Evidence map›Paper›PMID 40480199›Full record

ArticleAmerican journal of human genetics2025

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

Min Seon Park, Runjun D Kumar, Cristian Ovadiuc, Andrew Folta, Abbye E McEwen, Ashley Snyder, Rehan M Villani, Amanda B Spurdle, Douglas M Fowler, Alan F Rubin and 3 more

Abstract read
In one paragraph

Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Assessing the Clinical Relevance ofCurrent oncology (Toronto, Ont.) · 2026
    Article
  3. Article
  4. Article
  5. Phenotype-Specific Recalibration of MAVE Data Enables Repurposing ofmedRxiv : the preprint server for health sciences · 2026
    Article
  6. Not Forgotten: Patient Experiences with Genetic Variant Reclassifications.medRxiv : the preprint server for health sciences · 2026
    Article
  7. Review
  8. A scalable approach to resolving variants of uncertain significance.bioRxiv : the preprint server for biology · 2026
    Article
  9. Review
  10. Article
  11. Evidence forbioRxiv : the preprint server for biology · 2025
    Article
  12. MaveMD: A functional data resource for genomic medicine.medRxiv : the preprint server for health sciences · 2025
    Article
  13. Article
  14. Article
  15. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Min Seon ParkInstitute for Public Health Genetics, University of Washington, Seattle, WA, USA.
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; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seatle, WA, USA.
Andrew FoltaDivision of Medical Genetics, University of Washington, Seattle, WA, USA.
Abbye E McEwenDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
Ashley SnyderDepartment of Genome Sciences, University of Washington, Seattle, WA, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
Rehan M VillaniQIMR Berghofer, Brisbane, QLD, Australia.
Amanda B SpurdleQIMR Berghofer, Brisbane, QLD, Australia; University of Queensland, Brisbane, QLD, Australia.
Douglas M FowlerDepartment of Genome Sciences, University of Washington, Seattle, WA, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA, USA; Department of Bioengineering, University of Washington, Seattle, WA, USA.
Alan F RubinBioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.
Brian H ShirtsDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
Lea M StaritaDepartment of Genome Sciences, University of Washington, Seattle, WA, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA, USA. Electronic address: lstarita@uw.edu.
Andrew B StergachisDivision of Medical Genetics, University of Washington, Seattle, WA, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA, USA. Electronic address: absterga@uw.edu.

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

Variant-level functional data are a core component of clinical variant classification and can aid in reinterpreting variants of uncertain significance (VUSs). However, the usage of functional data by genetics professionals is currently unknown. An online survey was developed and distributed in the spring of 2024 to individuals actively engaged in variant interpretation. Quantitative and qualitative methods were used to assess responses. 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 assessing functional data. However, 67% indicated that functional data for variants of interest were rarely or never available, and 91% considered insufficient quality metrics or confidence in the accuracy of data as barriers to their 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. The results from this survey showed a demand for a comprehensive database with reliable quality metrics to support the 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.

Indexed as

Genetic VariationAdultFemaleGenetic TestingHumansMaleSurveys and Questionnairesfunctional dataMAVEmultiplexed assays for variant effectsurveyvariant classification

Identifiers

PMID40480199
PMCPMC12256793

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.