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Nat Rev Genet. 2016 Jul;17(7):392-406. doi: 10.1038/nrg.2016.27. Epub 2016 May 3.

Developing and evaluating polygenic risk prediction models for stratified disease prevention.

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Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University.
Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21205, USA.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA.


Knowledge of genetics and its implications for human health is rapidly evolving in accordance with recent events, such as discoveries of large numbers of disease susceptibility loci from genome-wide association studies, the US Supreme Court ruling of the non-patentability of human genes, and the development of a regulatory framework for commercial genetic tests. In anticipation of the increasing relevance of genetic testing for the assessment of disease risks, this Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors. Potential applications of models for primary and secondary disease prevention are illustrated through several case studies, and future challenges and opportunities are discussed.

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