Abstract
BackgroundObesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach.MethodsUsing a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data.ResultsDifferential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B.ConclusionsTo our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0229-0) contains supplementary material, which is available to authorized users.
Highlights
Obesity is a multi-factorial health problem in which genetic factors play an important role
Association of gene expression with degree of obesity The detection of differential expression (DE) genes can lead to a better understanding of genetic and biological differences between two different conditions and to the detection of predictive biomarkers [51, 52]
We found that several adipose tissues were highly significantly associated with the ciseQTLs
Summary
The pig population The animal resource used in this study was established using breeds that diverged with respect to obesity traits in the parental generation, resulting in a F2 population that was highly divergent with respect to obesity and obesity-related traits. EQTL mapping The integration of the SNP genotype and RNA-Seq data was obtained using an eQTL study approach [13, 29], following the method as described in Westra et al [30]. Integration of eQTLs with co-expression network analysis We previously conducted a weighted gene co-expression network analysis (WGCNA) on the same RNA-Seq data, and recently published the method and results [28]. Functional annotation Over-represented Gene Ontology (GO) terms and KEGG pathways among the DE genes were detected using the software GoSeq [49], because GoSeq corrects for length bias in RNA-Seq data. Functional annotation analysis of the detected eQTLs was performed using GeneNetwork (http://www.gene network.nl), which detects over-represented GO terms, KEGG pathways, and associated phenotypes and tissues [50]. Availability of supporting data The RNA-Seq expression data in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are available through accession number [GEO:GSE61271]
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