Prediction of individual genetic risk to prostate cancer using a polygenic score

Robert Szulkin, Thomas Whitington, Martin Eklund, Markus Aly, Rosalind A Eeles, Douglas F Easton, Zsofia Kote-Jarai, Ali Amin Al Olama, Sara Benlloch, Kenneth Muir, Graham G. Giles, Melissa C. Southey, Liesel M. Fitzgerald, Brian E Henderson, Fredrick Schumacher, Christopher A Haiman, Johanna Schleutker, Tiina Wahlfors, Teuvo L.J. Tammela, Børge G. NordestgaardTim J. Key, Ruth C Travis, David E. Neal, Jenny L. Donovan, Freddie C Hamdy, Paul D P Pharoah, Nora Pashayan, Kay-Tee Khaw, Janet L. Stanford, Stephen N. Thibodeau, Shannon K. McDonnell, Daniel J. Schaid, Christiane Maier, Walther Vogel, Manuel Luedeke, Kathleen Herkommer, Adam S. Kibel, Cezary Cybulski, Jan Lubiński, Wojciech Kluźniak, Lisa Cannon-Albright, Hermann Brenner, Katja Butterbach, Christa Stegmaier, Jong Y. Park, Thomas A Sellers, Hui Yi Lim, Chavdar Slavov, Radka P. Kaneva, Vanio Mitev, Jyotsna Batra, Judith A. Clements, The Australian Prostate Cancer BioResource, Amanda B Spurdle, Manuel R Teixeira, Paula Paulo, Sofia Maia, Hardev S Pandha, Agnieszka Michael, Andrzej Kierzek, The PRACTICAL consortium, Henrik Gronberg, Fredrik Wiklund

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45 Citations (Scopus)


BACKGROUND Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P-=-0.0012) and the net reclassification index with 0.21 (P-=-8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.

Original languageEnglish
Pages (from-to)1467-1474
Number of pages8
JournalThe Prostate
Issue number13
Publication statusPublished - 15 Sep 2015
Externally publishedYes


  • polygenic risk score
  • prostate cancer
  • risk prediction

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