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

Abstract

Background: Building 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).

Methods: A 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.

Results: The 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.

Conclusions: We 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.

Fig. 1

General overview of the simulation steps. ¹SNP, single nucleotide polymorphism; ²QTL: quantitative trait locus; ³MAF: minor allele frequency; ⁴VLD: very low density; ⁵BLUP: best linear unbiased prediction; ⁶ssGBLUP: single step genomic BLUP

Fig. 2

General overview of the classical and genomic designs. Sel. on PA EBV: truncation selection on parent average estimated breeding values; Sel. on EBV: truncation selection on estimated breeding values; Sel. on PA GEBV: truncation selection on parent average genomic estimated breeding values; Sel. on GEBV: truncation selection on genomic estimated breeding values; prog. test: males in progeny testing using artificial insemination (AI); Proven sires: AI sires selected on progeny testing; NM sires: natural mating sires; AI sires: artificial insemination sires