Genome wide association and gene enrichment analysis reveal membrane anchoring and structural proteins associated with meat quality in beef

Abstract

Background 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. 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. Results The 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. Conclusion The 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. Electronic supplementary material The online version of this article (10.1186/s12864-019-5518-3) contains supplementary material, which is available to authorized users.

Identification of eQTLs and sQTLs associated with meat quality in beef

“…The present results provide biological support to some of the previously identified pQTLs related to complex phenotypes in cattle and could contribute to discovery of potential causative polymorphisms. pQTL and eQTL colocalization for NTF3 (cooking loss) and GPR98 (tenderness) was evident in the present population [16]; however, more research is required in order to be able to determine if these genes harbor actual causative markers associated with meat quality. The use of causative polymorphisms in genomic prediction is the ideal scenario since it is not affected by recombination events between the actual pQTL and the marker being genotyped, over time.…”

“…NTF3 was identified in a previous study as highly associated with cooking loss [16] pointing out that markers inside this locus are able to explain variation at both the phenotype and gene expression level. This implicates NTF3 as a positional and functional gene with a potential role in meat quality.…”

Identification of eQTLs and sQTLs associated with meat quality in beef

“…pQTL and eQTL colocalization for NTF3 (cooking loss) and GPR98 (tenderness) was evident in the present population [16]; however, more research is required in order to be able to determine if these…”

RNA-seq analysis identifies cytoskeletal structural genes and pathways for meat quality in beef

“…Table 3. Genes uncovered by the expression and DE analysis, and previously identified as associated with meat quality related phenotypes using a genotype-phenotype association analysis in the present population [80,81]. (adjusted p-value ≤ 0.1) and 198 (adjusted p-value ≤ 0.1) genes were DE for WBSF, tenderness and marbling, respectively.…”

“…The key genes identified in the protein-protein interaction network (Figure 4), NFKB2, ABLIM1, EIF4E2, ARPC5L and ARF6 , are involved in multiple cellular functions such as actin polymerization, cytoskeletal structure and transcription factor activity [22]. Table 3 shows a list of genes that were simultaneously identified by Leal-Gutiérrez et al (2018c) [80] and Leal-Gutiérrez et al (2019) [81] using genotype-phenotype association in the present population and genes that were identified in the expression or DE analysis. A total of 14 genes were identified using genotype-phenotype and expression-phenotype association approaches simultaneously.…”