Multi-ancestry genome-wide association meta-analysis identifies candidate genes for computed tomography-based carcass...

Carcass composition traits, such as lean meat percentage, bone percentage, and number of ribs, are critical factors determining meat production and profitability of pigs. Traditional slaughter measurements are time-consuming, labor-intensive and invasive and cannot be evaluated on selection candidates. However, computed tomography scanning, a non-invasive technique, enables in vivo measurement of these traits, facilitating rapid accumulation of extensive phenotypic data. Despite these advances, the genetic mechanisms underlying computed tomography-based carcass traits remain largely unexplored.

In this study, we performed a multi-ancestry genome-wide association meta-analysis (MA-GWAMA) using low-coverage whole-genome sequencing data from four breeds (1222 Duroc, 582 Landrace, 1018 Yorkshire, and 448 Piétrain). In total, we identified 11 independent genome-wide significant loci associated with carcass composition traits in the meta-analysis. Compared to standard genomic best linear unbiased prediction, weighting MA-GWAMA-significant SNPs increased genomic prediction accuracy in an independent population (N = 365, including 136 Duroc, 65 Landrace, 50 Piétrain, and 114 Yorkshire) by 16.3% for lean meat percentage, by 6.1% for bone percentage, and by 79.4% for number of ribs. Integrating MA-GWAMA results with public eQTL and single-cell data prioritized ALPK2 as a candidate gene for lean meat percentage, and ABCD4 and SLC8A3 as candidate genes for the number of ribs.

Our study demonstrates the efficacy of computed tomography phenotyping coupled with multi-omics integration for dissecting the genetic architecture of porcine carcass composition traits. The prioritized variants and genes provide valuable targets for molecular breeding programs to enhance meat quality in pigs.