Development of a low-density panel for genomic selection of pigs in Russia1

Abstract

Genomic selection is routinely used worldwide in agricultural breeding. However, in Russia, it is still not used to its full potential partially due to high genotyping costs. The use of genotypes imputed from the low-density chips (LD-chip) provides a valuable opportunity for reducing the genotyping costs. Pork production in Russia is based on the conventional 3-tier pyramid involving 3 breeds; therefore, the best option would be the development of a single LD-chip that could be used for all of them. Here, we for the first time have analyzed genomic variability in 3 breeds of Russian pigs, namely, Landrace, Duroc, and Large White and generated the LD-chip that can be used in pig breeding with the negligible loss in genotyping quality. We have demonstrated that out of the 3 methods commonly used for LD-chip construction, the block method shows the best results. The imputation quality depends strongly on the presence of close ancestors in the reference population. We have demonstrated that for the animals with both parents genotyped using high-density panels high-quality genotypes (allelic discordance rate < 0.05) could be obtained using a 300 single nucleotide polymorphism (SNP) chip, while in the absence of genotyped ancestors at least 2,000 SNP markers are required. We have shown that imputation quality varies between chromosomes, and it is lower near the chromosome ends and drops with the increase in minor allele frequency. Imputation quality of the individual SNPs correlated well across breeds. Using the same LD-chip, we were able to obtain comparable imputation quality in all 3 breeds, so it may be suggested that a single chip could be used for all of them. Our findings also suggest that the presence of markers with extremely low imputation quality is likely to be explained by wrong mapping of the markers to the chromosomal positions.

LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays

“…LmTag performancce is compared against commonly used tag SNP selection methods including TagIt (Weale et al, 2003), FastTagger (Liu et al, 2010), EQ_uniform (Shashkova et al, 2020), and EQ_MAF (Herry et al, 2018) by two main metrics: functional enrichment and imputation performance. The benchmarking is performed in both in-house and public genomics datasets including pilot phase data from the 1000 Vietnamese Genomes Project (1KVG), and data of three super populations comrising obtained from the 1000 Genomes Project samples re-sequenced by New York Genome Center (1KGP-NYGC) (Byrska-Bishop et al, 2021).…”

“…We compare LmTag against commonly used methods in SNP array design including TagIt (Weale et al, 2003), FastTagger (Liu et al, 2010), EQ_uniform (Shashkova et al, 2020), and EQ_MAF (Herry et al, 2018) using various metrics including imputation accuracy and functional enrichment. We also compare imputation accuracies of tag SNPs selected by LmTag against those of tag SNP sets from various commercial genotyping arrays.…”

“…In terms of current methods for genotyping array design, the first two methods optimize imputation accuracy by maximizing linkage disequilibrium while the later methods select makers based on the equidistant principle. The distance-based methods are widely used in animal SNP array designs that involve dividing chromosomes into certain intervals with equal genomic length (Hayes et al, 2012;Shashkova et al, 2020;Joshi et al, 2018) and further optimized toward MAF (Dassonneville et al, 2012;Qiao et al, 2017;Herry et al, 2018). For each interval, the SNP with the highest MAF is selected as representative of all SNPs in the interval (Herry et al, 2018).…”

“…For example, in the early generation of human genotyping SNP array, tag SNPs were selected at intervals of approximately each 5-kilobase with a minor allele frequency of at least 5% (Gibbs et al, 2003). This strategy has also been widely adopted in animal genetics, commonly referred to as the equidistance method (Shashkova et al, 2020;Herry et al, 2018). On the other hand, the latter approach utilizes LDs among nearby SNPs to find tag SNPs with a greedy approach to maximize LD coverage (Weale et al, 2003;Sakurai-Yageta et al, 2020;Wojcik et al, 2018).…”

Application of Genomic Data for Reliability Improvement of Pig Breeding Value Estimates

“…Possibilities of the genomic data application for the needs of livestock breeding are also associated with a number of areas. These are the development of low-density SNP chips (less than 3000 markers) [28], which can significantly reduce the genotyping cost per animal; the preliminary analysis and selection of QTLs causing the majority of trait's genetic variation [13,29,30]; and the development of methods allowing the use of a large number of genotyped animals' genomic data [19,31].…”