Network Analysis Reveals Putative Genes Affecting Meat Quality in Angus Cattle

Abstract

Improvements in eating satisfaction will benefit consumers and should increase beef demand which is of interest to the beef industry. Tenderness, juiciness, and flavor are major determinants of the palatability of beef and are often used to reflect eating satisfaction. Carcass qualities are used as indicator traits for meat quality, with higher quality grade carcasses expected to relate to more tender and palatable meat. However, meat quality is a complex concept determined by many component traits making interpretation of genome-wide association studies (GWAS) on any one component challenging to interpret. Recent approaches combining traditional GWAS with gene network interactions theory could be more efficient in dissecting the genetic architecture of complex traits. Phenotypic measures of 23 traits reflecting carcass characteristics, components of meat quality, along with mineral and peptide concentrations were used along with Illumina 54k bovine SNP genotypes to derive an annotated gene network associated with meat quality in 2,110 Angus beef cattle. The efficient mixed model association (EMMAX) approach in combination with a genomic relationship matrix was used to directly estimate the associations between 54k SNP genotypes and each of the 23 component traits. Genomic correlated regions were identified by partial correlations which were further used along with an information theory algorithm to derive gene network clusters. Correlated SNP across 23 component traits were subjected to network scoring and visualization software to identify significant SNP. Significant pathways implicated in the meat quality complex through GO term enrichment analysis included angiogenesis, inflammation, transmembrane transporter activity, and receptor activity. These results suggest that network analysis using partial correlations and annotation of significant SNP can reveal the genetic architecture of complex traits and provide novel information regarding biological mechanisms and genes that lead to complex phenotypes, like meat quality, and the nutritional and healthfulness value of beef. Improvements in genome annotation and knowledge of gene function will contribute to more comprehensive analyses that will advance our ability to dissect the complex architecture of complex traits.

Powerful detection of polygenic selection and evidence of environmental adaptation in US beef cattle

“…All three breeds have been selected for increased growth and efficiency traits over the last 50 years 22 , and GPSM identifies two genomic regions that have been previously associated with feed efficiency and growth traits. The common peak on BTA12 (lead SNP rs1389713) is ~182 kb upstream of the DACH1 (Dachshund homolog 1), a transcription factor associated with post-weaning gain, various indicators of feed efficiency 32,33 , and backfat thickness 34 in cattle. The shared GPSM peak on BTA14 resides near a known large-effect QTL for post-weaning gain 35 .…”

A Gene-Set Enrichment and Protein–Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle

“…In total, 109 calcium-related genes were part of the gene set (Table 4, Figure 3). Calcium plays an important role in meat tenderization, feed efficiency, and muscle contraction, and several genes involved in calcium-related processes were also found to affect meat quality in Angus cattle [74,75]. Calcium signaling is also key for regulating muscle growth is beef cattle [76].…”

SNPs in AKIRIN2, TTN, EDG1 and MYBPC1 genes are associated with growth-related traits in Chinese Qinchuan cattle

“…The AKIRIN2, TTN, EDG1 and MYBPC1 genes were rst found in relationship with economic traits in cattle, because they showed different expression levels in the musculus longissimus muscle between JB and HOL [28]. These genes are located within genomic regions of QTLs for carcass and growth-related traits in JB, Angus and crossbred populations [6][7][8][9][10][11][12]. Since then, the c.*188G > A SNP, the g.231054C > T SNP, the g.1471620G > T SNP and the g.70014208A > G SNP were identi ed in the AKIRIN2, TTN, EDG1 and MYBPC1 genes of JB [13,14,16,17], respectively.…”

“…To date, numerous candidate genes and molecular markers associated with economic traits have been identi ed, and some of them have already been used in cattle breeding [3][4][5]. Among them, the akirin 2 (AKIRIN2) gene is located within genomic regions of quantitative trait loci (QTLs) for marbling score and longissimus muscle area in Japanese Black (JB) cattle [6], as well as marbling score in Angus cattle [7]; the titin (TTN) gene was found in the genomic regions within QTLs for marbling score, longissimus muscle area and subcutaneous fat thickness in JB [6], as well as subcutaneous fat thickness in a Brahman × Hereford sire developed half-sib family [8]; the endothelial differentiation sphingolipid G-protein-coupled receptor 1 (EDG1) gene is located within genomic regions of QTLs for marbling score and body weight in JB [6], subcutaneous fat thickness in Angus [9], as well as marbling score in a Belgian Blue × MARC III developed half-sib family and a Piedmontese × Angus sire developed half-sib family [10], and the myosin binding protein C, slow type (MYBPC1) is included in the genomics regions of QTLs for rib thickness and subcutaneous fat thickness in JB [6], marbling score in Angus [7], hip height, rump length, rump width and chest depth in a cattle population including 1554 AI bulls distributed in 14 half-sib families (nine in Holstein, three in Normande and two in Montbéliarde breeds [11], as well as intramuscular fat in a Brangus heifers population [12]. Thus, these genes could be considered as important candidate genes for growth-related traits in beef cattle.…”