Abstract

BackgroundMeat quality related phenotypes are difficult and expensive to measure and predict but are ideal candidates for genomic selection if genetic markers that account for a worthwhile proportion of the phenotypic variation can be identified. The objectives of this study were: 1) to perform genome wide association analyses for Warner-Bratzler Shear Force (WBSF), marbling, cooking loss, tenderness, juiciness, connective tissue and flavor; 2) to determine enriched pathways present in each genome wide association analysis; and 3) to identify potential candidate genes with multiple quantitative trait loci (QTL) associated with meat quality.ResultsThe WBSF, marbling and cooking loss traits were measured in longissimus dorsi muscle from 672 steers. Out of these, 495 animals were used to measure tenderness, juiciness, connective tissue and flavor by a sensory panel. All animals were genotyped for 221,077 markers and included in a genome wide association analysis. A total number of 68 genomic regions covering 52 genes were identified using the whole genome association approach; 48% of these genes encode transmembrane proteins or membrane associated molecules. Two enrichment analysis were performed: a tissue restricted gene enrichment applying a correlation analysis between raw associated single nucleotide polymorphisms (SNPs) by trait, and a functional classification analysis performed using the DAVID Bioinformatic Resources 6.8 server. The tissue restricted gene enrichment approach identified eleven pathways including “Endoplasmic reticulum membrane” that influenced multiple traits simultaneously. The DAVID functional classification analysis uncovered eleven clusters related to transmembrane or structural proteins. A gene network was constructed where the number of raw associated uncorrelated SNPs for each gene across all traits was used as a weight. A multiple SNP association analysis was performed for the top five most connected genes in the gene-trait network. The gene network identified the EVC2, ANXA10 and PKHD1 genes as potentially harboring multiple QTLs. Polymorphisms identified in structural proteins can modulate two different processes with direct effect on meat quality: in vivo myocyte cytoskeletal organization and postmortem proteolysis.ConclusionThe main result from the present analysis is the uncovering of several candidate genes associated with meat quality that have structural function in the skeletal muscle.

Highlights

  • Meat quality related phenotypes are difficult and expensive to measure and predict but are ideal candidates for genomic selection if genetic markers that account for a worthwhile proportion of the phenotypic variation can be identified

  • Genomic regions detected by the genome wide association analysis A total number of 68 genomic regions covering 52 genes were associated with meat quality traits (Additional file 1, and Figures 1 and 2) and 26.9% of these genes were involved in gene-expression, 15.4% with cell-signaling, 9.6% with cell-differentiation, 5.8% in apoptosis and 42.3% in other pathways

  • A total number of 68 genomic regions covering 52 genes were associated with meat quality traits and these genes are related to gene-expression, cell-signaling, cell-differentiation, and apoptosis; 48% of these genes encode transmembrane proteins or membrane associated molecules and 5.7% encode cytoskeletal proteins

Read more

Summary

Introduction

Meat quality related phenotypes are difficult and expensive to measure and predict but are ideal candidates for genomic selection if genetic markers that account for a worthwhile proportion of the phenotypic variation can be identified. All the components defining the meat quality complex are quantitative traits, controlled by many genes and impacted by environmental factors Most of these component traits are difficult and expensive to measure and not available to measure until late in life or after the animal has been harvested. Such traits are impractical to improve through traditional phenotypic selection but are ideal candidates for genomic selection if genetic markers that account for a worthwhile proportion of the phenotypic variation can be identified

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call