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    Transcriptome-wide association study of breast cancer risk by estrogen-receptor status

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    Author
    Feng, H; Gusev, A; Pasaniuc, B; Wu, L; Long, J; Abu-full, Z; Aittomaki, K; Andrulis, IL; Anton-Culver, H; Antoniou, AC; ...
    Date
    2020-03-01
    Source Title
    Genetic Epidemiology
    Publisher
    WILEY
    University of Melbourne Author/s
    Makalic, Enes; Milne, Roger; Southey, Melissa; Hopper, John; Schmidt, Daniel; James, Paul; Stone, Jennifer; Phillips, Kelly-Anne; Giles, Graham; Campbell, Ian; ...
    Affiliation
    Sir Peter MacCallum Department of Oncology
    Melbourne School of Population and Global Health
    Metadata
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    Document Type
    Journal Article
    Citations
    Feng, H., Gusev, A., Pasaniuc, B., Wu, L., Long, J., Abu-full, Z., Aittomaki, K., Andrulis, I. L., Anton-Culver, H., Antoniou, A. C., Arason, A., Arndt, V., Aronson, K. J., Arun, B. K., Asseryanis, E., Auer, P. L., Azzollini, J., Balmana, J., Barkardottir, R. B. ,... Jiang, X. (2020). Transcriptome-wide association study of breast cancer risk by estrogen-receptor status. GENETIC EPIDEMIOLOGY, 44 (5), pp.442-468. https://doi.org/10.1002/gepi.22288.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/252347
    DOI
    10.1002/gepi.22288
    Abstract
    Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER-). We further compared associations with ER+ and ER- subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER- breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER- breast cancer.

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