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Estimating disease prevalence in large datasets using genetic risk scores
journal contribution
posted on 2023-06-10, 03:41 authored by Benjamin D Evans, Piotr Slowinski, Andrew T Hattersley, Samuel E Jones, Seth Sharp, Robert A Kimmitt, Michael N Weedon, Richard A Oram, Krasimira Tsaneva-Atanasova, Nicholas J ThomasClinical classification is essential for estimating disease prevalence but is difficult, often requiring complex investigations. The widespread availability of population level genetic data makes novel genetic stratification techniques a highly attractive alternative. We propose a generalizable mathematical framework for determining disease prevalence within a cohort using genetic risk scores. We compare and evaluate methods based on the means of genetic risk scores’ distributions; the Earth Mover’s Distance between distributions; a linear combination of kernel density estimates of distributions; and an Excess method. We demonstrate the performance of genetic stratification to produce robust prevalence estimates. Specifically, we show that robust estimates of prevalence are still possible even with rarer diseases, smaller cohort sizes and less discriminative genetic risk scores, highlighting the general utility of these approaches. Genetic stratification techniques offer exciting new research tools, enabling unbiased insights into disease prevalence and clinical characteristics unhampered by clinical classification criteria.
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Publication status
- Published
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- Published version
Journal
Nature CommunicationsISSN
2041-1723Publisher
Nature ResearchExternal DOI
Issue
1Volume
12Page range
1-12Article number
a6441Event location
EnglandDepartment affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-05-26First Open Access (FOA) Date
2022-05-26First Compliant Deposit (FCD) Date
2022-05-26Usage metrics
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