Abstract
Although genome-wide association studies have identified many risk loci associated with colorectal cancer, the molecular basis of these associations are still unclear. We aimed to infer biological insights and highlight candidate genes of interest within GWAS risk loci. We used an in silico pipeline based on functional annotation, quantitative trait loci mapping of cis-acting gene, PubMed text-mining, protein-protein interaction studies, genetic overlaps with cancer somatic mutations and knockout mouse phenotypes, and functional enrichment analysis to prioritize the candidate genes at the colorectal cancer risk loci. Based on these analyses, we observed that these genes were the targets of approved therapies for colorectal cancer, and suggested that drugs approved for other indications may be repurposed for the treatment of colorectal cancer. This study highlights the use of publicly available data as a cost effective solution to derive biological insights, and provides an empirical evidence that the molecular basis of colorectal cancer can provide important leads for the discovery of new drugs.
Highlights
Since the advent of high-density single nucleotide polymorphism (SNP) genotyping arrays, researchers have used genome-wide association studies (GWAS) to identify innumerable loci associated with a multitude of diseases
For each gene in the risk loci, we evaluated if the gene was the nearest gene to the colorectal cancers (CRC) risk SNP within the risk locus
We evaluated whether the CRC risk SNPs and SNPs in the linkage disequilibrium (r2 > 0.80) were overlapping with H3K4me3 peaks of 34 cell types
Summary
Since the advent of high-density single nucleotide polymorphism (SNP) genotyping arrays, researchers have used genome-wide association studies (GWAS) to identify innumerable loci associated with a multitude of diseases. The step was to identify the genes that were affected by causal variants, which would enable us to translate the risk SNPs to meaningful insights on pathogenesis. Most reports have implicated the nearest gene to a GWAS hit as a target of the functional variant without any evidence[1]. The identification of expression quantitative trait loci (eQTL) has been proposed as a promising method to find the candidate genes associated with a disease risk[3][4]. It should be noted that identifying an eQTL provides only an indirect evidence of a link between genotype and gene transcription [1]
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