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Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

ABSTRACT: A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

SUBMITTER: Yang X

PROVIDER: S-EPMC2837947 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Yang Xia X Deignan Joshua L JL Qi Hongxiu H Zhu Jun J Qian Su S Zhong Judy J Torosyan Gevork G Majid Sana S Falkard Brie B Kleinhanz Robert R RR Karlsson Jenny J Castellani Lawrence W LW Mumick Sheena S Wang Kai K Xie Tao T Coon Michael M Zhang Chunsheng C Estrada-Smith Daria D Farber Charles R CR Wang Susanna S SS van Nas Atila A Ghazalpour Anatole A Zhang Bin B Macneil Douglas J DJ Lamb John R JR Dipple Katrina M KM Reitman Marc L ML Mehrabian Margarete M Lum Pek Y PY Schadt Eric E EE Lusis Aldons J AJ Drake Thomas A TA

Nature genetics 20090308 4

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and ...[more]

PMID: 19270708

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Project description:Obesity 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.Using 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.Differential 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.To 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.

Dataset Information

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Publications

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

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