Genome-Wide Association Study Based on Random Regression Model Reveals Candidate Genes Associated with Longitudinal Data in Chinese Simmental Beef Cattle

• Main Authors: Lili Du, Xinghai Duan, Bingxing An, Tianpeng Chang, Mang Liang, Lingyang Xu, Lupei Zhang, Junya Li, Guangxin E, Huijiang Gao
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Animals
• Subjects: random regression model; longitudinal trait; GWAS; Chinese Simmental beef cattle
• Online Access: https://www.mdpi.com/2076-2615/11/9/2524

Body weight (BW) is an important longitudinal trait that directly described the growth gain of bovine in production. However, previous genome-wide association study (GWAS) mainly focused on the single-record traits, with less attention paid to longitudinal traits. Compared with traditional GWAS models, the association studies based on the random regression model (GWAS-RRM) have better performance in the control of the false positive rate through considering time-stage effects. In this study, the BW trait data were collected from 808 Chinese Simmental beef cattle aged 0, 6, 12, and 18 months, then we performed a GWAS-RRM to fit the time-varied SNP effect. The results showed a total of 37 significant SNPs were associated with BW. Gene functional annotation and enrichment analysis indicated <i>FGF4</i>, <i>ANGPT4</i>, <i>PLA2G4A</i>, and <i>ITGA5</i> were promising candidate genes for BW. Moreover, these genes were significantly enriched in the signaling transduction pathway and lipid metabolism. These findings will provide prior molecular information for bovine gene-based selection, as well as facilitate the extensive application of GWAS-RRM in domestic animals.