Evidence map›Paper›PMID 42094147›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Evaluation of the genome-informed risk assessment (GIRA) approach from eMERGE in an independent health system.

Sandra Lapinska, Xinzhe Li, Ravi Mandla, Zhuozheng Shi, Veronica Tozzo, Alexander Flynn-Carroll, Marylyn D Ritchie, Daniel J Rader, Penn Medicine Biobank, Bogdan Pasaniuc

Abstract readPreprint
In one paragraph

Article 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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Sandra LapinskaGraduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Xinzhe LiGraduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Ravi MandlaGraduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-0782-0138
Zhuozheng ShiGraduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-9769-9027
Veronica TozzoDepartment of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0001-8538-9198
Alexander Flynn-CarrollGraduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Marylyn D RitchieDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Daniel J RaderDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-9245-9876
Penn Medicine Biobank
Bogdan PasaniucDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Phenotypic Diversity in COVID-19UL1TR001878 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2016 to 2025
$102.4M
NCATS NIH HHS UL1 TR001878
6 · The paper itself

Abstract

The Genome Informed Risk Assessment (GIRA) from eMERGE is an ongoing pragmatic prospective study designed to implement and evaluate genomic precision medicine across diverse clinical settings. Here, we examine the utility of the high-risk criteria from GIRA in assessing health risk through a retrospective evaluation of 9 adult conditions in a health system independent of eMERGE using the Penn Medicine Biobank (PMBB, n=48,279). We find a large proportion of patients - 30.4% (n=14,676) - labeled as high-risk based on the genomic components (monogenic and polygenic) of GIRA. Stratifying by ancestry revealed significant differences in high-risk classification, with higher rates in African/African American (56.6% vs. 50.1%, p=7.43x10

Identifiers

PMID42094147
PMCPMC13142589

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.