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

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    Author
    Fachal, L; Aschard, H; Beesley, J; Barnes, DR; Allen, J; Kar, S; Pooley, KA; Dennis, J; Michailidou, K; Turman, C; ...
    Date
    2020-01-07
    Source Title
    Nature Genetics
    Publisher
    NATURE RESEARCH
    University of Melbourne Author/s
    Campbell, Ian; Hopper, John; Dite, Gillian; Makalic, Enes; Milne, Roger; Schmidt, Daniel; MacInnis, Robert; James, Paul; McLean, Catriona; Delatycki, Martin; ...
    Affiliation
    Medicine and Radiology
    Melbourne School of Population and Global Health
    Sir Peter MacCallum Department of Oncology
    Paediatrics (RCH)
    Medical Biology (W.E.H.I.)
    Clinical Pathology
    Obstetrics and Gynaecology
    Florey Department of Neuroscience and Mental Health
    Metadata
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    Document Type
    Journal Article
    Citations
    Fachal, 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 Status
    Access this item via the Open Access location
    URI
    http://hdl.handle.net/11343/253977
    DOI
    10.1038/s41588-019-0537-1
    Open Access URL
    http://europepmc.org/articles/pmc6974400?pdf=render
    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.

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