Abstract

Simple SummaryHanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest. Gene set enrichment analysis indicated that the identified candidate genes were related to biological processes and functional terms that were involved in growth and lipid metabolism. In conclusion, these results suggest that the incorporation of GWAS results and network analysis can help us to better understand the genetic bases underlying growth and carcass traits.In recent years, studies on the biological mechanisms underlying complex traits have been facilitated by innovations in high-throughput genotyping technology. We conducted a weighted single-step genome-wide association study (WssGWAS) to evaluate backfat thickness, carcass weight, eye muscle area, marbling score, and yearling weight in a cohort of 1540 Hanwoo beef cattle using BovineSNP50 BeadChip. The WssGWAS uncovered thirty-three genomic regions that explained more than 1% of the additive genetic variance, mostly located on chromosomes 6 and 14. Among the identified window regions, seven quantitative trait loci (QTL) had pleiotropic effects and twenty-six QTL were trait-specific. Significant pathways implicated in the measured traits through Gene Ontology (GO) term enrichment analysis included the following: lipid biosynthetic process, regulation of lipid metabolic process, transport or localization of lipid, regulation of growth, developmental growth, and multicellular organism growth. Integration of GWAS results of the studied traits with pathway and network analyses facilitated the exploration of the respective candidate genes involved in several biological functions, particularly lipid and growth metabolism. This study provides novel insight into the genetic bases underlying complex traits and could be useful in developing breeding schemes aimed at improving growth and carcass traits in Hanwoo beef cattle.

Highlights

  • To date, demand for high-quality animal protein is increasing, bringing beef quality and consumer satisfaction to the spotlight within the beef industry

  • The five traits selected for genome-wide association study (GWAS) analysis were backfat thickness (BT), carcass weight (CW), eye muscle area (EMA) and marbling score (MS), and yearling weight (YW)

  • We identified genomic regions associated with five measured traits in Hanwoo cattle using weighted single-step genome-wide association study (WssGWAS)

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Summary

Introduction

Demand for high-quality animal protein is increasing, bringing beef quality and consumer satisfaction to the spotlight within the beef industry. The main aim of the Hanwoo beef industry is to increase the quantity and improve the quality of the meat. To achieve this goal, estimated breeding values (EBVs) for carcass weight (CW), backfat thickness (BT), eye muscle area (EMA), marbling score (MS), and yearling weight (YW). The advent of high throughput genotyping technologies has made it possible to perform a genome-wide association study (GWAS) for the detection and localization of genomic regions associated with traits of economic interest in different species [3]. Recent studies in beef cattle for growth and carcass traits have revealed major quantitative trait loci (QTL) on chromosomes 6, 8, 12, 14 and 20 using a GWAS approach [4,5,6]. More studies are needed to provide further insight into the chromosomal regions, causal markers and candidate genes that affect related traits [7]

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