Show Notes
Pimplaskar A et al., The American Journal of Human Genetics - In UCLA ATLAS EHR-linked biobank analyses, random forest-derived enrollment probabilities and inverse-probability weighting increased replication of known GWAS variants and altered PGS associations.
Study Highlights:
Using the UCLA ATLAS EHR-linked biobank, the authors trained random forest classifiers on demographics, healthcare utilization, and ICD-10 features to distinguish enrolled from background patients. They converted predicted enrollment probabilities into inverse-probability weights and applied these to GWAS replication tests and PGS-PheWAS scans. The classifier achieved AUROC≈0.85 and weighting increased replication of known GWAS variants by 54% while changing phenome-wide PGS association patterns. These results indicate that enrollment-driven inclusion bias can materially affect variant discovery and downstream PGS-based phenotypic associations in health-system biobanks.
Conclusion:
Inclusion bias in EHR-linked biobanks like UCLA ATLAS measurably affects common-variant discovery and PGS associations, and enrollment-aware inverse-probability weighting can improve replication while reducing effective sample size.
Music:
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Reference:
Pimplaskar A, Qiu J, Lapinska S, Tozzo V, Chiang JN, Pasaniuc B, Olde Loohuis LM. Inclusion bias affects common variant discovery and replication in a health-system linked biobank. The American Journal of Human Genetics. 2026;113:1–13. https://doi.org/10.1016/j.ajhg.2026.02.011
License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) - https://creativecommons.org/licenses/by/4.0/
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