Design of a low‐density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy

Abstract

Genotyping sheep for genome-wide SNPs at lower density and imputing to a higher density would enable cost-effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low-density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50-475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single-breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%.

Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx

“…Müller et al [15] considered the subset of~5 K SNPs that they used as a low-density marker panel in Eucalyptus benthamii and Eucalyptus pellita. Notably, low-density SNP chips are considered as a way to reduce the cost of high-density SNP panels in animal breeding and would enable cost-effective implementation of genomic studies [17,18]. In accordance with this, recently Silva et al [19] used linkage (LA) and linkage disequilibrium (LDA) analyses (termed 'LALDA') for low density-based genomic selection (GS) purposes in animal breeding.…”

Genotype imputation in the domestic dog

“…SNP imputation has been reported in the dog as part of a genomic breeding value study in which SNPs from the Illumina array were imputed using genotypes derived from the smaller Affymetrix array (Guo et al 2011); similar studies have been performed in pigs, sheep, and chickens (Huang et al 2012; Heidaritabar et al 2015; Bolormaa et al 2015). However, to the best of our knowledge, whole genome-level SNP imputation has never before been reported in the dog, and no study has been performed examining the accuracy of SNP imputation in this species.…”

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins

“…Though genotyping cost per SNP has been drastically decreased in the past 10 years, use of moderate-density (MD) or high-density (HD) SNP chips is still expensive for animal breeding and selection programs in practice. Consequently, when the advantage of GS is compared to traditional genetic selection in terms of genetic gain per unit of cost, it is clear that low-density (LD) SNP chips are preferred in order to fully exploit the genetic gain advantages of GS because they are cost-effective (Habier et al 2009;Weigel et al 2009;Biochard et al 2012;Bolormaa et al 2015).…”