Use of Genome-Wide Expression Data to Mine the ''Gray Zone'' of GWA Studies Leads to Novel Candidate Obesity Genes

To get beyond the ''low-hanging fruits'' so far identified by
genome-wide association (GWA) studies, new methods must be
developed in order to discover the numerous remaining genes that
estimates of heritability indicate should be contributing to
complex human phenotypes, such as obesity. Here we describe a novel
integrative method for complex disease gene identification
utilizing both genome-wide transcript profiling of adipose tissue
samples and consequent analysis of genome-wide association data
generated in large SNP scans. We infer causality of genes with
obesity by employing a unique set of monozygotic twin pairs
discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean
weight difference) and contrast the transcript profiles with those
from a larger sample of non-related adult individuals (N=77). Using
this approach, we were able to identify 27 genes with possibly
causal roles in determining the degree of human adiposity. Testing
for association of SNP variants in these 27 genes in the population
samples of the large ENGAGE consortium (N=21,000) revealed a
significant deviation of P-values from the expected (P=4x10(-4)). A
total of 13 genes contained SNPs nominally associated with BMI. The
top finding was blood coagulation factor F13A1 identified as a
novel obesity gene also replicated in a second GWA set of similar
to 2,000 individuals. This study presents a new approach to
utilizing gene expression studies for informing choice of candidate
genes for complex human phenotypes, such as obesity.