Reproductive traits are critical for improving productivity and profitability in the pig industry, and genome-wide association studies (GWASs) are a powerful tool in detecting genetic markers related to target traits. Genome imputation provides an effective approach to obtain a greater number of genetic markers from low-density sequencing data. China’s pig industry recently introduced an imputation panel and is now seeking to determine what types of data are required to meet breeding needs. In this study, we collected and analyzed two pig sequencing datasets, including Yorkshire pig (YY), Landrace pig (LL), and Duroc pig (DD), genotyped by either an SNP chip (n = 816) or genotyping-by-targeted sequencing (n = 314), and applied an imputation strategy before validation in a third dataset (n = 2401). The aim of this study was to identify SNPs associated with reproductive traits and compare imputation results of two different types of data to evaluate whether sample size or marker density more strongly impacts imputation-enabled GWAS performance. Through a GWAS, we identified 73 significant SNPs from imputed Chip data across seven reproductive traits, 94 SNPs from imputed GBTS data across three traits, and 34 SNPs from the combined dataset across seven traits. Seven of these SNPs passed validation and were associated with number born alive, number born healthy, and gestation length. Gestation length (GL) and number born alive (NBA) are the most noteworthy traits. LOXL2 and PTPRD are high-confidence candidate genes affecting GL and NBA, respectively. In addition to LOXL2, STC1, NKX2-6, HMGCLL1, MLIP, TINAG, FAM83B, GFRAL, HCRTR2, ENTPD4, MYH8, IER5L, and U5 are associated with GL. Moreover, in addition to PTPRD, KLHL32, U6, MMS22L, and FHL5 are associated with NBA. The results of this study indicate that sample size is of greater importance than marker density in imputation strategies and provide beneficial insights into genes affecting pigs’ reproductive traits.
Zhou et al. (Thu,) studied this question.