Identifying the genetic basis of improvement in pigs contributes to our understanding of the role of artificial selection in shaping the genome. Here we employed the Cross Population Extended Haplotype Homozogysity (XPEHH) and the Wright’s fixation index (F_ST) methods to detect trait-specific selection signatures by making phenotypic gradient differential population pairs, and then attempted to map functional genes of six backfat thickness traits in Yorkshire pigs. The results indicate that a total of 283 and 466 single nucleotide polymorphisms (SNPs) were identified as trait-specific selection signatures using F_ST and XPEHH, respectively. Functional annotation suggested that the genes overlapping with the trait-specific selection signatures such as OSBPL8, ASAH2, SMCO2, GBE1, and ABL1 are responsible for the phenotypes including fat metabolism, lean body mass and fat deposition, and transport in mouse. Overall, the study developed the methods of gene mapping on the basis of identification of selection signatures. The candidate genes putatively associated with backfat thickness traits can provide important references and fundamental information for future pig-breeding programs.

South China indigenous pigs are famous for their superior meat quality and crude feed tolerance. Saba and Baoshan pigs without saddleback were located in the high-altitude area of Yunnan Province, while Tunchang and Ding’an pigs with saddleback were located in the low-altitude area of Hainan Province. Although these pigs are different in appearance, the underlying genetic differences have not been investigated. In this study, based on the single-nucleotide polymorphism (SNP) genotypes of 124 samples, both the cross-population extended haplotype homozygosity (XP-EHH) and the fixation index (F_ST) statistic were used to identify potential signatures of selection in these pig breeds. We found nine potential signatures of selection detected simultaneously by two methods, annotated 22 genes in Hainan pigs, when Baoshan pigs were used as the reference group. In addition, eleven potential signatures of selection detected simultaneously by two methods, annotated 24 genes in Hainan pigs compared with Saba pigs. These candidate genes were most enriched in GO: 0048015~phosphatidylinositol-mediated signaling and ssc00604: Glycosphingolipid biosynthesis—ganglio series. These selection signatures were likely to overlap with quantitative trait loci associated with meat quality traits. Furthermore, one potential selection signature, which was associated with different coat color, was detected in Hainan pigs. These results contribute to a better understanding of the underlying genetic architecture of South China indigenous pigs.

Identifying genetic basis of domestication and improvement in livestock contributes to our understanding of the role of artificial selection in shaping the genome. Here we used whole-genome sequencing and the genotyping by sequencing approach to detect artificial selection signatures and identify the associated SNPs of two economic traits in Duroc pigs. A total of 38 candidate selection regions were detected by combining the fixation index and the Composite Likelihood Ratio methods. Further genome-wide association study revealed seven associated SNPs that were related with intramuscular fat content and feed conversion ratio traits, respectively. Enrichment analysis suggested that the artificial selection regions harbored genes, such as MSTN, SOD2, MC5R and CD83, which are responsible for economic traits including lean muscle mass, fertility and immunization. Overall, this study found a series of candidate genes putatively associated with the breeding improvement of Duroc pigs and the polygenic basis of adaptive evolution, which can provide important references and fundamental information for future breeding programs.

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.

In the process of pig genetic improvement, different commercial breeds have been bred for the same purpose, improving meat production. Most of the economic traits, such as growth and fertility, have been selected similarly despite the discrepant selection pressure, which is known as parallel selection. Here, 28 whole‐genome sequencing data of Danish Large White pigs with an approximately 25‐fold depth each were generated, resulting in about 12 million high‐quality SNPs for each individual. Combined with the sequencing data of 27 Duroc and 23 European wild boars, we investigated the parallel selection of Danish Large White and Duroc pigs using two complementary methods, Fst and iHS. In total, 67 candidate regions were identified as the signatures of parallel selection. The genes in candidate regions of parallel selection were mainly associated with sensory perception, growth rate, and body size. Further functional annotation suggested that the striking consistency of the terms may be caused by the polygenetic basis of quantitative traits, and revealing the complex genetic basis of parallel selection. Besides, some unique terms were enriched in population‐specific selection regions, such as the limb development related terms enriched in Duroc specific selection regions, suggesting unique selections of breed‐specific selected traits. These results will help us better understand the parallel selection process of different breeds. Moreover, we identified several potential casual SNPs that may contribute to the pig genetic breeding process.