Genomic selection using low density marker panels with application to a sire line in pigs

Abstract

BackgroundGenomic selection has become a standard tool in dairy cattle breeding. However, for other animal species, implementation of this technology is hindered by the high cost of genotyping. One way to reduce the routine costs is to genotype selection candidates with an SNP (single nucleotide polymorphism) panel of reduced density. This strategy is investigated in the present paper. Methods are proposed for the approximation of SNP positions, for selection of SNPs to be included in the low-density panel, for genotype imputation, and for the estimation of the accuracy of genomic breeding values. The imputation method was developed for a situation in which selection candidates are genotyped with an SNP panel of reduced density but have high-density genotyped sires. The dams of selection candidates are not genotyped. The methods were applied to a sire line pig population with 895 German Piétrain boars genotyped with the PorcineSNP60 BeadChip.ResultsGenotype imputation error rates were 0.133 for a 384 marker panel, 0.079 for a 768 marker panel, and 0.022 for a 3000 marker panel. Error rates for markers with approximated positions were slightly larger. Availability of high-density genotypes for close relatives of the selection candidates reduced the imputation error rate. The estimated decrease in the accuracy of genomic breeding values due to imputation errors was 3% for the 384 marker panel and negligible for larger panels, provided that at least one parent of the selection candidates was genotyped at high-density.Genomic breeding values predicted from deregressed breeding values with low reliabilities were more strongly correlated with the estimated BLUP breeding values than with the true breeding values. This was not the case when a shortened pedigree was used to predict BLUP breeding values, in which the parents of the individuals genotyped at high-density were considered unknown.ConclusionsGenomic selection with imputation from very low- to high-density marker panels is a promising strategy for the implementation of genomic selection at acceptable costs. A panel size of 384 markers can be recommended for selection candidates of a pig breeding program if at least one parent is genotyped at high-density, but this appears to be the lower bound.

Genomic selection in pigs: state of the art and perspectives

“…Batches of these panels can be manufactured including private SNPs that can be read only by the owner of this information or including patented markers and SNPs in a few genes associated with production traits (Table 2). Other studies simulated the lower number of SNPs needed according to different scenarios of genotyping and imputation with higher density SNP chips to obtain sufficient predicting ability of GEBV (Wellmann et al 2013;Stratz et al 2014;Xiang et al 2015). For example, based on simulations, a panel size of less than 1000 markers spread all over the pig genome (with the lower limit of 384 markers), if imputed to a higher density panel genotyped in at least one of the parents, could be used to obtain unbiased estimates of accuracy of genomic breeding values (Wellmann et al 2013).…”

“…The accuracy obtained with the imputation depends on several factors, such as the number of markers in the LD panel, the markers informativeness and their distribution across the genome, the relationship between the genotyped animals, the effective population size and the used method of imputation (Wellmann et al 2013). The reduction in the accuracy of direct GEBV is ranging only between 0.02 and 0.05, depending on the reference base, and the average reliability increased with large training populations (Dassonneville et al 2011).…”

“…With the selection candidates genotyped at LD and all animals used for breeding genotyped at HD, costs resulted in US$0.164 (in a 1000 sow maternal line nucleus herd) or US$0.21 (in a 600 sow terminal sire line) per weaned piglets. The decrease in GEBV accuracy due to imputation was estimated in a maximum of 3% in a scenario with a panel of only 384 markers if at least one parent of the selection candidates were genotyped at HD (Wellmann et al 2013). In another study (Jiao et al 2014), the accuracy of the prediction of GEBV was considered low in a population with 1047 boars genotyped at HD (e.g.…”

Future directions in breeding for disease resistance in aquaculture species

“…the offspring) at very-low density, but impute to high density -a technique that is relatively commonplace in terrestrial livestock breeding (e.g. Wellmann et al, 2013) and has shown promise in Atlantic salmon breeding (Tsai et al, 2017).…”

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F₂ populations by using genomic selection models

“…The development of this type of chip is important to reduce genotyping costs. In animal breeding, low density chips for cattle (Heaton et al, 2002;Boichard et al, 2012) and pigs (Wellmann et al, 2013) have been developed. Moreover, the genotyping cost of a low density chip is much lower than that of a high density chip (Habier et al, 2009).…”