Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation

Abstract

Background Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions. Methods The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data. Results Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by − 0.1 to ~ 6.9% for real traits with the medium-density panel. Conclusions Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation.

Identification of Genomic Regions for Traits Associated with Flowering in Cassava (Manihot esculenta Crantz)

“…SNP-based heritability ( h 2 ) estimates were relatively higher for stem height at the first branch ( h 2 = 0.35), followed by branching ( h 2 = 0.25), and branching levels ( h 2 = 0.19). These estimates could be attributed to LD unevenness or heterogeneity among marker regions due to overestimation or underestimation, as postulated by [ 76 ].…”

Multi-Trait Bayesian Models Enhance the Accuracy of Genomic Prediction in Multi-Breed Reference Populations

“…Block size can affect prediction accuracy of single-trait or multi-trait Bayesian models fitting heterogeneous genetic (co)variances [41,45]. In the present study, the prediction accuracy of mbBayesAB for different block sizes (one SNP; a group of 25, 50, 100, 200 adjacent SNPs or the whole genome) was compared.…”

Progress and Application of Genomic Selection in Sheep and Goat

“…Therefore, the higher marker density, the more accuracy of GS and better selection effect. In addition, some studies have shown that grouping SNPs based on regional LD can effectively reduce the adverse effect of the uneven distribution of LD on the genome, and greatly improve the efficiency of high-density SNP data for genome prediction and heritability estimation (Ren et al, 2022).…”