GWAS meets TCGA to illuminate mechanisms of cancer predisposition

Abstract

Genome-wide association studies (GWASs) have unraveled a large number of cancer risk alleles. Understanding how these allelic variants predispose to disease is a major bottleneck confronting translational application. In this issue, Li and colleagues combine GWASs with The Cancer Genome Atlas (TCGA) to disambiguate the contributions of germline and somatic variants to tumorigenic gene expression programs. They find that close to half of the known risk alleles for estrogen receptor (ER)-positive breast cancer are expression quantitative trait loci (eQTLs) acting upon major determinants of gene expression in tumors.

Figure 1. Bridging the Gap between Detection of Cancer Risk Loci and Development of Disease Prevention Strategies

High-resolution annotation of molecular correlates in tumor samples can be dovetailed with GWAS to elaborate causal relationships between risk loci and target genes. These target genes serve as both functional and predictive biomarkers for personalized medicine. This, in turn, can lead to testable mechanistic hypotheses and nomination of early detection and prevention strategies.