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

Lili Du,Mang Liang,Guangxin E,Tianpeng Chang,Bingxing An,Xinghai Duan,Lingyang Xu,Lupei Zhang,Junya Li,Huijiang Gao

doi:10.3390/ani11092524

Abstract

Simple SummaryGenome-wide association study (GWAS) has become the main approach for detecting functional genes that affects complex traits. For growth traits, the conventional GWAS method can only deal with the single-record traits observed at specific time points, rather than the longitudinal traits measured at multiple time points. Previous studies have reported the random regression model (RRM) for longitudinal data could overcome the limitation of the traditional GWAS model. Here, we present an association analysis based on RRM (GWAS-RRM) for 808 Chinese Simmental beef cattle at four stages of age. Ultimately, 37 significant single-nucleotide polymorphisms (SNPs) and several important candidate genes were screened to be associated with the body weight. Enrichment analysis showed these genes were significantly enriched in the signaling transduction pathway and lipid metabolism. This study not only offers a further understanding of the genetic basis for growth traits in beef cattle, but also provides a robust analytics tool for longitudinal traits in various species.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 FGF4, ANGPT4, PLA2G4A, and ITGA5 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.

Highlights

With the development of the high-throughput chip technologies and the completion of whole-genome sequencing of swine [1], cattle [2], sheep [3], chicken [4], and other domestic animals [5], genome-wide association study (GWAS) has become an indispensable statistical method that can detect significant single-nucleotide polymorphisms (SNPs) and functional genes affecting economical traits in domestic animals, including growth traits, fertility traits [6], and meat quality [7], which greatly contributes to improving animal breeding and reproduction
Genome-Wide Association Study Based on the Random Regression Model
The Manhattan plots showed that a total of 37 significant SNPs associated with Body weight (BW) trait were identified, most of which were located on Bos taurus autosome (BAT) 1, BAT 2, BAT, and BAT

Summary

Introduction

With the development of the high-throughput chip technologies and the completion of whole-genome sequencing of swine [1], cattle [2], sheep [3], chicken [4], and other domestic animals [5], genome-wide association study (GWAS) has become an indispensable statistical method that can detect significant single-nucleotide polymorphisms (SNPs) and functional genes affecting economical traits in domestic animals, including growth traits, fertility traits [6], and meat quality [7], which greatly contributes to improving animal breeding and reproduction.Numerous GWAS have been widely performed on single-record traits of beef cattle, such as birth weight, weaning weight, and yearling weight (YW) [8,9], and several significant SNPs and candidate genes were mapped. There are three main models for GWAS analysis of longitudinal traits, namely the two-stage analysis method [15], the point-by-point analysis method, and the analysis method based on the random regression model (RRM) [16] Among these analysis ideas, GWAS analysis based on RRM (GWAS-RRM) could result in the high accuracy of estimated breeding values and the decrease of false positive rate (FPR) in animals breeding [17]. The same method was conducted on Duroc for daily feed intake and average daily weight, and results showed candidate genes associated with these traits were mainly involved in metabolite homeostasis and insulin signaling [21] Taken together, these studies indicated that GWAS-RRM has been widely applied in the genetic evaluation of longitudinal traits in dairy cattle, especially for its milk production, but not in beef cattle [22,23]

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