Haplotype Block Analysis Reveals Candidate Genes and QTLs for Meat Quality and Disease Resistance in Chinese Jiangquhai Pig Breed

Abstract

The Jiangquhai (JQ) pig breed is one of the most widely recognized pig populations in China due to its unique and dominant characteristics. In this study, we examined the extent of Linkage disequilibrium (LD) and haplotype block structure of the JQ pig breed, and scanned the blocks for possible genes underlying important QTLs that could either be responsible for some adaptive features in these pigs or might have undergone some selection pressure. We compared some of our results with other Chinese and Western pig breeds. The results show that the JQ breed had the highest total block length (349.73 Mb ≈ 15% of its genome), and the coverage rate of blocks in most of its chromosomes was larger than those of other breeds except for Sus scrofa chromosome 4 (SSC4), SSC6, SSC7, SSC8, SSC10, SSC12, SSC13, SSC14, SSC17, SSC18, and SSCX. Moreover, the JQ breed had more SNPs that were clustered into haplotype blocks than the other breeds examined in this study. Our shared and unique haplotype block analysis revealed that the Hongdenglong (HD) breed had the lowest percentage of shared haplotype blocks while the Shanzhu (SZ) breed had the highest. We found that the JQ breed had an average r 2 > 0.2 at SNPs distances 10–20 kb and concluded that about 120,000–240,000 SNPs would be needed for a successful GWAS in the breed. Finally, we detected a total of 88 genes harbored by selected haplotype blocks in the JQ breed, of which only 4 were significantly enriched ( p-value ≤ 0.05). These genes were significantly enriched in 2 GO terms ( p-value < 0.01), and 2 KEGG pathways ( p-value < 0.02). Most of these enriched genes were related to health. Also, most of the overlapping QTLs detected in the haplotype blocks were related to meat and carcass quality, as well as health, with a few of them relating to reproduction and production. These results provide insights into the genetic architecture of some adaptive and meat quality traits observed in the JQ pig breed and also revealed the pattern of LD in the genome of the pig. Our result provides significant guidance for improving the statistical power of GWAS and optimizing the conservation strategy for this JQ pig breed.

Identification of candidate regulatory genes for intramuscular fatty acid composition in pigs by transcriptome analysis

“…While the relationship between UNC93A (unc-93 homolog A) and the lipid metabolism in muscle has yet to be explored, a study in mice determined that the expression of this putative solute carrier responded to nutrient availability [ 52 ]. In addition, UNC93A was also mentioned as a candidate gene in a quantitative trait loci (QTL) study for meat quality and disease resistance in the Chinese Jiangquhai pig breed [ 53 ].…”

Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs

“…Genome-wide association analysis (GWAS) is a comprehensive approach that explores genetic variation—particularly single nucleotide polymorphisms (SNPs)—across the entire genome to identify variants potentially associated with specific traits. In livestock breeding, GWAS serves as a valuable tool for identifying economically important traits such as growth, carcass [ 5 , 6 , 7 ], reproduction [ 8 , 9 ], immunity [ 10 , 11 ] and flesh quality [ 12 , 13 ], among other candidate genes studied. While GWAS is commonly employed to identify genetic markers associated with complex traits in livestock, it has some limitations, such as low reproducibility [ 14 ] and a high incidence of false positives [ 15 ].…”

GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data

“…Genome-wide association studies (GWAS) have emerged as an efficient method for dissecting the genetic mechanisms of complex traits. Recently, GWAS have been widely employed to identify the candidate genes on traits in pigs, such as growth [ 7 , 8 , 9 ], reproduction [ 10 , 11 , 12 ], meat quality [ 13 , 14 , 15 ], and disease resistance [ 16 , 17 ]. The variant detection power of GWAS is affected by marker density.…”