| Title: | A one-shot, lossless algorithm for cross-cohort learning in mixed-outcomes analysis |
| Journal: | Patterns |
| Published: | 30 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41040961/ |
| DOI: | https://doi.org/10.1016/j.patter.2025.101321 |
Publication 18703
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Abstract
In cross-cohort studies, integrating diverse datasets is essential and challenging due to cohort-specific variations, distributed data storage, and privacy concerns. Traditional methods often require data pooling or 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 electronic health record (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 4,530 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.</p>
12 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
1 Application
| Application ID | Title |
|---|---|
| 32133 | Integration of multi-organ imaging phenotypes, clinical phenotypes, and genomic data |
Enabling scientific discoveries that improve human health