Robert Szulkin, Thomas Whitington, Martin Eklund, Markus Aly, Rosalind A. Eeles, Douglas 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 LJ Tammela, Børge G. Nordestgaard, Tim J. Key, Ruth C. Travis, David E. Neal, Jenny L. Donovan, Freddie C. Hamdy, Paul 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 Sellers, Hui-Yi Lim, Chavdar Slavov, Radka Kaneva, Vanio Mitev, Jyotsna Batra, Judith A. Clements, The Australian Prostate Cancer BioResource, Amanda Spurdle, Manuel R. Teixeira, Paula Paulo, Sofia Maia, Hardev Pandha, Agnieszka Michael, Andrzej Kierzek, PRACTICAL consortium, Henrik Gronberg, Fredrik Wiklund

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. 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. 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. Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. Prostate 9999: XX-XX, 2015. © 2015 Wiley Periodicals, Inc.