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 and Nicholas J. Thomas ()
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Benjamin D. Evans: University of Exeter
Piotr Słowiński: 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: University of Exeter
Nicholas J. Thomas: University of Exeter

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Clinical 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26501-7

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DOI: 10.1038/s41467-021-26501-7

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