Evidence map›Paper›PMID 39625477›Full record

ArticleeLife2024

Understanding genetic variants in context.

Nasa Sinnott-Armstrong, Stanley Fields, Frederick Roth, Lea M Starita, Cole Trapnell, Judit Villen, Douglas M Fowler, Christine Queitsch

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. A scalable approach to resolving variants of uncertain significance.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Regulation ofInternational journal of molecular sciences · 2025
    Review
  7. Article
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

8 authors.

Nasa Sinnott-ArmstrongHerbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States.ORCID https://orcid.org/0000-0003-4490-0601
Stanley FieldsDepartment of Genome Sciences, University of Washington, Seattle, United States.ORCID https://orcid.org/0000-0001-5504-5925
Frederick RothDonnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, Canada.
Lea M StaritaDepartment of Genome Sciences, University of Washington, Seattle, United States.
Cole TrapnellDepartment of Genome Sciences, University of Washington, Seattle, United States.ORCID https://orcid.org/0000-0002-8105-4347
Judit VillenDepartment of Genome Sciences, University of Washington, Seattle, United States.
Douglas M FowlerDepartment of Genome Sciences, University of Washington, Seattle, United States.ORCID https://orcid.org/0000-0001-7614-1713
Christine QueitschDepartment of Genome Sciences, University of Washington, Seattle, United States.ORCID https://orcid.org/0000-0002-0905-4705

Funding

Technology to understand genetic variant effects in contextRM1HG010461 · NHGRI · UNIVERSITY OF WASHINGTON · PI Douglas M Fowler, Bruce Colston Trapnell · 2019 to 2026
$18.9M
Understanding gene regulation in contextR35GM139532 · NIGMS · UNIVERSITY OF WASHINGTON · PI Christine Queitsch · 2021 to 2026
$3.3M
Bruce G. Cochener Foundation Creativity AwardNHGRI NIH HHS RM1 HG010461NIGMS NIH HHS R35 GM139532NIH HHS 5RM1HG010461NIH HHS NIGMS R35GM139532
6 · The paper itself

Abstract

Over the last three decades, human genetics has gone from dissecting high-penetrance Mendelian diseases to discovering the vast and complex genetic etiology of common human diseases. In tackling this complexity, scientists have discovered the importance of numerous genetic processes - most notably functional regulatory elements - in the development and progression of these diseases. Simultaneously, scientists have increasingly used multiplex assays of variant effect to systematically phenotype the cellular consequences of millions of genetic variants. In this article, we argue that the context of genetic variants - at all scales, from other genetic variants and gene regulation to cell biology to organismal environment - are critical components of how we can employ genomics to interpret these variants, and ultimately treat these diseases. We describe approaches to extend existing experimental assays and computational approaches to examine and quantify the importance of this context, including through causal analytic approaches. Having a unified understanding of the molecular, physiological, and environmental processes governing the interpretation of genetic variants is sorely needed for the field, and this perspective argues for feasible approaches by which the combined interpretation of cellular, animal, and epidemiological data can yield that knowledge.

Indexed as

Genetic VariationAnimalsGenetic Predisposition to DiseaseGenomicsHumansPhenotypeepistasisgene–environment interactionsgeneticsgenomicsmultiplexed assays of variant effect

Identifiers

PMID39625477
PMCPMC11614383

What OpenQuestion holds

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LicenceCC BY
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.