Estimating population level disease prevalence using genetic risk scores

Benjamin D Evans, Piotr M Słowiński, Andrew T Hattersley, Samuel Edward Jones, Seth Andrew Sharp, Robert A Kimmitt, Michael N Weedon, Richard A Oram, Krasimira Tsaneva-Atanasova, Nicholas John Meyrick Thomas

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Received Date: 9th March 20

Clinical classification is essential for estimating disease prevalence in a population 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 the prevalence of a disease within a population using genetic risk scores. We compare and evaluate methods based on the means of the 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 assess the impact on estimates resulting from the population size and proportion of cases to non-cases. Using less discriminative genetic risk scores still results in robust estimates of proportion.  Genetic stratification techniques provide exciting research tools enabling unbiased insights into disease prevalence and clinical characteristics unhampered by clinical classification criteria.

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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

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