fitSNP

Candidate single nucleotide polymorphisms (SNPs) from genome-wide
association studies (GWASs) were often selected for validation based on
their functional annotation, which was inadequate and biased. We instead
proposed to use the more than 200,000 microarray studies in the Gene
Expression Omnibus to systematically prioritize candidate SNPs from GWASs.

We analyzed all human microarray studies from the Gene Expression Omnibus,
and calculated the observed frequency of differential expression, which we
called differential expression ratio, for every human gene. Analysis
conducted in a comprehensive list of curated disease genes revealed a
positive association between differential expression ratio values and the
likelihood of harboring disease-associated variants. By considering highly
differentially expressed genes, we were able to rediscover disease genes
with 79% specificity and 37% sensitivity. We successfully distinguished true
disease genes from false positives in multiple GWASs for multiple diseases.
We then derived a list of functionally interpolating SNPs (fitSNPs) to
analyze the top seven loci of Wellcome Trust Case Control Consortium type 1
diabetes mellitus GWASs, rediscovered all type 1 diabetes mellitus genes,
and predicted a novel gene (KIAA1109) for an unexplained locus 4q27. We
suggest that fitSNPs would work equally well for both Mendelian and complex
diseases (being more effective for cancer) and proposed candidate genes to
sequence for their association with 597 syndromes with unknown molecular
basis.

Our study demonstrates that highly differentially expressed genes are more
likely to harbor disease-associated DNA variants. FitSNPs can serve as an
effective tool to systematically prioritize candidate SNPs from GWASs.