Functional genomic dissection and prediction of body size traits in pigs

Body Size traits, particularly body weight (BW) and body mass index (BMI) at slaughter age, determine the meat yield and productivity of pigs. These phenotypes are shaped by numerous small-effect polygenes and regulated mostly by non-coding variants. Although genome-wide association studies (GWAS) have identified several loci and candidate functional variants, the regulatory mechanisms and causative genes of most traits remain uncharacterized, which limits the effectiveness of genomic prediction (GP). The purpose of this study was to bridge the gap between association studies and GP by integrating regulatory genomics into the GP framework to enhance prediction accuracy for body size traits.

Using imputation-based GWAS in 1226 Shanxia Long Black pigs, multiple genome-wide significant loci were identified to be associated with BW and BMI. Linkage disequilibrium (LD) analysis, SuSiE fine-mapping, and regulatory modeling with Basenji deep-learning predictions refined these associations to 10 quantitative trait loci (QTLs) with 45 high-confidence candidate functional variants. Integration of chromatin-state annotations and high-throughput chromosome conformation capture (Hi-C) data revealed receptor tissue regulatory architectures; BW-associated variants on Sus scrofa chromosome 2 (SSC2) were enriched for brain regulatory regions, whereas BMI-associated loci showed enhancer activity across adipose, brain, and liver tissues. Multi-omics analyses converged on ZER1, KLHL29, and HAO1 as high-confidence candidate genes, while OR2T27 was a putative candidate on SSC2. In Basenji prediction, several specific candidate variants were identified as a liver enhancer. Incorporating these top-prioritized functional variants into genomic prediction models, GP yielded up to 21% gains in accuracy.

This study dissects the multi-tissue regulatory architecture, identifying functional variants, effector tissues, and target genes underlying porcine BW and BMI. By leveraging these biologically prioritized loci, we established a functionally informed GP framework that enhances prediction accuracy and biological interpretability simultaneously and also offered a scalable strategy for genetic improvement of complex traits in livestock.