Estimating disease prevalence in large datasets using genetic risk scores

Evans, Benjamin D, Słowiński, Piotr, Hattersley, Andrew T, Jones, Samuel E, Sharp, Seth, Kimmitt, Robert A, Weedon, Michael N, Oram, Richard A, Tsaneva-Atanasova, Krasimira and Thomas, Nicholas J (2021) Estimating disease prevalence in large datasets using genetic risk scores. Nature Communications, 12 (1). a6441 1-12. ISSN 2041-1723

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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.

Item Type: Article
Keywords: Algorithms, Cohort Studies, Computer Simulation, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Humans, Models, Genetic, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Prevalence, Reproducibility of Results, Risk Factors, Sensitivity and Specificity
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 26 May 2022 10:21
Last Modified: 26 May 2022 10:30
URI: http://sro.sussex.ac.uk/id/eprint/106108

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