Estimating disease prevalence in large datasets using genetic risk scores

Benjamin D. Evans; Piotr Słowiński; Andrew T. Hattersley; Samuel E. Jones; Seth Sharp; Robert A. Kimmitt; Michael N. Weedon; Richard A. Oram; Krasimira Tsaneva-Atanasova; Nicholas J. Thomas

doi:10.1038/s41467-021-26501-7

Nature Communications (Nov 2021)

Estimating disease prevalence in large datasets using genetic risk scores

Benjamin D. Evans,
Piotr Słowiński,
Andrew T. Hattersley,
Samuel E. Jones,
Seth Sharp,
Robert A. Kimmitt,
Michael N. Weedon,
Richard A. Oram,
Krasimira Tsaneva-Atanasova,
Nicholas J. Thomas

Affiliations

Benjamin D. Evans: Department of Mathematics, University of Exeter
Piotr Słowiński: Department of Mathematics, University of Exeter
Andrew T. Hattersley: University of Exeter Medical School, Institute of Biomedical & Clinical Science
Samuel E. Jones: University of Exeter Medical School, Institute of Biomedical & Clinical Science
Seth Sharp: University of Exeter Medical School, Institute of Biomedical & Clinical Science
Robert A. Kimmitt: University of Exeter Medical School, Institute of Biomedical & Clinical Science
Michael N. Weedon: University of Exeter Medical School, Institute of Biomedical & Clinical Science
Richard A. Oram: University of Exeter Medical School, Institute of Biomedical & Clinical Science
Krasimira Tsaneva-Atanasova: Department of Mathematics, University of Exeter
Nicholas J. Thomas: Department of Mathematics, University of Exeter

DOI: https://doi.org/10.1038/s41467-021-26501-7
Journal volume & issue: Vol. 12, no. 1
pp. 1 – 12

Abstract

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Estimating disease prevalence in biobanks is prone to error, especially for self-reported traits. Here, the authors propose a method to estimate the prevalence of a disease within a cohort based on genetic risk scores.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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