Abstract
Abstract Breast cancer is the most common cancer affecting women in the developed world. However, the current knowledge of breast cancer genetic risk cannot explain as much as two-thirds of cases. While successful, GWAS have not: (1) contributed to explain the genetic causality of risk; (2) provided an indication of the biological mechanisms involved. Based on our previous results demonstrating that cis-regulatory variation is involved in breast cancer risk, we have performed whole-genome differential allelic expression (DAE) analysis of sixty-four normal breast tissue samples. We integrated this genome-wide DAE map with published breast cancer GWAS risk loci according to chromosome location, and linkage disequilibrium between DAE SNPs and GWAS associated SNPs. We found 15 loci (20%) displaying GWAS associated SNPs in strong linkage disequilibrium with SNPs displaying DAE. Potential regulatory SNPs in top two candidate loci are being currently mapped and functionally studied. We are now integrating the DAE whole-genome map with unpublished GWAS data, to test the efficiency of this approach to help prioritise loci for further risk-association validation. The large overlap between GWAS and DAE data confirms the importance of cis-regulation in the biology of breast cancer risk and provides a new powerful tool to prioritise and functionally analyse risk loci identified through GWAS. Citation Format: Joana Xavier, Roslin Russell, Bernardo P. Almeida, Nordiana Rosli, Catia Rocha, Shamith Samarajiwa, Suet-Feung Chin, Carlos Caldas, Bruce AJ Ponder, Ana-Teresa Maia. Integrative differential allelic expression analysis efficiently reveals the biology underlying risk to breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr A31.
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