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Cited by 697

The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.

Developing and evaluating polygenic risk prediction models for stratified disease prevention

Open access
Chatterjee, Nilanjan, Shi, Jianxin, García-Closas, Montserrat·2016·Nature Reviews Genetics

Abstract

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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Metadata

Authors
Chatterjee, N., Shi, J., & García-Closas, M.
Venue
Nature Reviews Genetics
Published
2016-05-03
Volume
17
Issue
7
Pages
392-406
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
OpenAlex
W2345958530
Cited by
697
References
110
FWCI
8.47
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Developing and evaluating polygenic risk prediction models for stratified disease prevention. https://app.cochat.ai/papers/10.1038/nrg.2016.27
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Chatterjee, N., Shi, J., & García-Closas, M. (2016). Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nature Reviews Genetics. https://doi.org/10.1038/nrg.2016.27

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