Genome-Wide Association Study and Phenotype Prediction of Reproductive Traits in Large White Pigs

In a study involving 385 Large White pigs, a genome-wide association study (GWAS) was conducted to investigate reproductive traits, specifically the number of healthy litters (NHs) and the number of weaned litters (NWs). Several SNP loci, including ALGA0098819, ALGA0037969, and H3GA0032302, were significantly associated with these traits. In the combined-parity analysis, candidate genes, such as <i>BLVRA</i>, <i>STK17A</i>, <i>PSMA2</i>, and <i>C7orf25</i>, were identified. GO and KEGG pathway enrichment analyses revealed that these genes are involved in key biological processes, including organic synthesis, the regulation of sperm activity, spermatogenesis, and meiosis. In the by-parity analysis, the <i>PLCXD3</i> gene was significantly associated with the NW trait in the second and fourth parities, while <i>RNASEH1</i>, <i>PYM1</i>, and <i>SEPTIN9</i> were linked to cell proliferation, DNA repair, and metabolism, suggesting their potential role in regulating reproductive traits. These findings provide new molecular markers for the genetic study of reproductive traits in Large White pigs. For the phenotypic prediction of NH and NW traits, several machine learning models (GBDT, RF, LightGBM, and Adaboost.R2), as well as traditional models (GBLUP, BRR, and BL), were evaluated using SNP data in varying proportions. After PCA processing, the GBDT model achieved the highest PCC for NH (0.141), while LightGBM reached the highest PCC for NW (0.146). The MAE, MSE, and RMSE results showed that the traditional models exhibited stable error rates, while the machine learning models performed comparatively better across the different SNP ratios. Overall, PCA processing provided some improvement in the predictive performance of all of the models, though the overall increase in accuracy was limited.