ArticleNature communications2021
Estimating disease prevalence in large datasets using genetic risk scores.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Clinical Utility of a Type 1 Diabetes Genetic Risk Score Measured as Part of MODY Genetic Testing.Diabetes care · 2026Article
- Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.Nature reviews. Endocrinology · 2025Review
- Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores.Scientific reports · 2025Article
- The challenges of identifying and studying type 1 diabetes in adults.Diabetologia · 2023Review
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
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