In this study, data genotyping by sequence (GBS) was used to perform single step GWAS (ssGWAS) to identify SNPs associated with the litter traits in domestic pigs and search for candidate genes in the region of significant SNPs. After quality control, 167,355 high-quality SNPs from 532 pigs were obtained. Phenotypic traits on 2112 gilt litters from 532 pigs were recorded including total number born (TNB), number born alive (NBA), and litter weight born alive (LWB). A single-step genomic BLUP approach (ssGBLUP) was used to implement the genome-wide association analysis at a 5% genome-wide significance level. A total of 8, 23 and 20 significant SNPs were associated with TNB, NBA, and LWB, respectively, and these significant SNPs accounted for 62.78%, 79.75%, and 58.79% of genetic variance. Furthermore, 1 (SSC14: 16314857), 4 (SSC1: 81986236, SSC1: 66599775, SSC1: 161999013, and SSC1: 267883107), and 5 (SSC9: 29030061, SSC2: 32368561, SSC5: 110375350, SSC13: 45619882 and SSC13: 45647829) significant SNPs for TNB, NBA, and LWB were inferred to be novel loci. At SSC1, the AIM1 and FOXO3 genes were found to be associated with NBA; these genes increase ovarian reproductive capacity and follicle number and decrease gonadotropin levels. The genes SLC36A4 and INTU are involved in cell growth, cytogenesis and development were found to be associated with LWB. These significant SNPs can be used as an indication for regions in the Sus scrofa genome for variability in litter traits, but further studies are expected to confirm causative mutations.

The domestic pigs have been undergone intense selection pressures for these development of interested traits following domestication and modern breeding. This has altered many traits in most of pig breeds, such as growth rate, body weight, fertility, and immunity. Thus, the objectives of this study were to (1) detect these selection signatures and identify the candidate genes that show evidences of recent artificial selection at the level of whole genome, (2) be beneficial to understand the relationship between genomic structure and phenotypic diversity, and (3) highlight the key roles of these candidate genes in growth and development in the two breeds. The data consisted of total raw number of 345570 single nucleotide polymorphisms (SNPs) in 1200 individuals from the Chinese Landrace pigs (L, n = 600) and Yorkshire pigs (Y, n = 600). Based on these SNPs data, two complementary methods, population differentiation (Fst) and composite likelihood ratio test (CLR), were carried out to detect the selection signatures in this study. A total of 540 potential selection regions (50 kb) which contained 111 candidate genes were detected for Landrace-Yorkshire pair (L-Y) by Fst. In addition, 73 and 125 candidate genes were found for Landrace pigs and Yorkshire pigs by CLR test based on 321 and 628 potential selection regions, respectively. Some candidate genes are associated with important traits and signaling pathways including the ACACA, MECR, COL11A1, GHR, IGF1R, IGF2R, IFNG, and MTOR gene. The ACACA and MECR gene are related to fatty acid biosynthesis. The COL11A1 gene is essential for the development of the normal differentiation. The GHR, IGF1R, and IGF2R gene are significant candidate genes which play major roles in the growth and development in animals. The IFNG gene is associated with some aspects of immune response. The MTOR gene regulates many signaling pathways and signaling transduction pathway.

The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic R 2) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934 (5,533,290) SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778 (2,468,645) SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs.

Socially affected traits are affected by direct genetic effects (DGE) and social genetic effects (SGE). DGE and SGE of an individual directly quantify the genetic influence of its own phenotypes and the phenotypes of other individuals, respectively. In the current study, a total of 3,276 Large White pigs from different pens were used, and each pen contained 10 piglets. DGE and SGE were estimated for six socially affected traits, and then a GWAS was conducted to identify SNPs associated with DGE and SGE. Based on the whole-genome re-sequencing, 40 Large White pigs were genotyped and 10,501,384 high quality SNPs were retained for single-locus and multi-locus GWAS. For single-locus GWAS, a total of 54 SNPs associated with DGE and 33 SNPs with SGE exceeded the threshold ( P < 5.00E-07) were detected for six growth traits. Of these, 22 SNPs with pleiotropic effects were shared by DGE and SGE. For multi-locus GWAS, a total of 72 and 110 putative QTNs were detected for DGE and SGE, respectively. Of these, 5 SNPs with pleiotropic effects were shared by DGE and SGE. It is noteworthy that 2 SNPs (SSC8: 16438396 for DGE and SSC17: 9697454 for SGE) were detected in single-locus and multi-locus GWAS. Furthermore, 15 positional candidate genes shared by SGE and DGE were identified because of their roles in behaviour, health and disease. Identification of genetic variants and candidate genes for DGE and SGE for socially affected traits will provide a new insight to understand the genetic architecture of socially affected traits in pigs.

The Chinese Qingyu pig breed is an invaluable indigenous genetic resource. However, few studies have investigated the genetic architecture of meat quality traits in Qingyu pigs. Here, 30 purebred Qingyu pigs were subjected to whole-genome sequencing. After quality control, 18,436,759 SNPs were retained. Genome-wide association studies (GWAS) were then performed for meat pH and color at three postmortem time points (45 min, 24 h and 48 h) using single-marker regression analysis. In total, 11 and 69 SNPs were associated with meat pH and color of the longissimus thoracis muscle (LTM), respectively, while 54 and 29 SNPs were associated with meat pH and color of the semimembranosus muscle (SM), respectively. Seven SNPs associated with pork pH were shared by all three postmortem time points. Several candidate genes for meat traits were identified, including four genes(CXXC5, RYR3, BNIP3, and MYCT1) related to skeletal muscle development, regulation of Ca2+ release in the muscle, and anaerobic respiration, which are promising candidates for selecting superior meat quality traits in Qingyu pigs. To our knowledge, this is the first study investigating the postmortem genetic architecture of pork pH and color in Qingyu pigs. Our findings further the current understanding of the genetic factors influencing meat quality.