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A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact

Authors

  • Robert J. MacInnis,

    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
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  • Antonis C. Antoniou,

    Corresponding author
    1. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
    • Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, United Kingdom
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  • Rosalind A. Eeles,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
    2. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom
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  • Gianluca Severi,

    1. Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Carlton, Victoria, Australia
    2. Cancer Epidemiology Centre, The Cancer Council Victoria, Carlton, Victoria, Australia
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  • Ali Amin Al Olama,

    1. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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  • Lesley McGuffog,

    1. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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  • Zsofia Kote-Jarai,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
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  • Michelle Guy,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
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  • Lynne T. O'Brien,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
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  • Amanda L. Hall,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
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  • Rosemary A. Wilkinson,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
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  • Emma Sawyer,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
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  • Audrey T. Ardern-Jones,

    1. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom
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  • David P. Dearnaley,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
    2. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom
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  • Alan Horwich,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
    2. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom
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  • Vincent S. Khoo,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
    2. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom
    3. Departments of Medicine and Radiation Oncology, Austin Health & Northern Health Hospital and University of Melbourne
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  • Christopher C. Parker,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
    2. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom
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  • Robert A. Huddart,

    1. The Institute of Cancer Research, Sutton, Surrey, United Kingdom
    2. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom
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  • Nicholas Van As,

    1. The Royal Marsden NHS Foundation Trust, Sutton, Surrey, and Fulham Road, London, United Kingdom
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  • Margaret R. McCredie,

    1. Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand
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  • Dallas R. English,

    1. Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Carlton, Victoria, Australia
    2. Cancer Epidemiology Centre, The Cancer Council Victoria, Carlton, Victoria, Australia
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  • Graham G. Giles,

    1. Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Carlton, Victoria, Australia
    2. Cancer Epidemiology Centre, The Cancer Council Victoria, Carlton, Victoria, Australia
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  • John L. Hopper,

    1. Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Carlton, Victoria, Australia
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  • Douglas F. Easton

    1. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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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 equation image. 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, equation image, and the residual polygenic component due to the postulated but as yet unknown genetic variants, equation image. 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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