The findings from genome-wide association studies hold enormous potential for novel insight into disease mechanisms. A major challenge in the field is to map these low-risk association signals to their underlying functional sequence variants (FSV). Simple sequence study designs are insufficient, as the vast numbers of statistically comparable variants and a limited knowledge of noncoding regulatory elements complicate prioritization. Furthermore, large sample sizes are typically required for adequate power to identify the initial association signals. One important question is whether similar sample sizes need to be sequenced to identify the FSVs. Here, we present a proof-of-principle example of an extreme discordant design to map FSVs within the 2q33 low-risk breast cancer locus. Our approach employed DNA sequencing of a small number of discordant haplotypes to efficiently identify candidate FSVs. Our results were consistent with those from a 2,000-fold larger, traditional imputation-based fine-mapping study. To prioritize further, we used expression-quantitative trait locus analysis of RNA sequencing from breast tissues, gene regulation annotations from the ENCODE consortium, and functional assays for differential enhancer activities. Notably, we implicate three regulatory variants at 2q33 that target CASP8 (rs3769823, rs3769821 in CASP8, and rs10197246 in ALS2CR12) as functionally relevant. We conclude that nested discordant haplotype sequencing is a promising approach to aid mapping of low-risk association loci. The ability to include more efficient sequencing designs into mapping efforts presents an opportunity for the field to capitalize on the potential of association loci and accelerate translation of association signals to their underlying FSVs. Cancer Res; 76(7); 1916-25. ©2016 AACR.
©2016 American Association for Cancer Research.