Introduction
+The ped2com function computes relationship matrices from
+pedigree data using a recursive algorithm based on parent-offspring
+connections. Central to this computation is the parent
+adjacency matrix, which defines how individuals in the
+pedigree are connected across generations. The adjacency matrix acts as
+the structural input from which genetic relatedness is propagated.
The function offers two methods for constructing this matrix:
+-
+
- The classic method, which assumes that all parents are known and +that the adjacency matrix is complete. +
- The partial parent method, which allows for missing values in the +parent adjacency matrix. +
When parent data are complete, both methods return equivalent +results. But when parental information is missing, their behavior +diverges. This vignette illustrates how and why these differences +emerge, and under what conditions the partial method provides more +accurate results.
+Hazard Data Example
+We begin with the hazard dataset. First, we examine
+behavior under complete pedigree data.
We compute the additive genetic relationship matrix using both the +classic and partial parent methods. Because the pedigree is complete, we +expect no differences in the resulting matrices.
+
+ped_add_partial_complete <- ped2com(df, isChild_method= "partialparent",
+ component = "additive",
+ adjacency_method = "direct")
+ped_add_classic_complete <- ped2com(df, isChild_method= "classic",
+ component = "additive", adjacency_method = "direct")The following plots display the full additive matrices. These +matrices should be identical.
+This can be confirmed visually and numerically.
+
+library(corrplot)
+#> Warning: package 'corrplot' was built under R version 4.4.3
+#> corrplot 0.95 loaded
+
+
+corrplot(as.matrix(ped_add_classic_complete),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Additive component - Classic method")
+corrplot(as.matrix(ped_add_partial_complete),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Additive component - Partial parent method")
To verify this, we subtract one matrix from the other and calculate +RMSE. The difference should be numerically zero. Indeed, it is 0.
+
+corrplot(as.matrix(ped_add_classic_complete-ped_add_partial_complete),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE)
+#> Warning in corrplot(as.matrix(ped_add_classic_complete -
+#> ped_add_partial_complete), : col.lim interval too wide, please set a suitable
+#> value
Introducing Missingness: Remove a Parent
+To observe how the two methods diverge when data are incomplete, we +remove one parent—starting with the mother of individual 4.
+ +
+ped_add_partial_mom <- ped_add_partial<- ped2com(df, isChild_method= "partialparent",
+ component = "additive",
+ adjacency_method = "direct")
+
+ped_add_classic_mom <- ped_add_classic <- ped2com(df, isChild_method= "classic",
+ component = "additive", adjacency_method = "direct")The two methods now treat individual 4 differently in the parent +adjacency matrix. The classic method applies a fixed contribution +because one parent remains. The partial parent method inflates the +individual’s diagonal contribution to account for the missing +parent.
+The resulting additive matrices reflect this difference. The RMSE +between the two matrices is 0.009811.
+corrplot(as.matrix(ped_add_classic),
+ method = 'color', type = 'lower', col.lim = c(0, 1),
+ is.corr = FALSE, title = "Classic (mother removed)")
+corrplot(as.matrix(ped_add_partial),
+ method = 'color', type = 'lower', col.lim = c(0, 1),
+ is.corr = FALSE, title = "Partial (mother removed)")
We quantify the overall matrix difference:
+ +Next, we compare each method to the matrix from the complete +pedigree. This evaluates how much each method deviates from the correct +additive structure.
+corrplot(as.matrix(ped_add_classic_complete - ped_add_classic),
+ method = 'color', type = 'lower', col.lim = c(0, 1),
+ is.corr = FALSE)
The RMSE between the true additive component and the classic method +is 0.0299137.
+
+corrplot(as.matrix(ped_add_classic_complete - ped_add_partial),
+ method = 'color', type = 'lower', col.lim = c(0, 1),
+ is.corr = FALSE)
The RMSE between the true additive component and the partial parent +method is 0.028259.
+The partial method shows smaller deviations from the complete matrix, +confirming that it better preserves relatedness structure when one +parent is missing.
+Removing the Father Instead
+We now repeat the same process, this time removing the father of +individual 4.
+
+data(hazard)
+
+df <- hazard # this is the data that we will use for the example
+
+
+ df$dadID[df$ID == 4] <- NA
+ # add
+ped_add_partial_dad <- ped_add_partial<- ped2com(df, isChild_method= "partialparent",
+ component = "additive",
+ adjacency_method = "direct")
+
+ped_add_classic_dad <- ped_add_classic <- ped2com(df, isChild_method= "classic",
+ component = "additive", adjacency_method = "direct")As we can see, the two matrices are different. The RMSE between the +two matrices is 0.009811.
+corrplot(as.matrix(ped_add_classic_dad),
+ method = 'color', type = 'lower', col.lim = c(0, 1),
+ is.corr = FALSE, title = "Classic (father removed)")
+corrplot(as.matrix(ped_add_partial_dad),
+ method = 'color', type = 'lower', col.lim = c(0, 1),
+ is.corr = FALSE, title = "Partial (father removed)")
Again, we compare to the true matrix from the complete pedigree:
+corrplot(as.matrix(ped_add_classic_complete - ped_add_classic),
+ method = 'color', type = 'lower', col.lim = c(0, 1),
+ is.corr = FALSE)
+corrplot(as.matrix(ped_add_classic_complete - ped_add_partial),
+ method = 'color', type = 'lower', col.lim = c(0, 1),
+ is.corr = FALSE)
The partial parent method again yields a matrix closer to the +full-data version.
+Inbreeding Dataset: Family-Level Evaluation
+To generalize the comparison across a larger and more varied set of
+pedigrees, we use the inbreeding dataset. Each family in
+this dataset is analyzed independently.
For each one, we construct the additive relationship matrix under +complete information and then simulate two missingness scenarios:
+-
+
Missing mother: One individual with a known mother is randomly +selected, and the mother’s ID is set to NA.
+Missing father: Similarly, one individual with a known father is +selected, and the father’s ID is set to NA.
+
In each condition, we recompute the additive matrix using both the +classic and partial parent methods. We then calculate the RMSE between +each estimate and the matrix from the complete pedigree. This allows us +to quantify which method more accurately reconstructs the original +relatedness structure when parental data are partially missing.
+inbreeding_list <- list()
+results <- data.frame(FamIDs = FamIDs,
+ RMSE_partial_dad = rep(NA, length(FamIDs)),
+ RMSE_partial_mom = rep(NA, length(FamIDs)),
+ RMSE_classic_dad = rep(NA, length(FamIDs)),
+ RMSE_classic_mom = rep(NA, length(FamIDs)),
+ max_R_classic_dad = rep(NA, length(FamIDs)),
+ max_R_partial_dad = rep(NA, length(FamIDs)),
+ max_R_classic_mom = rep(NA, length(FamIDs)),
+ max_R_partial_mom = rep(NA, length(FamIDs)),
+ max_R_classic = rep(NA, length(FamIDs)))The loop below performs this procedure for all families in the +dataset and stores both the RMSEs and the maximum relatedness +values.
+
+
+for (i in 1:length(FamIDs)) {
+
+# make three versions to filter down
+df_fam_dad<-df_fam_mom <- df_fam <- df[df$FamID == FamIDs[i],]
+
+results$RMSE_partial_mom[i] <- sqrt(mean((ped_add_classic_complete-ped_add_partial_mom)^2))
+
+
+ped_add_partial_complete <- ped2com(df_fam, isChild_method= "partialparent",
+ component = "additive",
+ adjacency_method = "direct")
+
+ped_add_classic_complete <- ped2com(df_fam, isChild_method= "classic",
+ component = "additive",
+ adjacency_method = "direct")
+
+
+# select first ID with a mom and dad
+momid_to_cut <- df_fam$ID[!is.na(df_fam$momID)] %>% head(1)
+dadid_to_cut <- df_fam$ID[!is.na(df_fam$dadID)] %>% head(1)
+
+df_fam_dad$dadID[df_fam$ID == dadid_to_cut] <- NA
+
+df_fam_mom$momID[df_fam$ID == momid_to_cut] <- NA
+
+ped_add_partial_dad <- ped2com(df_fam_dad, isChild_method= "partialparent",
+ component = "additive",
+ adjacency_method = "direct")
+ped_add_classic_dad <- ped2com(df_fam_dad, isChild_method= "classic",
+ component = "additive", adjacency_method = "direct")
+
+results$RMSE_partial_dad[i] <- sqrt(mean((ped_add_classic_complete-ped_add_partial_dad)^2))
+results$RMSE_classic_dad[i] <- sqrt(mean((ped_add_classic_complete-ped_add_classic_dad)^2))
+results$max_R_classic_dad[i] <- max(as.matrix(ped_add_classic_dad))
+results$max_R_partial_dad[i] <- max(as.matrix(ped_add_partial_dad))
+
+
+ped_add_partial_mom <- ped2com(df_fam_mom, isChild_method= "partialparent",
+ component = "additive",
+ adjacency_method = "direct")
+
+ped_add_classic_mom <- ped2com(df_fam_mom, isChild_method= "classic",
+ component = "additive", adjacency_method = "direct")
+
+results$RMSE_partial_mom[i] <- sqrt(mean((ped_add_classic_complete-ped_add_partial_mom)^2))
+results$RMSE_classic_mom[i] <- sqrt(mean((ped_add_classic_complete-ped_add_classic_mom)^2))
+results$max_R_classic_mom[i] <- max(as.matrix(ped_add_classic_mom))
+results$max_R_partial_mom[i] <- max(as.matrix(ped_add_partial_mom))
+results$max_R_classic[i] <- max(as.matrix(ped_add_classic_complete))
+
+inbreeding_list[[i]] <- list(df_fam=df_fam,
+ ped_add_partial_complete = ped_add_partial_complete,
+ ped_add_classic_complete = ped_add_classic_complete,
+ ped_add_partial_dad = ped_add_partial_dad,
+ ped_add_classic_dad = ped_add_classic_dad,
+ ped_add_partial_mom = ped_add_partial_mom,
+ ped_add_classic_mom = ped_add_classic_mom)
+
+}Example: Family 1
+To understand what these matrices look like, we visualize them for +one representative family. For this example, we select the first family +in the dataset.
+
# pull the first family from the list
+fam1 <- inbreeding_list[[1]]
+
+corrplot(as.matrix(fam1$ped_add_classic_complete),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Classic - Complete")
+corrplot(as.matrix(fam1$ped_add_classic_mom),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Classic - Mom Missing")
+corrplot(as.matrix(fam1$ped_add_partial_mom),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Partial - Mom Missing")
+corrplot(as.matrix(fam1$ped_add_classic_dad),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Classic - Dad Missing")
+corrplot(as.matrix(fam1$ped_add_partial_dad),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Partial - Dad Missing")
To visualize the differences from the true matrix:
+corrplot(as.matrix(fam1$ped_add_classic_complete - fam1$ped_add_classic_mom),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Classic Mom Diff from Complete")
+corrplot(as.matrix(fam1$ped_add_classic_complete - fam1$ped_add_partial_mom),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Partial Mom Diff from Complete")
+corrplot(as.matrix(fam1$ped_add_classic_complete - fam1$ped_add_classic_dad),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Classic Dad Diff from Complete")
+corrplot(as.matrix(fam1$ped_add_classic_complete - fam1$ped_add_partial_dad),
+ method = 'color', type = 'lower', col.lim = c(0,1),
+ is.corr = FALSE, title = "Partial Dad Diff from Complete")
These plots show how each method responds to missing data, and +whether it maintains consistency with the complete pedigree. We observe +that the partial parent method typically introduces smaller deviations. +If desired, this same diagnostic can be repeated for additional +families, such as inbreeding_list[[2]].
+Summary
+Across all families in the inbreeding dataset, the results show a +consistent pattern: the partial parent method outperforms the classic +method in reconstructing the additive genetic relationship matrix when +either a mother or a father is missing.
+To make this explicit, we calculate the RMSE difference between +methods. A positive value means that the partial method had lower RMSE +(i.e., better accuracy) than the classic method:
+
+results <- results %>% as.data.frame() %>% mutate(RMSE_diff_dad = RMSE_classic_dad - RMSE_partial_dad,
+ RMSE_diff_mom = RMSE_classic_mom - RMSE_partial_mom)We can then summarize the pattern across families:
+
+results %>%
+ select(RMSE_diff_mom, RMSE_diff_dad) %>%
+ summary()
+#> RMSE_diff_mom RMSE_diff_dad
+#> Min. :0.001222 Min. :0.001222
+#> 1st Qu.:0.001869 1st Qu.:0.002036
+#> Median :0.002538 Median :0.002520
+#> Mean :0.005763 Mean :0.005786
+#> 3rd Qu.:0.005625 3rd Qu.:0.005625
+#> Max. :0.024221 Max. :0.024221In all families, both RMSE_diff_mom and
+RMSE_diff_dad are positive—indicating that the classic
+method produces larger the errors relative to the partial method. This
+holds regardless of whether the missing parent is a mother or a
+father.
To verify this directly:
+mean(results$RMSE_diff_mom > 0, na.rm = TRUE)
+#> [1] 1
+mean(results$RMSE_diff_dad > 0, na.rm = TRUE)
+#> [1] 1These proportions show how often the partial method produces a lower +RMSE across the dataset. This confirms the earlier findings: when +pedigree data are incomplete, the partial parent method more faithfully +reconstructs the full-data relatedness matrix.
+results %>% as.data.frame() %>% select(-FamIDs,-RMSE_diff_mom,-RMSE_diff_dad,-max_R_classic_dad,
+ -max_R_partial_dad,-max_R_classic_mom,-max_R_partial_mom,-max_R_classic) %>% summary()
+#> RMSE_partial_dad RMSE_partial_mom RMSE_classic_dad RMSE_classic_mom
+#> Min. :0.04773 Min. :0.04773 Min. :0.04895 Min. :0.04895
+#> 1st Qu.:0.05570 1st Qu.:0.05349 1st Qu.:0.05774 1st Qu.:0.05555
+#> Median :0.06206 Median :0.06899 Median :0.06457 Median :0.07158
+#> Mean :0.07545 Mean :0.07686 Mean :0.08124 Mean :0.08262
+#> 3rd Qu.:0.08237 3rd Qu.:0.08323 3rd Qu.:0.08866 3rd Qu.:0.08866
+#> Max. :0.15547 Max. :0.15547 Max. :0.17969 Max. :0.17969