Understanding the molecular and cellular mechanisms underlying complex traits in pigs is crucial for enhancing genetic gain via artificial selection and utilizing pigs as models for human disease and biology. Here, we conducted comprehensive genome-wide association studies (GWAS) followed by a cross-breed meta-analysis for 232 complex traits and a within-breed meta-analysis for 12 traits, using 28.3 million imputed sequence variants in 70 328 animals across 14 pig breeds. We identified 6878 quantitative trait loci (QTL) for 139 complex traits. Leveraging the Pig Genotype-Tissue Expression resource, we systematically investigated the biological context and regulatory mechanisms behind these trait-QTLs, ultimately prioritizing 14 829 variant-gene-tissue-trait regulatory circuits. For instance, rs344053754 regulates UGT2B31 expression in the liver and intestines, potentially by modulating enhancer activity, ultimately influencing litter weight at weaning in pigs. Furthermore, we observed conservation of certain genetic and regulatory mechanisms underlying complex traits between humans and pigs. Overall, our cross-breed meta-GWAS in pigs provides invaluable resources and novel insights into the genetic regulatory and evolutionary mechanisms of complex traits in mammals.

Background: Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions. Methods: The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data. Results: Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by − 0.1 to ~ 6.9% for real traits with the medium-density panel. Conclusions: Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation.

Semen traits play a key role in the pig industry because boar semen is widely used in purebred and crossbred pigs. The production of high-quality semen is crucial to ensuring a good result in artificial insemination. With the wide application of artificial insemination in the pig industry, more and more attention has been paid to the improvement of semen traits by genetic selection. The purpose of this study was to identify the genetic regions and candidate genes associated with semen traits of Duroc boars. We used weighted single-step GWAS to identify candidate genes associated with sperm motility, sperm progressive motility, sperm abnormality rate and total sperm count in Duroc pigs. In Duroc pigs, the three most important windows for sperm motility—sperm progressive motility, sperm abnormality rate, and total sperm count—explained 12.45%, 9.77%, 15.80%, and 12.15% of the genetic variance, respectively. Some genes that are reported to be associated with spermatogenesis, testicular function and male fertility in mammals have been detected previously. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. We found a common candidate gene, STRA8, in sperm motility and sperm progressive motility, and common candidate genes ZSWIM7, TEKT3 and UBB in sperm motility and sperm abnormality rate, which confirms the hypothesis of gene pleiotropy. Gene network enrichment analysis showed that STRA8, UBB and CATSPER1 were enriched in the common biological process and participated in male meiosis and spermatogenesis. The SNPs of candidate genes can be given more weight in genome selection to improve the ability of genome prediction. This study provides further insight into the understanding the genetic structure of semen traits in Duroc boars.

Chicken is a valuable model for understanding fundamental biology, vertebrate evolution and diseases, as well as a major source of nutrient-dense and lean-protein-enriched food globally. Although it is the first non-mammalian amniote genome to be sequenced, the chicken genome still lacks a systematic characterization of functional impacts of genetic variants. Here, through integrating 7,015 RNA-Seq and 2,869 whole-genome sequence data, the Chicken Genotype- Tissue Expression (ChickenGTEx) project presents the pilot reference of regulatory variants in 28 chicken tissue transcriptomes, including millions of regulatory effects on primary expression (including protein-coding genes, lncRNA and exon) and post-transcriptional modifications (alternative splicing and 3’ untranslated region alternative polyadenylation). We explored the tissue-sharing and context-specificity of these regulatory variants, their underlying molecular mechanisms of action, and their utility in interpreting adaptation and genome-wide associations of 108 chicken complex traits. Finally, we illustrated shared and lineage-specific features of gene regulation between chickens and mammals, and demonstrated how the ChickenGTEx resource can further assist with translating genetic findings across species. One-Sentence Summary: The ChickenGTEx provides a multi-tissue reference of regulatory variants for chicken genetics and genomics, functional genomics, precision breeding, veterinary medicine, vertebrate evolution and even human biomedicine.

Transcriptome-wide association study (TWAS) is a powerful approach for investigating the molecular mechanisms linking genetic loci to complex phenotypes. However, the complexity of the TWAS analytical pipeline, including the construction of gene expression reference panels, gene expression prediction, and association analysis using data from genome-wide association studies (GWASs), poses challenges for genetic studies in many species. In this study, we provide the Farm Animal Genotype-Tissue Expression (FarmGTEx) TWAS-server, an interactive and user-friendly multispecies platform designed to streamline the translation of genetic findings across tissues and species. The server incorporates gene expression data from 49 human tissues (838 individuals), 34 pig tissues (5457 individuals), and 23 cattle tissues (4889 individuals), providing prediction models for 38,180 human genes, 21,037 pig genes, and 17,942 cattle genes. It supports genotype-based gene expression prediction, GWAS summary statistics imputation, customizable TWAS analysis, functional annotation, and result visualization. Additionally, we provide 479,203, 1208, and 657 tissue–gene–trait associations for 1129 human traits, 41 cattle traits, and 11 pig traits, respectively. Utilizing the TWAS-server, we validated the association of the ABCD4 gene with pig teat number. Furthermore, we identified that pig backfat thickness may share genetic similarities with human diastolic blood pressure, sarcoidosis (Löfgren syndrome), and body mass index. The FarmGTEx TWAS-server offers a comprehensive and accessible platform for researchers to perform TWAS analyses across tissues and species. It is freely available at https://twas.farmgtex.org, with regular updates planned as the FarmGTEx project expands to include more species.

The teat number is a pivotal reproductive trait that significantly influences the survival rate of piglets. A meta-analysis is a robust instrument, enhancing the universality of research findings and improving statistical power by increasing the sample size. This study aimed to identify universal candidate genes associated with teat number traits using a genome-wide association study (GWAS) meta-analysis with three breeds. We identified 21 chromosome threshold significant single-nucleotide polymorphisms (SNPs) associated with five teat number traits in single-breed and cross-breed meta-GWAS analyses. Using a co-localization analysis of expression quantitative trait loci and GWAS loci, we detected four unique genes that were co-localized with cross-breed GWAS loci associated with teat number traits. Through a meta-analysis and integrative analysis, we identified more reliable candidate genes associated with multiple-breed teat number traits. Our research provides new information for exploring the genetic mechanism affecting pig teat number for breeding selection and improvement.