A one-shot, lossless algorithm for cross-cohort learning in mixed-outcomes analysis.
Ruowang Li1, Luke Benz2, Rui Duan2
1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90049, USA.
Patterns (New York, N.Y.)
|October 3, 2025
Summary
A new algorithm, mixWAS, efficiently integrates distributed electronic health record (EHR) data using summary statistics. This lossless method enhances cross-cohort genetic association discovery for improved healthcare research insights.
Area of Science:
- Bioinformatics
- Computational Biology
- Genetics
Background:
- Integrating diverse electronic health record (EHR) datasets for cross-cohort studies is challenging due to data heterogeneity, distributed storage, and privacy issues.
- Traditional data pooling or harmonization methods can be inefficient and limit the scope of cross-cohort analyses.
- Existing approaches may not preserve cohort-specific information or support simultaneous multi-outcome analyses.
Purpose of the Study:
- To introduce mixWAS, a novel one-shot, lossless algorithm for efficient integration of distributed EHR datasets.
- To enable precise cross-cohort learning while preserving cohort-specific covariate associations and supporting mixed-outcome analyses.
- To improve the accuracy and efficiency of identifying genetic associations across multiple EHR datasets.
Main Methods:
- mixWAS algorithm utilizes summary statistics for lossless integration of distributed EHR data.
- The algorithm preserves cohort-specific covariate associations.
- Simulations were conducted to compare mixWAS performance against conventional methods.
Main Results:
- mixWAS demonstrated superior accuracy and efficiency compared to traditional methods in simulations.
- Application to seven US EHR cohorts identified 4,530 significant cross-cohort genetic associations for traits including blood lipids, BMI, and circulatory diseases.
- Validation using an independent UK EHR dataset confirmed 97.7% of the identified associations, highlighting the algorithm's robustness.
Conclusions:
- mixWAS enables lossless integration of distributed EHR data, enhancing precision in multi-outcome analyses.
- The algorithm facilitates robust cross-cohort genetic association discovery, improving healthcare research.
- mixWAS expands the potential for generating actionable insights from large-scale, distributed health data.
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