Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes
AuthorFachal, L; Aschard, H; Beesley, J; Barnes, DR; Allen, J; Kar, S; Pooley, KA; Dennis, J; Michailidou, K; Turman, C; ...
Source TitleNature Genetics
University of Melbourne Author/sCampbell, Ian; Hopper, John; Dite, Gillian; Makalic, Enes; Milne, Roger; Schmidt, Daniel; MacInnis, Robert; James, Paul; McLean, Catriona; Delatycki, Martin; ...
AffiliationMedicine and Radiology
Melbourne School of Population and Global Health
Sir Peter MacCallum Department of Oncology
Medical Biology (W.E.H.I.)
Obstetrics and Gynaecology
Florey Department of Neuroscience and Mental Health
Document TypeJournal Article
CitationsFachal, L., Aschard, H., Beesley, J., Barnes, D. R., Allen, J., Kar, S., Pooley, K. A., Dennis, J., Michailidou, K., Turman, C., Soucy, P., Lemacon, A., Lush, M., Tyrer, J. P., Ghoussaini, M., Marjaneh, M. M., Jiang, X., Agata, S., Aittomaki, K. ,... Sachchithananthan, M. (2020). Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. NATURE GENETICS, 52 (1), pp.56-73. https://doi.org/10.1038/s41588-019-0537-1.
Access StatusAccess this item via the Open Access location
Open Access URLhttp://europepmc.org/articles/pmc6974400?pdf=render
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.
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