A One-Shot Lossless Algorithm for Cross-Cohort Learning in Mixed-Outcomes Analysis

Overview

Journal medRxiv

Date 2024 Jan 23

PMID 38260403

Authors

Ruowang Li

Luke Benz

Rui Duan

Joshua C Denny

Hakon Hakonarson

Jonathan D Mosley

Jordan W Smoller

Wei-Qi Wei

Thomas Lumley

Marylyn D Ritchie

Jason H Moore

Yong Chen

Affiliations

Soon will be listed here.

Abstract

In cross-cohort studies, integrating diverse datasets, such as electronic health records (EHRs), is both essential and challenging due to cohort-specific variations, distributed data storage, and data privacy concerns. Traditional methods often require data pooling or complex data harmonization, which can reduce efficiency and limit the scope of cross-cohort learning. We introduce mixWAS, a one-shot, lossless algorithm that efficiently integrates distributed EHR datasets via summary statistics. Unlike existing approaches, mixWAS preserves cohort-specific covariate associations and supports simultaneous mixed-outcome analyses. Simulations demonstrate that mixWAS outperforms conventional methods in accuracy and efficiency across various scenarios. Applied to EHR data from seven cohorts in the US, mixWAS identified 4,534 significant cross-cohort genetic associations among traits such as blood lipids, BMI, and circulatory diseases. Validation with an independent UK EHR dataset confirmed 97.7% of these associations, underscoring the algorithm's robustness. By enabling lossless cross-cohort integration, mixWAS improves the precision of multi-outcome analyses and expands the potential for actionable insights in healthcare research.

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