Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

ABCTB Investigators, GEMO Study Collaborators, EMBRACE Collaborators, KConFab Investigators, HEBON Investigators, Laura Fachal, Hugues Aschard, Jonathan Beesley, Daniel R. Barnes, Jamie Allen, Siddhartha Kar, Karen A. Pooley, Joe Dennis, Kyriaki Michailidou, Constance Turman, Penny Soucy, Audrey Lemaçon, Michael Lush, Jonathan P. Tyrer, Maya GhoussainiMahdi Moradi Marjaneh, Xia Jiang, Simona Agata, Kristiina Aittomäki, M. Rosario Alonso, Irene L. Andrulis, Hoda Anton-Culver, Natalia N. Antonenkova, Adalgeir Arason, Volker Arndt, Kristan J. Aronson, Banu K. Arun, Bernd Auber, Paul L. Auer, Jacopo Azzollini, Judith Balmaña, Rosa B. Barkardottir, Daniel Barrowdale, Alicia Beeghly-Fadiel, Javier Benitez, Marina Bermisheva, Katarzyna Białkowska, Amie M. Blanco, Carl Blomqvist, William Blot, Natalia V. Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Bernardo Bonanni, Ake Borg, Siranoush Manoukian, Marco Montagna, Bernard Peissel, Paolo Radice, Alessandra Viel

Research output: Contribution to journalArticlepeer-review

Abstract

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
Original languageEnglish
Pages (from-to)56-73
Number of pages18
JournalNature Genetics
Volume52
Issue number1
DOIs
Publication statusPublished - Jan 1 2020

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