Evidence map›Paper›PMID 37905042›Full record

ArticlebioRxiv : the preprint server for biology2023

Assigning credit where it's due: An information content score to capture the clinical value of Multiplexed Assays of Variant Effect.

John Michael O Ranola, Carrie Horton, Tina Pesaran, Shawn Fayer, Lea M Starita, Brian H Shirts

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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

6 authors.

John Michael O RanolaAmbry Genetics, Aliso Viejo, California.
Carrie HortonAmbry Genetics, Aliso Viejo, California.
Tina PesaranAmbry Genetics, Aliso Viejo, California.
Shawn FayerUniversity of Washington, Department of Genome Sciences, Seattle, Washington.
Lea M StaritaUniversity of Washington, Department of Genome Sciences, Seattle, Washington.
Brian H ShirtsBrotman Baty Institute, Seattle, Washington.

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

Background: A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, we hypothesized that this measure of utility underrepresents the information gained from MAVEs and that an information theory approach which includes data that does not reclassify variants will better reflect true information gain. We used this information theory approach to evaluate the information gain, in bits, for MAVEs of Results: Conclusions: An information content approach will more accurately portray information gained through MAVE mapping efforts than counting the number of variants reclassified. This information content approach may also help define the impact of modifying information definitions used to classify many variants, such as guideline rule changes.

Identifiers

PMID37905042
PMCPMC10614968

What OpenQuestion holds

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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.