Szulkin, R and Whitington, T and Eklund, M and Aly, M and Eeles, RA and Easton, D and Kote-Jarai, Z and Al Olama, AA and Benlloch, S and Muir, K and Giles, GG and Southey, MC and Fitzgerald, LM and Henderson, BE and Schumacher, F and Haiman, CA and Schleutker, J and Wahlfors, T and Tammela, TLJ and Nordestgaard, BG and Key, TJ and Travis, RC and Neal, DE and Donovan, JL and Hamdy, FC and Pharaoh, P and Pashayan, N and Khaw, K-T and Stanford, JL and Thibdoeau, SN and McDonnell, SK and Schaid, DJ and Maier, C and Vogel, W and Luedeke, M and Herkommer, K and Kibel, AS and Cybulski, C and Lubinski, J and Kluzniak, W and Cannon-Albright, L and Brenner, H and Butterbach, K and Stegmaier, C and Park, JY and Sellers, T and Lim, H-Y and Slavov, C and Kaneva, R and Mitev, V and Batra, J and Clements, JA and Spurdle, A and Teixeira, MR and Paulo, P and Maia, S and Pandha, H and Michael, A and Kierzek, A and Gronberg, H and Wiklund, F and The Australian Prostate Cancer BioResource, Prediction of Individual Genetic Risk to Prostate Cancer Using a Polygenic Score; PRACTICAL consortium, The Prostate, 75, (13) pp. 1467-1474. ISSN 0270-4137 (2015) [Refereed Article]
Copyright 2015 Wiley Periodicals, Inc.
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.
|Item Type:||Refereed Article|
|Keywords:||Prostate Cancer Risk Analysis|
|Research Division:||Biomedical and Clinical Sciences|
|Research Group:||Oncology and carcinogenesis|
|Research Field:||Cancer genetics|
|Objective Group:||Clinical health|
|Objective Field:||Clinical health not elsewhere classified|
|UTAS Author:||Fitzgerald, LM (Dr Liesel Fitzgerald)|
|Web of Science® Times Cited:||43|
|Deposited By:||Menzies Institute for Medical Research|
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