Genomic prediction in Nordic Red dairy cattle considering breed origin of alleles

Abstract

This study investigated the reliability of genomic prediction (GP) using breed origin of alleles (BOA) approach in the Nordic Red (RDC) population, which has an admixed population structure. The RDC population consists of animals with varying degrees of genetic materials from the Danish Red (RDM), Swedish Red (SRB), Finnish Ayrshire (FAY), and Holstein (HOL) because bulls have been used across the breeds. The BOA approach was tested using 39,550 RDC animals in the reference population and 11,786 in the validation population. Deregressed proofs (DRP) of milk, fat and protein were used as response variable for GP. Direct genomic breeding values (DGV) for animals in the validation population were calculated with (BOA model) or without (joint model) considering breed origin of alleles. The joint model assumed homogeneous marker effects and a single set of marker effects were estimated, whereas BOA model assumed heterogeneous marker effects, and different sets of marker effects were estimated across the breeds. For the BOA approach, we tested scenarios assuming both correlated (BOA_cor) and uncorrelated (BOA_uncor) marker effects between the breeds. Additionally, we investigated GP using a standard Illumina 50K chip and including SNP selected from imputed whole-genome sequencing (50K+WGS). We also studied the effect of estimating (co)variances for genome regions of different sizes to exploit the information of the genome regions contributing to the (co) variance between the breeds. Region sizes were set as 1 SNP, a group of 30 or 100 adjacent SNP, or the whole genome. Reliability of DGV was measured as squared correlations between DGV and DRP divided by the reliability of DRP. Across the 3 traits, in general, RS30 and RS100 SNP yielded the highest reliabilities. Including WGS SNP improved reliabilities in almost all scenarios (0.297 on average for 50K and 0.307 on average for 50K+WGS). The BOA_uncor (0.233 on average) was inferior to the joint model (0.339 on average), but the reliabilities obtained using BOA_cor (0.334 on average) in most cases were not significantly different from those obtained using the joint model. The results indicate that both including additional whole-genome sequencing SNP and dividing the genome into fixed regions improve GP in the RDC. The BOA models have the potential to increase the reliability of GP, but the benefit is limited in populations with a high exchange of genetic material for a long time, as is the case for RDC.

341. Genomic prediction in multi-breed Irish beef cattle population using a breed origin of alleles model

“…The estimates of high correlations between the SNP effects of the breeds may occur for different reasons: i) there were five breeds combined in the OT group (beef and dairy breeds) which may lead to problems in assignments of alleles, ii) unassigned alleles were included in the OT group, iii) the imputation of genotypes was carried out jointly in a multi-breed setting for the entire population which might lead to errors in genotypes, and therefore in predictions using BOA. It has been shown that errors in assignments of alleles lead to an increase in the correlation of SNP effects between the breeds (Guillenea et al, 2022). Even with correlations higher than expected, the BOA model outperformed PBLUP but more importantly, also out-performed the SNP-BLUP model for animals of the Irish cattle population.…”

Genomic predictions for crossbred dairy cows by combining solutions from purebred evaluation based on breed origin of alleles

“…However, when no crossbreds were in the training set, a BOA model did not outperform a model with combined training set of the 3 pure breeds without accounting for BOA. For empirical data, comparison of models with and without accounting for BOA for the admixed Nordic Red Dairy Cattle (RDC) populations did not show higher reliability from the BOA model (Guillenea et al, 2022). However, in that study, first, the pure subpopulations contributing to RDC were not clearly defined and had to be inferred from genotypes, and second, there were many generations of crossbreeding, which altogether made accurate detection of BOA difficult.…”

Genome‐wide marker effect heterogeneity is associated with a large effect dormancy locus in winter malting barley

“…(2019) saw variable results as compared with a standard GBLUP. More generally, in stratified prediction, models that consider group effects (without specifically using a structured GBLUP framework) led to improvements in prediction accuracy (Guillenea et al., 2022; Karaman et al., 2021; Lyra et al., 2018; Ramstein & Casler, 2019). Here we show that a model developed for highly differentiated populations can improve prediction accuracy within a highly related population due to the presence of group‐specific marker effects associated with a large‐effect locus.…”

“…The marker effects may be estimated as the combination of two or more different marker effects, with one effect size per marker from each group of individuals carrying each large effect allele. Heterogeneity of marker effects has been established across diverse populations (Guillenea et al., 2022; Legarra et al., 2021; Lehermeier et al., 2015; Liu et al., 2011; Rio et al., 2020; Schulz‐Streeck et al., 2012; Veturi et al., 2019); however, no study has explored this possibility in the context of large‐effect loci in related populations. Common ways tested to increase prediction accuracy across populations have included stratified analysis or the addition of random group‐specific marker deviation terms to standard regression models (Lehermeier et al., 2015).…”