Integrating Multiple Database Resources to Elucidate the Gene Flow in Southeast Asian Pig Populations.

The domestic pig (Sus scrofa) and its subfamilies have experienced long-term and extensive gene flow, particularly in Southeast Asia. Here, we analyzed 236 pigs, focusing on Yunnan indigenous, European commercial, East Asian, and Southeast Asian breeds, using the Pig Genomics Reference Panel (PGRP v1) of Pig Genotype-Tissue Expression (PigGTEx) to investigate gene flow and associated complex traits by integrating multiple database resources. In this study, we discovered evidence of admixtures from European pigs into the genome of Yunnan indigenous pigs. Additionally, we hypothesized that a potential conceptual gene flow route that may have contributed to the genetic composition of the Diannan small-ear pig is a gene exchange from the Vietnamese pig. Based on the most stringent gene introgression scan using the fd statistic, we identified three specific loci on chromosome 8, ranging from 51.65 to 52.45 Mb, which exhibited strong signatures of selection and harbored the NAF1, NPY1R, and NPY5R genes. These genes are associated with complex traits, such as fat mass, immunity, and litter weight, in pigs, as supported by multiple bio-functionalization databases. We utilized multiple databases to explore the potential dynamics of genetic exchange in Southeast Asian pig populations and elucidated specific gene functionalities.

Mapping and functional characterization of structural variation in 1060 pig genomes.

BACKGROUND: Structural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence. RESULTS: We present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome. This catalog includes 42,487 deletions, 37,913 mobile element insertions, 3308 duplications, 1664 inversions, and 45,184 break ends. Estimates of breed ancestry and hybridization using genotyped SVs align well with those from single nucleotide polymorphisms. Geographically stratified deletions are observed, along with known duplications of the KIT gene, responsible for white coat color in European pigs. Additionally, we identify a recent SINE element insertion in MYO5A transcripts of European pigs, potentially influencing alternative splicing patterns and coat color alterations. Furthermore, a Yorkshire-specific copy number gain within ABCG2 is found, impacting chromatin interactions and gene expression across multiple tissues over a stretch of genomic region of ~200 kb. Preliminary investigations into SV's impact on gene expression and traits using the Pig Genotype-Tissue Expression (PigGTEx) data reveal SV associations with regulatory variants and gene-trait pairs. For instance, a 51-bp deletion is linked to the lead eQTL of the lipid metabolism regulating gene FADS3, whose expression in embryo may affect loin muscle area, as revealed by our transcriptome-wide association studies. CONCLUSIONS: This SV catalog serves as a valuable resource for studying diversity, evolutionary history, and functional shaping of the pig genome by processes like domestication, trait-based breeding, and adaptive evolution.

Evaluating three strategies of genome-wide association analysis for integrating data from multiple populations.

In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single-GWAS, meta-GWAS, and mega-GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p-values (pC ). In Yorkshire, with the largest sample size, mega-GWAS, meta-GWAS and single-GWAS detected 149, 38 and 20 significant SNPs (pC < 1E-5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega-GWAS was the lowest, followed by meta-GWAS and single-GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta-GWAS and mega-GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega-GWAS versus single-GWAS.

A compendium of genetic regulatory effects across pig tissues.

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.