , , 42.03600, - 28 , 15617 , 9782 , 443 , M , , 44.80161, - 29 , 444 , , , F , , 37.22262, - 29 , 14219 , , , M , , 38.95235, - 29 , 18775 , , , M , , 38.97694, - 29 , 548 , 14219 , 444 , F , , 35.30647, - 29 , 5986 , 18775 , 548 , M , , 36.50408, - 29 , 2154 , 18775 , 548 , M , , 34.31488, - 29 , 14640 , 18775 , 548 , F , , 33.96409, - 31 , 5777 , , , F , , 33.60183, - 31 , 6885 , , , F , , 35.03186, - 31 , 9381 , , , M , , 36.96853, - 31 , 13772 , , , F , , 30.74825, - 31 , 14586 , , , F , , 33.75263, - 31 , 17673 , , , M , , 40.68325, - 31 , 23058 , , , M , , 43.76010, - 31 , 24094 , , , M , , 39.95246, - 31 , 24938 , , , M , , 39.85548, - 31 , 26817 , , , M , , 39.67360, - 31 , 27016 , , , M , , 34.66556, - 31 , 27017 , , , M , , 39.52413, - 31 , 19392 , 26817 , 13772 , F , , 34.72278, - 31 , 10472 , 24094 , 6885 , F , , 36.70687, - 31 , 21403 , 27016 , 10472 , F , , 33.88466, - 31 , 12608 , 27017 , 10472 , F , , 31.18318, - 31 , 13725 , 23058 , 12608 , F , , 31.67216, - 31 , 24385 , 23058 , 12608 , M , , 43.58843, - 31 , 1365 , 23058 , 12608 , F , , 39.73192, - 31 , 10372 , 24094 , 6885 , F , , 34.76781, - 31 , 15884 , 9381 , 10372 , F , , 32.62191, - 31 , 152 , 24938 , 15884 , F , , 33.83999, - 31 , 14705 , 9381 , 10372 , M , , 42.26222, - 31 , 15802 , 14705 , 5777 , M , , 42.26790, - 31 , 19770 , 9381 , 10372 , F , , 33.04774, - 31 , 26007 , 17673 , 19770 , M , , 39.55751, - 31 , 24905 , 17673 , 19770 , M , , 44.72676, - 31 , 16785 , 24905 , 14586 , M , , 42.46374, - 31 , 4512 , 17673 , 19770 , F , , 33.45711, - 31 , 18037 , 17673 , 19770 , F , , 36.69001, - 31 , 4257 , 9381 , 10372 , M , , 40.66950, - 31 , 19358 , 4257 , 13772 , M , , 41.79858, - 31 , 9277 , 4257 , 13772 , M , , 38.85976, - 31 , 16139 , 4257 , 13772 , M , , 42.21514, - 31 , 10439 , 4257 , 13772 , M , , 40.26029, - 31 , 63 , 17673 , 19770 , F , , 36.09045, - 10006 , 66 , , , M , , 37.72721, - 10008 , 92 , , , M , , 40.46004, - 10014 , 186 , , , M , , 36.59842, - 10027 , 374 , , , M , , 40.16524, - 10029 , 434 , , , M , , 44.12219, - 10033 , 333 , , , M , , 37.79624, - 10040 , 234 , , , M , , 38.96260, - 10045 , 789 , , , M , , 40.51312, diff --git a/VCTest/SNP_29a.log b/VCTest/SNP_29a.log deleted file mode 100644 index 2403391..0000000 --- a/VCTest/SNP_29a.log +++ /dev/null @@ -1,21 +0,0 @@ -PLINK v2.00a1 64-bit (11 Feb 2018) -Options in effect: - --bfile SNP_29a - --export vcf - --out SNP_29a - -Hostname: Jin-Zhous-MacBook-Pro.local -Working directory: /Users/jzhou/Box/OpenMendelTutorials/VCTest -Start time: Thu Oct 18 21:52:01 2018 - -Random number seed: 1539924721 -16384 MB RAM detected; reserving 8192 MB for main workspace. -Using up to 8 compute threads. -212 samples (0 females, 0 males, 212 ambiguous; 0 founders) loaded from -SNP_29a.fam. -253141 variants loaded from SNP_29a.bim. -1 categorical phenotype loaded (212 values). -Warning: Duplicate sample ID(s) present in exported VCF file. ---export vcf to SNP_29a.vcf ... done. - -End time: Thu Oct 18 21:52:02 2018 From 41d222bc66aeabfd135f9fb78c740b29ac0b7c73 Mon Sep 17 00:00:00 2001 From: Ruoyao Shi Date: Mon, 12 Aug 2019 15:16:26 -0700 Subject: [PATCH 4/6] Delete original files --- VCTest/chr19_chr20_gene_start_end.txt | 3793 ------------------------- 1 file changed, 3793 deletions(-) delete mode 100644 VCTest/chr19_chr20_gene_start_end.txt diff --git a/VCTest/chr19_chr20_gene_start_end.txt b/VCTest/chr19_chr20_gene_start_end.txt deleted file mode 100644 index 00ed3d2..0000000 --- a/VCTest/chr19_chr20_gene_start_end.txt +++ /dev/null @@ -1,3793 +0,0 @@ -Gene name,Gene end (bp),Gene start (bp),Chromosome/scaffold name -WASH5P,71626,60951,19 -AC008993.1,69178,68403,19 -AC008993.2,69972,69167,19 -AC008993.3,72718,71778,19 -MIR1302-11,72110,71973,19 -FAM138F,77686,76163,19 -OR4G3P,94974,94062,19 -OR4G1P,106077,99175,19 -OR4F17,117102,107104,19 -WBP1LP11,145812,145485,19 -OR4F8P,157215,156279,19 -AC092192.1,177913,176896,19 -SEPT14P19,195696,186373,19 -CICP19,195595,193239,19 -AC092299.1,198066,197310,19 -AC010507.1,200775,197961,19 -LINC01002,239440,198264,19 -AC010507.2,201174,200499,19 -RNU6-1076P,223261,223158,19 -AC016588.2,251571,248551,19 -PLPP2,291504,281040,19 -MIER2,344815,301444,19 -AC016588.1,306467,305573,19 -THEG,376670,361747,19 -AC010641.1,399173,397589,19 -C2CD4C,409170,405438,19 -SHC2,460996,416583,19 -RNA5SP462,453245,453134,19 -ODF3L2,474983,463346,19 -MADCAM1,505342,489176,19 -AC005775.1,507833,490046,19 -TPGS1,519654,507497,19 -CDC34,542092,531712,19 -GZMM,549924,544034,19 -AC009005.1,572228,567210,19 -BSG,583493,571277,19 -HCN2,617159,589893,19 -AC005559.1,591300,590430,19 -POLRMT,633604,617224,19 -AC004449.1,637537,637105,19 -FGF22,644371,639879,19 -RNF126,663277,647526,19 -AC004156.1,669500,663482,19 -FSTL3,683399,676365,19 -PRSS57,695498,685546,19 -RPS2P52,691172,690353,19 -PALM,748329,708939,19 -MISP,764318,751126,19 -AC006273.1,781862,781002,19 -LINC01836,785080,782755,19 -PTBP1,812327,797075,19 -MIR4745,805001,804940,19 -PLPPR3,821977,812488,19 -MIR3187,813653,813584,19 -AZU1,832018,825097,19 -PRTN3,848175,840960,19 -ELANE,856247,851014,19 -CFD,863630,859643,19 -MED16,893218,867630,19 -RNU6-9,893590,893484,19 -R3HDM4,913245,896503,19 -KISS1R,921015,917287,19 -ARID3A,975934,925781,19 -AC005379.1,928817,928259,19 -AC005391.1,958149,956485,19 -WDR18,998438,984271,19 -AC004528.1,1002757,999796,19 -GRIN3B,1009732,1000419,19 -TMEM259,1021179,1009648,19 -AC004528.2,1010907,1010221,19 -RNU6-2,1021628,1021522,19 -CNN2,1039068,1026581,19 -AC011558.1,1039064,1038727,19 -ABCA7,1065572,1040101,19 -ARHGAP45,1086628,1065923,19 -POLR2E,1095380,1086579,19 -GPX4,1106791,1103926,19 -SBNO2,1174283,1107636,19 -STK11,1228435,1177558,19 -HMGB2P1,1203842,1203209,19 -CBARP,1238027,1228287,19 -AC004221.1,1239522,1238179,19 -ATP5F1D,1244826,1241746,19 -MIDN,1259140,1248553,19 -CIRBP,1274880,1259384,19 -CIRBP-AS1,1270241,1267814,19 -C19orf24,1279249,1275438,19 -EFNA2,1300237,1286154,19 -RPS15P9,1307719,1307401,19 -AC005330.1,1322846,1321225,19 -MUM1,1378431,1354711,19 -AC004623.1,1374364,1372238,19 -AC005329.2,1377520,1376773,19 -NDUFS7,1395589,1383527,19 -AC005329.3,1389651,1386804,19 -AC005329.1,1396467,1392170,19 -GAMT,1401570,1397026,19 -DAZAP1,1435687,1407569,19 -RPS15,1440494,1438358,19 -AC027307.3,1441938,1440839,19 -APC2,1473244,1446302,19 -AC027307.2,1458580,1457670,19 -C19orf25,1479556,1461143,19 -PCSK4,1490752,1481428,19 -REEP6,1497927,1490747,19 -ADAMTSL5,1513604,1505018,19 -AC027307.1,1508963,1508375,19 -PLK5,1536046,1524074,19 -MEX3D,1568058,1554669,19 -RN7SL477P,1570860,1570575,19 -MBD3,1592801,1573596,19 -AC005943.2,1577086,1576939,19 -AC005943.1,1605445,1578339,19 -UQCR11,1605490,1597172,19 -TCF3,1652605,1609290,19 -RNU6-1223P,1616595,1616492,19 -RF00017,1724395,1724115,19 -AC005256.1,1748745,1748056,19 -ONECUT3,1780988,1752373,19 -ATP8B3,1812276,1782075,19 -REXO1,1848463,1815246,19 -AC012615.2,1815873,1815249,19 -MIR1909,1816238,1816159,19 -AC012615.6,1824542,1822089,19 -AC012615.3,1853622,1852382,19 -KLF16,1863568,1852399,19 -AC012615.4,1862019,1860250,19 -AC012615.1,1876169,1874871,19 -AC012615.5,1875992,1875016,19 -ABHD17A,1885547,1876810,19 -SCAMP4,1926013,1905214,19 -ADAT3,1913447,1905378,19 -CSNK1G2,1981338,1941149,19 -CSNK1G2-AS1,1954586,1952531,19 -BTBD2,2034881,1985438,19 -AC005306.1,1990370,1989401,19 -AC004678.2,2003581,2002580,19 -AC004678.1,2014656,2013728,19 -AC007136.1,2031051,2030853,19 -MKNK2,2051244,2037465,19 -MOB3A,2096673,2071038,19 -IZUMO4,2099593,2096429,19 -AP3D1,2164468,2100988,19 -DOT1L,2232578,2164149,19 -AC004490.1,2215565,2212029,19 -PLEKHJ1,2237704,2230084,19 -MIR1227,2234149,2234062,19 -MIR6789,2235926,2235829,19 -SF3A2,2248679,2236504,19 -AMH,2252073,2249309,19 -MIR4321,2250718,2250639,19 -JSRP1,2269759,2252252,19 -OAZ1,2273490,2269509,19 -AC005258.1,2341172,2269525,19 -PEAK3,2282176,2274622,19 -LINGO3,2292024,2289784,19 -LSM7,2328620,2321517,19 -SPPL2B,2354806,2328615,19 -TMPRSS9,2426239,2360238,19 -TIMM13,2427894,2425624,19 -LMNB2,2456996,2427638,19 -MIR7108,2435000,2434914,19 -LINC01775,2462185,2458935,19 -GADD45B,2478259,2476122,19 -RNU6-993P,2503141,2503047,19 -GNG7,2702709,2511219,19 -ELOCP28,2538754,2538112,19 -RN7SL121P,2595291,2595019,19 -AC092068.2,2611862,2610155,19 -MIR7850,2630793,2630715,19 -AC092068.1,2632849,2631954,19 -AC092068.3,2643853,2641838,19 -AC006538.3,2701266,2700689,19 -DIRAS1,2721418,2714567,19 -AC006538.2,2740048,2717769,19 -AC006538.1,2729327,2727743,19 -SLC39A3,2740152,2732204,19 -SGTA,2783371,2754714,19 -THOP1,2815807,2785460,19 -ZNF554,2835773,2819874,19 -ZNF555,2860484,2841435,19 -AC006130.1,2858716,2847227,19 -ZNF556,2883445,2867335,19 -AC006130.2,2875592,2874950,19 -AC119403.2,2897222,2896625,19 -ZNF57,2918476,2900898,19 -AC119403.1,2926807,2915146,19 -ZNF77,2944971,2933218,19 -AC006277.1,2950644,2949035,19 -TLE6,2995184,2977446,19 -TLE2,3047635,2997638,19 -AC005944.1,3053724,3052910,19 -AES,3063107,3052910,19 -GNA11,3124004,3094410,19 -AC005262.2,3119304,3118665,19 -KF456478.1,3122128,3121116,19 -AC005262.1,3131398,3130717,19 -GNA15,3163769,3136193,19 -AC005264.1,3155175,3141576,19 -S1PR4,3180332,3172346,19 -NCLN,3209575,3185563,19 -CELF5,3297076,3224703,19 -AC010649.1,3303003,3296765,19 -NFIC,3469217,3314403,19 -AC007792.1,3355955,3355554,19 -AC005514.1,3371602,3371395,19 -SMIM24,3480542,3473986,19 -AC005551.1,3483446,3482804,19 -DOHH,3500940,3490822,19 -FZR1,3538330,3506273,19 -RF00212,3521320,3521248,19 -MFSD12,3574290,3538261,19 -C19orf71,3544030,3539154,19 -AC005786.3,3557569,3544199,19 -AC005786.2,3558486,3558015,19 -AC005786.4,3567959,3567278,19 -AC005786.1,3568273,3568120,19 -HMG20B,3579088,3572777,19 -GIPC3,3593541,3585553,19 -TBXA2R,3606840,3594506,19 -CACTIN-AS1,3613930,3607247,19 -CACTIN,3626815,3610641,19 -PIP5K1C,3700479,3630183,19 -AC004637.1,3674295,3672582,19 -TJP3,3750813,3708109,19 -APBA3,3761699,3750819,19 -AC005954.2,3756517,3753840,19 -AC005954.1,3756659,3754620,19 -MRPL54,3768575,3762664,19 -RAX2,3772221,3769089,19 -AC005777.1,3776182,3775996,19 -MATK,3802129,3777970,19 -ZFR2,3869032,3804024,19 -FTLP5,3872107,3870991,19 -ATCAY,3928079,3879864,19 -RN7SL202P,3920506,3920245,19 -NMRK2,3942416,3933103,19 -DAPK3,3971123,3958453,19 -MIR637,3961512,3961414,19 -EEF2,3985469,3976056,19 -SNORD37,3982572,3982507,19 -PIAS4,4039386,4007646,19 -ZBTB7A,4066945,4044364,19 -AC016586.1,4062749,4061615,19 -MAP2K2,4124129,4090321,19 -RF00017,4127732,4127492,19 -CREB3L3,4173054,4153601,19 -SIRT6,4182604,4174109,19 -ANKRD24,4224814,4183354,19 -EBI3,4237531,4229498,19 -AC005578.1,4247358,4246339,19 -YJU2,4269090,4247079,19 -SHD,4290724,4278601,19 -TMIGD2,4302431,4292232,19 -FSD1,4323843,4304600,19 -STAP2,4342786,4324043,19 -AC104521.1,4343491,4339577,19 -MPND,4360086,4343527,19 -AC007292.3,4354057,4347244,19 -EIF1P6,4349061,4347780,19 -AC007292.2,4358448,4356637,19 -SH3GL1,4400547,4360370,19 -AC007292.1,4364640,4363789,19 -CHAF1A,4445018,4402662,19 -AC011498.3,4430934,4429689,19 -UBXN6,4457822,4444999,19 -MIR4746,4446048,4445978,19 -AC011498.2,4448217,4447304,19 -AC011498.7,4450836,4448810,19 -AC011498.6,4455286,4454014,19 -AC011498.1,4471493,4457962,19 -HDGFL2,4502211,4472287,19 -PLIN4,4518465,4502180,19 -PLIN5,4535224,4522531,19 -AC011498.4,4540067,4528427,19 -LRG1,4540474,4536409,19 -SEMA6B,4559808,4542593,19 -AC011498.5,4622307,4621194,19 -TNFAIP8L1,4655568,4639518,19 -MYDGF,4670370,4641374,19 -AC005339.1,4655524,4654964,19 -DPP9,4724673,4675224,19 -DPP9-AS1,4685948,4679282,19 -MIR7-3HG,4772533,4769140,19 -MIR7-3,4770779,4770670,19 -AC005523.1,4791207,4785120,19 -FEM1A,4801273,4791681,19 -AC005523.2,4795559,4791745,19 -TICAM1,4831704,4815932,19 -AC027319.1,4838907,4838332,19 -PLIN3,4867768,4838341,19 -ARRDC5,4902867,4890437,19 -UHRF1,4962154,4903080,19 -MIR4747,4932740,4932687,19 -KDM4B,5153595,4969113,19 -PTPRS,5340803,5158495,19 -AC022517.1,5178464,5178119,19 -RPL32P34,5278133,5277734,19 -AC005790.1,5294696,5294247,19 -ZNRF4,5456856,5455431,19 -TINCR,5578349,5558167,19 -SNRPEP4,5576938,5576660,19 -SAFB2,5624046,5586999,19 -SAFB,5668478,5623035,19 -RPL36,5691876,5674947,19 -C19orf70,5680896,5678421,19 -HSD11B1L,5688523,5680604,19 -LONP1,5720572,5691834,19 -CATSPERD,5778734,5720677,19 -PRR22,5784765,5782960,19 -AC011499.1,5785619,5784005,19 -DUS3L,5791238,5784832,19 -NRTN,5828324,5823802,19 -FUT6,5839731,5830610,19 -FUT3,5851474,5842888,19 -AC024592.2,5858239,5847467,19 -FUT5,5870540,5865826,19 -AC024592.3,5903787,5866171,19 -NDUFA11,5904006,5891276,19 -AC024592.1,5901511,5899895,19 -AC104532.1,5914707,5904841,19 -VMAC,5910853,5904858,19 -AC104532.2,5913899,5911578,19 -CAPS,5915877,5911707,19 -RANBP3,5978142,5916139,19 -AC011444.1,6020363,5978403,19 -RFX2,6199572,5993164,19 -AC011444.2,6077119,6067953,19 -AC011444.4,6073527,6072823,19 -AC011444.3,6125797,6109322,19 -ACSBG2,6193094,6135247,19 -AC011471.1,6176442,6176256,19 -AC011471.4,6205121,6199405,19 -MLLT1,6279948,6210379,19 -AC011471.2,6259799,6259157,19 -AC011471.3,6263469,6263068,19 -ACER1,6333629,6306142,19 -AC011491.3,6362149,6343761,19 -CLPP,6368910,6361452,19 -ALKBH7,6375251,6372433,19 -PSPN,6379058,6375148,19 -GTF2F1,6393981,6379569,19 -AC011491.2,6390400,6386762,19 -MIR6885,6389703,6389638,19 -MIR6790,6392983,6392921,19 -AC011491.1,6412404,6393794,19 -KHSRP,6424794,6413348,19 -MIR3940,6416511,6416410,19 -SLC25A41,6433779,6426037,19 -SLC25A23,6465203,6436079,19 -CRB3,6467221,6463777,19 -DENND1C,6482557,6467207,19 -AC010503.4,6470152,6469465,19 -TUBB4A,6502848,6494319,19 -AC010503.2,6494805,6494320,19 -AC010503.1,6495025,6494320,19 -TNFSF9,6535928,6530999,19 -AC010503.3,6554494,6552358,19 -CLIC4P2,6563848,6562123,19 -RF00019,6573057,6572956,19 -CD70,6604103,6583183,19 -RPL7P50,6594055,6593329,19 -TNFSF14,6670588,6661253,19 -C3,6730562,6677704,19 -AC008760.2,6717742,6716386,19 -GPR108,6737603,6729914,19 -MIR6791,6736778,6736712,19 -TRIP10,6751526,6737925,19 -AC008760.1,6751467,6748293,19 -SH2D3A,6767588,6752160,19 -VAV1,6857366,6772714,19 -ADGRE1,6940459,6887566,19 -AC020895.2,6896832,6894994,19 -AC020895.1,6898686,6898299,19 -RF00017,6928629,6928345,19 -ADGRE4P,6997872,6952500,19 -AC025278.1,6954904,6953211,19 -FLJ25758,7006074,7005012,19 -AC025278.2,7012679,7011884,19 -MBD3L2B,7021450,7018969,19 -MBD3L5,7033011,7030578,19 -MBD3L4,7040179,7037748,19 -MBD3L2,7051735,7049321,19 -MBD3L3,7058640,7056209,19 -ZNF557,7087968,7069444,19 -INSR,7294034,7112255,19 -AC119396.1,7383385,7348943,19 -AC008878.3,7472477,7382834,19 -AC119396.2,7395039,7390522,19 -ARHGEF18,7472477,7395113,19 -PEX11G,7497449,7476875,19 -TEX45,7508450,7492976,19 -AC008878.1,7519622,7507052,19 -ZNF358,7521026,7515292,19 -AC008878.4,7520460,7519916,19 -MCOLN1,7534009,7522626,19 -AC008878.2,7540059,7533595,19 -PNPLA6,7561764,7534004,19 -CAMSAP3,7618304,7595902,19 -MIR6792,7617505,7617439,19 -XAB2,7629565,7619525,19 -PET100,7631956,7629737,19 -AC008763.2,7643048,7629796,19 -PCP2,7633748,7631611,19 -AC008763.1,7636990,7633766,19 -STXBP2,7647873,7636881,19 -RPS27AP19,7666476,7665717,19 -RETN,7670454,7669044,19 -MCEMP1,7679826,7676628,19 -AC008763.3,7682854,7678501,19 -TRAPPC5,7687703,7680843,19 -FCER2,7702146,7688758,19 -CLEC4G,7733906,7728957,19 -CD209,7747564,7739994,19 -RPL21P129,7751161,7750684,19 -CLEC4M,7769605,7763149,19 -CLEC4GP1,7790621,7787549,19 -EXOSC3P2,7814425,7813615,19 -EVI5L,7864976,7830233,19 -PRR36,7874379,7868719,19 -AC010336.3,7871296,7870561,19 -LYPLA2P2,7880120,7879445,19 -RNA5SP463,7887096,7886977,19 -AC010336.7,7887096,7886977,19 -LRRC8E,7902021,7888505,19 -AC010336.1,7903542,7898804,19 -MAP2K7,7914478,7903843,19 -AC010336.4,7913518,7912648,19 -TGFBR3L,7919097,7916145,19 -AC010336.2,7919157,7918652,19 -SNAPC2,7923250,7920316,19 -CTXN1,7926166,7924485,19 -AC010336.5,7926810,7926001,19 -TIMM44,7943920,7926718,19 -ELAVL1,8005659,7958579,19 -AC010336.6,7960012,7959123,19 -AC008946.1,8016025,8008729,19 -CCL25,8062650,8052767,19 -FBN3,8149846,8065402,19 -AC022146.1,8175567,8173272,19 -CERS4,8262421,8206736,19 -AC022146.2,8239648,8222153,19 -CD320,8308356,8302127,19 -AC010323.1,8321379,8308283,19 -NDUFA7,8321396,8308768,19 -RPS28,8323340,8321158,19 -KANK3,8343262,8322584,19 -AC010323.2,8347687,8346517,19 -ANGPTL4,8374373,8363289,19 -RAB11B-AS1,8390685,8374373,19 -MIR4999,8389380,8389290,19 -RAB11B,8404434,8389981,19 -MARCH2,8439017,8413270,19 -AC136469.1,8427827,8423355,19 -AC136469.2,8445597,8444570,19 -HNRNPM,8489114,8444767,19 -PRAM1,8503112,8490056,19 -ZNF414,8514164,8509678,19 -MYO1F,8577577,8520790,19 -AC092316.1,8550139,8549506,19 -ADAMTS10,8610735,8580242,19 -AC130469.1,8582715,8581160,19 -LINC01862,8638649,8630671,19 -AC243312.1,8677584,8676844,19 -AC243312.2,8696321,8695721,19 -ACTL9,8698822,8697397,19 -OR2Z1,8732160,8721634,19 -RPL23AP78,8729204,8728735,19 -AC012616.1,8778861,8778665,19 -ZNF558,8832328,8806170,19 -AC008734.1,8824110,8821501,19 -MBD3L1,8843340,8832398,19 -AC008734.2,8833541,8833065,19 -MUC16,8981342,8848844,19 -BOLA3P2,9019984,9019344,19 -OR1M4P,9083398,9083112,19 -OR1M1,9095669,9087061,19 -OR7G2,9107475,9100407,19 -OR7G1,9115763,9114828,19 -OR7G15P,9122492,9122160,19 -OR7G3,9126950,9126012,19 -ZNF317,9163424,9140380,19 -OR7D2,9188817,9178979,19 -ELOCP29,9183048,9182369,19 -OR7E16P,9192170,9191286,19 -OR7E25P,9210067,9198354,19 -OR7D4,9219589,9210276,19 -OR7D1P,9236076,9235144,19 -OR7E24,9252625,9247344,19 -OR7E18P,9261716,9261015,19 -OR7E19P,9268819,9265440,19 -OR7H1P,9279369,9278448,19 -AC011451.1,9294482,9291515,19 -ZNF699,9309838,9294275,19 -ZNF559,9351162,9323772,19 -ZNF559-ZNF177,9382617,9324174,19 -AC011451.2,9348703,9348106,19 -ZNF177,9382617,9363020,19 -AC011451.3,9407055,9383581,19 -ZNF266,9435578,9412461,19 -ZNF560,9498607,9466507,19 -AC008567.2,9510079,9498678,19 -AC008567.1,9519341,9518150,19 -ZNF426,9538645,9523224,19 -ZNF426-DT,9539734,9538733,19 -ZNF121,9584533,9560353,19 -ZNF561,9621399,9604680,19 -ZNF561-AS1,9645896,9621291,19 -ZNF562,9675086,9641808,19 -RPS4XP22,9684073,9683294,19 -ZNF812P,9700776,9689924,19 -AC008759.3,9722410,9721903,19 -AC008759.1,9725086,9724008,19 -AC008759.2,9731943,9730853,19 -ZNF846,9793180,9751993,19 -AC008752.3,9756863,9756152,19 -UBE2L4,9774960,9774497,19 -AC008752.1,9802301,9793621,19 -FBXL12,9827816,9810267,19 -RF00156,9820094,9819954,19 -RPL10P15,9820455,9820141,19 -UBL5,9830115,9827892,19 -AC008752.2,9835013,9834079,19 -PIN1,9849682,9835257,19 -OLFM2,9936552,9853718,19 -COL5A3,10010471,9959561,19 -AC008742.1,9997163,9995997,19 -RDH8,10022279,10013249,19 -C3P1,10074135,10037803,19 -MIR5589,10038413,10038354,19 -C19orf66,10093252,10086122,19 -AC020931.1,10090377,10089032,19 -ANGPTL6,10102796,10092338,19 -PPAN-P2RY11,10114780,10106223,19 -PPAN,10111634,10106289,19 -SNORD105,10107735,10107651,19 -SNORD105B,10109835,10109757,19 -P2RY11,10115372,10111538,19 -EIF3G,10119918,10115017,19 -DNMT1,10231286,10133345,19 -S1PR2,10231331,10221433,19 -MIR4322,10230485,10230413,19 -MRPL4,10260045,10251901,19 -AC011511.2,10285108,10252268,19 -AC011511.3,10260045,10259109,19 -ICAM1,10286615,10270835,19 -AC011511.5,10289019,10285801,19 -ICAM4,10288522,10286967,19 -ICAM5,10296778,10289981,19 -ZGLP1,10309880,10304803,19 -AC011511.1,10316009,10305427,19 -FDX2,10316015,10310045,19 -AC011511.4,10320678,10315471,19 -RAVER1,10333640,10316212,19 -AC114271.1,10336248,10333436,19 -ICAM3,10339823,10333776,19 -TYK2,10380676,10350529,19 -CDC37,10420121,10391134,19 -MIR1181,10403538,10403458,19 -PDE4A,10469631,10416773,19 -KEAP1,10503741,10486120,19 -AC011461.1,10491000,10490339,19 -S1PR5,10517931,10512742,19 -ATG4D,10553418,10543895,19 -RNU7-140P,10551092,10551033,19 -MIR1238,10552204,10552122,19 -KRI1,10566037,10553078,19 -CDKN2D,10569059,10566462,19 -AP1M2,10587315,10572671,19 -AC011475.1,10598965,10598001,19 -SLC44A2,10644559,10602457,19 -ILF3-DT,10653844,10651862,19 -ILF3,10692417,10654261,19 -QTRT1,10713437,10701430,19 -DNM2,10833488,10718079,19 -MIR638,10718503,10718404,19 -MIR4748,10780335,10780254,19 -MIR199A1,10817496,10817426,19 -MIR6793,10829035,10828973,19 -TMED1,10836318,10832438,19 -C19orf38,10869790,10836575,19 -AC007229.1,10842250,10841839,19 -CARM1,10923070,10871513,19 -YIPF2,10928681,10922185,19 -TIMM29,10933535,10928733,19 -SMARCA4,11079426,10960825,19 -AC011442.1,11016011,11010917,19 -RN7SL192P,11012006,11011708,19 -RF02271,11033912,11033727,19 -LDLR,11133816,11089362,19 -MIR6886,11113534,11113474,19 -SPC24,11155808,11131520,19 -KANK2,11197791,11164267,19 -DOCK6,11262481,11199295,19 -AC011472.1,11216168,11203628,19 -AC011472.5,11208702,11208195,19 -AC011472.4,11221573,11221083,19 -ANGPTL8,11241943,11237450,19 -TSPAN16,11326996,11296139,19 -AC011472.2,11324441,11300777,19 -RAB3D,11346270,11322046,19 -AC011472.3,11324195,11322156,19 -TMEM205,11346518,11342776,19 -CCDC159,11354944,11344684,19 -PLPPR2,11365698,11355386,19 -AC024575.1,11374935,11368123,19 -SWSAP1,11376951,11374685,19 -EPOR,11384342,11377205,19 -RGL3,11419342,11384341,19 -RF00019,11408880,11408772,19 -CCDC151,11435782,11420604,19 -PRKCSH,11450968,11435288,19 -ELAVL3,11481046,11451326,19 -AC008481.3,11505698,11466240,19 -ZNF653,11505923,11483427,19 -MIR7974,11495622,11495544,19 -ECSIT,11529174,11505916,19 -RN7SL833P,11516330,11516047,19 -AC008481.2,11522498,11521968,19 -AC008481.1,11523831,11523436,19 -CNN1,11550323,11538717,19 -ELOF1,11559236,11551147,19 -ZNF627,11619135,11559374,19 -ACP5,11579008,11574660,19 -GAPDHP76,11587135,11585246,19 -AC008543.1,11686569,11639776,19 -ZNF887P,11652877,11648364,19 -HNRNPA1P10,11667030,11666069,19 -ZNF833P,11686259,11680944,19 -ZNF823,11739009,11721265,19 -AC008543.3,11730783,11725961,19 -AC008543.2,11759933,11756533,19 -ZNF441,11784078,11767000,19 -AC008543.5,11798598,11796084,19 -ZNF491,11809622,11797667,19 -ZNF440,11835201,11814284,19 -AC008543.4,11842439,11841241,19 -ZNF439,11883750,11848726,19 -AC020951.1,11858302,11856032,19 -ZNF69,11914329,11887784,19 -ZNF700,11950773,11925068,19 -AC008770.2,11979290,11925075,19 -AC008770.3,11953381,11939959,19 -ZNF763,11980381,11965037,19 -ZNF433-AS1,12046275,11987617,19 -RNA5SP464,11996916,11996809,19 -ZNF433,12035741,12014732,19 -AC008770.1,12052967,12017888,19 -RNA5SP465,12028021,12027913,19 -ZNF878,12052939,12043805,19 -ZNF844,12081565,12064699,19 -RNA5SP466,12070271,12070164,19 -RF00019,12088346,12088245,19 -ZNF788P,12137235,12092263,19 -ZNF20,12140407,12092843,19 -RSL24D1P8,12096915,12096424,19 -RNA5SP467,12106614,12106497,19 -ZNF625-ZNF20,12156731,12132117,19 -AC022415.1,12143322,12141806,19 -ZNF625,12156729,12142090,19 -ZNF136,12189881,12163064,19 -AC012618.3,12237767,12195015,19 -ZNF44,12294887,12224686,19 -AC012618.1,12260537,12260126,19 -AC012618.2,12285185,12284337,19 -ZNF563,12333720,12317477,19 -ZNF442,12365905,12345949,19 -RPS29P23,12373731,12373609,19 -AC008758.4,12401274,12379189,19 -AC008758.3,12383687,12379746,19 -ZNF799,12401271,12390016,19 -AC008758.5,12441082,12391949,19 -ZNF443,12441112,12429707,19 -AC008758.6,12484816,12433103,19 -AC008758.2,12460705,12450935,19 -ZNF709,12513854,12461184,19 -AC008758.1,12551474,12461559,19 -MTND2P40,12504106,12502675,19 -MTCO1P27,12504890,12504665,19 -MTCO2P27,12505976,12504905,19 -MTATP6P27,12508030,12506249,19 -PPIAP20,12508263,12508129,19 -ZNF564,12551542,12525370,19 -AC010422.6,12580975,12525720,19 -AC010422.4,12532973,12529768,19 -AC010422.1,12553644,12552597,19 -PGK1P2,12561105,12559571,19 -ZNF490,12640098,12577961,19 -ZNF791,12633840,12610918,19 -RPL10P16,12643919,12643275,19 -AC010422.3,12648397,12643831,19 -MAN2B1,12666742,12646511,19 -AC010422.5,12669397,12664828,19 -WDR83,12675832,12666800,19 -WDR83OS,12669489,12668071,19 -AC010422.8,12681834,12668071,19 -AC010422.7,12671023,12670079,19 -DHPS,12681902,12675717,19 -AC010422.2,12687279,12682693,19 -GNG14,12688422,12687998,19 -FBXW9,12696643,12688053,19 -AC018761.4,12689238,12688922,19 -TNPO2,12724011,12699194,19 -SNORD41,12706518,12706449,19 -AC018761.1,12725886,12725517,19 -TRIR,12734775,12730640,19 -ASNA1,12748323,12737139,19 -BEST2,12758458,12751702,19 -HOOK2,12872740,12763003,19 -AC018761.3,12785101,12784539,19 -MIR5684,12787192,12787128,19 -JUNB,12793315,12791496,19 -PRDX2,12801859,12796820,19 -AC018761.2,12801849,12796823,19 -THSD8,12813581,12802031,19 -RNASEH2A,12815201,12806556,19 -RTBDN,12835428,12825478,19 -AC020934.1,12832983,12825711,19 -MAST1,12874951,12833951,19 -MIR6794,12852327,12852260,19 -DNASE2,12881468,12875211,19 -AC020934.2,12884088,12880969,19 -KLF1,12887199,12884422,19 -GCDH,12914207,12891026,19 -RPS6P25,12894880,12894133,19 -SYCE2,12919276,12898786,19 -MIR5695,12920404,12920320,19 -FARSA,12934037,12922479,19 -FARSA-AS1,12933296,12930522,19 -CALR,12944489,12938578,19 -MIR6515,12940540,12940484,19 -AC092069.1,12944487,12944118,19 -RAD23A,12953642,12945855,19 -GADD45GIP1,12957236,12953119,19 -DAND5,12974762,12965159,19 -NFIX,13098796,12995608,19 -AC138474.1,13014511,13013662,19 -AC007787.1,13071374,13069792,19 -AC007787.2,13082564,13081966,19 -LYL1,13103161,13099033,19 -TRMT1,13117567,13104902,19 -NACC1,13141141,13118103,19 -AC005546.1,13132410,13131571,19 -AC011446.1,13141147,13139617,19 -STX10,13150383,13144058,19 -IER2,13154908,13150415,19 -AC011446.2,13154193,13153071,19 -RPL12P42,13158788,13157959,19 -CACNA1A,13633025,13206442,19 -CCDC130,13763296,13731760,19 -MRI1,13774282,13764532,19 -AC008686.1,13774118,13772118,19 -C19orf53,13778462,13774168,19 -ZSWIM4,13832230,13795460,19 -AC020916.2,13796933,13796574,19 -RN7SL619P,13815319,13815062,19 -AC020916.1,13842928,13823880,19 -MIR24-2,13836359,13836287,19 -MIR27A,13836517,13836440,19 -MIR23A,13836659,13836587,19 -NANOS3,13880757,13862063,19 -MIR181C,13874808,13874699,19 -MIR181D,13875011,13874875,19 -C19orf57,13906452,13882348,19 -CC2D1A,13930879,13906201,19 -PODNL1,13953392,13931187,19 -DCAF15,13961449,13952492,19 -RFX1,14007039,13961538,19 -AC022098.4,14006773,14006422,19 -RLN3,14031042,14028148,19 -AC022098.2,14031557,14030855,19 -IL27RA,14053216,14031748,19 -PALM3,14059159,14053365,19 -EEF1DP1,14071237,14070342,19 -MISP3,14075062,14072536,19 -MIR1199,14073479,14073361,19 -C19orf67,14085875,14081619,19 -SAMD1,14091036,14087840,19 -PRKACA,14118084,14091688,19 -AC022098.3,14119537,14119106,19 -ASF1B,14136956,14119509,19 -AC022098.1,14171267,14137179,19 -ADGRL1,14206187,14147743,19 -RN7SL231P,14213323,14213052,19 -AC011509.2,14293205,14254189,19 -AC011509.1,14269377,14267807,19 -LINC01841,14370196,14305458,19 -LINC01842,14343916,14333743,19 -ADGRE5,14408725,14380501,19 -AC008569.1,14408723,14402717,19 -DDX39A,14419383,14408819,19 -PKN1,14471867,14433053,19 -AC008569.2,14459366,14458401,19 -PTGER1,14475362,14472466,19 -GIPC1,14496149,14477760,19 -SNRPGP15,14489609,14489388,19 -DNAJB1,14529770,14514770,19 -TECR,14565980,14517085,19 -MIR639,14529640,14529543,19 -NDUFB7,14572062,14566078,19 -CLEC17A,14611157,14583084,19 -RN7SL337P,14584395,14584095,19 -RN7SL842P,14588656,14588357,19 -ADGRE3,14690027,14619117,19 -AC022149.1,14622242,14621634,19 -AC022149.2,14628801,14628599,19 -ZNF333,14733746,14689801,19 -ADGRE2,14778541,14732393,19 -OR7C1,14835376,14789260,19 -AC005255.1,14792533,14792318,19 -OR7A5,14835285,14824251,19 -OR7A10,14848922,14840466,19 -OR7A8P,14852944,14852032,19 -OR7A2P,14867358,14863714,19 -OR7A17,14886132,14878203,19 -OR7A18P,14887115,14886839,19 -OR7A1P,14897845,14891737,19 -OR7A3P,14904138,14903232,19 -OR7A11P,14917380,14916428,19 -OR7A15P,14928060,14927052,19 -OR7C2,14942448,14941489,19 -SLC1A6,15022990,14950034,19 -CCDC105,15023269,15010744,19 -CASP14,15058293,15049384,19 -OR1I1,15092970,15082211,19 -SYDE1,15114988,15107403,19 -ILVBL,15125785,15114984,19 -AC003956.1,15126174,15124160,19 -OR10B1P,15142062,15141105,19 -RNU6-782P,15145318,15145215,19 -NOTCH3,15200981,15159038,19 -MIR6795,15179350,15179283,19 -AC004257.1,15222297,15221015,19 -EPHX3,15233435,15226919,19 -BRD4,15332545,15235519,19 -AC005776.1,15339237,15338948,19 -AC005776.2,15348417,15346068,19 -AKAP8,15379798,15353385,19 -AC005785.1,15381194,15379076,19 -AKAP8L,15419141,15380048,19 -AC005785.2,15383832,15383202,19 -AC006128.1,15400356,15398696,19 -WIZ,15449951,15419980,19 -MIR1470,15449608,15449548,19 -RASAL3,15464571,15451624,19 -PGLYRP2,15498956,15468645,19 -CYP4F22,15552317,15508493,19 -AC011492.1,15516836,15515342,19 -CYP4F23P,15584709,15564074,19 -AD000091.1,15611909,15611623,19 -RPL23AP2,15612122,15611657,19 -AC093072.1,15614553,15613247,19 -CYP4F8,15630638,15615218,19 -CYP4F3,15662825,15640897,19 -CYP4F10P,15669826,15664106,19 -CYP4F12,15697174,15672757,19 -OR10H2,15729060,15728020,19 -OR10H3,15742343,15737985,19 -CYP4F24P,15779909,15760241,19 -AC004597.1,15770828,15760375,19 -OR10H5,15801025,15787661,19 -OR10H1,15815664,15804549,19 -ZNF861P,15823797,15821796,19 -LINC01764,15835853,15827045,19 -UCA1,15836326,15828206,19 -RF02202,15834804,15834730,19 -LINC01835,15864904,15851993,19 -CYP4F36P,15871852,15871667,19 -CYP4F2,15898120,15878023,19 -AC005336.2,15903238,15902300,19 -AC005336.3,15907234,15904556,19 -AC005336.1,15911824,15910582,19 -CYP4F11,15934867,15912367,19 -OR10H4,15950350,15944299,19 -CYP4F9P,15999020,15979835,19 -LINC00661,16027462,16015287,19 -AC004790.2,16020290,16019719,19 -LINC00905,16042116,16031307,19 -SNX33P1,16036537,16034776,19 -AC004790.1,16038455,16037009,19 -OR1AB1P,16052689,16051929,19 -LINC01855,16066312,16065737,19 -TPM4,16103005,16067021,19 -AC008894.3,16077395,16074293,19 -AC008894.1,16104254,16103761,19 -RAB8A,16134234,16111629,19 -AC008894.2,16139892,16123661,19 -HSH2D,16158575,16134028,19 -CIB3,16173525,16161368,19 -FAM32A,16192046,16185380,19 -AC020911.1,16189458,16186276,19 -RF00017,16187566,16187313,19 -AP1M1,16245907,16197578,19 -AC020911.2,16324514,16283359,19 -KLF2,16327874,16324817,19 -AC020917.3,16353182,16352462,19 -EPS15L1,16472085,16355239,19 -AC020917.4,16358327,16356329,19 -AC020917.1,16413576,16412684,19 -AC020917.2,16443584,16440946,19 -CALR3,16496192,16479057,19 -AC008764.1,16628204,16479067,19 -C19orf44,16521352,16496311,19 -CHERP,16542530,16517889,19 -RN7SL146P,16539984,16539688,19 -AC008764.6,16544814,16542746,19 -SLC35E1,16572382,16549831,19 -AC008764.3,16552328,16551773,19 -AC008764.5,16566330,16565551,19 -AC008764.8,16575340,16572624,19 -MED26,16629062,16574907,19 -AC008764.4,16660115,16577642,19 -AC008764.7,16587985,16586905,19 -AC008764.9,16589409,16586905,19 -AC008764.10,16612203,16610374,19 -AC008764.2,16636531,16610411,19 -AC024075.3,16643942,16630743,19 -SMIM7,16660442,16630751,19 -AC024075.2,16635269,16633797,19 -AC024075.1,16640668,16639967,19 -TMEM38A,16690029,16661127,19 -AC008737.2,16712434,16712317,19 -NWD1,16817963,16719976,19 -AC008737.3,16801714,16801426,19 -SIN3B,16880353,16829400,19 -AC008737.1,16846473,16844025,19 -F2RL3,16892606,16888860,19 -CPAMD8,17026815,16892947,19 -RN7SL835P,16907723,16907462,19 -RN7SL823P,16910784,16910490,19 -AC020908.2,17013460,17009189,19 -HAUS8,17075625,17049729,19 -AC020908.1,17055087,17051994,19 -MYO9B,17214537,17075781,19 -AC020908.3,17099307,17095418,19 -AC020913.1,17168051,17152588,19 -AC020913.3,17178476,17177511,19 -AC020913.2,17208010,17207138,19 -USE1,17219829,17215346,19 -OCEL1,17229219,17226204,19 -NR2F6,17245940,17231883,19 -AC010646.1,17255448,17235940,19 -USHBP1,17282786,17249176,19 -BABAM1,17281249,17267350,19 -AC010463.1,17282966,17267418,19 -ANKLE1,17287646,17281645,19 -ABHD8,17310236,17292131,19 -MRPL34,17306843,17292609,19 -AC010463.3,17296695,17296196,19 -DDA1,17323301,17309518,19 -ANO8,17334829,17323223,19 -GTPBP3,17342735,17334920,19 -PLVAP,17377350,17351448,19 -AC010463.2,17362753,17360932,19 -CCDC194,17394158,17390509,19 -BST2,17405648,17402939,19 -BISPR,17419324,17405686,19 -MVB12A,17433724,17405722,19 -AC010319.4,17422324,17414257,19 -AC010319.1,17418288,17417031,19 -AC010319.3,17419774,17419305,19 -TMEM221,17448567,17435509,19 -AC010319.2,17460804,17436611,19 -AC010319.5,17452969,17452211,19 -NXNL1,17460954,17455425,19 -SLC27A1,17506168,17468769,19 -AC010618.3,17511889,17488990,19 -PGLS,17521288,17511629,19 -AC010618.2,17520565,17518330,19 -FAM129C,17553839,17523301,19 -COLGALT1,17583162,17555594,19 -AC010618.1,17590071,17589595,19 -UNC13A,17688365,17601328,19 -AC008761.2,17614430,17613722,19 -AC008761.3,17623338,17622447,19 -MAP1S,17734516,17719242,19 -AC008761.1,17734513,17727840,19 -FCHO1,17788568,17747718,19 -B3GNT3,17813082,17794828,19 -INSL3,17821574,17816512,19 -JAK3,17848071,17824780,19 -RPL18A,17864153,17859876,19 -SNORA68,17862720,17862588,19 -AC005796.1,17871705,17871273,19 -SLC5A5,17895174,17871973,19 -CCDC124,17943991,17933016,19 -KCNN1,18000080,17951293,19 -RNA5SP468,17972117,17972025,19 -ARRDC2,18014102,18001132,19 -AC020904.3,18009881,18009440,19 -AC020904.2,18032099,18022403,19 -AC020904.1,18041083,18040355,19 -RPS18P13,18049090,18048637,19 -IL12RB1,18098944,18058995,19 -MAST3,18151692,18097793,19 -AC007192.2,18151691,18144522,19 -PIK3R2,18170540,18153118,19 -AC007192.1,18178117,18153158,19 -IFI30,18178117,18173162,19 -MPV17L2,18196948,18193182,19 -RAB3A,18204074,18196784,19 -AC005759.1,18220480,18204730,19 -AC008397.2,18255419,18207961,19 -PDE4C,18248200,18208652,19 -AC008397.1,18255419,18249950,19 -IQCN,18274509,18257097,19 -JUND,18281622,18279760,19 -MIR3188,18282161,18282077,19 -RPL39P38,18285495,18285343,19 -LSM4,18323274,18306230,19 -RN7SL513P,18333573,18333276,19 -PGPEP1,18369950,18340587,19 -GDF15,18389176,18374731,19 -MIR3189,18386634,18386562,19 -LRRC25,18397617,18391144,19 -SSBP4,18434562,18418864,19 -ISYNA1,18438301,18434388,19 -AC010335.1,18443597,18441419,19 -ELL,18522127,18442663,19 -AC010335.2,18448802,18448275,19 -AC010335.3,18448805,18448275,19 -AC005387.1,18532632,18531613,19 -FKBP8,18544077,18531751,19 -AC005387.2,18536188,18532908,19 -KXD1,18569387,18557762,19 -AC005253.1,18561560,18557775,19 -AC005253.2,18569375,18568506,19 -UBA52,18577550,18571730,19 -CRLF1,18607741,18572220,19 -REX1BD,18592336,18588685,19 -TMEM59L,18621039,18607430,19 -RN7SL155P,18631908,18631646,19 -KLHL26,18671714,18636965,19 -CRTC1,18782333,18683677,19 -COMP,18791314,18782773,19 -UPF1,18868236,18831938,19 -GDF1,18896096,18868545,19 -CERS1,18896727,18868545,19 -AC005197.1,18883376,18881680,19 -COPE,18919397,18899514,19 -AC002985.1,18934397,18907013,19 -DDX49,18928633,18919675,19 -HOMER3,18941261,18929201,19 -HOMER3-AS1,18946831,18940322,19 -RN7SL70P,18958258,18957957,19 -SUGP2,19034023,18990888,19 -ARMC6,19060311,19033575,19 -SLC25A42,19112888,19063999,19 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-AC011477.6,19752544,19750618,19 -LINC00663,19776423,19757366,19 -AC011477.4,19821716,19761038,19 -AC011477.5,19768989,19768731,19 -AC011477.1,19836073,19776602,19 -ZNF506,19821751,19785839,19 -AC011477.2,19790531,19788755,19 -BNIP3P9,19857527,19856975,19 -ZNF253,19894674,19865886,19 -BNIP3P10,19871299,19870723,19 -AC011477.3,19895847,19892433,19 -ZNF93,19963464,19900913,19 -AC007204.1,19909146,19908268,19 -AC007204.2,19979610,19978190,19 -ZNF682,20039506,19997058,19 -AC006539.2,20034332,20033444,19 -AC006539.3,20056594,20056466,19 -BNIP3P13,20063320,20062755,19 -ZNF90,20127076,20077994,19 -AC006539.1,20080539,20079818,19 -AC011447.5,20087576,20087013,19 -AC011447.1,20125484,20125044,19 -AC011447.3,20321305,20125377,19 -AC011447.2,20151908,20131894,19 -BNIP3P15,20137233,20136933,19 -ZNF486,20200490,20167228,19 -BNIP3P16,20177063,20175013,19 -AC011447.4,20206954,20206844,19 -AC011447.6,20209789,20207192,19 -AC011447.7,20222186,20220597,19 -BNIP3P17,20222463,20221869,19 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-VN1R80P,21064261,21063924,19 -ZNF714,21125270,21082159,19 -RNA5SP469,21113236,21113128,19 -VN1R81P,21124188,21123817,19 -ZNF431,21196053,21142024,19 -RPL7AP10,21150438,21149648,19 -VN1R82P,21188953,21188598,19 -AC010620.1,21222746,21221645,19 -AC010620.2,21245241,21225098,19 -RPL36AP51,21233652,21233335,19 -AC022432.1,21276332,21276023,19 -VN1R83P,21289998,21289554,19 -ZNF708,21329425,21291160,19 -BNIP3P25,21318719,21318161,19 -ZNF738,21379302,21358930,19 -AC010615.3,21381921,21381220,19 -AC010615.1,21387177,21382865,19 -ZNF493,21427573,21397119,19 -AC010615.4,21491266,21405159,19 -AC010615.2,21463908,21444241,19 -LINC00664,21503238,21483374,19 -ZNF429,21556270,21496682,19 -BNIP3P26,21521896,21521343,19 -AC123912.1,21569237,21554640,19 -AC123912.4,21587322,21570822,19 -AC123912.2,21594628,21587432,19 -AC123912.6,21593041,21592727,19 -AC123912.5,21635175,21634833,19 -AC123912.3,21656300,21637974,19 -MTDHP3,21678221,21677550,19 -MTDHP4,21710191,21709522,19 -VN1R84P,21720035,21719801,19 -ZNF100,21767628,21722766,19 -AC092364.2,21753438,21750747,19 -AC092364.1,21793860,21768772,19 -BRI3BPP1,21801266,21800480,19 -ZNF43,21852125,21804949,19 -BNIP3P27,21827279,21826513,19 -ZNF208,22010949,21932958,19 -MTDHP2,21941533,21940820,19 -AC003973.3,21968529,21965708,19 -BNIP3P28,21998840,21997648,19 -AC003973.2,22036315,22017959,19 -AC003973.1,22050005,22043500,19 -ZNF257,22091480,22052452,19 -BNIP3P29,22066398,22065828,19 -ZNF92P2,22130318,22128627,19 -AC073539.2,22130489,22130325,19 -PCGF7P,22138981,22137931,19 -MTDHP5,22141117,22140532,19 -VN1R85P,22175191,22174766,19 -ZNF676,22215775,22179091,19 -BNIP3P30,22204003,22203128,19 -AC073539.7,22215661,22215634,19 -AC073539.1,22245347,22243254,19 -AC073539.3,22261335,22259693,19 -ZNF729,22317176,22286408,19 -BNIP3P31,22299868,22298977,19 -AC011494.1,22362825,22362589,19 -RPL34P34,22368952,22368599,19 -ZNF98,22532485,22391019,19 -AC011516.1,22426045,22422513,19 -BNIP3P32,22448777,22447934,19 -AC025811.1,22456459,22455988,19 -BNIP3P33,22458212,22457329,19 -ZNF209P,22473036,22463922,19 -AC004004.1,22501948,22501699,19 -AC011467.1,22527949,22520995,19 -LINC01233,22533494,22532626,19 -RNU6-1179P,22555367,22555261,19 -AC011467.6,22596163,22595479,19 -GOLGA2P9,22603550,22596257,19 -RN7SL860P,22601707,22601485,19 -AC011467.3,22608501,22603269,19 -AC011467.4,22608211,22605739,19 -AC011467.5,22610601,22609606,19 -LINC01785,22623971,22615557,19 -AC011467.2,22620724,22620483,19 -ZNF492,22667670,22634324,19 -AC024563.1,22716991,22665552,19 -ZNF849P,22686732,22685167,19 -RPL34P33,22699685,22699352,19 -AC024563.2,22709314,22708834,19 -VN1R87P,22743410,22742924,19 -ZNF99,22784107,22752183,19 -AC008626.2,22770622,22770375,19 -AC008626.1,22771191,22770937,19 -BNIP3P34,22774424,22773853,19 -ZNF723,22858667,22832321,19 -BNIP3P35,22840921,22840432,19 -VN1R88P,22861594,22861303,19 -AC022145.1,22914289,22907086,19 -AC022145.2,22925547,22925377,19 -ZNF728,23003176,22975468,19 -BNIP3P36,22995847,22994968,19 -LINC01859,23023697,23015075,19 -LINC01858,23061752,23046958,19 -AC074135.2,23071476,23068473,19 -AC074135.1,23100361,23075201,19 -ZNF730,23147219,23075210,19 -BNIP3P37,23124088,23123188,19 -AC124856.1,23128543,23125665,19 -SNX6P1,23127792,23126569,19 -AC092329.2,23204556,23204010,19 -ZNF724,23250390,23221599,19 -AC092329.3,23274221,23222755,19 -BNIP3P38,23236851,23236308,19 -IPO5P1,23257939,23255053,19 -AC092329.1,23274232,23257255,19 -IPO5P1,23274251,23259906,19 -VN1R90P,23289345,23288620,19 -VN1R91P,23291017,23290849,19 -ZNF91,23395560,23304991,19 -AC010300.1,23329101,23323968,19 -BNIP3P8,23380876,23380317,19 -CDC42EP3P1,23385293,23384530,19 -LINC01224,23416075,23399233,19 -VN1R92P,23445189,23444262,19 -AC074140.1,23480468,23479762,19 -ZNF725P,23512656,23492428,19 -ZNF675,23687220,23525631,19 -AC073544.1,23598159,23596122,19 -RPS27P29,23597592,23597341,19 -VN1R93P,23603106,23602567,19 -ZNF681,23758891,23739195,19 -AC139769.1,23833314,23762944,19 -BNIP3P39,23799477,23798924,19 -AC139769.2,23874701,23817599,19 -RPSAP58,23828049,23827162,19 -AC139769.3,23843230,23842549,19 -ZNF726,23945159,23914876,19 -AC011503.1,23957930,23919081,19 -AC011503.2,23929287,23927788,19 -ZNF92P3,23936173,23935220,19 -RNA5-8SP4,24004507,24004358,19 -ZNF254,24129961,24033405,19 -AC092279.1,24066909,24033445,19 -AC092279.2,24080669,24078481,19 -BNIP3P40,24098980,24098425,19 -AC073534.1,24147081,24146701,19 -HAVCR1P1,24163425,24162370,19 -ERVK-28,27646483,27638483,19 -AC112702.1,27639665,27639300,19 -LINC00662,27793940,27684580,19 -AC006504.1,27760849,27757184,19 -AC006504.5,27918863,27793463,19 -AC006504.7,27984984,27793488,19 -AC006504.8,27803472,27802838,19 -AC006504.3,27804060,27803538,19 -SLC25A1P5,27806205,27805423,19 -AC006504.2,27889222,27849730,19 -AC006504.4,27898927,27892495,19 -AC005357.2,27984743,27902595,19 -AC006504.6,27908554,27908356,19 -AC005758.1,27929370,27918943,19 -AC005357.1,27983332,27983032,19 -AC010511.1,28125430,28065267,19 -AC020905.1,28313500,28294093,19 -AC005580.1,28386204,28316705,19 -AC093074.1,28396428,28394851,19 -AC005307.1,28429490,28418483,19 -AC005394.2,28727680,28435388,19 -AC005381.1,28535277,28437060,19 -AC005394.1,28632728,28491864,19 -AC005616.1,28518728,28509238,19 -AC079466.2,28648303,28602379,19 -AC079466.1,28615229,28606688,19 -AC005524.1,28727777,28683071,19 -MAN1A2P1,28792871,28790812,19 -AC007795.1,28879170,28869796,19 -AC008991.1,28953245,28883430,19 -AC008991.2,28895949,28892931,19 -AC011524.1,28950200,28949437,19 -AC011524.2,28964943,28960506,19 -LINC00906,28970874,28965131,19 -RNA5SP470,29001775,29001670,19 -LINC01532,29013955,29002555,19 -UQCRFS1,29213541,29205321,19 -AC007786.1,29215815,29213235,19 -AC007786.2,29221666,29220864,19 -AC007786.3,29223137,29222542,19 -RN7SL340P,29233385,29233090,19 -AC011474.1,29525752,29287011,19 -AC011474.3,29448380,29448038,19 -AC011474.2,29526948,29489775,19 -AC011474.4,29509041,29506966,19 -VSTM2B,29564479,29526499,19 -POP4,29617237,29604017,19 -PLEKHF1,29675457,29665056,19 -C19orf12,29715789,29698886,19 -CCNE1,29824308,29811898,19 -AC008798.1,29901869,29901696,19 -AC008507.3,29917830,29915862,19 -PPIAP58,29921653,29921182,19 -URI1,30016608,29923644,19 -RPL9P32,29956988,29956369,19 -AC008507.2,30029916,30028741,19 -TAF9P3,30037411,30037285,19 -AC008507.1,30038181,30037423,19 -AC005597.1,30228715,30219666,19 -ZNF536,30713538,30228290,19 -AC011504.1,30558058,30553956,19 -AC011478.1,30668647,30665274,19 -AC011507.1,30872507,30850975,19 -LINC01834,30909155,30907199,19 -AC020897.1,30957192,30946582,19 -AC020912.1,31147981,31100304,19 -LINC01791,31207663,31167516,19 -TSHZ3,31349547,31274945,19 -AC025809.1,31417794,31348881,19 -AC025809.2,31366098,31364297,19 -AC008992.1,31427302,31421997,19 -AC008794.2,31466431,31465653,19 -AC008794.1,31554797,31542039,19 -AC011525.1,31595720,31588040,19 -AC011525.2,31593781,31588196,19 -RNA5SP471,31655461,31655356,19 -RNU6-967P,31787255,31787148,19 -LINC01837,32072082,31859103,19 -AC011456.1,31908284,31886704,19 -LINC01533,32056865,32025862,19 -LINC01782,32105916,32102088,19 -AC011518.1,32106537,32103583,19 -RNA5SP472,32244083,32243965,19 -ZNF507,32387667,32345594,19 -AC007773.1,32405560,32390050,19 -DPY19L3,32485895,32405543,19 -PDCD5,32587452,32581068,19 -ANKRD27,32676597,32597007,19 -SNORA68B,32608469,32608337,19 -RPS12P31,32636475,32636078,19 -RN7SL789P,32655308,32655016,19 -AC008474.1,32673210,32666298,19 -RGS9BP,32678300,32675407,19 -AC008736.1,32691750,32687089,19 -NUDT19,32713796,32691961,19 -AC008736.2,32719595,32718298,19 -TDRD12,32829580,32719753,19 -AC008805.1,32824505,32824142,19 -SLC7A9,32869766,32830509,19 -AC008805.2,32833168,32831295,19 -RN7SKP22,32863092,32862800,19 -CEP89,32971991,32875925,19 -RPL31P60,32963865,32963499,19 -FAAP24,32978222,32972209,19 -RHPN2,33064888,32978593,19 -AC008521.1,33051701,33051515,19 -AC008521.2,33067433,33067175,19 -GPATCH1,33130542,33080880,19 -WDR88,33175795,33132090,19 -LRP3,33208867,33177603,19 -AC008738.1,33178651,33178056,19 -AC008738.7,33207639,33207129,19 -SLC7A10,33225850,33208664,19 -AC008738.4,33221170,33217817,19 -AC008738.6,33285739,33283978,19 -AC008738.3,33301168,33299934,19 -CEBPA,33302564,33299934,19 -AC008738.5,33301940,33301279,19 -CEBPA-DT,33305054,33302857,19 -AC008738.2,33309387,33305036,19 -RPS3AP50,33341984,33340686,19 -AKR1B1P7,33361448,33360497,19 -CEBPG,33382686,33373330,19 -PEPD,33521794,33386950,19 -AC010485.1,33557470,33555568,19 -RPL21P131,33589688,33589206,19 -CHST8,33773509,33621955,19 -KCTD15,33815763,33795933,19 -AC008556.1,33908391,33906352,19 -RN7SL150P,33927625,33927364,19 -RPS4XP23,34021895,34021659,19 -RPS4XP20,34023427,34022656,19 -RPS4XP21,34093310,34092561,19 -CHCHD2P3,34127423,34126973,19 -LSM14A,34229515,34172504,19 -KIAA0355,34355586,34254537,19 -RPL29P33,34296592,34296277,19 -AC010504.1,34359412,34348356,19 -GPI,34402413,34359480,19 -AC092073.1,34409364,34396315,19 -PDCD2L,34426168,34404384,19 -RN7SL154P,34422311,34421988,19 -AC008747.1,34428273,34426112,19 -UBA2,34471251,34428352,19 -WTIP,34512304,34481638,19 -RPS26P55,34534084,34533427,19 -ZNF807,34545395,34542462,19 -SCGB1B2P,34577701,34576733,19 -SCGB2B2,34675699,34593329,19 -AC020910.3,34635717,34634844,19 -RF00611,34637781,34637696,19 -AC020910.1,34640689,34638122,19 -AC020910.6,34646838,34645380,19 -SCGB2B3P,34646070,34645662,19 -AC020910.5,34677581,34675717,19 -ZNF302,34686397,34677639,19 -AC020910.4,34733837,34733298,19 -ZNF181,34745378,34734155,19 -ZNF599,34773229,34758076,19 -AC020910.2,34780021,34779412,19 -LINC01801,34832869,34788582,19 -AC008555.1,34814345,34811589,19 -AC008555.7,34825996,34825414,19 -AC008555.2,34855304,34837889,19 -AC008555.5,34860576,34849278,19 -AC008555.4,34866919,34866352,19 -LINC00904,34898943,34892029,19 -AC008555.3,34904195,34902202,19 -AC008555.6,34905057,34904392,19 -LINC01838,34908188,34905315,19 -ZNF30-AS1,34926812,34923299,19 -ZNF30,34945170,34926903,19 -AC008555.8,34951205,34949038,19 -ZNF792,34964049,34956354,19 -GRAMD1A,35026471,34994784,19 -AC020907.3,35000170,34998233,19 -AC020907.4,35025335,35014961,19 -SCN1B,35040449,35030466,19 -HPN,35066571,35040506,19 -HPN-AS1,35106304,35059059,19 -AC020907.1,35113664,35090930,19 -AC020907.6,35124297,35106510,19 -FXYD3,35124324,35115879,19 -MIR6887,35122764,35122700,19 -LGI4,35142451,35124513,19 -AC020907.5,35139580,35137645,19 -FXYD1,35143109,35138808,19 -AC020907.2,35151336,35138824,19 -FXYD7,35154301,35143250,19 -FXYD5,35169883,35154730,19 -FAM187B,35228729,35224800,19 -FAM187B2P,35233003,35232291,19 -LSR,35267964,35248330,19 -AC002128.2,35253639,35251440,19 -AC002128.1,35264804,35262846,19 -USF2,35279821,35268978,19 -HAMP,35285143,35280716,19 -MAG,35313804,35292125,19 -CD22,35347355,35319261,19 -U62631.1,35331920,35330843,19 -MIR5196,35345627,35345513,19 -FFAR1,35353862,35351552,19 -FFAR3,35360485,35358460,19 -GPR42,35372962,35370929,19 -EEF1A1P7,35384052,35382172,19 -LINC01531,35419385,35399511,19 -AC002511.3,35408493,35407770,19 -RN7SL491P,35415149,35414881,19 -AC002511.2,35424652,35424062,19 -AC002511.1,35434642,35432957,19 -FFAR2,35451767,35443907,19 -KRTDAP,35495558,35487324,19 -DMKN,35513658,35497220,19 -SBSN,35528351,35523367,19 -GAPDHS,35545316,35533412,19 -TMEM147-AS1,35546029,35540738,19 -TMEM147,35547526,35545595,19 -ATP4A,35563658,35550043,19 -AD000090.1,35581954,35557956,19 -PMIS2,35587296,35586161,19 -LINC01766,35601501,35597907,19 -AC002115.1,35612666,35608285,19 -HAUS5,35625349,35612744,19 -RBM42,35637686,35629030,19 -ETV2,35644871,35641745,19 -COX6B1,35658861,35648223,19 -UPK1A,35678483,35666516,19 -UPK1A-AS1,35673291,35667948,19 -TYMSP2,35684201,35683778,19 -ZBTB32,35717038,35704527,19 -KMT2B,35738878,35718019,19 -IGFLR1,35742453,35738801,19 -AD000671.2,35745432,35739252,19 -U2AF1L4,35745445,35742464,19 -PSENEN,35747519,35745114,19 -AD000671.1,35754519,35745678,19 -AD000671.3,35753415,35747057,19 -LIN37,35754519,35748361,19 -AC002398.2,35755490,35754566,19 -HSPB6,35758079,35754569,19 -PROSER3,35771028,35758143,19 -AC002398.1,35771028,35769144,19 -ARHGAP33,35788822,35774532,19 -LINC01529,35797902,35785697,19 -PRODH2,35813299,35799988,19 -NPHS1,35869287,35825964,19 -KIRREL2,35867109,35855861,19 -APLP1,35879791,35867899,19 -RN7SL402P,35882153,35881890,19 -NFKBID,35902303,35887653,19 -AD000864.1,35903855,35901561,19 -HCST,35904377,35902480,19 -TYROBP,35908295,35904401,19 -LRFN3,35945767,35935358,19 -AF038458.3,35959192,35947115,19 -AF038458.1,35957382,35957121,19 -AF038458.2,35964063,35960512,19 -SDHAF1,35996315,35995199,19 -SYNE4,36008793,36003299,19 -AC002116.1,36014235,36008638,19 -ALKBH6,36014239,36009120,19 -AC002116.2,36045972,36014508,19 -CLIP3,36033343,36014660,19 -THAP8,36054762,36034985,19 -WDR62,36105106,36054881,19 -AD000813.1,36071070,36070239,19 -OVOL3,36113711,36111151,19 -POLR2I,36115346,36113710,19 -TBCB,36125947,36114289,19 -AD001527.2,36115146,36114362,19 -CAPNS1,36150353,36139575,19 -AD001527.1,36148348,36144081,19 -COX7A1,36152869,36150922,19 -AC002984.1,36173853,36173406,19 -ZNF565,36246257,36182060,19 -RF00019,36203459,36203347,19 -ZNF146,36238774,36214602,19 -AC012617.1,36312668,36304580,19 -LINC00665,36331718,36313067,19 -ZFP14,36379199,36334453,19 -AC092296.1,36378832,36378028,19 -AC092296.3,36380912,36379351,19 -ZFP82,36418656,36383120,19 -AC092296.2,36422534,36421536,19 -ZNF566,36489902,36445119,19 -AC092295.2,36491040,36489649,19 -CTBP2P7,36498159,36496916,19 -ZNF260,36528660,36510695,19 -AC092295.1,36535235,36528318,19 -ZNF529,36605276,36534774,19 -ZNF529-AS1,36594708,36573070,19 -ZNF382,36634113,36604817,19 -ZNF461,36666853,36637192,19 -AC074138.1,36669404,36668102,19 -LINC01534,36687449,36685440,19 -ZNF567,36727701,36687612,19 -ZNF850,36772825,36714383,19 -AC020928.1,36777078,36773153,19 -AC020928.2,36775908,36773712,19 -ZNF790-AS1,36828115,36797518,19 -ZNF790,36850787,36817428,19 -ZNF345,36913029,36850361,19 -RF00019,36858329,36858227,19 -ZNF829,36916291,36888124,19 -RPL31P61,36902117,36901742,19 -ZNF568,36998700,36916329,19 -ZNF420,37130314,37007857,19 -AC010632.3,37025254,37024886,19 -AC010632.1,37078605,37075113,19 -AC010632.2,37092564,37091341,19 -AC012309.1,37210512,37093019,19 -ZNF585A,37172741,37106734,19 -AC012309.2,37141640,37128679,19 -ZNF585B,37218153,37181579,19 -ZNF383,37248738,37217926,19 -LINC01535,37265535,37251912,19 -AC016590.1,37271518,37265939,19 -HKR1,37369365,37312837,19 -AC016590.3,37315620,37314868,19 -AC016590.2,37337743,37337236,19 -ZNF527,37393066,37371061,19 -AC008806.1,37403846,37401404,19 -ZNF569,37469275,37411155,19 -ZNF570,37488652,37467585,19 -ZNF793-AS1,37507046,37497159,19 -AC022148.2,37504465,37503906,19 -ZNF793,37548762,37506939,19 -AC022148.1,37549171,37545470,19 -ZNF571-AS1,37587348,37548914,19 -ZNF540,37614097,37551406,19 -ZNF571,37594790,37554782,19 -ZFP30,37692337,37613749,19 -ZNF781,37692322,37667751,19 -AC093227.2,37719188,37689865,19 -SELENOKP1,37693564,37693280,19 -ZNF607,37719790,37696363,19 -AC093227.1,37730643,37728586,19 -ZNF573,37817300,37735833,19 -AC016582.2,37792865,37779686,19 -AC016582.3,37832160,37817359,19 -AC016582.1,37855215,37823722,19 -WDR87,37906677,37884823,19 -SIPA1L3,38208372,37907228,19 -AC008395.1,37964790,37963853,19 -AC011465.1,38109533,38108500,19 -AC011479.3,38186265,38184376,19 -AC011479.2,38200934,38199836,19 -RN7SL663P,38200532,38200234,19 -DPF1,38229714,38211006,19 -SPINT2,38292614,38244035,19 -PPP1R14A,38256591,38251237,19 -AC011479.1,38304910,38289151,19 -RF00019,38294636,38294544,19 -C19orf33,38305009,38304161,19 -YIF1B,38317273,38305104,19 -KCNK6,38332076,38319844,19 -CATSPERG,38370943,38335775,19 -AC005625.1,38362484,38361795,19 -PSMD8,38383824,38374536,19 -GGN,38388082,38384265,19 -AC005789.1,38386759,38385522,19 -SPRED3,38399587,38388421,19 -FAM98C,38409088,38403135,19 -RASGRP4,38426305,38409051,19 -RYR1,38587564,38433699,19 -AC067969.2,38528555,38526954,19 -AC067969.1,38537128,38535517,19 -MAP4K1,38618882,38587641,19 -AC008649.1,38601694,38596346,19 -EIF3K,38636955,38619082,19 -ACTN4,38731583,38647649,19 -AC008649.2,38693606,38683873,19 -CAPN12,38769904,38730187,19 -AC022144.1,38739863,38738284,19 -LGALS7,38773492,38770971,19 -LGALS7B,38791749,38789211,19 -RNU6-140P,38797109,38797002,19 -LGALS4,38813364,38801671,19 -ECH1,38832005,38815422,19 -AC008982.1,38840178,38817471,19 -AC104534.1,38823223,38820166,19 -HNRNPL,38852347,38836388,19 -AC008982.2,38845499,38844729,19 -RINL,38878279,38867834,19 -AC011455.1,38873763,38870159,19 -SIRT2,38899862,38878555,19 -NFKBIB,38908893,38899700,19 -CCER2,38912158,38908980,19 -SARS2,38930896,38915266,19 -AC011455.2,38949855,38915404,19 -MRPS12,38933162,38930548,19 -ERVK9-11,38938632,38935297,19 -FBXO17,38975910,38941401,19 -FBXO27,39032785,38990714,19 -AC010605.1,39031323,39030436,19 -ACP7,39111493,39083913,19 -PAK4,39182816,39125770,19 -AC011443.1,39136463,39134882,19 -NCCRP1,39201884,39196961,19 -SYCN,39204266,39202831,19 -IFNL3P1,39237293,39236433,19 -IFNL3,39245129,39243553,19 -IFNL4,39248856,39246314,19 -MSRB1P1,39255269,39254916,19 -IFNL4P1,39265817,39264382,19 -IFNL2,39270092,39268514,19 -IFNL1,39298673,39296325,19 -LRFN1,39315336,39306568,19 -AC011445.1,39320858,39314651,19 -GMFG,39342372,39328353,19 -AC011445.2,39341945,39341773,19 -SAMD4B,39385710,39342396,19 -RN7SL566P,39369448,39369153,19 -PAF1,39391195,39385852,19 -MED29,39400637,39391303,19 -ZFP36,39409412,39406813,19 -MIR4530,39409678,39409623,19 -PLEKHG2,39428415,39412585,19 -RPS16,39435948,39433207,19 -SUPT5H,39476670,39436156,19 -TIMM50,39493785,39480412,19 -DLL3,39508481,39498895,19 -SELENOV,39520686,39515113,19 -EID2B,39532854,39530990,19 -AC011500.3,39534422,39532412,19 -TDGF1P7,39535021,39534463,19 -EID2,39540333,39538705,19 -AC011500.2,39555356,39553034,19 -AC011500.1,39586489,39584042,19 -RPS29P24,39589125,39588955,19 -RPS29P25,39591151,39590981,19 -RPS29P26,39598002,39597832,19 -LGALS13,39607476,39602501,19 -AC005176.2,39631446,39631300,19 -AC005176.1,39642401,39638685,19 -RPS29P30,39644612,39644502,19 -LGALS16,39660647,39655894,19 -LGALS17A,39686373,39679374,19 -AC005515.1,39694577,39685244,19 -AC093063.1,39696258,39693925,19 -RPS29P27,39698073,39697904,19 -LGALS14,39709444,39704306,19 -AC006133.1,39726775,39726030,19 -CLC,39738028,39731250,19 -LEUTX,39786167,39776595,19 -AC005393.1,39813555,39812972,19 -DYRK1B,39834201,39825350,19 -MIR6719,39829802,39829716,19 -FBL,39846414,39834458,19 -FCGBP,39934626,39863323,19 -PRR13P5,39943680,39943239,19 -PSMC4,39981441,39971005,19 -ZNF546,40021041,39984134,19 -AC007842.1,40025502,40023384,19 -ZNF780B,40056209,40028260,19 -AC005614.2,40090925,40048757,19 -ZNF780A,40090938,40069152,19 -AC005614.1,40094406,40090754,19 -VN1R96P,40122168,40121311,19 -MAP3K10,40215575,40191744,19 -TTC9B,40218399,40216058,19 -CNTD2,40226697,40222208,19 -AKT2,40285536,40230317,19 -AC118344.1,40275479,40273489,19 -MIR641,40282641,40282543,19 -RNU6-945P,40299584,40299478,19 -C19orf47,40348527,40319536,19 -RF00019,40339164,40339059,19 -RF00019,40342441,40342329,19 -PLD3,40380439,40348456,19 -AC118344.2,40360606,40355842,19 -MIR6796,40369907,40369846,19 -HIPK4,40390187,40379271,19 -PRX,40413366,40393768,19 -SERTAD1,40426025,40421592,19 -AC010271.2,40426702,40426115,19 -SERTAD3,40444705,40440844,19 -AC010271.1,40444087,40443436,19 -BLVRB,40465840,40447765,19 -RF00017,40450480,40450225,19 -SPTBN4,40576464,40466241,19 -SHKBP1,40591399,40576851,19 -LTBP4,40629818,40592883,19 -NUMBL,40690972,40665905,19 -COQ8B,40718207,40691529,19 -RNU6-195P,40708163,40708055,19 -ITPKC,40740860,40717103,19 -C19orf54,40751553,40740856,19 -SNRPA,40765389,40750637,19 -MIA,40777490,40771648,19 -MIA-RAB4B,40796943,40771648,19 -RAB4B,40796938,40778216,19 -RAB4B-EGLN2,40808418,40778242,19 -AC008537.4,40796943,40779780,19 -EGLN2,40808433,40798996,19 -AC008537.1,40898282,40801297,19 -CYP2T1P,40812100,40808474,19 -CYP2F2P,40826772,40818414,19 -AC008537.2,40837210,40831221,19 -AC008537.3,40842039,40840159,19 -CYP2A6,40850447,40843538,19 -CYP2A7,40882752,40875439,19 -CYP2G1P,40900508,40890826,19 -CYP2A7P2,40910685,40908480,19 -CYP2B7P,40950660,40924219,19 -AC092071.1,40947450,40946347,19 -CYP2B6,41018398,40991250,19 -CYP2A7P1,41027720,41024268,19 -CYP2G2P,41062444,41050449,19 -AC008962.1,41074107,41073804,19 -CYP2A13,41096195,41088472,19 -CYP2F1,41128366,41114432,19 -CYP2T3P,41137308,41134722,19 -RPL36P16,41170166,41169861,19 -RN7SL718P,41191229,41190927,19 -CYP2S1,41207539,41193049,19 -AXL,41261766,41219203,19 -AC011510.1,41222051,41221426,19 -HNRNPUL1,41307598,41262496,19 -RN7SL34P,41286693,41286424,19 -TGFB1,41353911,41301587,19 -CCDC97,41324883,41310189,19 -AC011462.1,41425001,41350853,19 -TMEM91,41384083,41350911,19 -B9D2,41364173,41354421,19 -AC011462.4,41374419,41373971,19 -EXOSC5,41397479,41386374,19 -BCKDHA,41425005,41397460,19 -AC011462.3,41400365,41399372,19 -AC011462.2,41426237,41425359,19 -B3GNT8,41428730,41425359,19 -DMAC2,41440717,41431318,19 -RF00017,41442392,41442102,19 -ERICH4,41444765,41443158,19 -PCAT19,41500649,41454169,19 -AC243960.2,41506898,41506152,19 -PLEKHA3P1,41521989,41521043,19 -AC243960.3,41532174,41531206,19 -LINC01480,41536904,41535183,19 -AC243960.1,41555462,41545192,19 -CEACAM21,41586844,41549518,19 -AC243960.7,41552274,41551568,19 -CEACAMP3,41605984,41599735,19 -DNAJC19P2,41605671,41605341,19 -CEACAM4,41627074,41618971,19 -DNAJC19P3,41638899,41638569,19 -AC243960.5,41640602,41639659,19 -AC243960.4,41652963,41652676,19 -CEACAM7,41706976,41673307,19 -CEACAM5,41729798,41708585,19 -AC243967.1,41761282,41708612,19 -CEACAM6,41772208,41750977,19 -AC243967.2,41786893,41757898,19 -CEACAM3,41811553,41796437,19 -HNRNPA1P52,41830938,41829692,19 -AC243967.3,41835950,41833686,19 -LYPD4,41844697,41837074,19 -DMRTC2,41852333,41844743,19 -RPS19,41872926,41859918,19 -MIR6797,41869698,41869627,19 -CD79A,41881372,41877120,19 -ARHGEF1,41930150,41883161,19 -AC010616.1,41928516,41907705,19 -RABAC1,41959390,41956681,19 -AC010616.2,41994232,41956879,19 -ATP1A3,41997497,41966582,19 -GRIK5,42069498,41998321,19 -ZNF574,42081549,42068477,19 -POU2F2,42196585,42086110,19 -AC010247.1,42137099,42132555,19 -MIR4323,42133513,42133445,19 -AC010247.2,42157523,42152569,19 -DEDD2,42220140,42198598,19 -ZNF526,42228201,42220271,19 -GSK3A,42242625,42230186,19 -AC006486.1,42255132,42234583,19 -AC006486.2,42244973,42242775,19 -ERF,42255157,42247572,19 -CIC,42295797,42268537,19 -PAFAH1B3,42303546,42297033,19 -PRR19,42310821,42302098,19 -TMEM145,42325062,42313325,19 -MEGF8,42378769,42325609,19 -MIR8077,42351205,42351131,19 -CNFN,42390287,42387019,19 -LIPE-AS1,42652355,42397128,19 -LIPE,42427426,42401507,19 -AC011497.1,42425071,42424384,19 -CXCL17,42443048,42428288,19 -RNU4-60P,42473009,42472875,19 -AC011497.2,42485714,42485076,19 -CEACAM1,42561234,42507304,19 -CEACAMP2,42561750,42560433,19 -CEACAM8,42595055,42580241,19 -CEACAMP1,42633034,42620212,19 -CEACAMP5,42669076,42654200,19 -RPS10P28,42670816,42670005,19 -PSG3,42740569,42721638,19 -PSG8,42855691,42752686,19 -CEACAMP6,42786595,42781206,19 -PSG8-AS1,42826878,42821863,19 -PSG10P,42855718,42839368,19 -PSG1,42879822,42866464,19 -PSG6,42919563,42902079,19 -PSG7,42937178,42924132,19 -CEACAMP7,42950387,42949503,19 -AC004784.1,43171515,42977463,19 -PSG11,43026512,43007656,19 -CEACAMP8,43041248,43035869,19 -PSG2,43083045,43064211,19 -CEACAMP9,43097360,43096456,19 -AC005392.1,43160862,43153279,19 -PSG5,43186536,43166256,19 -PSG4,43207299,43192702,19 -CEACAMP10,43248646,43211791,19 -PSG9,43269530,43211791,19 -CEACAMP11,43284318,43278933,19 -CEACAMP4,43304523,43303171,19 -AC005392.2,43331430,43329295,19 -CD177,43363172,43353659,19 -AC005392.3,43368970,43360685,19 -CD177P1,43379123,43372742,19 -TEX101,43418597,43401496,19 -LYPD3,43465660,43460787,19 -PHLDB3,43504935,43474954,19 -ETHE1,43527244,43506719,19 -ZNF575,43536130,43525497,19 -XRCC1,43580473,43543040,19 -L34079.2,43555494,43553445,19 -L34079.1,43593036,43554692,19 -L34079.3,43575618,43574638,19 -PINLYP,43583964,43576800,19 -IRGQ,43596135,43584369,19 -ZNF576,43601157,43596392,19 -SRRM5,43614497,43596617,19 -ZNF428,43619874,43607219,19 -CADM4,43639839,43622368,19 -PLAUR,43670547,43646095,19 -RN7SL368P,43660776,43660500,19 -AC005622.1,43700508,43690002,19 -IRGC,43720021,43716010,19 -SMG9,43754990,43727992,19 -KCNN4,43781261,43766533,19 -AC115522.1,43795658,43794309,19 -LYPD5,43827206,43795929,19 -ZNF283,43852017,43827292,19 -ZNF404,43901385,43872363,19 -AC006213.6,43895411,43891233,19 -AC006213.2,43901805,43891804,19 -AC006213.3,43926545,43902001,19 -ZNF45,43935278,43912629,19 -ZNF221,43967709,43951223,19 -ZNF155,43998325,43967862,19 -AC006213.4,43977448,43976815,19 -AC006213.5,43978663,43978376,19 -AC006213.1,44002836,43996896,19 -ZNF230,44013926,44002948,19 -AC067968.2,44025262,44023604,19 -ZNF222,44033112,44025342,19 -AC067968.1,44087318,44025354,19 -ZNF223,44067991,44051367,19 -ZNF284,44089613,44072144,19 -RNU6-902P,44085303,44085213,19 -ZNF224,44109886,44094339,19 -AC021092.1,44113145,44105463,19 -ZNF225,44134816,44112181,19 -ZNF234,44160309,44141557,19 -ZNF226,44178381,44165073,19 -ZNF227,44237268,44207547,19 -ZNF235,44305046,44228729,19 -AC138470.1,44248374,44246478,19 -ZNF233,44275317,44259880,19 -AC138473.1,44290911,44290081,19 -NDUFA3P1,44297355,44297104,19 -ZNF112,44367217,44326555,19 -AC245748.1,44401608,44329695,19 -ZNF285,44401608,44382298,19 -AC245748.2,44448452,44392439,19 -ZNF229,44448578,44417519,19 -ZNF285B,44473227,44467641,19 -ZNF180,44500524,44474428,19 -CEACAM20,44529788,44501677,19 -AC245748.3,44536613,44536263,19 -CEACAM22P,44620821,44537058,19 -AC243964.1,44570564,44570304,19 -IGSF23,44636781,44613630,19 -AC243964.2,44718759,44632199,19 -PVR,44663583,44643798,19 -MIR4531,44653732,44653686,19 -CEACAM19,44684359,44662278,19 -AC243964.3,44666158,44664131,19 -CEACAM16,44710714,44699151,19 -RF00285,44726058,44725984,19 -BCL3,44760044,44747705,19 -MIR8085,44758721,44758657,19 -RF00156,44765100,44764950,19 -AC092066.1,44765464,44765148,19 -CBLC,44800652,44777869,19 -AC092306.1,44804010,44803701,19 -BCAM,44821421,44809059,19 -NECTIN2,44889228,44846175,19 -AC011481.2,44890876,44882027,19 -TOMM40,44903689,44890569,19 -APOE,44909393,44905754,19 -AC011481.3,44914968,44909375,19 -APOC1,44919349,44914247,19 -APOC1P1,44931386,44926804,19 -APOC4,44945496,44942238,19 -APOC4-APOC2,44949565,44942238,19 -APOC2,44949565,44945982,19 -AC011481.1,44954007,44950044,19 -CLPTM1,44993341,44954585,19 -RELB,45038198,45001430,19 -CLASRP,45070956,45039040,19 -RNU6-611P,45047561,45047458,19 -ZNF296,45076588,45071500,19 -GEMIN7-AS1,45090391,45076510,19 -GEMIN7,45091524,45079195,19 -MARK4,45305283,45079288,19 -PPP1R37,45148077,45091396,19 -EIF5AP3,45129030,45128569,19 -AC005757.1,45136977,45135993,19 -NKPD1,45158737,45149750,19 -TRAPPC6A,45178237,45162928,19 -BLOC1S3,45181801,45178745,19 -AC005779.2,45202444,45179822,19 -AC005779.1,45216933,45187388,19 -EXOC3L2,45245431,45212621,19 -CKM,45322977,45306414,19 -RPS16P9,45333124,45332698,19 -KLC3,45351520,45333434,19 -ERCC2,45370918,45349837,19 -PPP1R13L,45406349,45379634,19 -CD3EAP,45410766,45406209,19 -ERCC1,45478828,45407333,19 -MIR6088,45436704,45436654,19 -FOSB,45475179,45467995,19 -RTN2,45497061,45485289,19 -PPM1N,45502510,45488777,19 -VASP,45526983,45506579,19 -OPA3,45602212,45527427,19 -GPR4,45602208,45589764,19 -EML2,45645629,45606994,19 -MIR330,45639087,45638994,19 -EML2-AS1,45642840,45641494,19 -RN7SL836P,45651805,45651510,19 -GIPR,45683724,45668244,19 -MIR642A,45675024,45674928,19 -MIR642B,45675008,45674932,19 -SNRPD2,45692569,45687454,19 -QPCTL,45703989,45692483,19 -FBXO46,45730904,45710629,19 -AC007191.1,45717381,45714387,19 -BHMG1,45764534,45733251,19 -SIX5,45769226,45764785,19 -AC074212.1,45769806,45764785,19 -DM1-AS,45772504,45767796,19 -DMPK,45782552,45769717,19 -AC011530.1,45785973,45779437,19 -DMWD,45792802,45782947,19 -RSPH6A,45815319,45795710,19 -SYMPK,45863290,45815410,19 -AC092301.1,45831108,45830164,19 -FOXA3,45873797,45863989,19 -IRF2BP1,45886170,45883607,19 -MYPOP,45902604,45890020,19 -NANOS2,45914870,45913214,19 -NOVA2,45973546,45933734,19 -CCDC61,46021318,45995461,19 -MIR769,46019049,46018932,19 -PGLYRP1,46023065,46019153,19 -AC007785.3,46030161,46027654,19 -IGFL4,46077118,46039748,19 -AC007785.1,46196218,46101122,19 -AC007785.2,46113863,46111079,19 -IGFL3,46124674,46120071,19 -TGIF1P1,46137547,46136746,19 -IGFL2,46161299,46143106,19 -AC006262.2,46180647,46163893,19 -IGFL2-AS1,46203083,46189166,19 -IGFL1P1,46197323,46196238,19 -AC006262.1,46214837,46203426,19 -RPL12P41,46219920,46219423,19 -AC006262.3,46230388,46228964,19 -IGFL1,46231243,46229752,19 -AC006262.4,46250163,46249394,19 -IGFL1P2,46260858,46260420,19 -HIF3A,46343433,46297046,19 -RNU6-924P,46306577,46306470,19 -AC007193.2,46340004,46320197,19 -PPP5C,46392981,46346994,19 -AC007193.1,46383169,46382492,19 -AC007193.3,46390852,46390515,19 -CCDC8,46413662,46410329,19 -PNMA8C,46428951,46424697,19 -PNMA8A,46471563,46466491,19 -PPP5D1,46601200,46480796,19 -PNMA8B,46496498,46486906,19 -AC011484.1,46496502,46494508,19 -AC011551.1,46534351,46533669,19 -AC093503.1,46600861,46547056,19 -GAPDHP38,46559642,46558641,19 -CALM3,46610793,46601074,19 -AC093503.2,46610779,46609277,19 -PTGIR,46625118,46620468,19 -GNG8,46634685,46634076,19 -AC093503.3,46639161,46636810,19 -DACT3,46661138,46647612,19 -DACT3-AS1,46677447,46660364,19 -PRKD2,46717127,46674275,19 -RN7SL364P,46689068,46688797,19 -MIR320E,46709345,46709293,19 -STRN4,46746994,46719507,19 -AC008635.1,46732700,46728603,19 -RF00019,46736419,46736312,19 -FKRP,46776988,46746046,19 -SLC1A5,46788594,46774883,19 -AC008622.2,46789043,46787815,19 -AC008622.3,46810182,46809583,19 -HNRNPMP2,46810183,46809585,19 -AC008622.1,46828440,46826895,19 -AP2S1,46850992,46838136,19 -ARHGAP35,47005077,46918676,19 -AC008895.1,46949156,46946535,19 -NPAS1,47045775,47019820,19 -TMEM160,47048630,47045907,19 -ZC3H4,47113752,47064187,19 -SAE1,47210636,47113274,19 -BBC3,47232766,47220822,19 -MIR3190,47227021,47226942,19 -MIR3191,47227019,47226944,19 -CCDC9,47273701,47255980,19 -INAFM1,47275707,47274453,19 -C5AR1,47322066,47290023,19 -C5AR2,47347327,47332147,19 -DHX34,47382704,47349281,19 -MEIS3,47419523,47403124,19 -SLC8A2,47472168,47428017,19 -KPTN,47484268,47475144,19 -NAPA-AS1,47501597,47484282,19 -NAPA,47515240,47487637,19 -AC073548.1,47489519,47487638,19 -ZNF541,47555856,47520685,19 -RN7SL322P,47581321,47581023,19 -AC010519.1,47733098,47607524,19 -AC010331.1,47608454,47607873,19 -BICRA,47703277,47608196,19 -BICRA-AS1,47626395,47615699,19 -AC008985.1,47663895,47661657,19 -EHD2,47743134,47713343,19 -NOP53,47757058,47745522,19 -SNORD23,47755962,47755853,19 -NOP53-AS1,47768840,47757036,19 -SELENOW,47784686,47778572,19 -RPL23AP80,47794890,47794467,19 -AC008745.1,47794920,47794650,19 -TPRX1,47819051,47801243,19 -CRX,47843330,47819779,19 -TPRX2P,47861512,47860113,19 -LINC01595,47865341,47863483,19 -SULT2A1,47886397,47870466,19 -BSPH1,47992170,47968046,19 -ELSPBP1,48025153,47994651,19 -CABP5,48044053,48029953,19 -PLA2G4C,48110817,48047843,19 -PLA2G4C-AS1,48064945,48061371,19 -LIG1,48170603,48115445,19 -AC011466.3,48127706,48118432,19 -AC011466.2,48147413,48145908,19 -ZSWIM9,48197620,48170692,19 -CARD8,48255946,48180770,19 -AC011466.4,48199720,48198107,19 -AC011466.1,48213154,48204083,19 -CARD8-AS1,48258199,48255675,19 -ZNF114-AS1,48271283,48262900,19 -ZNF114,48287608,48270102,19 -CCDC114,48321894,48296457,19 -EMP3,48330553,48321509,19 -TMEM143,48364237,48332356,19 -SYNGR4,48376376,48364395,19 -KDELR1,48391553,48382570,19 -GRIN2D,48444931,48394875,19 -GRWD1,48457022,48445773,19 -KCNJ14,48466980,48455509,19 -AC008403.2,48469693,48465593,19 -AC008403.1,48472431,48465837,19 -CYTH2,48482314,48469032,19 -LMTK3,48513935,48485271,19 -AC008403.3,48513660,48513207,19 -SULT2B1,48599425,48552075,19 -FAM83E,48614854,48600810,19 -SPACA4,48607714,48606743,19 -RPL18,48619536,48615328,19 -AC022154.1,48624132,48619272,19 -SPHK2,48630717,48619291,19 -DBP,48637438,48630030,19 -CA11,48646312,48637942,19 -SEC1P,48682245,48638071,19 -NTN5,48673081,48661407,19 -FUT2,48705950,48695971,19 -MAMSTR,48719721,48712742,19 -RASIP1,48740721,48720587,19 -IZUMO1,48746909,48740852,19 -FUT1,48755390,48748011,19 -FGF21,48758333,48755559,19 -RNU6-317P,48793258,48793152,19 -BCAT2,48811029,48795062,19 -HSD17B14,48836678,48813017,19 -PLEKHA4,48868632,48837097,19 -PPP1R15A,48876057,48872392,19 -TULP2,48898733,48880965,19 -NUCB1,48923372,48900050,19 -NUCB1-AS1,48918891,48910930,19 -AC026803.1,48926444,48926171,19 -DHDH,48944969,48933682,19 -BAX,48961798,48954815,19 -AC026803.2,48965158,48963975,19 -FTL,48966878,48965301,19 -GYS1,48993310,48968125,19 -RUVBL2,49015995,48993448,19 -MIR6798,49009972,49009906,19 -LHB,49017091,49015980,19 -AC008687.4,49020523,49017496,19 -CGB3,49024333,49022869,19 -AC008687.1,49036895,49022953,19 -NTF6A,49026020,49025461,19 -NTF6G,49030341,49029726,19 -CGB2,49033238,49031912,19 -CGB1,49036895,49035610,19 -NTF6B,49039038,49038479,19 -AC008687.5,49042731,49042174,19 -CGB5,49045311,49043884,19 -CGB8,49049106,49047638,19 -AC008687.6,49050846,49050217,19 -CGB7,49058860,49054275,19 -AC008687.8,49065076,49055793,19 -AC008687.7,49058761,49056088,19 -AC008687.2,49061132,49060613,19 -NTF4,49065054,49061066,19 -AC008687.3,49064856,49064259,19 -KCNA7,49072941,49067418,19 -RN7SL708P,49081630,49081332,19 -SNRNP70,49108605,49085419,19 -LIN7B,49118460,49114324,19 -C19orf73,49119140,49118397,19 -PPFIA3,49151026,49119389,19 -HRC,49155424,49151198,19 -TRPM4,49211836,49157741,19 -SLC6A21P,49256102,49255472,19 -SUMO1P4,49275420,49275119,19 -SLC6A16,49325225,49289638,19 -MIR4324,49308868,49308797,19 -AC011450.1,49340303,49331617,19 -CD37,49343335,49335171,19 -TEAD2,49362457,49340595,19 -AC010524.1,49357769,49356152,19 -DKKL1,49375116,49361783,19 -AC010643.1,49388081,49368705,19 -CCDC155,49417994,49388218,19 -PTH2,49423441,49422414,19 -GFY,49428818,49423749,19 -SLC17A7,49442360,49429401,19 -PIH1D1,49453497,49446298,19 -ALDH16A1,49471048,49453169,19 -AC010619.1,49486231,49462752,19 -FLT3LG,49486231,49474207,19 -AC010619.2,49487637,49474586,19 -RPL13A,49492308,49487554,19 -SNORD32A,49490048,49489965,19 -SNORD33,49490699,49490615,19 -SNORD34,49490974,49490904,19 -SNORD35A,49491260,49491175,19 -RPS11,49499689,49496365,19 -SNORD35B,49497806,49497720,19 -MIR150,49500868,49500785,19 -COX6CP7,49502639,49502432,19 -FCGRT,49526333,49506816,19 -RCN3,49546962,49527618,19 -NOSIP,49590262,49555711,19 -RF00019,49566772,49566672,19 -PRRG2,49591015,49580646,19 -PRR12,49626439,49591643,19 -AC011495.2,49626439,49625994,19 -RRAS,49640201,49635292,19 -SCAF1,49658642,49642125,19 -IRF3,49665875,49659569,19 -BCL2L12,49673916,49665142,19 -PRMT1,49689029,49675786,19 -MIR5088,49682195,49682117,19 -ADM5,49690575,49688664,19 -AC011495.3,49690573,49688853,19 -CPT1C,49713731,49690898,19 -AC011495.1,49720081,49719498,19 -TSKS,49763330,49739753,19 -RNU6-841P,49764630,49764524,19 -AP2A1,49807113,49766968,19 -MIR6799,49791934,49791866,19 -FUZ,49817376,49806869,19 -AC006942.1,49809738,49808933,19 -MED25,49840383,49818279,19 -MIR6800,49832099,49832018,19 -PTOV1-AS1,49851676,49838639,19 -PTOV1,49860744,49850735,19 -AC018766.1,49854967,49852887,19 -MIR4749,49854651,49854591,19 -PTOV1-AS2,49859289,49856970,19 -PNKP,49878351,49859882,19 -AKT1S1,49878459,49869033,19 -TBC1D17,49888749,49877425,19 -MIR4750,49888230,49888175,19 -IL4I1,49929539,49889654,19 -NUP62,49929763,49906825,19 -AC011452.1,49958391,49909501,19 -ATF5,49933935,49928702,19 -MIR4751,49933137,49933064,19 -SIGLEC11,49961172,49948985,19 -RF00012,49949527,49949316,19 -SIGLEC16,49975814,49969673,19 -VRK3,50025946,49976467,19 -RF00017,50019320,50019019,19 -ZNF473,50053414,50025714,19 -AC010624.3,50051062,50043196,19 -AC010624.1,50066793,50050589,19 -AC010624.4,50060232,50058882,19 -AC010624.2,50075902,50075123,19 -IZUMO2,50163195,50152548,19 -MYH14,50310544,50188186,19 -RF00017,50258722,50258443,19 -AC008655.1,50310539,50310022,19 -KCNC3,50333515,50311937,19 -NR1H2,50382982,50329653,19 -NAPSB,50344767,50333796,19 -NAPSA,50365830,50358477,19 -AC008655.2,50369331,50365880,19 -POLD1,50418016,50384204,19 -AC020909.1,50428409,50415799,19 -SPIB,50431313,50418938,19 -MYBPC2,50466321,50432903,19 -FAM71E1,50476753,50466785,19 -EMC10,50490870,50476400,19 -AC020909.3,50483351,50480119,19 -AC020909.2,50487638,50486810,19 -JOSD2,50511353,50505998,19 -ASPDH,50514690,50511600,19 -LRRC4B,50568045,50516892,19 -AC008743.1,50557969,50555370,19 -SYT3,50639827,50621307,19 -C19orf81,50659310,50649445,19 -SHANK1,50719450,50661827,19 -CLEC11A,50725718,50723329,19 -GPR32P1,50759192,50758382,19 -GPR32,50771732,50770464,19 -AC010325.1,50793142,50776141,19 -ACP4,50795224,50790415,19 -AC010325.2,50793584,50792685,19 -C19orf48,50804929,50797704,19 -SNORD88B,50799122,50799032,19 -SNORD88A,50799532,50799442,19 -SNORD88C,50802418,50802328,19 -AC010325.3,50812707,50808145,19 -LINC01869,50818880,50808874,19 -KLK1,50823787,50819148,19 -KLK15,50837213,50825289,19 -AC011523.1,50851089,50830530,19 -KLK3,50860764,50854915,19 -KLK2,50880567,50861568,19 -KLKP1,50896398,50882096,19 -KLK4,50910738,50906352,19 -PPIAP59,50927571,50927079,19 -KLK5,50953093,50943303,19 -AC011483.2,50963649,50950185,19 -KLK6,50969673,50958631,19 -AC011483.3,50962781,50962197,19 -AC011483.1,51012129,50968210,19 -KLK7,50984099,50976482,19 -KLK8,51002711,50996007,19 -AC011473.4,51009581,50996018,19 -KLK9,51009634,51003111,19 -KLK10,51020175,51012739,19 -AC011473.2,51014734,51014374,19 -KLK11,51028039,51022216,19 -KLK12,51035230,51029092,19 -AC011473.3,51034335,51030076,19 -AC011473.1,51052077,51043806,19 -KLK13,51065114,51056206,19 -KLK14,51084245,51077495,19 -CTU1,51108370,51097606,19 -SIGLEC18P,51122965,51119987,19 -SIGLEC9,51136651,51124908,19 -AC063977.2,51138634,51137970,19 -SIGLEC7,51153526,51142299,19 -AC063977.1,51144196,51143826,19 -AC063977.6,51181966,51152923,19 -AC063977.7,51155624,51155288,19 -SIGLEC17P,51173524,51167328,19 -SIGLEC20P,51190809,51184905,19 -AC063977.4,51195540,51194118,19 -SIGLEC21P,51201449,51198855,19 -MIR8074,51207009,51206929,19 -SIGLEC22P,51212088,51211080,19 -CD33,51243860,51225064,19 -SIGLECL1,51269330,51246348,19 -AC063977.3,51271179,51251231,19 -LINC01872,51281234,51271266,19 -AC063977.5,51283022,51280862,19 -IGLON5,51330354,51311848,19 -VSIG10L,51342124,51331536,19 -AC008750.3,51344117,51340695,19 -AC008750.4,51353293,51345169,19 -ETFB,51366418,51345169,19 -AC008750.8,51368099,51350373,19 -AC008750.5,51365544,51361712,19 -CLDND2,51369003,51367098,19 -NKG7,51372715,51371606,19 -LIM2,51387960,51379909,19 -C19orf84,51390574,51388289,19 -AC008750.7,51403650,51394488,19 -NIFKP6,51403526,51396755,19 -SIGLEC10,51417803,51410021,19 -AC008750.1,51414965,51414298,19 -AC008750.2,51417425,51415724,19 -AC008750.6,51426668,51426347,19 -AC020914.5,51442650,51442379,19 -SIGLEC8,51458456,51450847,19 -AC020914.3,51468636,51468360,19 -CEACAM18,51490952,51478622,19 -SIGLEC12,51501789,51491357,19 -AC020914.2,51505362,51505059,19 -SIGLEC27P,51509311,51508573,19 -SIGLEC6,51531856,51519525,19 -AC020914.1,51520260,51519731,19 -AC020914.4,51560590,51560122,19 -SIGLEC29P,51564910,51563797,19 -ZNF175,51592508,51571298,19 -AC018755.3,51606150,51594561,19 -AC018755.1,51600470,51599289,19 -SIGLEC5,51645545,51611927,19 -AC018755.2,51646889,51612091,19 -RPL9P33,51621701,51621136,19 -AC018755.4,51639931,51639478,19 -SIGLEC14,51646801,51642553,19 -RPL7P51,51649610,51648862,19 -SPACA6P-AS,51693456,51685363,19 -SPACA6,51712387,51689128,19 -MIR99B,51692681,51692612,19 -MIRLET7E,51692864,51692786,19 -MIR125A,51693339,51693254,19 -HAS1,51723994,51713112,19 -FPR1,51804110,51745172,19 -FPR2,51770526,51752026,19 -FPR3,51826189,51795163,19 -ZNF577,51890950,51804816,19 -AC006272.1,51840945,51839771,19 -AC006272.2,51843315,51842878,19 -AC074141.1,51863236,51860242,19 -ZNF649-AS1,51900463,51888025,19 -ZNF649,51905040,51889224,19 -AC011460.1,51917703,51914675,19 -ZNF613,51948759,51927147,19 -ZNF350-AS1,51981367,51949134,19 -ZNF350,51986856,51964343,19 -ZNF615,52008230,51991332,19 -ZNF614,52030240,52012765,19 -ZNF432,52095738,52031379,19 -AC011468.5,52049754,52049007,19 -AC011468.1,52063703,52058490,19 -ZNF841,52095765,52064466,19 -AC011468.4,52110726,52110318,19 -ZNF616,52139922,52113091,19 -AC011468.2,52171533,52130548,19 -AC011468.3,52144156,52142626,19 -RPL37P23,52143336,52143043,19 -ZNF836,52171643,52153864,19 -AC010320.1,52177002,52171158,19 -PPP2R1A,52229533,52190039,19 -MIR6801,52222098,52222020,19 -AC010320.2,52231294,52222225,19 -AC010320.5,52242440,52241765,19 -RF00019,52259187,52259076,19 -ZNF766,52296046,52269571,19 -MIR643,52281893,52281797,19 -AC010320.3,52297920,52284780,19 -ZNF480,52325922,52297177,19 -AC010320.4,52345229,52300693,19 -ZNF610,52367778,52336245,19 -ZNF880,52385795,52369917,19 -ZNF528-AS1,52397766,52388842,19 -AC010332.1,52392755,52392659,19 -ZNF528,52418412,52397849,19 -DPPA5P1,52426202,52425863,19 -ZNF534,52452315,52429187,19 -ZNF578,52512154,52453576,19 -AC010332.2,52475239,52473336,19 -AC022150.3,52512342,52511282,19 -ZNF808,52564464,52527652,19 -ZNF701,52587174,52555457,19 -RPL39P34,52564231,52564076,19 -AC022150.1,52585561,52575883,19 -ZNF137P,52597345,52588505,19 -ZNF83,52690496,52594060,19 -AC022150.2,52598887,52597699,19 -AC022150.4,52653284,52650437,19 -ZNF611,52735073,52702813,19 -ZNF600,52786807,52749942,19 -ZNF28,52857600,52797409,19 -AC008813.1,52810708,52809824,19 -ZNF468,52890375,52838008,19 -AC010487.1,52864151,52861807,19 -ZNF320,52897693,52863790,19 -AC010487.4,52865429,52865369,19 -AC010487.3,52962768,52880663,19 -ZNF888,52923481,52904417,19 -AC010487.2,52924075,52923382,19 -ZNF321P,52942601,52927135,19 -ZNF816-ZNF321P,52962823,52928475,19 -ZNF816,52962911,52949379,19 -AC010328.3,52981654,52956041,19 -ZNF702P,53037898,52968251,19 -AC010328.2,53005150,53004040,19 -AC010328.1,53013180,53007512,19 -ERVV-1,53016122,53013921,19 -ERVV-2,53051076,53044738,19 -ZNF160,53103436,53066606,19 -ZNF415,53133077,53107879,19 -ZNF347,53159075,53124072,19 -ZNF665,53193386,53159213,19 -AC092070.1,53163563,53162428,19 -AC092070.2,53214522,53197111,19 -NDUFV2P1,53224879,53223973,19 -ZNF677,53254898,53235381,19 -AC092070.3,53241913,53240675,19 -VN1R2,53261837,53258292,19 -VN1R4,53267723,53266676,19 -AC092070.4,53270932,53267870,19 -FAM90A27P,53284944,53281559,19 -BIRC8,53291626,53289601,19 -FAM90A28P,53308644,53299051,19 -VN1R6P,53316043,53315137,19 -ZNF845,53356906,53333746,19 -ZNF525,53392217,53365693,19 -AC125388.1,53386388,53386233,19 -ZNF765,53430413,53389793,19 -RPL39P36,53413345,53413190,19 -AC022137.2,53426499,53426019,19 -TPM3P9,53444670,53431984,19 -ZNF761,53458261,53431993,19 -ZNF813,53496255,53467735,19 -TPM3P6,53480091,53479350,19 -AC022137.1,53496037,53495887,19 -AC011487.1,53512687,53503392,19 -AC011487.4,53512616,53512092,19 -ZNF331,53580269,53519527,19 -AC011487.2,53608451,53599628,19 -AC011487.3,53617254,53616802,19 -RN7SL317P,53630041,53629713,19 -DPRX,53637009,53632056,19 -RNU6-698P,53653791,53653685,19 -AC011453.2,53658462,53657400,19 -MIR512-1,53666762,53666679,19 -MIR512-2,53669254,53669157,19 -MIR1323,53672040,53671968,19 -MIR498,53674320,53674197,19 -MIR520E,53675797,53675711,19 -MIR515-1,53679085,53679003,19 -MIR519E,53680023,53679940,19 -MIR520F,53682245,53682159,19 -MIR515-2,53685091,53685009,19 -MIR519C,53686555,53686469,19 -MIR1283-1,53688567,53688481,19 -MIR520A,53690965,53690881,19 -MIR526B,53694475,53694393,19 -MIR519B,53695293,53695213,19 -MIR525,53697617,53697533,19 -MIR523,53698471,53698385,19 -MIR518F,53700101,53700015,19 -MIR520B,53701287,53701227,19 -MIR518B,53702819,53702737,19 -MIR526A1,53706336,53706252,19 -MIR520C,53707539,53707453,19 -MIR518C,53708835,53708735,19 -MIR524,53711088,53711002,19 -MIR517A,53712354,53712268,19 -MIR519D,53713434,53713347,19 -MIR521-2,53716680,53716594,19 -RNU6-803P,53719165,53719064,19 -MIR520D,53720182,53720096,19 -MIR517B,53721142,53721076,19 -MIR520G,53722255,53722166,19 -MIR516B2,53725526,53725442,19 -MIR526A2,53726986,53726922,19 -MIR518E,53729925,53729838,19 -MIR518A1,53731090,53731006,19 -RNU6-980P,53733083,53732981,19 -MIR518D,53734963,53734877,19 -MIR516B1,53736934,53736845,19 -MIR518A2,53739419,53739333,19 -MIR517C,53741407,53741313,19 -MIR520H,53742599,53742512,19 -RNU6-982P,53745105,53745004,19 -MIR521-1,53748722,53748636,19 -RNU6-751P,53749951,53749851,19 -MIR522,53751297,53751211,19 -MIR519A1,53752481,53752397,19 -MIR527,53754102,53754018,19 -MIR516A1,53756830,53756741,19 -MIR1283-2,53758318,53758232,19 -RNU6-1041P,53760188,53760087,19 -MIR516A2,53761222,53761133,19 -AC011453.1,53764199,53761961,19 -MIR519A2,53762430,53762344,19 -RNU6-165P,53766632,53766534,19 -HMGN1P32,53768553,53767971,19 -SEPT7P8,53773280,53772506,19 -AC008753.1,53775882,53775599,19 -AC008753.3,53788169,53787597,19 -MIR371A,53787741,53787675,19 -MIR371B,53787742,53787677,19 -MIR372,53787956,53787890,19 -MIR373,53788773,53788705,19 -AC008753.2,53789168,53788782,19 -NLRP12,53824394,53793603,19 -AC008440.1,53869107,53854581,19 -AC008440.3,53866140,53864763,19 -MYADM,53876437,53866223,19 -AC008440.2,53876049,53874626,19 -PRKCG,53907652,53879190,19 -CACNG7,53943941,53909335,19 -CACNG8,53990215,53963040,19 -MIR935,53982397,53982307,19 -CACNG6,54012669,53992288,19 -AC012314.20,54031504,54031412,19 -VSTM1,54063953,54040825,19 -TARM1,54081365,54069895,19 -OSCAR,54102692,54094668,19 -NDUFA3,54109257,54102728,19 -TFPT,54115675,54107013,19 -PRPF31,54131719,54115410,19 -AC245052.4,54125343,54119511,19 -CNOT3,54155708,54137717,19 -LENG1,54159882,54155161,19 -TMC4,54173250,54160108,19 -MBOAT7,54189882,54173412,19 -TSEN34,54194536,54189938,19 -AC245052.1,54199971,54199454,19 -RPS9,54249003,54200742,19 -AC245052.2,54209418,54209092,19 -LILRB3,54223506,54216278,19 -AC245052.7,54224881,54224523,19 -AC245052.3,54230213,54229036,19 -LILRA6,54242791,54236592,19 -AC245052.6,54247322,54246101,19 -LILRB5,54257301,54249431,19 -RNU6-1307P,54264053,54263951,19 -AC245052.5,54269045,54268005,19 -LILRB2,54281184,54273821,19 -MIR4752,54282180,54282109,19 -AC245884.5,54294366,54294049,19 -AC245884.12,54296252,54296194,19 -AC245884.7,54298814,54298105,19 -AC245884.2,54306026,54301758,19 -LILRA5,54313139,54307070,19 -AC245884.11,54337168,54308915,19 -VN1R104P,54321212,54320204,19 -AC245884.6,54331434,54328272,19 -LILRA4,54339150,54333185,19 -LAIR1,54370558,54351384,19 -AC245884.4,54379960,54379489,19 -TTYH1,54436900,54415219,19 -AC245884.1,54434698,54430654,19 -AC245884.9,54434665,54434183,19 -AC245884.10,54438346,54438010,19 -AC245884.8,54439544,54438665,19 -LENG8-AS1,54449045,54444813,19 -AC245884.3,54447047,54446830,19 -LENG8,54462037,54448887,19 -LENG9,54463711,54461796,19 -CDC42EP5,54473264,54465026,19 -LAIR2,54510687,54497879,19 -AC245036.1,54522031,54521723,19 -KIR3DX1,54545771,54532692,19 -AC245036.2,54552256,54551970,19 -LILRA2,54590287,54572920,19 -AC245036.5,54590287,54589441,19 -LILRA1,54602090,54593582,19 -AC245036.4,54608376,54607870,19 -LILRB1,54637528,54617158,19 -LILRB1-AS1,54638892,54635722,19 -LILRB4,54670359,54643889,19 -MIR8061,54645383,54645309,19 -VN1R105P,54648853,54647944,19 -AC245036.3,54652230,54652085,19 -AC243962.1,54672591,54671231,19 -LILRP1,54700905,54696604,19 -AC245128.2,54704235,54703952,19 -LILRP2,54713453,54707916,19 -KIR3DL3,54736536,54724479,19 -AC245128.1,54798285,54724496,19 -KIR2DL3,54753052,54738515,19 -KIR2DP1,54767371,54755023,19 -KIR2DL1,54784322,54769811,19 -KIR3DP1,54790318,54786355,19 -KIR2DL4,54814517,54803535,19 -KIR3DL1,54830778,54816468,19 -KIR2DS4,54848569,54832676,19 -KIR3DL2,54867207,54850443,19 -RNU6-222P,54869045,54868939,19 -FCAR,54890472,54874248,19 -AC245128.3,54891420,54890673,19 -NCR1,54916140,54906150,19 -NLRP7,54966312,54923509,19 -AC011476.8,54948993,54948864,19 -NLRP2,55001142,54953130,19 -AC011476.1,54974417,54973817,19 -AC011476.3,55048086,55006193,19 -GP6,55038264,55013705,19 -AC011476.2,55053928,55027593,19 -RDH13,55071291,55039108,19 -EPS8L1,55087923,55072020,19 -PPP1R12C,55117559,55090913,19 -AC010327.2,55120383,55119250,19 -MIR7975,55123292,55123225,19 -TNNT1,55149354,55132794,19 -TNNI3,55157773,55151767,19 -AC010327.1,55160671,55154757,19 -DNAAF3,55166722,55158661,19 -AC010327.3,55177540,55158939,19 -RF01210,55167274,55167171,19 -SYT5,55180441,55171199,19 -PTPRH,55209506,55181248,19 -AC010327.4,55221616,55216660,19 -TMEM86B,55229264,55226639,19 -AC010327.5,55230279,55227219,19 -PPP6R1,55258995,55229780,19 -MIR6804,55230952,55230885,19 -MIR6802,55239976,55239912,19 -MIR6803,55245250,55245186,19 -HSPBP1,55280381,55262231,19 -BRSK1,55312533,55282072,19 -AC020922.2,55312495,55312029,19 -TMEM150B,55334048,55312801,19 -AC020922.4,55339283,55338945,19 -KMT5C,55348120,55339853,19 -COX6B2,55354814,55349306,19 -AC020922.1,55359516,55352301,19 -FAM71E2,55363260,55354908,19 -IL11,55370463,55364389,19 -TMEM190,55378244,55376836,19 -AC020922.3,55378125,55377579,19 -TMEM238,55384598,55379245,19 -RPL28,55403250,55385345,19 -MIR6805,55388242,55388181,19 -UBE2S,55407777,55399745,19 -SHISA7,55442863,55428740,19 -ISOC2,55462343,55452985,19 -C19orf85,55464707,55463002,19 -AC008735.5,55467934,55467438,19 -AC008735.3,55476482,55475983,19 -ZNF628,55484487,55476332,19 -NAT14,55487568,55485004,19 -SSC5D,55519098,55488404,19 -SBK2,55537089,55529733,19 -SBK3,55545543,55540656,19 -ZNF579,55580845,55576770,19 -FIZ1,55601970,55591371,19 -ZNF524,55603138,55600022,19 -ZNF865,55617269,55605405,19 -AC008735.4,55613097,55612490,19 -ZNF784,55624601,55620742,19 -ZNF580,55643469,55635016,19 -ZNF581,55645622,55635459,19 -CCDC106,55653161,55641062,19 -U2AF2,55674715,55654146,19 -AC008735.2,55674715,55661901,19 -AC008735.1,55672069,55670632,19 -EPN1,55709858,55675226,19 -AC010525.1,55694558,55694118,19 -AC010525.2,55706172,55705978,19 -AC008749.1,55707855,55707550,19 -NLRP9,55738402,55708432,19 -RN7SKP109,55737600,55737285,19 -RFPL4A,55763175,55759014,19 -RFPL4AL1,55773179,55769141,19 -RFPL4AP1,55778900,55777553,19 -NLRP11,55836800,55785397,19 -NLRP4,55881854,55836578,19 -NLRP13,55932336,55891699,19 -NLRP8,55988629,55947832,19 -NLRP5,56061813,55999726,19 -LINC01864,56078801,56066684,19 -ZNF787,56121280,56087366,19 -RF00019,56105510,56105416,19 -AC024580.2,56127466,56127236,19 -ZNF444,56160893,56132599,19 -AC024580.1,56150490,56149817,19 -GALP,56185775,56176020,19 -ZSCAN5B,56197920,56189570,19 -ZSCAN5C,56209452,56202301,19 -AC011506.1,56217777,56217168,19 -ZSCAN5A,56368383,56219670,19 -ZSCAN5DP,56247482,56244043,19 -EDDM13,56310450,56272769,19 -AC006116.5,56312486,56311928,19 -AC006116.8,56341287,56314703,19 -RF00019,56316636,56316524,19 -VN2R17P,56318008,56317177,19 -AC006116.1,56322310,56319790,19 -AC006116.3,56338648,56338288,19 -AC006116.2,56343419,56342432,19 -AC006116.6,56348183,56347081,19 -AC006116.9,56368053,56354493,19 -ZNF542P,56379828,56368099,19 -ZNF582,56393545,56375846,19 -AC006116.10,56377284,56376704,19 -AC006116.7,56377792,56377037,19 -SLC25A36P1,56386690,56386226,19 -AC006116.4,56388424,56387412,19 -ZNF582-AS1,56399172,56393656,19 -ZNF583,56436035,56397966,19 -ZNF667,56478065,56439325,19 -ZNF667-AS1,56500666,56477250,19 -AC004696.2,56483977,56480397,19 -AC004696.1,56494280,56485923,19 -ZNF471,56530221,56507843,19 -AC005498.1,56535763,56535299,19 -AC005498.2,56538575,56536156,19 -ZFP28,56556810,56538948,19 -AC005498.3,56567411,56545566,19 -ZNF470,56588911,56567511,19 -SIGLEC31P,56590281,56589495,19 -ZNF71,56626481,56595264,19 -SMIM17,56655766,56643145,19 -ZNF835,56671783,56661981,19 -AC006115.1,56684653,56672268,19 -AC007228.1,56673901,56672574,19 -OR5AH1P,56761215,56690686,19 -AC006115.3,56791197,56707293,19 -ZIM2-AS1,56799646,56765344,19 -ZIM2,56840729,56774547,19 -AC006115.2,56793249,56793048,19 -PEG3,56840728,56810077,19 -MIMT1,56848556,56840902,19 -AC004792.1,56848204,56848068,19 -AC044792.1,56922213,56922018,19 -RPL7AP69,57066139,57065334,19 -AC025588.4,57112027,57111120,19 -USP29,57131926,57119138,19 -ZIM3,57145202,57134096,19 -AC025588.2,57150978,57150513,19 -AC025588.3,57151860,57151587,19 -DUXA,57167443,57154021,19 -AC025588.1,57177921,57175233,19 -ZNF264,57222846,57191500,19 -AURKC,57235548,57230802,19 -ZNF805,57255135,57240685,19 -AC005261.3,57262738,57261354,19 -ZNF460-AS1,57280334,57267220,19 -ZNF460,57293569,57280051,19 -AC005261.5,57294469,57293083,19 -AC005261.1,57308562,57304305,19 -AC005261.4,57317562,57317208,19 -ZNF543,57330776,57320509,19 -AC005261.2,57352012,57350848,19 -ZNF304,57359898,57351307,19 -ZNF547,57379565,57363477,19 -TRAPPC2B,57365353,57363511,19 -AC003002.1,57398584,57363566,19 -ZNF548,57402992,57389850,19 -AC003002.3,57419994,57389884,19 -AC003002.2,57438457,57394183,19 -ZNF17,57421939,57411163,19 -ZNF749,57445485,57435329,19 -AC004076.1,57477536,57437970,19 -AC004076.2,57453011,57449689,19 -VN1R1,57457142,57454790,19 -VN1R107P,57460120,57459912,19 -ZNF772,57477570,57466663,19 -AC003005.2,57482996,57477649,19 -ZNF419,57496097,57487711,19 -AC003005.1,57505381,57487718,19 -ZNF773,57518404,57499915,19 -ZNF549,57557542,57527325,19 -ZNF550,57559863,57535257,19 -ZNF416,57578927,57571566,19 -ZIK1,57593777,57578456,19 -ZNF530,57612722,57599885,19 -ZNF134,57624723,57614233,19 -AC003682.1,57626950,57625032,19 -ZNF211,57642779,57630395,19 -TPRG1LP1,57665039,57664280,19 -ZSCAN4,57679152,57668935,19 -ZNF551,57717301,57681969,19 -AC003006.1,57753575,57682045,19 -ZNF154,57709194,57697367,19 -ZNF671,57727624,57719751,19 -ZNF776,57758159,57746796,19 -ZNF586,57819939,57769655,19 -ZNF552,57814913,57803841,19 -AC010522.1,57858941,57819719,19 -ZNF587B,57846238,57819741,19 -FKBP1AP1,57826778,57826461,19 -ZNF814,57889074,57848731,19 -ZNF587,57865112,57849857,19 -AC010326.3,57868172,57867038,19 -AC010326.4,57868834,57867885,19 -AC010326.2,57916591,57876765,19 -ZNF417,57916610,57900296,19 -ZNF418,57935393,57921884,19 -ZNF256,57947675,57940833,19 -AC010326.1,57953759,57953158,19 -C19orf18,57974534,57958437,19 -ZNF606,58003349,57977053,19 -AC008969.1,58011232,58002061,19 -VN2R19P,58025926,58012589,19 -ZSCAN1,58054631,58034032,19 -LETM1P2,58046769,58044592,19 -HNRNPDLP4,58049346,58049046,19 -ZNF135,58086310,58059239,19 -RN7SL526P,58070288,58069970,19 -ZSCAN18,58118427,58083838,19 -ZNF329,58155110,58126252,19 -AC008751.1,58137054,58136713,19 -AC008751.2,58167145,58166166,19 -ZNF274,58213562,58183029,19 -RPS15AP36,58216561,58216180,19 -AC020915.4,58225469,58224506,19 -ZNF544,58277495,58228594,19 -AC020915.5,58315183,58228914,19 -AC020915.3,58278808,58257270,19 -AC020915.2,58267685,58266635,19 -ZNF8,58302805,58278951,19 -AC020915.1,58315197,58278966,19 -ERVK3-1,58315663,58305319,19 -AC010642.2,58326933,58310449,19 -AC010642.1,58320489,58319277,19 -ZSCAN22,58342332,58327019,19 -MIR6806,58334751,58334688,19 -A1BG,58353499,58345178,19 -AC012313.3,58362751,58346854,19 -A1BG-AS1,58355183,58347751,19 -AC012313.10,58351720,58350795,19 -ZNF497,58362848,58354357,19 -AC012313.2,58359603,58357999,19 -AC012313.6,58366591,58362585,19 -RNA5SP473,58363562,58363438,19 -ZNF837,58381060,58367618,19 -RPS5,58394806,58386400,19 -MIR4754,58386858,58386770,19 -RNF225,58397079,58396090,19 -LINC02560,58400679,58400221,19 -ZNF584,58418327,58401504,19 -AC012313.1,58408484,58404238,19 -AC012313.4,58419310,58418560,19 -AC012313.5,58431148,58428632,19 -ZNF132,58440222,58432814,19 -AC012313.8,58445849,58440448,19 -ZNF324B,58457833,58451604,19 -ZNF324,58475436,58467045,19 -ZNF446,58481230,58474017,19 -AC012313.7,58475763,58475355,19 -SLC27A5,58512413,58479512,19 -RNU6-1337P,58483843,58483749,19 -RN7SL693P,58491075,58490797,19 -AC012313.9,58501137,58500505,19 -ZBTB45,58538911,58513530,19 -RN7SL525P,58543578,58543299,19 -TRIM28,58550722,58544091,19 -MIR6807,58550376,58550285,19 -CHMP2A,58555124,58551566,19 -UBE2M,58558960,58555712,19 -MZF1-AS1,58574797,58559129,19 -MZF1,58573575,58561931,19 -CENPBD1P1,58599801,58573503,19 -AC016629.3,58581912,58581426,19 -AC016629.1,58586356,58586158,19 -AC016629.2,58599355,58593896,19 -FP565260.4,5017145,5011799,21 -FP565260.3,5040666,5022493,21 -FP565260.5,5087867,5073458,21 -GATD3B,5128425,5079294,21 -FP565260.2,5133805,5116343,21 -FP565260.1,5154734,5130871,21 -FP565260.6,5165472,5155499,21 -AC079801.1,5243833,5232668,21 -LINC01670,5502542,5499151,21 -CU633967.1,5614880,5553637,21 -RF00019,5597490,5597390,21 -FP236315.2,5705637,5703182,21 -FP236315.3,5707160,5705345,21 -FP236315.1,5709456,5707004,21 -H2BFS,5973383,5972924,21 -CU639417.2,6008810,6008604,21 -CU639417.1,6036093,6034690,21 -LINC01669,6076305,6060340,21 -CU639417.3,6082585,6081193,21 -CU639417.4,6091407,6084364,21 -SIK1B,6123739,6111134,21 -RF00019,6223580,6223480,21 -CU633906.1,6267317,6228966,21 -CU633906.4,6276532,6272135,21 -CU633906.3,6312948,6309161,21 -CU633906.2,6360415,6318434,21 -RF00019,6366055,6365955,21 -CBSL,6468040,6444869,21 -U2AF1L5,6499261,6484623,21 -FP236240.3,6507389,6507017,21 -FP236240.2,6520670,6520344,21 -FP236240.1,6553955,6550749,21 -CRYAA2,6564489,6560714,21 -CU638689.4,6670695,6630182,21 -CU638689.1,6670667,6667304,21 -CU638689.3,6679962,6676178,21 -CU638689.2,6716361,6712596,21 -CU638689.5,6725209,6721812,21 -FP475955.3,6812297,6789592,21 -FP475955.1,6897263,6858539,21 -MIR8069-1,6859256,6859171,21 -FP475955.4,6899280,6897291,21 -FP475955.2,6906692,6904884,21 -CU634019.6,6997765,6986450,21 -CU634019.4,6997737,6994374,21 -CU634019.5,7007022,7003248,21 -CU634019.2,7025166,7020599,21 -CU634019.1,7087229,7048891,21 -RF00019,7092716,7092616,21 -CU634019.7,7130287,7129103,21 -CU634019.3,7137789,7135334,21 -CU633904.2,7388799,7385058,21 -CU633904.3,7425839,7421442,21 -CU633904.1,7469007,7430659,21 -RF00019,7474494,7474394,21 -FP236241.2,7627528,7626344,21 -RF00019,7664011,7663911,21 -FP236241.1,7681742,7669397,21 -SMIM11B,7777853,7744962,21 -FAM243B,7770591,7768884,21 -SMIM34B,7793954,7784482,21 -KCNE1B,7829926,7816675,21 -RF00614,7826225,7826098,21 -FP671120.2,8103706,8101251,21 -FP671120.1,8227646,8197620,21 -MIR6724-1,8205406,8205315,21 -RF01518,8205940,8205851,21 -MIR3648-1,8208652,8208473,21 -MIR3687-1,8208904,8208844,21 -FP671120.3,8211306,8210384,21 -RNA5-8SN2,8212724,8212572,21 -MIR6724-2,8249596,8249505,21 -RF01518,8250149,8250060,21 -FP671120.4,8255514,8254592,21 -RNA5-8SN2,8256933,8256781,21 -FP236383.1,8410645,8380665,21 -MIR6724-3,8388453,8388362,21 -RF01518,8388987,8388898,21 -FP236383.2,8394341,8393419,21 -RNA5-8SN2,8395759,8395607,21 -MIR6724-4,8432621,8432530,21 -RF01518,8433174,8433085,21 -FP236383.3,8438551,8437629,21 -RNA5-8SN2,8439975,8439823,21 -RPSAP68,8603984,8603547,21 -CR381572.2,8680934,8680538,21 -CR381572.1,8701562,8701461,21 -LINC01666,8761335,8759077,21 -SNX18P10,8801880,8801362,21 -MTCO1P1,8846646,8845566,21 -CR381670.1,8880976,8857260,21 -MIR3648-2,8987178,8986999,21 -MIR3687-2,8987430,8987370,21 -CR392039.3,9018670,8996496,21 -CR392039.2,9027329,9026821,21 -CR392039.5,9059060,9053122,21 -TEKT4P2,9129752,9068361,21 -CR392039.4,9083101,9082601,21 -CR392039.1,9088391,9088188,21 -CR381653.1,9368775,9325013,21 -SNX18P12,9365225,9364467,21 -CR381653.2,9376645,9369285,21 -AC124864.2,9559155,9558970,21 -AC124864.1,9580415,9580024,21 -RF00003,9647000,9646825,21 -LINC01667,9814009,9781918,21 -AC018692.3,9810503,9810381,21 -AC018692.2,9810848,9810501,21 -RN7SL52P,9902627,9902344,21 -RF00156,9908010,9907916,21 -AC018692.1,9914846,9913117,21 -CR382285.1,10119309,9975017,21 -CR382285.2,10029855,10028361,21 -CR382287.2,10129029,10122273,21 -CR382287.1,10137004,10136419,21 -AP003900.1,10342737,10328411,21 -EIF3FP1,10331537,10330732,21 -VN1R7P,10358620,10357400,21 -AF254983.2,10397778,10397644,21 -BAGE2,10516431,10413477,21 -AF254983.1,10605716,10482738,21 -TPTE,10606140,10521553,21 -CYCSP41,10576587,10576292,21 -SLC25A15P4,10613379,10612455,21 -AF254982.1,10640338,10640028,21 -IGHV1OR21-1,10649835,10649400,21 -AP001464.1,13016692,12999676,21 -ANKRD30BP2,13067033,13038160,21 -RNU6-614P,13047689,13047583,21 -ZNF355P,13113790,13095305,21 -AJ239321.1,13120807,13120244,21 -AJ239318.1,13314944,13305152,21 -FGF7P2,13350648,13349265,21 -ANKRD30BP1,13427773,13384249,21 -MIR3156-3,13406460,13406384,21 -GTF2IP2,13489176,13443373,21 -VN1R8P,13477263,13476371,21 -SNX19P1,13528579,13525599,21 -OR4K11P,13545185,13544210,21 -LINC01674,13558461,13546033,21 -OR4K12P,13582004,13581060,21 -POTED,13641585,13609858,21 -RNU6-286P,13621683,13621577,21 -MIR3118-1,13644850,13644775,21 -AL050303.3,13654356,13654073,21 -GRAMD4P1,13667546,13665868,21 -CXADRP1,13677116,13676022,21 -AL050303.4,13681138,13679300,21 -AL050303.2,13705518,13704853,21 -MIR8069-2,13724274,13724189,21 -FEM1AP1,13764332,13762338,21 -AL050303.1,13771740,13769932,21 -TERF1P1,13777266,13776086,21 -FAM207CP,13793141,13792635,21 -GXYLT1P2,13825635,13824406,21 -CNN2P7,13827627,13826696,21 -ZNF114P1,13839409,13839118,21 -CYP4F29P,13848364,13843133,21 -SNX18P13,13906488,13905960,21 -ANKRD20A11P,13980437,13909574,21 -RHOT1P2,13937325,13936993,21 -RNU6-954P,13968595,13968489,21 -NF1P3,14005279,14000927,21 -PPP6R2P1,14014880,14007134,21 -FRG2MP,14020725,14019060,21 -AP001347.1,14144468,14027421,21 -ANKRD20A18P,14069087,14064325,21 -RNA5SP488,14070986,14070871,21 -RF01518,14076038,14075950,21 -LIPI,14210891,14108813,21 -ERLEC1P1,14144158,14143581,21 -RBM11,14228372,14216130,21 -ABCC13,14362754,14236206,21 -HSPA13,14383484,14371115,21 -SAMSN1,14658821,14485228,21 -SAMSN1-AS1,14598303,14582202,21 -AF127936.2,14753863,14746762,21 -POLR2CP1,14758352,14757588,21 -AF127936.1,14763090,14761710,21 -GAPDHP16,14775262,14774301,21 -AF127577.4,15014430,14818843,21 -LINC02246,14918552,14819699,21 -RBMX2P1,14829989,14829154,21 -AF127577.3,14947096,14918534,21 -NRIP1,15065936,14961235,21 -AF127577.1,14964233,14961309,21 -AF127577.2,14992854,14971470,21 -AF127577.6,15052379,15050189,21 -AF127577.5,15067837,15067070,21 -AF246928.1,15346738,15346652,21 -AJ009632.2,15627342,15370500,21 -CYCSP42,15490767,15490530,21 -AJ009632.1,15496124,15493932,21 -RNU6-1326P,15614389,15614283,21 -RAD23BP3,15696581,15694300,21 -USP25,15880069,15730025,21 -RBPMSLP,15745080,15744004,21 -MIR99AHG,16645065,15928296,21 -RNU6-426P,16035509,16035413,21 -VDAC2P1,16095372,16094415,21 -RPS26P5,16121644,16121310,21 -SNORD74B,16284768,16284696,21 -AP001172.1,16308133,16291791,21 -AP001172.2,16419080,16417139,21 -AP000962.2,16527019,16525919,21 -MIR99A,16539169,16539089,21 -MIRLET7C,16539911,16539828,21 -AP000962.1,16582637,16574718,21 -MIR125B2,16590325,16590237,21 -RNU1-98P,16719157,16718998,21 -AF130359.1,16815688,16754519,21 -AF212831.1,16873691,16862875,21 -NEK4P1,17211347,17210469,21 -AP000457.1,17296596,17296219,21 -RNU6-113P,17431647,17431547,21 -LINC01549,17449185,17438890,21 -RPL39P40,17500824,17500679,21 -RN7SL163P,17506728,17506453,21 -CXADR,17593579,17512382,21 -BTF3L4P1,17518993,17518526,21 -RF00019,17527247,17527140,21 -RF00019,17576906,17576798,21 -BTG3,17612947,17593653,21 -BTG3-AS1,17633199,17611744,21 -AP000432.1,17660384,17659276,21 -AP000432.2,17707455,17705408,21 -C21orf91-OT1,17792523,17763315,21 -C21orf91,17819386,17788967,21 -AL109761.1,17810845,17793488,21 -CHODL-AS1,17885608,17835016,21 -RPL37P3,17894485,17894193,21 -CHODL,18267373,17901263,21 -AF130417.1,18114904,18022370,21 -TMPRSS15,18485879,18269116,21 -AL109763.1,18486599,18477358,21 -MIR548XHG,18760003,18561265,21 -AF240627.1,18650360,18614431,21 -MIR548X,18686164,18686090,21 -PPIAP22,18858276,18857779,21 -AL157359.3,18917429,18917213,21 -AL157359.2,18935859,18917934,21 -AL157359.1,18967058,18953264,21 -AP000431.2,19047684,19046310,21 -AP000431.1,19134423,19130523,21 -SLC6A6P1,19246392,19244543,21 -AP000855.1,19303739,19301613,21 -RNU1-139P,19345312,19345148,21 -RPL37P4,19594108,19593841,21 -AP000403.1,19621545,19620695,21 -AP000568.1,19740150,19739709,21 -C1QBPP1,19760128,19759359,21 -LINC01683,19899755,19893279,21 -LINC02573,20258820,20256752,21 -RNU6-772P,20355852,20355748,21 -RN7SKP147,20356896,20356653,21 -FDPSP6,20388845,20388334,21 -KRT18P2,20426206,20424949,21 -RPS3AP1,20430762,20430443,21 -AP001506.1,20598163,20597953,21 -LINC00320,20803216,20742590,21 -PPIAP1,20828622,20828127,21 -NCAM2,21543329,20998315,21 -AP001136.1,21226829,21223295,21 -AP001117.1,21615437,21566763,21 -AF241725.1,21686329,21655038,21 -LINC00317,21737319,21723293,21 -LINC01425,21797415,21746973,21 -AP000472.1,21975681,21933315,21 -LINC01687,22098459,22008944,21 -LINC00308,22116528,22098617,21 -AP000705.2,22136083,22134698,21 -AP000705.1,22154299,22153950,21 -RNU4-45P,22205332,22205192,21 -AP000561.1,22420819,22209939,21 -MAPK6P2,22438349,22436290,21 -AP000959.1,22520058,22500604,21 -AP000949.1,22884000,22882582,21 -AP001116.1,23131206,23065491,21 -MIR6130,23079392,23079284,21 -ZNF299P,23091831,23090028,21 -MSANTD2P1,23103074,23101223,21 -RNU2-55P,23281909,23281736,21 -D21S2088E,23384861,23361104,21 -EEF1A1P1,23390996,23390258,21 -TUBAP,23409128,23407658,21 -RF00019,23432282,23432181,21 -AP000459.1,23490724,23477641,21 -AP000474.1,23890541,23888798,21 -AP000477.2,23967273,23960905,21 -AP000477.1,24050286,24043257,21 -AP000470.1,24188342,24155170,21 -LINC01689,24321377,24304546,21 -LINC01684,24547942,24428740,21 -LINC01692,25057746,24840550,21 -AP000402.1,24902756,24886676,21 -AP000146.1,24992817,24938431,21 -AP001342.1,25031529,25031005,21 -AP000235.1,25069906,25055535,21 -AP000233.2,25103670,25095022,21 -RN7SKP236,25101415,25101132,21 -AP000233.1,25135247,25127469,21 -AP001341.1,25333825,25169431,21 -RNA5SP489,25202315,25202207,21 -RPL13AP7,25362431,25361821,21 -AP001340.1,25372043,25371827,21 -LINC00158,25431701,25385820,21 -AP000221.1,25518338,25515473,21 -MIR155HG,25575168,25561909,21 -MIR155,25574044,25573980,21 -LINC00515,25583326,25582770,21 -MRPL39,25607517,25585656,21 -JAM2,25717562,25639272,21 -RNGTTP1,25644216,25643489,21 -FDX1P2,25692554,25692180,21 -ATP5PF,25735673,25716503,21 -GABPA,25772460,25734570,21 -LLPHP2,25763333,25762938,21 -APP,26171128,25880550,21 -AP001442.1,25942686,25928754,21 -RNU6-123P,25943145,25943044,21 -AP001439.1,26175824,26158091,21 -AP000229.1,26217381,26170871,21 -RNU6-926P,26190813,26190707,21 -AP001595.1,26350634,26349780,21 -AP001596.1,26471698,26378552,21 -CYYR1-AS1,26569252,26393635,21 -AP001596.2,26460214,26459903,21 -CYYR1,26573284,26466209,21 -ADAMTS1,26845409,26835747,21 -AP001599.1,26939742,26889376,21 -ADAMTS5,26966513,26917912,21 -MIR4759,26954043,26953961,21 -GPX1P2,27143949,27143344,21 -AP001605.1,27448579,27358885,21 -AP001604.1,27395787,27361164,21 -EIF4A1P1,27367947,27367014,21 -RPL10P1,27421023,27420380,21 -NCSTNP1,27492261,27492118,21 -LINC01673,27653491,27638693,21 -LINC00113,27751233,27722379,21 -AJ006995.1,27985295,27954922,21 -LINC00314,28023233,28013363,21 -LINC01697,28137611,28048404,21 -LINC01695,28228667,28116094,21 -AF165147.1,28674848,28439346,21 -LINC00161,28540355,28539318,21 -RF00026,28743291,28743208,21 -N6AMT1,28885371,28872191,21 -HSPD1P7,28889272,28887280,21 -THUMPD1P1,28903697,28901779,21 -LTN1,28992956,28928144,21 -RPL23P2,28998033,28997613,21 -RWDD2B,29019378,29004384,21 -AF129075.2,29024890,29024255,21 -USP16,29054488,29024629,21 -CCT8,29073797,29055805,21 -AF129075.1,29060095,29058073,21 -MAP3K7CL,29175889,29077471,21 -RPL12P9,29128188,29127701,21 -RF00019,29139384,29139283,21 -RF00012,29180639,29180425,21 -AF124730.1,29187795,29182027,21 -LINC00189,29288205,29193480,21 -BACH1,29630751,29194071,21 -GAPDHP14,29223257,29222321,21 -BACH1-IT1,29361894,29351634,21 -AP000240.1,29359453,29359002,21 -BACH1-AS1,29376339,29370019,21 -BACH1-IT2,29373709,29370497,21 -BACH1-IT3,29500386,29496047,21 -GRIK1,29940033,29536933,21 -AP000238.1,29657831,29657406,21 -GRIK1-AS1,29764002,29748175,21 -AF096876.1,30097836,30089717,21 -CLDN17,30166756,30165564,21 -LINC00307,30211783,30209151,21 -CLDN8,30216073,30214006,21 -RPL8P2,30263881,30263380,21 -KRTAP24-1,30282958,30281309,21 -KRTAP25-1,30289514,30289145,21 -KRTAP26-1,30320316,30319124,21 -KRTAP27-1,30337694,30337013,21 -KRTAP23-1,30348609,30348399,21 -KRTAP13-6P,30356885,30356515,21 -KRTAP13-2,30372257,30371391,21 -MIR4327,30375378,30375294,21 -KRTAP13-1,30396822,30396074,21 -KRTAP13-3,30425968,30425265,21 -KRTAP13-4,30431026,30430230,21 -KRTAP13-5P,30436964,30436466,21 -KRTAP15-1,30440945,30440275,21 -KRTAP19-1,30480344,30479699,21 -KRTAP19-2,30487436,30487057,21 -KRTAP19-3,30491985,30491464,21 -KRTAP19-4,30497133,30496824,21 -KRTAP19-5,30502117,30501657,21 -KRTAP19-9P,30510497,30510307,21 -KRTAP19-10P,30514799,30514613,21 -AP000567.1,30526701,30526546,21 -KRTAP19-11P,30537381,30537278,21 -KRTAP19-6,30541864,30541535,21 -KRTAP19-7,30561314,30560875,21 -KRTAP22-2,30590397,30590105,21 -KRTAP6-3,30593075,30592440,21 -KRTAP6-2,30598902,30598590,21 -KRTAP22-1,30601382,30601087,21 -KRTAP6-1,30613930,30613431,21 -KRTAP20-1,30616699,30616425,21 -KRTAP20-4,30620850,30620627,21 -KRTAP20-2,30635619,30635236,21 -KRTAP20-3,30643136,30642864,21 -KRTAP21-3,30718777,30718525,21 -KRTAP21-4P,30742595,30741780,21 -KRTAP21-2,30747233,30746794,21 -KRTAP21-1,30755428,30754830,21 -AP000244.1,30762882,30762682,21 -KRTAP8-2P,30802427,30802242,21 -KRTAP8-3P,30807119,30806932,21 -KRTAP8-1,30813252,30812697,21 -KRTAP7-1,30829759,30829039,21 -KRTAP11-1,30881555,30880644,21 -KRTAP19-8,31038476,31038159,21 -UBE3AP2,31063168,31060600,21 -TIAM1,31559977,31118416,21 -AP000563.1,31453223,31452466,21 -AP000251.1,31560487,31559245,21 -FBXW11P1,31628600,31627127,21 -AP000253.1,31659500,31653593,21 -SOD1,31668931,31659622,21 -RF01241,31664482,31664306,21 -AP000254.1,31667247,31666728,21 -SCAF4,31732075,31671033,21 -HMGN1P2,31706864,31706555,21 -AP000255.1,31736407,31735732,21 -TPT1P1,31840858,31840341,21 -HUNK,32044633,31873315,21 -HUNK-AS1,32021782,32020966,21 -LINC00159,32197813,32080316,21 -AP000265.1,32261585,32259804,21 -MIS18A,32279069,32268219,21 -MIS18A-AS1,32280988,32277863,21 -MRAP,32314784,32291813,21 -AP000266.1,32308737,32306464,21 -URB1,32393026,32311018,21 -SNORA80A,32377322,32377187,21 -URB1-AS1,32393960,32393130,21 -EVA1C,32515397,32412006,21 -EXOSC3P1,32497311,32496812,21 -RF00438,32538434,32538299,21 -RNA5SP490,32563210,32563075,21 -LINC00846,32575881,32572238,21 -TCP10L,32587373,32574841,21 -AP000275.2,32612281,32578822,21 -CFAP298,32612866,32592079,21 -OR7E23P,32622083,32621084,21 -AP000275.1,32625096,32624416,21 -SYNJ1,32728048,32628759,21 -PAXBP1-AS1,32743122,32728115,21 -PAXBP1,32771858,32733899,21 -C21orf62-AS1,32893735,32772100,21 -AP000280.1,32791504,32790673,21 -C21orf62,32813743,32793564,21 -AP000281.1,32841811,32841496,21 -RF00156,32841995,32841861,21 -AP000281.2,32849934,32844367,21 -AP000282.1,33071104,32913649,21 -LINC01690,32960566,32958888,21 -OLIG2,33029196,33025845,21 -LINC00945,33064983,33057829,21 -OLIG1,33072420,33070144,21 -AP000289.1,33123703,33111699,21 -AP000290.1,33159110,33156632,21 -LINC01548,33170649,33165470,21 -IFNAR2,33265675,33229901,21 -AP000295.1,33297160,33229901,21 -IL10RB-DT,33266260,33263873,21 -IL10RB,33310187,33266358,21 -IFNAR1,33359862,33324477,21 -USF1P1,33335096,33334571,21 -IFNGR2,33479348,33402896,21 -TMEM50B,33480011,33432485,21 -RPS5P3,33482195,33481580,21 -AP000302.1,33484258,33482499,21 -DNAJC28,33491720,33485530,21 -GART,33543491,33503931,21 -BTF3P6,33519108,33518610,21 -SON,33577481,33542618,21 -MIR6501,33550728,33550662,21 -DONSON,33588708,33559542,21 -AP000311.1,33931607,33584687,21 -CRYZL1,33643926,33589341,21 -ITSN1,33899861,33642400,21 -ATP5PO,33915980,33903453,21 -LINC00649,33977691,33915534,21 -RN7SL740P,33919338,33918995,21 -AP000569.1,33968573,33967101,21 -MRPS6,34143034,34073224,21 -SLC5A3,34106262,34073570,21 -AP000317.2,34360033,34073592,21 -RPS5P2,34136071,34135432,21 -LINC00310,34190244,34157724,21 -AP000317.1,34325034,34205055,21 -KCNE2,34371389,34364024,21 -AP000320.1,34375348,34370802,21 -SMIM11A,34407866,34375480,21 -FAM243A,34401072,34400317,21 -AP000322.2,34412587,34412200,21 -SMIM34A,34423966,34418715,21 -AP000322.1,34426017,34425508,21 -KCNE1,34512275,34446688,21 -RF00614,34456237,34456110,21 -RCAN1,34615142,34513142,21 -CLIC6,34718227,34669389,21 -LINC00160,34737181,34723807,21 -LINC01426,34784886,34745757,21 -RUNX1,36004667,34787801,21 -AF015262.1,35139222,35136638,21 -RPL34P3,35472432,35472095,21 -EZH2P1,35600022,35599732,21 -AF015720.1,35732942,35713139,21 -MIR802,35720808,35720715,21 -RPS20P1,35725100,35724747,21 -PPP1R2P2,35887807,35887195,21 -LINC01436,36007838,36005338,21 -RPL23AP3,36016546,36016079,21 -SETD4,36079389,36034541,21 -RIMKLBP1,36051377,36050214,21 -AP000688.4,36064408,36060432,21 -RNU6-992P,36066652,36066545,21 -AP000688.1,36126640,36069642,21 -CBR1,36073166,36069941,21 -AP000688.3,36090414,36082859,21 -AP000688.2,36109690,36104881,21 -MEMO1P1,36131376,36130489,21 -CBR3-AS1,36175815,36131767,21 -RPS9P1,36133032,36132450,21 -CBR3,36146566,36134912,21 -DOP1B,36294274,36156782,21 -RPL3P1,36170180,36168970,21 -SRSF9P1,36295702,36295173,21 -AP000692.2,36320670,36319792,21 -MORC3,36386148,36320189,21 -AP000692.1,36362040,36360630,21 -CHAF1B,36419015,36385378,21 -ATP5MFP1,36389112,36388878,21 -AP000695.1,36481070,36430360,21 -AP000695.2,36532408,36445731,21 -CLDN14,36576569,36460621,21 -AP000695.3,36487760,36485867,21 -PSMD4P1,36487411,36485983,21 -AP000696.1,36637033,36632681,21 -AP000697.1,36701564,36698773,21 -SIM2,36749917,36699133,21 -HLCS,36990236,36750888,21 -HLCS-IT1,36806284,36803984,21 -RNA5SP491,36852028,36851911,21 -DPRXP5,36944125,36943267,21 -AP000704.1,36975164,36966492,21 -RF00019,36986851,36986739,21 -MRPL20P1,36995075,36994643,21 -RIPPLY3,37019659,37006150,21 -RNU6-696P,37045636,37045530,21 -PIGP,37073170,37059170,21 -TTC3,37203112,37073226,21 -AP001429.1,37101343,37100814,21 -TTC3-AS1,37193926,37187666,21 -DSCR9,37221736,37208503,21 -RN7SL678P,37215903,37215605,21 -AP001432.1,37237744,37221419,21 -VPS26C,37267919,37223420,21 -AP001412.1,37268497,37267784,21 -AP001437.1,37365932,37365477,21 -DYRK1A,37526358,37365573,21 -KCNJ6,38121345,37607376,21 -KCNJ6-AS1,37719569,37717102,21 -DSCR4,38121360,37951425,21 -DSCR4-IT1,38010618,38006544,21 -DSCR8,38156511,38121451,21 -KCNJ15,38307357,38155549,21 -AP001425.1,38206080,38204141,21 -DSCR10,38208644,38206156,21 -AP001434.1,38238201,38237217,21 -SPATA20P1,38238664,38238227,21 -LINC01423,38333421,38323635,21 -ERG,38661780,38380027,21 -SNRPGP13,38502621,38502445,21 -LINC00114,38747460,38739021,21 -ETS2,38824955,38805307,21 -AP001042.1,38848644,38846247,21 -AP001043.1,38956467,38863676,21 -AP001043.2,38903905,38888772,21 -RF00091,38894867,38894785,21 -RPSAP64,38895252,38894917,21 -LINC01700,38977774,38974429,21 -AF064858.1,39006153,38988707,21 -AF064858.3,39011329,39006648,21 -AF064858.2,39029128,39028536,21 -RPL23AP12,39128040,39127568,21 -PCBP2P1,39172106,39171130,21 -RF02271,39171560,39171462,21 -PSMG1,39183851,39174769,21 -BRWD1,39321559,39184176,21 -AF129408.1,39184899,39184469,21 -TIMM9P2,39217506,39216624,21 -BRWD1-IT1,39219805,39217093,21 -METTL21AP1,39236020,39235386,21 -BRWD1-AS2,39314962,39313935,21 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-DSTNP1,46654022,46653558,21 -RPL23AP4,46691226,46690764,21 From 35e068e5032371751b0b7b9ec79f01839379030d Mon Sep 17 00:00:00 2001 From: Ruoyao Shi Date: Mon, 12 Aug 2019 15:18:35 -0700 Subject: [PATCH 5/6] Upload new input files --- VCTest/SNP_24.bed | Bin 0 -> 5500003 bytes VCTest/SNP_24.bim | 10000 +++++++++++++++++++++++++++++++++++++++++ VCTest/SNP_24.fam | 2200 +++++++++ VCTest/SNP_def24a.in | 10000 +++++++++++++++++++++++++++++++++++++++++ 4 files changed, 22200 insertions(+) create mode 100644 VCTest/SNP_24.bed create mode 100644 VCTest/SNP_24.bim create mode 100644 VCTest/SNP_24.fam create mode 100644 VCTest/SNP_def24a.in diff --git a/VCTest/SNP_24.bed b/VCTest/SNP_24.bed new file mode 100644 index 0000000000000000000000000000000000000000..e8664c6190e263accd10190485437f87aa15e677 GIT binary patch literal 5500003 zcmY)W&5jzm`^OFc?wQRf8Fdce*^D}y7DO;gHW5T{Lia&LolOfOIE5B$kd5pJHmC~O zqhu371g8+e234V|2sWsU?B`=<{?hYQ(%n$ycVDmH>vH|S|DXS-3S|~@<2Jzsl^0&^ 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insertions(+) create mode 100644 VCTest/VC test_new.html diff --git a/VCTest/VC test_new.html b/VCTest/VC test_new.html new file mode 100644 index 0000000..cf82baf --- /dev/null +++ b/VCTest/VC test_new.html @@ -0,0 +1,13398 @@ + + + +VC test_draftnewJSS08102019 + + + + + + + + + + + + + + + + + + + +

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New variance component test for SNP set analysis

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Based on the oringinal tutorial, this one changes a new dataset for SNP 24 and focuses on genes with 3 or more SNPs.

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Check version

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This tutorial has been checked with Julia version 1.1.1

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In [1]:
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versioninfo()
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Julia Version 1.1.0
+Commit 80516ca202 (2019-01-21 21:24 UTC)
+Platform Info:
+  OS: macOS (x86_64-apple-darwin14.5.0)
+  CPU: Intel(R) Core(TM) i7-6567U CPU @ 3.30GHz
+  WORD_SIZE: 64
+  LIBM: libopenlibm
+  LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
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Install packages when first use

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You need to have the statistial software R and R package "pracma" installed.

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In [ ]:
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using Pkg
+Pkg.add(PackageSpec(url="https://github.com/OpenMendel/SnpArrays.jl.git"))
+Pkg.add(PackageSpec(url="https://github.com/jinjinzhou/VarianceComponentTest.jl.git"))
+Pkg.add(PackageSpec(url="https://github.com/OpenMendel/MendelPlots.jl.git"))
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Use packages

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In [2]:
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using SnpArrays
+using CSV
+using DataFrames, Query
+using Distributions, DelimitedFiles, LinearAlgebra
+using VarianceComponentTest
+using StatsModels, Random, StatsBase, TraitSimulation
+using Random
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┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/Query/wGX7b.ji for Query [1a8c2f83-1ff3-5112-b086-8aa67b057ba1]
+└ @ Base loading.jl:1184
+┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/VarianceComponentTest/QfNDj.ji for VarianceComponentTest [1b8b39c6-5c46-50f5-9c36-ac18d7f28ae0]
+└ @ Base loading.jl:1184
+┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/TraitSimulation/VikWX.ji for TraitSimulation [dec3038e-29bc-11e9-2207-9f3d5855a202]
+└ @ Base loading.jl:1184
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Reproducibility

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We set a random seed using the Random.jl package for reproducibility. If you wish to end up with different data, you will need to comment out these commands or use another value in Random.seed!().

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In [3]:
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Random.seed!(100);
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Enter the folder path

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In [4]:
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;pwd
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/Users/janets/Documents/lap_top_janet/janet/genetics/variance_components/Ruoyao_notebooksAugust2019/newvctest/VCtest_24_input_file
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You can change the folder path according to your own computer. For example:

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In [ ]:
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;cd /Users/apple/Desktop/Julia/VCtest_24
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Load and filter missing value of the definition file

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The gene annotation is contained in the definition file in the last (sixth) column. This data is from Option 24 of the Mendel data.

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In [5]:
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df= (CSV.read("SNP_def24a.in",header=false))[:,[1,2,3,6]]
+chromosomes = df[:, 2]
+AdjBasepairs = df[:,3]
+genes = df[:, end];
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Set missing value as -1 and count the number of unique genes because we are only interested in genes with 3 or more snps.

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In [6]:
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genes[ismissing.(genes)] .= -1 # missing value as -1
+chromosomes[ismissing.(genes)] .= -1
+uniquegenes = unique(genes)
+genes_number = length(uniquegenes)
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Out[6]:
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4824
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Read three files containing information about the names of snps, locations and phenotype messages.

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In [7]:
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plinkFile = "SNP_24"
+## parse data from files
+plinkBedfile = string(plinkFile, ".bed");
+plinkBimfile = string(plinkFile, ".bim");
+plinkFamfile = string(plinkFile, ".fam");
+SNP_24 = SnpData("SNP_24");
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In [8]:
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# BIM file: chr, rs#, morgan, bp position, allele1, allele2
+bimdata = readdlm(plinkBimfile, String);
+chrID = bimdata[:, 1];
+snpID = bimdata[:, 2];
+snpPos = bimdata[:, 4];
+nSNP = length(snpID);
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In [9]:
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# FAM file: fam ID, ind ID, father ID, mother ID, sex, phenotype
+famdata = readdlm(plinkFamfile,',')
+nPer = length(famdata[:, 1])
+Fam_Person_id = DataFrame(FamID = famdata[:,1],PID =famdata[:,2])
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Out[9]:
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2,200 rows × 2 columns

FamIDPID
AnyAny
111
221
331
441
551
661
771
881
991
10101
11111
12121
13131
14141
15151
16161
17171
18181
19191
20201
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22221
23231
24241
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⋮⋮⋮
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Trait Simulation

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We will use TraitSimulation.jl package to simulate traits from genotype data for unrelateds by Generalized Linear Models (GLMs). We will simulate our continuous trait treating sex and three snps as fixed effects in the model.

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In [10]:
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snpdata = SnpArray("Snp_24.bed")
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Out[10]:
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2200×10000 SnpArray:
+ 0x02  0x03  0x03  0x00  0x02  0x03  …  0x02  0x03  0x03  0x03  0x02  0x03
+ 0x02  0x02  0x03  0x02  0x02  0x03     0x03  0x02  0x02  0x03  0x02  0x02
+ 0x03  0x02  0x03  0x02  0x02  0x03     0x03  0x02  0x02  0x02  0x02  0x03
+ 0x03  0x02  0x03  0x02  0x03  0x03     0x03  0x00  0x03  0x03  0x03  0x02
+ 0x03  0x03  0x03  0x00  0x02  0x03     0x03  0x03  0x03  0x03  0x02  0x03
+ 0x03  0x02  0x03  0x02  0x03  0x03  …  0x03  0x03  0x03  0x03  0x02  0x03
+ 0x03  0x02  0x02  0x02  0x03  0x03     0x02  0x00  0x02  0x03  0x02  0x02
+ 0x03  0x00  0x02  0x03  0x00  0x03     0x03  0x02  0x02  0x03  0x02  0x03
+ 0x03  0x02  0x00  0x02  0x02  0x03     0x03  0x02  0x03  0x03  0x02  0x03
+ 0x03  0x02  0x03  0x02  0x03  0x03     0x02  0x03  0x03  0x03  0x03  0x03
+ 0x02  0x02  0x02  0x00  0x03  0x03  …  0x03  0x03  0x03  0x03  0x02  0x02
+ 0x03  0x03  0x03  0x00  0x03  0x03     0x02  0x00  0x00  0x02  0x03  0x02
+ 0x02  0x02  0x03  0x02  0x02  0x03     0x03  0x03  0x02  0x03  0x03  0x03
+    ⋮                             ⋮  ⋱           ⋮                        
+ 0x03  0x03  0x02  0x03  0x03  0x03     0x02  0x03  0x02  0x03  0x02  0x02
+ 0x03  0x02  0x03  0x03  0x03  0x03     0x02  0x02  0x02  0x03  0x02  0x02
+ 0x03  0x02  0x00  0x02  0x02  0x03  …  0x03  0x02  0x03  0x03  0x03  0x02
+ 0x03  0x02  0x03  0x00  0x02  0x03     0x03  0x03  0x03  0x03  0x03  0x02
+ 0x00  0x03  0x02  0x03  0x03  0x03     0x03  0x03  0x03  0x03  0x03  0x03
+ 0x02  0x03  0x03  0x02  0x02  0x03     0x02  0x00  0x00  0x02  0x02  0x03
+ 0x02  0x02  0x03  0x00  0x03  0x03     0x02  0x03  0x03  0x03  0x02  0x02
+ 0x02  0x02  0x02  0x02  0x02  0x03  …  0x00  0x03  0x03  0x03  0x02  0x03
+ 0x03  0x02  0x03  0x03  0x03  0x03     0x03  0x00  0x00  0x03  0x02  0x02
+ 0x03  0x02  0x03  0x03  0x03  0x02     0x03  0x02  0x02  0x03  0x03  0x02
+ 0x03  0x03  0x02  0x00  0x00  0x02     0x02  0x03  0x03  0x03  0x00  0x03
+ 0x03  0x03  0x03  0x00  0x03  0x03     0x03  0x03  0x00  0x03  0x03  0x02
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In [11]:
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snpdef24 = readdlm("SNP_24.bim";header = false)
+snpid = map(x -> strip(string(x)), snpdef24[:, 2])
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Out[11]:
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10000-element Array{SubString{String},1}:
+ "rs3934834" 
+ "rs6603793" 
+ "rs2843153" 
+ "rs7535528" 
+ "rs12046158"
+ "rs2817134" 
+ "rs4494089" 
+ "rs4648505" 
+ "rs1181883" 
+ "rs10915433"
+ "rs4609397" 
+ "rs600933"  
+ "rs350187"  
+ ⋮           
+ "rs135720"  
+ "rs138407"  
+ "rs2413552" 
+ "rs139426"  
+ "rs137636"  
+ "rs132572"  
+ "rs137948"  
+ "rs739182"  
+ "rs12159200"
+ "rs9611502" 
+ "rs6519287" 
+ "rs11090076"
+
+ +
+ +
+
+ +
+
+
+
+
+

We focus on genes with 3 or more snps. Although it can be altered, we use rs649296, rs11122146, rs1875238 and rs12489977 in this tutorial.
+These four snps are from four different genes and chromosomes. Among them, rs649296 is from gene 24175 (on chromosome 8) which has 20 snps; rs11122146 is from gene 158 (on chromosome 1) which has 5 snps; rs1875238 is from gene 25064 (on chromosome 9); rs12489977 is from gene 17442 (on chromosome 3).

+ +
+
+
+
+
+
In [12]:
+
+
+
ind_rs649296= findall(x -> x == "rs649296",snpid)[1]
+ind_rs11122146= findall(x -> x == "rs11122146",snpid)[1]
+ind_rs1875238=findall(x -> x == "rs1875238",snpid)[1]
+ind_rs12489977 = findall(x -> x == "rs12489977",snpid)[1]
+
+ +
+
+
+ +
+
+ + +
+ +
Out[12]:
+ + + + +
+
1834
+
+ +
+ +
+
+ +
+
+
+
In [13]:
+
+
+
snp1 = convert(Vector{Float64}, @view(snpdata[:, ind_rs649296]))
+snp2 = convert(Vector{Float64}, @view(snpdata[:, ind_rs11122146]))
+snp3 = convert(Vector{Float64}, @view(snpdata[:, ind_rs1875238]))
+snp4 = convert(Vector{Float64}, @view(snpdata[:, ind_rs12489977]))
+sex = Float64.(famdata[:, 5])
+X = DataFrame(sex = sex, snp1 = snp1, snp2 = snp2,snp3 = snp3,snp4 = snp4)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[13]:
+ + + +
+

2,200 rows × 5 columns

sexsnp1snp2snp3snp4
Float64Float64Float64Float64Float64
11.02.02.02.02.0
20.01.02.02.02.0
31.02.02.02.01.0
41.02.02.02.01.0
51.02.02.01.02.0
60.02.02.02.01.0
70.01.02.02.01.0
81.02.02.02.01.0
91.02.02.02.01.0
100.02.01.02.00.0
111.02.02.01.02.0
120.02.02.02.01.0
131.02.02.01.02.0
141.02.02.02.02.0
150.02.02.02.00.0
161.02.02.02.02.0
170.02.02.01.00.0
181.02.00.02.01.0
191.02.02.01.01.0
201.02.00.02.01.0
211.01.02.02.00.0
221.02.02.02.01.0
230.02.02.01.01.0
240.02.02.02.02.0
250.02.02.01.01.0
260.02.02.02.02.0
270.02.01.02.02.0
280.02.01.02.00.0
291.02.02.02.02.0
301.02.01.02.02.0
⋮⋮⋮⋮⋮⋮
+
+ +
+ +
+
+ +
+
+
+
+
+

Generalized Linear Model

+
+
+
+
+
+
+
+

The snp parameters can be changed by the user. Note make the value large enough or the final pvalue will not be significant. The following ones just are examples and should be modified according to the sample size and application and may not reflect a user's actual data.

+ +
+
+
+
+
+
+
+ +
mean_formula = "20 + 3(sex) - 0.36(snp1) + 0.45(snp2) - 0.44(snp3) + 0.46(snp4)" # 4diff   
+             = "20 + 3(sex) + 0.35(snp1) - 0.56(snp2) + 0.60(snp3)" # 3diff
+ +
+
+
+
+
+
In [14]:
+
+
+
mean_formula = "20 + 3(sex) - 0.36(snp1) + 0.45(snp2) - 0.44(snp3) + 0.46(snp4)"
+GLM_trait_model = GLMTrait(mean_formula, X, NormalResponse(2), IdentityLink())
+Simulated_GLM_trait = simulate(GLM_trait_model)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[14]:
+ + + +
+

2,200 rows × 1 columns

trait1
Float64
121.9037
221.3475
321.5572
422.337
526.8175
623.8251
720.9121
821.0186
921.3486
1022.1254
1123.0516
1219.332
1325.4339
1426.3005
1519.031
1623.0451
1718.9116
1821.0699
1925.8202
2025.3199
2122.2284
2221.7282
2320.5106
2418.9595
2519.4494
2620.2138
2722.0342
2818.706
2923.5814
3023.8188
⋮⋮
+
+ +
+ +
+
+ +
+
+
+
In [15]:
+
+
+
describe(Simulated_GLM_trait, stats = [:mean, :std, :min, :q25, :median, :q75, :max, :eltype])
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
┌ Warning: The `stats` keyword argument has been deprecated. Use describe(df, stats...) instead.
+│   caller = ip:0x0
+└ @ Core :-1
+
+
+
+ +
+ +
Out[15]:
+ + + +
+

1 rows × 9 columns

variablemeanstdminq25medianq75maxeltype
SymbolFloat64Float64Float64Float64Float64Float64Float64DataType
1trait121.53682.550113.771319.715321.636223.355529.6356Float64
+
+ +
+ +
+
+ +
+
+
+
In [16]:
+
+
+
Trait1_GLM = hcat(Fam_Person_id, Simulated_GLM_trait, X)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[16]:
+ + + +
+

2,200 rows × 8 columns

FamIDPIDtrait1sexsnp1snp2snp3snp4
AnyAnyFloat64Float64Float64Float64Float64Float64
11121.90371.02.02.02.02.0
22121.34750.01.02.02.02.0
33121.55721.02.02.02.01.0
44122.3371.02.02.02.01.0
55126.81751.02.02.01.02.0
66123.82510.02.02.02.01.0
77120.91210.01.02.02.01.0
88121.01861.02.02.02.01.0
99121.34861.02.02.02.01.0
1010122.12540.02.01.02.00.0
1111123.05161.02.02.01.02.0
1212119.3320.02.02.02.01.0
1313125.43391.02.02.01.02.0
1414126.30051.02.02.02.02.0
1515119.0310.02.02.02.00.0
1616123.04511.02.02.02.02.0
1717118.91160.02.02.01.00.0
1818121.06991.02.00.02.01.0
1919125.82021.02.02.01.01.0
2020125.31991.02.00.02.01.0
2121122.22841.01.02.02.00.0
2222121.72821.02.02.02.01.0
2323120.51060.02.02.01.01.0
2424118.95950.02.02.02.02.0
2525119.44940.02.02.01.01.0
2626120.21380.02.02.02.02.0
2727122.03420.02.01.02.02.0
2828118.7060.02.01.02.00.0
2929123.58141.02.02.02.02.0
3030123.81881.02.01.02.02.0
⋮⋮⋮⋮⋮⋮⋮⋮⋮
+
+ +
+ +
+
+ +
+
+
+
In [18]:
+
+
+
CSV.write("Trait1_GLM.csv", Trait1_GLM)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[18]:
+ + + + +
+
"Trait1_GLM.csv"
+
+ +
+ +
+
+ +
+
+
+
+
+

Extract phenotype and covariates

+
+
+
+
+
+
In [19]:
+
+
+
ytrait = readdlm("Trait1_GLM.csv", ',';header = false, skipstart=1)
+y = convert(Vector{Float64}, ytrait[:, 3])
+
+ +
+
+
+ +
+
+ + +
+ +
Out[19]:
+ + + + +
+
2200-element Array{Float64,1}:
+ 21.903727897598223
+ 21.347506361432973
+ 21.557158012566536
+ 22.33696667367645 
+ 26.81747848096493 
+ 23.825127838651703
+ 20.91214104662801 
+ 21.018594298261547
+ 21.348637996132272
+ 22.125421882328816
+ 23.05157370228284 
+ 19.33201631688663 
+ 25.43389985511292 
+  ⋮                
+ 21.266162824991035
+ 20.1899787106751  
+ 20.265726565300394
+ 17.679625219688123
+ 20.658765295147006
+ 21.851078808252776
+ 22.22852457488234 
+ 16.65477571667752 
+ 16.908007881309874
+ 24.632551836965924
+ 24.300409661278852
+ 20.64711104441882
+
+ +
+ +
+
+ +
+
+
+
In [20]:
+
+
+
# covariates
+#2 change to 1.0; 1 change to 0.0
+X = [ones(size(sex,1)) Float64.(sex)]
+
+ +
+
+
+ +
+
+ + +
+ +
Out[20]:
+ + + + +
+
2200×2 Array{Float64,2}:
+ 1.0  1.0
+ 1.0  0.0
+ 1.0  1.0
+ 1.0  1.0
+ 1.0  1.0
+ 1.0  0.0
+ 1.0  0.0
+ 1.0  1.0
+ 1.0  1.0
+ 1.0  0.0
+ 1.0  1.0
+ 1.0  0.0
+ 1.0  1.0
+ ⋮       
+ 1.0  0.0
+ 1.0  0.0
+ 1.0  0.0
+ 1.0  0.0
+ 1.0  0.0
+ 1.0  0.0
+ 1.0  0.0
+ 1.0  0.0
+ 1.0  1.0
+ 1.0  1.0
+ 1.0  1.0
+ 1.0  1.0
+
+ +
+ +
+
+ +
+
+
+
+
+

Loop over genes

+
+
+
+
+
+
+
+

We will conduct gene set analysis for valid genes with 3 or more snps. In this example the time reasonably short because we have only a small number of genes with 3 or more snps but depending on the user's computer it may take around 2 - 10 minutes. To track the process we write out the progress as we go along.

+ +
+
+
+
+
+
In [21]:
+
+
+
nNullSimPts = 1000000;
+nPtsChi2 = 500;
+pvalELRT = Array{Float64}(undef, genes_number)
+pvalERLRT = Array{Float64}(undef, genes_number)
+pvalEscore = Array{Float64}(undef, genes_number)
+@time for i in 1:length(uniquegenes)
+    if uniquegenes[i] == -1 #missing values for genes were coded as -1
+        pvalELRT[i] = NaN
+        pvalERLRT[i] = NaN
+        pvalEscore[i] = NaN
+        continue
+    end
+    snpset_index = genes.== uniquegenes[i]
+    if (sum((snpset_index)) < 3 )
+        pvalELRT[i] = NaN
+        pvalERLRT[i] = NaN
+        pvalEscore[i] = NaN
+        continue
+    else
+        G = convert(Matrix{Float64}, SNP_24.snparray[:, snpset_index])
+        # get rid of mono-snp
+        maf = sum(G,dims=1)/(2*nPer);
+        G = G[:, map(x -> maf[x] > 0, 1:size(G,2))];
+        windowSize = size(G,2);
+    
+        if(windowSize > 2 && windowSize < 50)
+            (b, vc0, vc1, pvalELRT[i]) =
+                vctest(y, X, G, 
+                   tests = "eLRT", 
+                   Vform = "half", 
+                    nPtsChi2 = nPtsChi2,
+                    windowSize = windowSize,
+                    pvalueComputings = "chi2",
+#                     pvalueComputings = "MonteCarlo", 
+                    nNullSimPts = nNullSimPts);  
+            (b, vc0, vc1, pvalERLRT[i]) =
+                vctest(y, X, G, 
+                tests = "eRLRT", 
+                Vform = "half",
+                tolX = 1e-6,
+                nPtsChi2 = nPtsChi2,
+                windowSize = windowSize,
+                pvalueComputings = "chi2",
+#                 pvalueComputings = "MonteCarlo", 
+                nNullSimPts = nNullSimPts);
+### 
+## eScore uses integration to calculate p-value -currently Julia's package not updated used R package 
+## used R package pracma, need to first instll: install.packages("pracma")
+            (b, vc0, vc1, pvalEscore[i]) =
+                vctest(y, X, G, tests = "eScore", 
+                    Vform = "half",
+                    windowSize = windowSize)
+            println("finished $i")
+            
+        else
+            pvalELRT[i] = NaN
+            pvalERLRT[i] = NaN
+            pvalEscore[i] = NaN
+        end
+    end    
+end
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
┌ Warning: RCall.jl: Loading required package: pracma
+└ @ RCall /Users/janets/.julia/packages/RCall/ffM0W/src/io.jl:113
+
+
+
+ +
+ +
+ + +
+
finished 15
+finished 111
+finished 128
+finished 195
+finished 359
+finished 370
+finished 412
+finished 416
+finished 418
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+finished 4304
+finished 4314
+finished 4341
+finished 4560
+finished 4571
+finished 4604
+finished 4644
+finished 4666
+finished 4681
+finished 4702
+finished 4711
+finished 4771
+finished 4793
+finished 4795
+275.009100 seconds (316.08 M allocations: 198.556 GiB, 55.40% gc time)
+
+
+
+ +
+
+ +
+
+
+
+
+

Output results

+
+
+
+
+
+
+
+

Because of a problem in the CSV package, the number format of very small pvalues in .txt files is not convenient to read when you read them in Julia or other languages. You can remove the commented code to read the files.

+ +
+
+
+
+
+
In [22]:
+
+
+
chromosomes = Array{Int64}(undef, genes_number)
+for i in 1:length(uniquegenes)
+    chromosomes[i] = df[findfirst(genes .== uniquegenes[i]), 2]
+end
+
+ +
+
+
+ +
+
+
+
In [23]:
+
+
+
AdjBasepairs = Array{Int64}(undef, genes_number)
+for j in 1:length(uniquegenes)
+    AdjBasepairs[j] = df[findfirst(genes .== uniquegenes[j]),3]
+end
+
+ +
+
+
+ +
+
+
+
In [24]:
+
+
+
index = .!isnan.(pvalELRT)
+plot_ELRT = DataFrame(GeneID = uniquegenes[index],
+    Chromosome = chromosomes[index], 
+    AdjBasepair = AdjBasepairs[index],
+    Pvalue = pvalELRT[index])
+CSV.write("exactVC_eLRT_pVal_4diffsnps.txt", plot_ELRT)
+
+#= read all the pvalues
+df1 = CSV.read("exactVC_eLRT_pVal_3diffsnps.txt")[4]
+for i in 1:length(df1)
+    println(df1[i])
+end
+=#
+
+#= read pvalues which are not equal to 1
+df1 = CSV.read("exactVC_eLRT_pVal_4diffsnps.txt")[4]
+for i in 1:length(df1)
+    if df1[i] != 1
+        println(df1[i])
+    end
+end
+=#
+
+ +
+
+
+ +
+
+ + +
+ +
Out[24]:
+ + + + +
+
"exactVC_eLRT_pVal_4diffsnps.txt"
+
+ +
+ +
+
+ +
+
+
+
In [25]:
+
+
+
index = .!isnan.(pvalERLRT)
+plot_ERLRT = DataFrame(GeneID = uniquegenes[index],
+    Chromosome = chromosomes[index], 
+    AdjBasepair = AdjBasepairs[index],
+    Pvalue = pvalERLRT[index])
+CSV.write("exactVC_eRLRT_pVal_4diffsnps.txt", plot_ERLRT)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[25]:
+ + + + +
+
"exactVC_eRLRT_pVal_4diffsnps.txt"
+
+ +
+ +
+
+ +
+
+
+
In [26]:
+
+
+
index = .!isnan.(pvalEscore)
+plot_Escore = DataFrame(GeneID = uniquegenes[index],
+    Chromosome = chromosomes[index], 
+    AdjBasepair = AdjBasepairs[index],
+    Pvalue = pvalEscore[index])
+CSV.write("exactVC_Escore_pVal_4diffsnps.txt", plot_Escore)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[26]:
+ + + + +
+
"exactVC_Escore_pVal_4diffsnps.txt"
+
+ +
+ +
+
+ +
+
+
+
+
+

Manhattan Plot

+
+
+
+
+
+
+
+

The following code produces the Manhattan plots for the three different methods and saves them as .png files.

+ +
+
+
+
+
+
In [27]:
+
+
+
using MendelPlots
+
+manhattan(plot_ELRT; pvalvar = "Pvalue", chrvar = "Chromosome", 
+    posvar = "AdjBasepair", outfile = "exactVC_eLRT_manhattan.png", 
+    fontsize = 18pt, linecolor = "red")
+
+manhattan(plot_ERLRT; pvalvar = "Pvalue", chrvar = "Chromosome", 
+    posvar = "AdjBasepair", outfile = "exactVC_eRLRT_manhattan.png", 
+    fontsize = 18pt, linecolor = "red")
+
+manhattan(plot_Escore; pvalvar = "Pvalue", chrvar = "Chromosome", 
+    posvar = "AdjBasepair", outfile = "exactVC_eScore_manhattan.png", 
+    fontsize = 18pt, linecolor = "red")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
┌ Info: Recompiling stale cache file /Users/janets/.julia/compiled/v1.1/MendelPlots/5R3iq.ji for MendelPlots [7ab2ea78-72dd-4c03-85d8-fc2b0db252af]
+└ @ Base loading.jl:1184
+┌ Info: Loading DataFrames support into Gadfly.jl
+└ @ Gadfly /Users/janets/.julia/packages/Gadfly/09PWZ/src/mapping.jl:228
+
+
+
+ +
+
+ +
+
+
+
+
+

eRLRT

+
+
+
+
+
+
In [28]:
+
+
+
display("image/png", read("exactVC_eRLRT_manhattan.png"))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

eLRT

+
+
+
+
+
+
In [29]:
+
+
+
display("image/png", read("exactVC_eLRT_manhattan.png"))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

eScore

+
+
+
+
+
+
In [30]:
+
+
+
display("image/png", read("exactVC_eScore_manhattan.png"))
+
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