Integrative transcriptomics and machine learning reveal key regulatory genes for meat quality traits in pigs

作者：Shuya Ma, Xiezong Hu, Jianwei Yang, Kunpeng Shi, Xiaodong Zhang, Yueyun Ding, Xudong Wu, Mengting Zhu, Zongjun Yin, Xianrui Zheng · 发表于：BMC Genomics · 年份：2026 · DOI：10.1186/s12864-026-12734-7 · 研究领域：Genetic Mapping and Diversity in Plants and Animals、Meat and Animal Product Quality、Genetic and phenotypic traits in livestock

BACKGROUND: Meat quality traits are typically regulated by multiple genes, each contributing a small effect. In this study, to pinpoint candidate genes involved in meat quality traits, we performed transcriptome profiles of porcine longissimus dorsi (LD) muscle and applied machine learning (ML) models to analyze RNA-seq data. We also carried out Gene Set Enrichment Analysis (ssGSEA), Weighted gene co-expression network analysis (WGCNA) and functional validation of putative target genes to better support the biological relevance of our findings. RESULTS: In this study, LD muscle samples were collected from 142 Huoshou Black (HSH) pigs and 191 Anqing Six-end-white (AQLB) pigs. Based on results of the estimated breeding values (EBV) analysis, of meat quality traits, we selected 101 HSH pigs and 99 AQLB pigs for transcriptomic analysis. Using an integrative analytical framework that combined ssGSEA and WGCNA, we identified 197 candidate genes 197 candidate genes. These genes were significantly associated with various metabolic pathways, including fatty-acid elongation and metabolism, amino-acid catabolism, protein turnover, and biosynthetic processes. To further refine the identification of key regulatory genes, we systematically evaluated ten ML models, ultimately selecting XGBoost, Random Forest, and Lasso Regression for subsequent analysis. This approach pinpointed CYSLTR1 and LPCAT2 as the key regulatory genes. To investigate the functional roles of CYSLTR1 and LPCAT2 in intr...