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Evaluation of polygenic risk scores for predicting breast and prostate cancer risk

Authors

  • Mitchell J. Machiela,

    1. Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
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    • Mitchell J. Machiela and Chia-Yen Chen contributed equally to this work.

  • Chia-Yen Chen,

    1. Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
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    • Mitchell J. Machiela and Chia-Yen Chen contributed equally to this work.

  • Constance Chen,

    1. Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
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  • Stephen J. Chanock,

    1. Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
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  • David J. Hunter,

    1. Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
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  • Peter Kraft

    Corresponding author
    1. Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
    • 655 Huntington Avenue, Building II Room 207, Boston, MA 02115
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Abstract

Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores. Genet. Epidemiol. 2011. © 2011 Wiley-Liss, Inc. 35: 506-514, 2011

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