Abstract
Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.
Highlights
Cancer, like many common diseases, is influenced by a variety of genetic and environmental factors
BridGE takes as input human genotypes from matched disease/control groups, typical of that used for Genome-wide association studies (GWAS), together with a set of pathways as defined by curated functional standards (e.g. KEGG[43], Reactome [44], Biocarta[45])
We found significant discoveries across 5 of the breast cancer cohorts examined, suggesting that genetic interactions play a role in determining breast cancer risk
Summary
Like many common diseases, is influenced by a variety of genetic and environmental factors. A recent comprehensive study reported excess familial risk for 20 of 23 cancer types with an overall heritability estimate of 33% [1]. It has been proposed that rare variants, which are not measured by most microarray-based genotyping platforms, may be responsible [8,9,10,11,12, 14]. Another possible explanation for our inability to explain the genetic component of disease is genetic interactions between combinations of common and/or rare loci [10, 11, 13, 15, 16]
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