The Electronic Medical Records and Genomics study: Design and analytic framework for assessing the impact of genome-informed risk assessments.

Clinical Snapshot

Key Findings

• P Value: α = 0.05 with 80% power for primary analysis

• Effect Size: Study powered to detect 2-11% differences in adoption rates

• Primary Outcome: Provider/participant adoption of recommended healthcare actions

• Nnt Or Sensitivity: Not applicable - study protocol without results

• Confidence Interval: Not reported - study protocol

Clinical Application

Requires genomic testing infrastructure, risk calculation algorithms, and provider education - feasible in major Australian health networks Relevant to Australian Genomics implementation, potential PBS considerations for genomic testing, and integration with My Health Record system Adults receiving genomic risk assessment for chronic disease prevention in healthcare systems with electronic health records

Abstract

The Electronic Medical Records and Genomics (eMERGE) Network developed and implemented a genome-informed risk assessment (GIRA) to communicate genomic (polygenic risk scores [PRSs], integrated risk scores [IRSs], and monogenic results), clinical, and family history-based risk for 11 chronic diseases and provide recommended healthcare recommendations. GIRA reports have now been returned to 23,840 participants and their providers in a large prospective cohort study. We present here the study design and analysis framework for assessing the attributable impact of GIRA return. Pre-specified outcomes include (1) provider/participant adoption of recommended healthcare actions, (2) new diagnosis of disease, (3) treatment initiation/intensification, and (4) clinical outcomes (surrogate markers or clinical events). We assess outcomes in high risk vs. not-high-risk participants, adjusting for covariates. We evaluate the effect of PRS/IRS at pre-established high-risk thresholds using regression discontinuity (RD), a quasi-experimental method that mimics randomization near a cutoff, enabling estimation of causal effects and controlling for unobserved confounders. Monogenic and family history-based risk stratification are analyzed using logistic regression. With 23,840 participants and 12 months of follow-up, the study is powered to detect differences of 2%-11% with 80% power (α = 0.05 in the adoption outcome). Longer follow-up will be required to enable assessment of new disease diagnosis, treatment changes, and clinical outcomes. Through innovative RD analyses and defined outcomes and comparison groups, this study will provide new insights into the real-world clinical impact of genomic risk assessment, address critical evidence gaps, advance understanding of genomic medicine outcomes, and inform future research.

References

1. 1.Limdi, N., Beasley, T. M., Cortopassi, J., Davis, B., Bangash, H., Chen, J., Chisholm, R. L., Chung, W. K., Cimino, J. J., Connolly, J., Crosslin, D. R., Davis, K. W., DiVietro, A., Esplin, E. D., Freimuth, B., Gordon, A., Hakonarson, H., Hamed, M., He, M., Hoell, C., Holm, I., Hripscak, G., Irvin, M. R., Jarvik, G. P., Karavite, D., Karlson, E. W., Kenny, E. E., Khan, A., Kiryluk, K., ... Veenstra, D. L. (2026). The Electronic Medical Records and Genomics study: Design and analytic framework for assessing the impact of genome-informed risk assessments. American Journal of Human Genetics. https://doi.org/10.1016/j.ajhg.2026.02.018