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

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 Lemacon, Michael Lush, Jonathan P. Tyrer, Maya Ghoussaini, Mahdi Moradi Marjaneh, Xia Jiang, Simona Agata, Kristiina Aittomaki, M. Rosario AlonsoIrene 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 Balmana, Rosa B. Barkardottir, Daniel Barrowdale, Alicia Beeghly-Fadiel, Javier Benitez, Marina Bermisheva, Katarzyna Bialkowska, Amie M. Blanco, Carl Blomqvist, William Blot, Natalia Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Bernardo Bonanni, Ake Borg, Kristin Bosse, Hiltrud Brauch, Hermann Brenner, Maaike de Boer, Encarna Gomez Garcia, Marinus Blok, GEMO Study Collaborators, EMBRACE Collaborators, KConFab Investigators, HEBON Investigators, ABCTB Investigators, Alison M. Dunning*, Peter Kraft*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

58 Citations (Web of Science)

Abstract

Fine-mapping of causal variants and integration of epigenetic and chromatin conformation data identify likely target genes for 150 breast cancer risk regions.

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 2020

Keywords

  • GENOME-WIDE ASSOCIATION
  • COMPREHENSIVE MOLECULAR PORTRAITS
  • ESTROGEN-RECEPTOR
  • INTEGRATIVE ANALYSIS
  • FUNCTIONAL VARIANTS
  • SUSCEPTIBILITY LOCI
  • PROTEIN FUNCTION
  • CHROMATIN
  • ENHANCER
  • REVEALS

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