Identification of putative regulatory regions and transcription factors associated with intramuscular fat content traits

Abstract

BackgroundIntegration of high throughput DNA genotyping and RNA-sequencing data allows for the identification of genomic regions that control gene expression, known as expression quantitative trait loci (eQTL), on a whole genome scale. Intramuscular fat (IMF) content and carcass composition play important roles in metabolic and physiological processes in mammals because they influence insulin sensitivity and consequently prevalence of metabolic diseases such as obesity and type 2 diabetes. However, limited information is available on the genetic variants and mechanisms associated with IMF deposition in mammals. Thus, our hypothesis was that eQTL analyses could identify putative regulatory regions and transcription factors (TFs) associated with intramuscular fat (IMF) content traits.ResultsWe performed an integrative eQTL study in skeletal muscle to identify putative regulatory regions and factors associated with intramuscular fat content traits. Data obtained from skeletal muscle samples of 192 animals was used for association analysis between 461,466 SNPs and the transcription level of 11,808 genes. This yielded 1268 cis- and 10,334 trans-eQTLs, among which we identified nine hotspot regions that each affected the expression of > 119 genes. These putative regulatory regions overlapped with previously identified QTLs for IMF content. Three of the hotspots respectively harbored the transcription factors USF1, EGR4 and RUNX1T1, which are known to play important roles in lipid metabolism. From co-expression network analysis, we further identified modules significantly correlated with IMF content and associated with relevant processes such as fatty acid metabolism, carbohydrate metabolism and lipid metabolism.ConclusionThis study provides novel insights into the link between genotype and IMF content as evident from the expression level. It thereby identifies genomic regions of particular importance and associated regulatory factors. These new findings provide new knowledge about the biological processes associated with genetic variants and mechanisms associated with IMF deposition in mammals.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4871-y) contains supplementary material, which is available to authorized users.

Potential Biomarkers for Feed Efficiency-Related Traits in Nelore Cattle Identified by Co-expression Network and Integrative Genomics Analyses

“…Phenotypes from 192 Nelore steers were previously described elsewhere (Cesar et al, 2018;de Lima, 2019). These steers were a subsample from a broader experimental population of 593 steers (de Oliveira et al, 2014) for which muscle RNAseq data were available.…”

“…A total of 192 samples from the Longissimus thoracis muscle of Nelore steers were used in this study. The RNA sequencing from these samples was performed at the Genomics Core Facility at ESALQ (Piracicaba, SP, Brazil), as previously described elsewhere (Cesar et al, 2018) and archived on the European Nucleotide Archive (ENA) under accessions: PRJEB13188, PRJEB10898, and PRJEB19421.…”

“…The data quality control (QC), alignment, and quantification were previously performed and described elsewhere (Cesar et al, 2018). In summary, the data QC of raw reads was performed by FastQC v.0.11.2 (Andrews, 2010) and MultiQc v.1.4 (Ewels et al, 2018).…”

Potential Functional Variants in Innate Immune Response Genes Associated with Feed Efficiency in Beef Cattle

“…A usual strategy is the use of molecular markers for genomic selection based on SNPs (single nucleotide polymorphisms) genotyping, which allows the detection of DNA variants associated with FE. However, the majority of the SNPs previously identified in Genome-Wide Association studies (GWAs) and expression quantitative loci (eQTL) association study (10) are non-causal variants (11-13), which are located in intron or intergenic genomic regions. There is an urgent need to establish the functional (causal) variants of this important phenotype.…”

Omics multi-layers networks and identification of genes involved in fat storage and metabolism in poultry using interactomics approach

“…Many studies have been reported identi ed genes related to storage lipids in broilers [4,5,6]. On the other hand, the integration of high-throughput genomic DNA and RNA sequencing leads to identify genomic regions that control traits at the whole genome scale [7]. Some studies that using high-density microarrays in two groups of obese (high-fat) and lean (low-fat) birds indicated genes associated with lipogenic pathways [8,9].…”