Genome-Wide Association Study on Reproductive Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs

Abstract

Reproductive traits have a key impact on production efficiency in the pig industry. It is necessary to identify the genetic structure of potential genes that influence reproductive traits. In this study, a genome-wide association study (GWAS) based on chip and imputed data of five reproductive traits, namely, total number born (TNB), number born alive (NBA), litter birth weight (LBW), gestation length (GL), and number of weaned (NW), was performed in Yorkshire pigs. In total, 272 of 2844 pigs with reproductive records were genotyped using KPS Porcine Breeding SNP Chips, and then chip data were imputed to sequencing data using two online software programs: the Pig Haplotype Reference Panel (PHARP v2) and Swine Imputation Server (SWIM 1.0). After quality control, we performed GWAS based on chip data and the two different imputation databases by using fixed and random model circulating probability unification (FarmCPU) models. We discovered 71 genome-wide significant SNPs and 25 potential candidate genes (e.g., SMAD4, RPS6KA2, CAMK2A, NDST1, and ADCY5). Functional enrichment analysis revealed that these genes are mainly enriched in the calcium signaling pathway, ovarian steroidogenesis, and GnRH signaling pathways. In conclusion, our results help to clarify the genetic basis of porcine reproductive traits and provide molecular markers for genomic selection in pig breeding.

The SWine IMputation (SWIM) haplotype reference panel enables nucleotide resolution genetic mapping in pigs

“…Given the large number of existing genome-wide association studies in pigs 6 , we expect this resource to be highly utilized and impactful. Indeed, more than 130,000 genomes were imputed in the first year since the server became public, including a recent study that found SWIM imputed genomes to detect more significant SNPs compared to other platforms 28 . All existing studies using SNP arrays can be improved by a simple imputation followed by GWAS without additional data.…”

Research progress and applications of genome‐wide association study in farm animals

“…However, testing multiple markers simultaneously is more advantageous, and this can be done by fitting pseudo QTNs in addition to the testing markers in a stepwise MLM, which is called multi-locus mixed model [60]. Liu et al developed an improved method by using fixedeffect model and random-effect models iteratively, namely the FarmCPU, which has strong advantages for eliminating confounding effects, improving statistical power, and reducing computing time [49], and this approach has been applied to investigate the genetic markers for meat quality, reproduction, and growth traits in pigs [61][62][63]. Kaler et al have compared the different statistical models used for GWAS, which indicated that the FarmCPU performed better than other models based on the Q-Q plot and the identification of the known QTLs in a simulation dataset [64].…”