Abstract
The discovery of single nucleotide polymorphisms (SNP) and the subsequent genotyping of large numbers of animals have enabled large-scale analyses to begin to understand the biological processes that underpin variation in animal populations. In beef cattle, genome-wide association studies using genotype arrays have revealed many quantitative trait loci (QTL) for various production traits such as growth, efficiency and meat quality. Most studies regarding meat quality have focused on marbling, which is a key trait associated with meat eating quality. However, other important traits like meat color, texture and fat color have not commonly been studied. Developments in genome sequencing technologies provide new opportunities to identify regions associated with these traits more precisely. The objective of this study was to estimate variance components and identify significant variants underpinning variation in meat quality traits using imputed whole genome sequence data. Phenotypic and genomic data from 2,110 Hanwoo cattle were used. The estimated heritabilities for the studied traits were 0.01, 0.16, 0.31, and 0.49 for fat color, meat color, meat texture and marbling score, respectively. Marbling score and meat texture were highly correlated. The genome-wide association study revealed 107 significant SNPs located on 14 selected chromosomes (one QTL region per selected chromosome). Four QTL regions were identified on BTA2, 12, 16, and 24 for marbling score and two QTL regions were found for meat texture trait on BTA12 and 29. Similarly, three QTL regions were identified for meat color on BTA2, 14 and 24 and five QTL regions for fat color on BTA7, 10, 12, 16, and 21. Candidate genes were identified for all traits, and their potential influence on the given trait was discussed. The significant SNP will be an important inclusion into commercial genotyping arrays to select new breeding animals more accurately.
Highlights
The availability of genome-wide single nucleotide polymorphism (SNP) panels has enabled the implementation of genomic prediction in many livestock species. (Goddard and Hayes, 2009; Meuwissen, 2009)
Apart from BTA2, McClure et al (2010) identified a potential Quantitative Trait Loci (QTL) on BTA12 spanned at 6.9–11.0 Mb in Angus cattle; in the current study, we identified a QTL region on BTA12 at a genomic position of 23 Mb in Hanwoo beef cattle
We identified new QTL regions that were associated with meat quality traits across the genome that are not reported elsewhere
Summary
The availability of genome-wide single nucleotide polymorphism (SNP) panels has enabled the implementation of genomic prediction in many livestock species. (Goddard and Hayes, 2009; Meuwissen, 2009). Many genome-wide association studies (GWAS) have been performed using this SNP information to identify Quantitative Trait Loci (QTL). Results from GWAS, can provide information on the genetic architecture of the quantitative trait and identify potential causative mutations. Significantly associated regions have large confidence intervals, which often result in many candidate genes, which makes it challenging to identify the causative mutation itself. Despite the fact that a large number of regions have been associated with traits of economic importance at the 50K or high-density SNP panel densities, only very few causative mutations from large QTL have been validated so far (Fortes et al, 2013; Teissier et al, 2018)
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