Abstract
BackgroundIn 2017, genomic selection was implemented in French dairy goats using the single-step genomic best linear unbiased prediction (ssGBLUP) method, which assumes that all single nucleotide polymorphisms explain the same fraction of genetic variance. However, ssGBLUP is not suitable for protein content, which is controlled by a major gene, i.e. αs1casein. This gene explains about 40% of the genetic variation in protein content. In this study, we evaluated the accuracy of genomic prediction using different genomic methods to include the effect of the αs1casein gene.MethodsGenomic evaluation for protein content was performed with data from the official genetic evaluation on 2955 animals genotyped with the Illumina goat SNP50 BeadChip, 7202 animals genotyped at the αs1casein gene and 6,767,490 phenotyped females. Pedigree-based BLUP was compared with regular unweighted ssGBLUP and with three weighted ssGBLUP methods (WssGBLUP, WssGBLUPMax and WssGBLUPSum), which give weights to SNPs according to their effect on protein content. Two other methods were also used: trait-specific marker-derived relationship matrix (TABLUP) using pre-selected SNPs associated with protein content and gene content based on a multiple-trait genomic model that includes αs1casein genotypes. We estimated accuracies of predicted genomic estimated breeding values (GEBV) in two populations of goats (Alpine and Saanen).ResultsAccuracies of GEBV with ssGBLUP improved by + 5 to + 7 percent points over accuracies from the pedigree-based BLUP model. With the WssGBLUP methods, SNPs that are located close to the αs1casein gene had the biggest weights and contributed substantially to the capture of signals from quantitative trait loci. Improvement in accuracy of genomic predictions using the three weighted ssGBLUP methods delivered up to + 6 percent points of accuracy over ssGBLUP. A similar accuracy was obtained for ssGBLUP and TABLUP considering the 20,000 most important SNPs. Incorporating information on the αs1casein genotypes based on the gene content method gave similar results as ssGBLUP.ConclusionsThe three weighted ssGBLUP methods were efficient for detecting SNPs associated with protein content and for a better prediction of genomic breeding values than ssGBLUP. They also combined fast computing, simplicity and required ssGBLUP to be run only twice.
Highlights
In 2017, genomic selection was implemented in French dairy goats using the single-step genomic best linear unbiased prediction method, which assumes that all single nucleotide polymorphisms explain the same fraction of genetic variance
We used WssGBLUP because we wanted to identify the weights given to single nucleotide polymorphism (SNP) with this method, in order to determine if the chromosomal region including the αs1 casein gene was considered in the analyses
Using the trait-specific marker-derived relationship matrix did not improve accuracy of genomic evaluation, which was the same as that obtained by single-step genomic best linear unbiased prediction (ssGBLUP) with a selection of the 20,000 most important SNPs for protein content
Summary
In 2017, genomic selection was implemented in French dairy goats using the single-step genomic best linear unbiased prediction (ssGBLUP) method, which assumes that all single nucleotide polymorphisms explain the same fraction of genetic variance. SsGBLUP is not suitable for protein content, which is controlled by a major gene, i.e. αs casein. This gene explains about 40% of the genetic variation in protein content. We evaluated the accuracy of genomic prediction using different genomic methods to include the effect of the αs casein gene. Meuwissen et al [10] proposed genomic prediction of animals based on dense single nucleotide polymorphism (SNP) maps, by deriving the effects of SNPs from a reference population, for which animals are both phenotyped and genotyped. Genomic estimated breeding values (GEBV) of selection candidates (i.e., usually young individuals with genotypes but without phenotypes) can be estimated by summing up the effects of the SNP alleles carried by each animal
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