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Genome regulation and gene interaction networks inferred from muscle transcriptome underlying feed efficiency in Pigs

ABSTRACT: Feed efficiency (FE) is a key trait in pig production, as it has both high economic and environmental impact. FE is a challenging phenotype to study, as it is complex and affected by many factors, such as metabolism, growth and activity level. Furthermore, testing for FE is expensive, as it requires costly equipment to measure feed intake of individual animals, making FE biomarkers valuable. Therefore, there has been a desire to find single nucleotide polymorphisms (SNPs) as biomarkers, to assist with improved selection and improve our biological understanding of FE. We have done a cis- and trans- eQTL (expressed quantitative trait loci) analysis, in a population of Danbred Durocs (N=11) and Danbred Landrace (N=27) using both a linear and Anova model. We used bootstrapping and enrichment analysis to validate and analyze our detected eQTLs. We identified 15 eQTLs with FDR < 0.01, affecting several genes found in previous studies of commercial pig breeds. Examples include IFI6, PRPF39, TMEM222, CSRNP1,PARK7 and MFF. The bootstrapping results showed statistically significant enrichment of eQTLs with p-value < 0.01 (p-value < 2.2x0-16) in both cis and trans-eQTLs. Based on this, enrichment analysis of top trans-eQTLs revealed high enrichment for gene categories and gene ontologies associated with genomic context and expression regulation. This includes transcription factors (p-value=1.0x10-13), DNA-binding (GO:0003677, p-value=8.9x10-14), DNA-binding transcription factor activity (GO:0003700,) nucleus gene (GO:0005634, p-value<2.2x10-16), positive regulation of expression (GO:0010628), negative regulation of expression (GO:0010629, p-value<2.2x10-16). These results would be useful for future genome assisted breeding of pigs to improve FE, and in the improved understanding of the functional mechanism of trans-eQTLs.

ORGANISM(S): Sus scrofa

PROVIDER: GSE148889 | GEO | 2020/06/01

REPOSITORIES: GEO

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Project description:Genetics of gene expression (eQTLs or expression QTLs) has proved an indispensable tool for understanding biological pathways and pathomechanisms of trait associated SNPs. However, power of most genome-wide eQTL studies is still limited. We performed a large eQTL study in peripheral blood mononuclear cells of 2,112 individuals increasing the power to detect trans-effects genome-wide. Going beyond univariate SNP-transcript associations, we analyse relations of eQTLs to biological pathways, polygenetic effects of expression regulation, trans-clusters, and enrichment of co-localised functional elements. We found eQTLs for about 85% of analysed genes, 18% of genes were trans-regulated. Local eSNPs were enriched up to a distance of 5 MB to the transcript challenging typically implemented ranges of cis-regulations. Pathway enrichment within regulated genes of GWAS-related eSNPs supported functional relevance of identified eQTLs. We demonstrate that nearest genes of GWAS-SNPs might often be misleading functional candidates. We identified novel trans-clusters of potential functional relevance for GWAS-SNPs of several phenotypes including obesity-related traits, HDL-cholesterol levels, and haematological phenotypes. We used chromatin immunoprecipitation data for demonstrating biological effects. We show for strongly heritable transcripts that a considerable gap still exists between total heritability resulting from all trans-chromosomes and explained variance of all identified trans-eSNPs. In contrast, the vast majority of most cis-heritability of these genes is already explained. Dissection of co-localised functional elements indicated a prominent role of SNPs in loci of pseudogenes and non-coding RNAs for the regulation of coding genes. In summary, our study substantially increases the catalogue of human eQTLs and improves our understanding of the complex genetic regulation of gene-expression, pathways and disease related processes. Gene expression from human blood mononuclear cells from individuals of the Leipzig LIFE Heart Study was analyzed applying Illumina HT-12 v4 Expression BeadChips. After preprocessing, 28,295 expression probes for 2,112 individuals remained for analysis. In a population-based analysis, we identified associations between DNA variants and RNA expression levels and characterized these findings.

Project description:BACKGROUND: Chronic obstructive pulmonary disease (COPD) exacerbations significantly contribute to morbidity and mortality. The complex nature of COPD presents challenges in accurately predicting and understanding frequent exacerbations.OBJECTIVE: This study aimed to investigate the metabolic characteristics of frequent exacerbation of COPD (COPD-FE) phenotype, identify potential metabolic biomarkers associated with COPD-FE risk and explore underlying pathogenic mechanisms.METHODS: An internal cohort of 30 stable COPD patients was recruited. A widely targeted metabolomics approach was employed to detect and compare serum metabolite expressions between patients with COPD-FE and non-frequent exacerbations (COPD-NE). Subsequently, bioinformatics analysis was utilized for pathway enrichment of the identified metabolites; Spearman correlation analysis explored associations between metabolites and clinical indicators, and receiver operating characteristic (ROC) analysis assessed the predictive performance of identified metabolites. An external cohort of 20 patients with COPD validated findings from the internal cohort.RESULTS: Out of 484 detected metabolites, 25 exhibited significant differences between COPD-FE and COPD-NE. Metabolomic analysis revealed differences in lipid, energy, amino acid and immunity pathways. Spearman correlation analysis showed associations between metabolites and clinical indicators of acute exacerbation risk. ROC analysis revealed that D-fructose 1,6-bisphosphate (AUC=0.871), arginine (AUC=0.836), L-2-Hydroxyglutarate (L-2HG, AUC=0.849), Diacylglycerol (DG)(16:0/20:5) (AUC=0.827), DG(16:0/20:4) (AUC=0.818) and carnitine-C18:2 (AUC=0.804) had area under the curve (AUC) values above 0.8, highlighting their superior discriminative capacity between the two groups. External validation results showed that DG(16:0/20:5), DG(16:0/20:4), carnitine-C18:2 and L-2HG were significantly different between COPD-FE patients and COPD-NE patients (p-value<0.05).CONCLUSION: This study offers insights into early identification, mechanistic understanding and personalized management of COPD-FE phenotype.

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Genome regulation and gene interaction networks inferred from muscle transcriptome underlying feed efficiency in Pigs

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