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Prediction of individual genetic risk to prostate cancer using a polygenic score
Author(s) -
Szulkin Robert,
Whitington Thomas,
Eklund Martin,
Aly Markus,
Eeles Rosalind A.,
Easton Douglas,
KoteJarai ZSofia,
Amin Al Olama Ali,
Benlloch Sara,
Muir Kenneth,
Giles Graham G.,
Southey Melissa C.,
Fitzgerald Liesel M.,
Henderson Brian E.,
Schumacher Fredrick,
Haiman Christopher A.,
Schleutker Johanna,
Wahlfors Tiina,
Tammela Teuvo LJ,
Nordestgaard Børge G.,
Key Tim J.,
Travis Ruth C.,
Neal David E.,
Donovan Jenny L.,
Hamdy Freddie C.,
Pharoah Paul,
Pashayan Nora,
Khaw KayTee,
Stanford Janet L.,
Thibodeau Stephen N.,
McDonnell Shan K.,
Schaid Daniel J.,
Maier Christiane,
Vogel Walther,
Luedeke Manuel,
Herkommer Kathleen,
Kibel Adam S.,
Cybulski Cezary,
Lubiński Jan,
Kluźniak Wojciech,
CanAlbright Lisa,
Brenner Hermann,
Butterbach Katja,
Stegmaier Christa,
Park Jong Y.,
Sellers Thomas,
Lim HuiYi,
Slavov Chavdar,
Kaneva Radka,
Mitev Vanio,
Batra Jyotsna,
Clements Judith A.,
BioResource The Australian Prostate Cancer,
Spurdle Amanda,
Teixeira Manuel R.,
Paulo Paula,
Maia Sofia,
Pandha Hardev,
Michael Agnieszka,
Kierzek Andrzej,
consortium the PRACTICAL,
Gronberg Henrik,
Wiklund Fredrik
Publication year - 2015
Publication title -
the prostate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.295
H-Index - 123
eISSN - 1097-0045
pISSN - 0270-4137
DOI - 10.1002/pros.23037
Subject(s) - biostatistics , epidemiology , public health , medicine , clinical epidemiology , family medicine , gerontology , library science , pathology , computer science
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. Prostate 75:1467–1474, 2015 . © 2015 Wiley Periodicals, Inc.

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