title: Functional annotation of putative regulatory elements at cancer susceptibility Loci.
abstract: Most cancer-associated genetic variants identified from genome-wide association studies (GWAS) do not obviously change protein structure, leading to the hypothesis that the associations are attributable to regulatory polymorphisms. Translating genetic associations into mechanistic insights can be facilitated by knowledge of the causal regulatory variant (or variants) responsible for the statistical signal. Experimental validation of candidate functional variants is onerous, making bioinformatic approaches necessary to prioritize candidates for laboratory analysis. Thus, a systematic approach for recognizing functional (and, therefore, likely causal) variants in noncoding regions is an important step toward interpreting cancer risk loci. This review provides a detailed introduction to current regulatory variant annotations, followed by an overview of how to leverage these resources to prioritize candidate functional polymorphisms in regulatory regions.
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
authors.forename: Stephanie A
authors.initials: SA
authors.lastname: Rosse
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. ; School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
authors.forename: Paul L
authors.initials: PL
authors.lastname: Auer
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. ; Department of Epidemiology, University of Washington, Seattle, WA, USA.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Carlson
keywords: bioinformatics; functional follow-up; regulatory prediction; variant annotation