A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact |
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| Authors: | Robert J. MacInnis Antonis C. Antoniou Rosalind A. Eeles Gianluca Severi Ali Amin Al Olama Lesley McGuffog Zsofia Kote‐Jarai Michelle Guy Lynne T. O'Brien Amanda L. Hall Rosemary A. Wilkinson Emma Sawyer Audrey T. Ardern‐Jones David P. Dearnaley Alan Horwich Vincent S. Khoo Christopher C. Parker Robert A. Huddart Nicholas Van As Margaret R. McCredie Dallas R. English Graham G. Giles John L. Hopper Douglas F. Easton |
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| Affiliation: | 1. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom;2. Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Carlton, Victoria, Australia;3. Cancer Epidemiology Centre, The Cancer Council Victoria, Carlton, Victoria, Australia;4. The Institute of Cancer Research, Sutton, Surrey, United Kingdom;5. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom;6. Departments of Medicine and Radiation Oncology, Austin Health & Northern Health Hospital and University of Melbourne;7. Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand |
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| Abstract: | ![]()
Genome wide association studies have identified several single nucleotide polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population‐based samples of 4,390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where  . Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs,  , and the residual polygenic component due to the postulated but as yet unknown genetic variants,  . The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population‐based family data and established risk variants exist. Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. 35: 549‐556, 2011 |
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| Keywords: | risk model common variant GWAS prostate cancer |
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