Using a very low-density SNP panel for genomic selection in a breeding program for sheep

Abstract

BackgroundBuilding an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs).MethodsA population under selection for a maternal trait was simulated using real genotypes. Genetic gains realized from classical selection and genomic selection designs were compared. Genomic selection scenarios that differed in reference population structure (whether or not dams were included in the reference) and genotype quality (medium-density or imputed to medium-density from very low-density) were evaluated.ResultsThe genomic design increased genetic gain by 26% when the reference population was based on sire medium-density genotypes and by 54% when the reference population included both sire and dam medium-density genotypes. When medium-density genotypes of male candidates and dams were replaced by imputed genotypes from very low-density SNP genotypes (1000 SNPs), the increase in gain was 22% for the sire reference population and 42% for the sire and dam reference population. The rate of increase in inbreeding was lower (from − 20 to − 34%) for the genomic design than for the classical design regardless of the genomic scenario.ConclusionsWe show that very low-density genotypes of male candidates and dams combined with an imputation process result in a substantial increase in genetic gain for small sheep breeding programs.

Genomic selection strategies to improve maternal traits in Norwegian White Sheep

“…Other studies have shown that the rate of inbreeding per generation is reduced when genomic selection is implemented, because genomic selection increases in particular the accuracy of estimates of the individual Mendelian sampling component of breeding values (Daetwyler et al, 2007; Granleese et al, 2019; Raoul, Swan, & Elsen, 2017). However, a reduced generation interval in genomic selection schemes can increase rate of inbreeding per year despite a reduction in rate of inbreeding per generation (Schaeffer, 2006), due to faster turnover of generations.…”

“…Whether implementation of GS is economically profitable will depend on the genotyping costs. Other studies of implementation strategies of GS in sheep have shown that low‐cost genotyping is more profitable, than medium to high density, because of significantly reduced costs and smaller reductions in genetic gain (Raoul et al, 2017; Santos, Werf, Gibson, Byrne, & Amer, 2017). The large number of animals assumed to be genotyped before GS was implemented would represent a large investment cost which comes on top of the running costs of these schemes.…”

Linkage disequilibrium in Brazilian Santa Inês breed, Ovis aries

“…The appropriate panel density could be specific to each species and breed depending on overall LD structure. Unfortunately, the current genotyping costs in sheep are greater than the economic value of breeding animals 21 . Consequently, we also aimed to provide an estimate of the marker density required for genomic studies in the Santa Inês breed.…”

NanoGBS: A Miniaturized Procedure for GBS Library Preparation

“…Reducing genotyping cost per sample via increased sample multiplexing can make the use of molecular markers more costeffective in various types of genetic studies, from conservation biology to breeding (Davey et al, 2011). In addition, recent studies have documented that relatively low-density genotyping can be as efficient as high-density genotyping in many cases (Raoul et al, 2016). For example, Abed et al (2018) showed that no significant decrease in the accuracy of genomic selection was seen when using as few as~1K SNPs compared to using 35K SNPs, even in a species such as barley that has a large genome (5.3 Gb).…”