Resolving disease causality by integrating polygenic risk into propensity score matching

This dataset contains summary-level result tables supporting the conclusions of the PRIMA manuscript. The files include pairwise disease association results, PRS-informed propensity score matching results, logistic regression results, Mendelian randomization results, and survival analysis results. No individual-level UK Biobank data are included.

Authors

Publication Details

Journal
Genome Medicine
Published
2026-10-27
DOI
https://doi.org/10.1186/s13073-026-01804-w
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
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article

Resolving disease causality by integrating polygenic risk into propensity score matching

H Kaufman, Nadav Rappoport, Michal Linial B
1 links
Genome Medicine
Genetic Associations and Epidemiology
article

Resolving disease causality by integrating polygenic risk into propensity score matching

H Kaufman, Nadav Rappoport, Michal Linial B
article en

Abstract

This dataset contains summary-level result tables supporting the conclusions of the PRIMA manuscript. The files include pairwise disease association results, PRS-informed propensity score matching results, logistic regression results, Mendelian randomization results, and survival analysis results. No individual-level UK Biobank data are included.

Genome Medicine
Good health and well-being
Openalex Percentile: Top 55%
Genetic Associations and Epidemiology
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Resolving disease causality by integrating polygenic risk into propensity score matching — H Kaufman, Nadav Rappoport, et al. · Genome Medicine (2026) | TGRS Research Map | TGRS