ArticlemedRxiv : the preprint server for health sciences2026
Analytic Choices Shape Genomic Risk Estimates from Electronic Health Records: Coronary Heart Disease in eMERGE IV.
Jingheng H Chen, Sarah A Knerr, David L Veenstra, Noura S Abul-Husn, Sarah C Hanks, Eimear E Kenny, Nita A Limdi, Josh B Cortopassi, David Crosslin, Gail P Jarvik and 1 more
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In one paragraphArticle in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 · The registryThe trial behind it
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4 · The recordCorrections and comments
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5 · Who and what moneyAuthors and funding
11 authors.
Jingheng H ChenInstitute of Public Health Genetics, University of Washington, Seattle, WA, USA.
Sarah A KnerrInstitute of Public Health Genetics, University of Washington, Seattle, WA, USA.
David L VeenstraDepartment of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, USA.
Noura S Abul-HusnInstitute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0002-5179-1944 Sarah C HanksInstitute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0003-2978-5289 Eimear E KennyInstitute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Nita A LimdiDepartment of Neurology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA.
Josh B CortopassiDepartment of Neurology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA.ORCID 0009-0007-8131-3947 David CrosslinDivision of Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, LA, USA.
Gail P JarvikDivision of Medical Genetics, School of Medicine, University of Washington, Seattle, WA.ORCID 0000-0002-6710-8708 Iftikhar J KulloDivision of Cardiovascular Medicine and the Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.ORCID 0000-0002-6524-3471 Funding
The eMERGE Risk Assessment Network - Coordinating CenterU01HG011166 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Niall John Lennon, Joseph F. Peterson · 2020 to 2026
$16.8MGenomic Basis of Susceptibility to COVID-19 Infection and its ComplicationsU01HG006379 · NHGRI · MAYO CLINIC ROCHESTER · PI Richard R. Sharp · 2011 to 2026
$16.5MFinding Genomic Profiles of COVID-19 Phenotypes from the EHRU01HG008685 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIZABETH W KARLSON, Matthew S Lebo · 2015 to 2026
$13.6MVariation, Function, and Disease Supplement ProgramU01HG008657 · NHGRI · UNIVERSITY OF WASHINGTON · PI David Russell Crosslin, Gail Pairitz Jarvik · 2015 to 2026
$13.4MOMOP information model for eMERGE phenotypingU01HG008680 · NHGRI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Wendy K Chung, GEORGE M HRIPCSAK · 2015 to 2026
$13.3MGenomic risk in clinic care to promote health equity in New York City patientsU01HG011176 · NHGRI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI NOURA SERENE ABUL-HUSN, Eimear Elizabeth Kenny · 2020 to 2026
$10.4MUtilizing Polygenic Risk to Understand and Improve Outcomes: A Model For Overturning Health Disparities Through Minority-Enriched Genomics HealthcareU01HG011175 · NHGRI · CHILDREN'S HOSP OF PHILADELPHIA · PI Hakon Hakonarson · 2020 to 2026
$10.3MIntegrating Genomic Risk Assessment for Disease Management in a Clinical PopulationU01HG011167 · NHGRI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI JAMES J CIMINO, NITA A LIMDI · 2020 to 2026
$9.3MNorthwestern Genomic Risk Assessment and Management ProgramU01HG011169 · NHGRI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI REX L CHISHOLM, Elizabeth M McNally · 2020 to 2026
$7.4MPolygenic Risk Scores for Healthier African American FamiliesU01HG011172 · NHGRI · CINCINNATI CHILDRENS HOSP MED CTR · PI Leah Claire Kottyan, LISA J MARTIN · 2020 to 2026
$7.2MVanderbilt Genome-Electronic Records (VGER) ProjectU01HG011181 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI DAN M RODEN, Digna R Velez Edwards · 2020 to 2026
$7.0MNHGRI NIH HHS U01 HG006379NHGRI NIH HHS U01 HG008657NHGRI NIH HHS U01 HG008680NHGRI NIH HHS U01 HG008685NHGRI NIH HHS U01 HG011166NHGRI NIH HHS U01 HG011167NHGRI NIH HHS U01 HG011169NHGRI NIH HHS U01 HG011172NHGRI NIH HHS U01 HG011175NHGRI NIH HHS U01 HG011176NHGRI NIH HHS U01 HG011181
6 · The paper itselfAbstract
Background: Electronic health records (EHR) are an important data source for genomic studies, but challenges exist in ascertaining cases and observation start time. We used data derived from the Electronic Medical Records and Genomics (eMERGE) IV study to examine how analytic assumptions regarding case ascertainment and EHR entry time influence estimation of monogenic and polygenic risks for coronary heart disease (CHD). Methods: We assessed agreement between CHD cases ascertained from EHR phenotyping and survey. Associations of monogenic variants and high (top 5%) PRS of CHD were evaluated using multivariate relative risk (RR) regression under three alternative case definitions: EHR-algorithm-defined, self-reported, and combined. Time-to-event analyses (Kaplan-Meier method and Cox proportional hazards models) were conducted under three entry time specifications: (1) entry at the first EHR record, (2) entry at the start of the latest consecutive observation period, and (3) no left truncation. Results: The agreement between CHD cases ascertained by the EHR-based algorithm versus self-report was 37.2% among individuals identified as cases by at least one source, with the EHR algorithm demonstrating higher accuracy. The adjusted RR [95% confidence interval (CI)] associated with high PRS was 2.05 [1.50, 2.81] for EHR-defined, 1.49 [1.04, 2.13] for self-reported, and 1.66 [1.27, 2.18] for combined CHD. Estimated cumulative incidence by age 75 was 0.188 using the first EHR code as left truncation and 0.225 using the most recent observation period. Hazard ratio (HR) estimates were similar across the three left truncation scenarios. Conclusion: The choice of case definition meaningfully influenced RR estimates, whereas alternative specifications of EHR entry time affected absolute cumulative incidence estimates but has minimal impact on HR. These findings highlight the impact of analytical choices in EHR and survey-data-based studies that are applicable beyond the context of CHD.
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PMID42094145
PMCPMC13142575
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