Validation of Candidate Causal Genes for Abdominal Obesity Which Affect Shared Metabolic Pathways and Networks
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.
Post Comment
No comments