Evidence map›Paper›PMID 39974143›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Insights on improving accessibility and usability of functional data to unlock its potential for variant interpretation.

Min Seon Park, Runjun D Kumar, Cristian Ovadiuc, Andrew Folta, Abbye E McEwen, Ashley Snyder, Douglas M Fowler, Alan F Rubin, Brian H Shirts, Lea M Starita and 1 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. 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

5 · Who and what money

Authors and funding

11 authors.

Min Seon ParkInstitute for Public Health Genetics, University of Washington, Seattle, WA, USA.ORCID 0000-0003-1172-9967
Runjun D KumarDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
Cristian OvadiucInstitute for Public Health Genetics, University of Washington, Seattle, WA, USA.
Andrew FoltaDivision of Medical Genetics, University of Washington, Seattle, WA, USA.ORCID 0009-0004-0395-8430
Abbye E McEwenDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.ORCID 0000-0001-7187-7975
Ashley SnyderDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Douglas M FowlerDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0001-7614-1713
Alan F RubinBioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.ORCID 0000-0003-1474-605X
Brian H ShirtsDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.ORCID 0000-0002-7188-6764
Lea M StaritaDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0003-2870-5099
Andrew B StergachisDivision of Medical Genetics, University of Washington, Seattle, WA, USA.ORCID 0000-0002-1299-3674

Funding

The Center for Actionable Variant Analysis; measuring variant function at scaleUM1HG011969 · NHGRI · UNIVERSITY OF WASHINGTON · PI Douglas M Fowler, Lea Starita · 2021 to 2026
$9.9M
Advancing the implementation of variant-level functional data into clinical databases and clinical practiceR01HG013025 · NHGRI · UNIVERSITY OF WASHINGTON · PI Lea Starita, Andrew Ben Stergachis · 2023 to 2026
$3.1M
NHGRI NIH HHS R01 HG013025NHGRI NIH HHS UM1 HG011969
6 · The paper itself

Abstract

Introduction: Variant-level functional data is a core component of clinical variant classification and can aid in reinterpreting variants of uncertain significance (VUS). However, the usage of functional data by genetics professionals is currently unknown. Methods: An online survey was developed and distributed in spring of 2024 to individuals actively engaged in variant interpretation. Quantitative and qualitative methods were used to assess responses. Results: 190 eligible individuals responded, with 93% reporting interpreting 26 or more variants per year. The median respondent reported 11-20 years of experience. The most common professional roles were laboratory medical geneticists (23%) and variant review scientists (23%). 77% reported using functional data for variant interpretation in a clinical setting and overall respondents felt confident in assessing functional data. However, 67% indicated that functional data for variants of interest was rarely or never available, and 91% considered insufficient quality metrics or confidence in the accuracy of data as barriers to its use. 94% of respondents noted that better access to primary functional data and standardized interpretation of functional data would improve usage. Respondents also indicated that handling conflicting functional data is a common challenge in variant interpretation that is not performed in a systematic manner across institutions. Discussion: The results from this survey showed a demand for a comprehensive database with reliable quality metrics to support use of functional evidence in clinical variant interpretation. The results also highlight a need for guidelines regarding how putatively conflicting functional data should be used for variant classification.

Identifiers

PMID39974143
PMCPMC11838987

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Registered trials

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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.