Abstract
BackgroundImputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species.ResultsIn this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed MRAP2, LEPROT, PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs.ConclusionOur post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.
Highlights
Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species
A previous study [10] showed the advantage of a two-step imputation strategy in cattle, where step 1: impute a low-density (50k) single nucleotide polymorphism (SNP) array marker set to a high-density (700k) SNP array marker set; and step 2: impute the imputed high-density marker set to whole-genome sequencing (WGS)
As the current computational facility available to us was limited, we could not run genome-wide association studies (GWAS) analysis including all animals from a breed, and we adopted the strategy to split each of the breeds into three subpopulations, and combined results using within-breed meta-analysis
Summary
Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. Imputation from a low-density marker set to a high-density marker set, and even up to WGS level, has shown high accuracy at an affordable cost for large-scale GWAS [3,4,5,6] and investigation of the genetic architecture of complex traits [7,8,9]. Brøndum et al [11] showed that a multi-breed reference panel can increase imputation accuracy in cattle. Both strategies, namely the two-step imputation and multibreed reference population, can be used to increase imputation accuracy in other livestock species like pigs. We know that multi-breed reference could improve imputation accuracy, it is worth examining whether available high-density (HD) genotypes from crossbred pigs can be used as the intermediate reference panel for purebred pigs
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