Validation of Candidate Causal Genes for Abdominal Obesity Which Affect Shared Metabolic Pathways and Networks

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The discovery of novel genes which contribute to complex human disorders remains a challenge for geneticists.  The conventional methodology of determining whether a particular locus is involved in a given disease involves testing for inheritance of specific genomic regions in successive generations of affected individuals.  This typically leads to multiple loci (known as quantitative trait loci, or QTLs), each of which contributes modestly to the overall phenotype.  Each locus may contain hundreds of genes, making the elucidation of the underlying gene or genes labor intensive and time consuming.  Additionally, differentiating genes that are causal for the disease from those that are reactive to the biological alterations resulting from the disease has been difficult. 

The advent of microarray technology has enabled scientists to simultaneously examine alterations in the mRNA levels of thousands of transcripts in a sample.  Since microarrays yield quantitative estimates of gene expression changes, the loci that control their expression can be mapped.  These loci are known as expression QTLs (or eQTLs).  eQTLs that map near the gene and are likely to regulate gene expression in cis are termed cis-eQTLs ¹.  Genes with cis-eQTLs that are coincident with a clinical disease-related trait QTL (or cQTL) have an increased likelihood of contributing causally to the particular disorder, especially if expression of the gene is correlated with the severity of the disease trait ^2,3.

However, correlation between an expression trait and a clinical phenotype does not imply a causal/reactive relationship due to linked causal mutations, and particular alleles may also influence RNA levels and phenotypes independently, further confounding the analysis.  There is unambiguous biological directionality in that DNA changes influence alterations in transcript abundances and clinical phenotypes, so the number of possible relationships among correlated traits can be greatly reduced.  For example, among two traits which are correlated and controlled by a unique DNA locus, only three likely relationship models exist, namely causal, reactive, and independent ^4,5.  Therefore, after constructing a network, one can simultaneously integrate all possible DNA variants and their underlying changes in transcript levels, and each relationship can be supported as being causal, reactive, or independent in relation to a particular phenotype such as obesity.  This is referred to as the likelihood-based causality model selection (LCMS) procedure ⁴.

Using our LCMS procedure, we have predicted ~100 causal genes for abdominal obesity using an F2 intercross between the C57BL/6J and DBA/2J strains of mice (the BXD cross) ⁴.  In order to validate the predictive power of LCMS, we carried out phenotypic characterization of transgenic or knockout mouse models for nine of the top candidate genes, and report here that in total eight out of the nine genes under characterization were found to influence obesity-related traits.  We analyzed liver gene expression signatures of the transgenic and knockout mouse models and demonstrated that all nine genes affect common pathways and subnetworks that relate to metabolic pathways, suggesting that obesity is driven by a gene network instead of a single gene.