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. 2015 Sep;75(13):1467-74.
doi: 10.1002/pros.23037. Epub 2015 Jul 14.

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

Robert Szulkin  1 Thomas Whitington  1 Martin Eklund  1 Markus Aly  1   2 Rosalind A Eeles  3   4 Douglas Easton  5 Z Sofia Kote-Jarai  3 Ali Amin Al Olama  5 Sara Benlloch  5 Kenneth Muir  6   7 Graham G Giles  8   9 Melissa C Southey  10 Liesel M Fitzgerald  8 Brian E Henderson  11 Fredrick Schumacher  11 Christopher A Haiman  11 Johanna Schleutker  12   13 Tiina Wahlfors  13 Teuvo L J Tammela  14 Børge G Nordestgaard  15   16 Tim J Key  17 Ruth C Travis  17 David E Neal  18   19 Jenny L Donovan  20 Freddie C Hamdy  21 Paul Pharoah  22 Nora Pashayan  22   23 Kay-Tee Khaw  24 Janet L Stanford  25   26 Stephen N Thibodeau  27 Shannon K McDonnell  27 Daniel J Schaid  27 Christiane Maier  28 Walther Vogel  29 Manuel Luedeke  28 Kathleen Herkommer  30 Adam S Kibel  31 Cezary Cybulski  32 Jan Lubiński  32 Wojciech Kluźniak  32 Lisa Cannon-Albright  33   34 Hermann Brenner  35   36   37 Katja Butterbach  35 Christa Stegmaier  38 Jong Y Park  39 Thomas Sellers  39 Hui-Yi Lin  40 Chavdar Slavov  41 Radka Kaneva  42 Vanio Mitev  42 Jyotsna Batra  43 Judith A Clements  43 Australian Prostate Cancer BioResource  43   44 Amanda Spurdle  45 Manuel R Teixeira  46   47 Paula Paulo  46 Sofia Maia  46 Hardev Pandha  48 Agnieszka Michael  48 Andrzej Kierzek  48 Practical ConsortiumHenrik Gronberg  1 Fredrik Wiklund  1
Collaborators, Affiliations

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

Robert Szulkin et al. Prostate. 2015 Sep.

Erratum in

  • Erratum.
    [No authors listed] [No authors listed] Prostate. 2015 Dec;75(16):1972. doi: 10.1002/pros.23113. Prostate. 2015. PMID: 26469353 No abstract available.

Abstract

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.

Keywords: polygenic risk score; prostate cancer; risk prediction.

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Figures

Fig. 1.
Fig. 1.
Prediction performance in different study populations included in the training set. The left plot shows prediction performance when up to 5,000 novel SNPs are added to the prediction model. The right plot is zoomed in on the part where the predictions increase. The black line corresponds to the mean AUC.

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