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

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 mode...

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Bibliographic Details
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
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Online Access:https://www.mdpi.com/2076-2615/11/9/2524
Description
Summary: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.
ISSN:2076-2615