Journal article

Two-stage Study of Familial Prostate Cancer by Whole-exome Sequencing and Custom Capture Identifies 10 Novel Genes Associated with the Risk of Prostate Cancer

Daniel J Schaid, Shannon K McDonnell, Liesel M FitzGerald, Lissa DeRycke, Zachary Fogarty, Graham G Giles, Robert J MacInnis, Melissa C Southey, Tu Nguyen-Dumont, Geraldine Cancel-Tassin, Oliver Cussenot, Alice S Whittemore, Weiva Sieh, Nilah Monnier Ioannidis, Chih-Lin Hsieh, Janet L Stanford, Johanna Schleutker, Cheryl D Cropp, John Carpten, Josef Hoegel Show all

EUROPEAN UROLOGY | ELSEVIER | Published : 2021

Abstract

BACKGROUND: Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease. OBJECTIVE: To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa. DESIGN, SETTING, AND PARTICIPANTS: A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, sy..

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Grants

Awarded by U.S. Public Health Service, National Institutes of Health


Awarded by NIH


Awarded by National Health and Medical Research Council


Awarded by WES


Funding Acknowledgements

This research was supported by the U.S. Public Health Service, National Institutes of Health, contract grant U01CA08960 (Stephen N. Thibodeau, ICPCG). Additional grant support for collection of samples and personnel efforts are as follows: NIH CA89600 (William Isaacs); CA080122, CA056678, CA082664, CA092579, and P30-CA015704 (Janet L. Stanford); Intramural Research Program of the National Human Genome Research Institute (Joan E. Bailey-Wilson and Elaine A. Ostrander); National Health and Medical Research Council (APP IDs 940394, 126402, 209057, APP1028280, and APP1074383); and WES datasets for the ARIC study (dbGaP accession study number phs000398.v1.p1).