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Prostate. 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.

Author information

1
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
2
Department of Clinical Sciences at Danderyds Hospital, Stockholm, Sweden.
3
The Institute of Cancer Research, London, UK.
4
Royal Marsden National Health Service (NHS) Foundation Trust, London and Sutton, UK.
5
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, UK.
6
Institute of Population Health, University of Manchester, Manchester, UK.
7
Warwick Medical School, University of Warwick, Coventry, UK.
8
Cancer Epidemiology Centre, The Cancer Council Victoria, Melbourne Victoria, Australia.
9
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.
10
Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Victoria, Australia.
11
Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California.
12
Department of Medical Biochemistry and Genetics Institute of Biomedicine Kiinamyllynkatu 10, University of Turku, Finland.
13
BioMediTech, University of Tampere and FimLab Laboratories, Tampere, Finland.
14
Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Finland.
15
Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark.
16
Faculty of Health and Medical Sciences, University of Copenhagen.
17
Cancer Epidemiology, Nuffield Department of Population Health University of Oxford, Oxford, UK.
18
University of Cambridge, Department of Oncology, Addenbrooke's Hospital, Cambridge.
19
Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK.
20
School of Social and Community Medicine, University of Bristol, Bristol, UK.
21
Nuffield Department of Surgical Sciences, University of Oxford, Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK.
22
Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
23
University College London, Department of Applied Health Research, London, UK.
24
Clinical Gerontology Unit, University of Cambridge, Cambridge, UK.
25
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
26
Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington.
27
Mayo Clinic, Rochester, Minnesota.
28
Department of Urology, University Hospital Ulm, Germany.
29
Institute of Human Genetics, University Hospital Ulm, Germany.
30
Department of Urology, Klinikum rechts der Isar der Technischen Universitaet Muenchen, Munich, Germany.
31
Division of Urologic Surgery, Brigham and Womens Hospital, Dana-Farber Cancer Institute, Boston, Massachesetts.
32
International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.
33
Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah.
34
George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah.
35
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Division of Preventive Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
37
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
38
Saarland Cancer Registry, Saarbrücken, Germany.
39
Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida.
40
Biostatistics Program, Moffitt Cancer Center, Tampa, Florida.
41
Department of Urology and Alexandrovska University Hospital, Medical University, Sofia, Bulgaria.
42
Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, Bulgaria.
43
Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia.
44
Australian Prostate Cancer BioResource, Brisbane, Queensland, Australia.
45
Molecular Cancer Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia.
46
Department of Genetics, Portuguese Oncology Institute, Porto, Portugal.
47
Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal.
48
The University of Surrey, Guildford, Surrey, UK.

Erratum in

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

PMID:
26177737
DOI:
10.1002/pros.23037
[Indexed for MEDLINE]

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