Variation to Biology: Optimizing Functional Analysis of Cancer Risk Variants.

Abstract

Research conducted over the past 15+ years has identified hundreds of common germline genetic variants associated with cancer risk but understanding the biological impact of these primarily non-protein coding variants has been challenging [1]. The National Cancer Institute sought to better understand and address those challenges by requesting input from the scientific community via a survey and a 2-day virtual meeting, which focused on discussions among participants. Here, we discuss challenges identified through the survey as important to advancing functional analysis of common cancer risk variants: 1) When is a variant truly characterized; 2) Developing and standardizing databases and computational tools; 3) Optimization and implementation of high throughput assays; 4) Use of model organisms for understanding variant function; 5) Diversity in data and assays; and 6) Creating and improving large multidisciplinary collaborations. We define these six challenges, describe how success in addressing them may look, propose potential solutions, and note issues that span all the challenges. Implementation of these ideas could help develop a framework for methodically analyzing common cancer risk variants to understand their function and make effective and efficient use of the wealth of existing genomic association data.