Evidence map›Paper›PMID 39389016›Full record

ArticleCell genomics2024

FUSE: Improving the estimation and imputation of variant impacts in functional screening.

Tian Yu, James D Fife, Vineel Bhat, Ivan Adzhubey, Richard Sherwood, Christopher A Cassa

Abstract read
In one paragraph

Article in Cell genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Language models reveal evidence gaps in variants of uncertain significance.medRxiv : the preprint server for health sciences · 2026
    Article
  5. Article
  6. Review
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Tian YuDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
James D FifeDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Vineel BhatDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Ivan AdzhubeyDepartment of Biomedical Informatics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
Richard SherwoodDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: rsherwood@bwh.harvard.edu.
Christopher A CassaDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: ccassa@bwh.harvard.edu.

Funding

Integrated pathogenicity assessment of clinically actionable genetic variantsR01HG010372 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI CASSA, CHRISTOPHER · 2018 to 2022
$3.5M
High-throughput investigation of human genetic variants affecting cholesterol uptake and effluxR01HL164409 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SHERWOOD, RICHARD I · 2022 to 2025
$3.1M
Integrative computational-experimental approaches to stratify monogenic disease riskR56HG012681 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI CASSA, CHRISTOPHER, SHERWOOD, RICHARD I · 2023 to 2023
$300k
American Heart Association-American Stroke Association 24TPA1300072NHGRI NIH HHS R01 HG010372NHGRI NIH HHS R56 HG012681NHLBI NIH HHS R01 HL164409
6 · The paper itself

Abstract

Deep mutational scanning enables high-throughput functional assessment of genetic variants. While phenotypic measurements from screening assays generally align with clinical outcomes, experimental noise may affect the accuracy of individual variant estimates. We developed the FUSE (functional substitution estimation) pipeline, which leverages measurements collectively within screening assays to improve the estimation of variant impacts. Drawing data from 115 published functional assays, FUSE assesses the mean functional effect per amino acid position and makes estimates for individual allelic variants. It enhances the correlation of variant functional effects from different assay platforms and increases the classification accuracy of missense variants in ClinVar across 29 genes (area under the receiver operating characteristic [ROC] curve [AUC] from 0.83 to 0.90). In UK Biobank patients with rare missense variants in BRCA1, LDLR, or TP53, FUSE improves the classification accuracy of associated phenotypes. FUSE can also impute variant effects for substitutions not experimentally screened. This approach improves accuracy and broadens the utility of data from functional screening.

Indexed as

BRCA1 ProteinGenetic VariationHumansMutation, MissensePhenotypeReceptors, LDLTumor Suppressor Protein p53BRCA1 ProteinBRCA1 protein, humanLDLR protein, humanReceptors, LDLTP53 protein, humanTumor Suppressor Protein p53base editingclinical risk assessmentDeep mutational scanningfunctional modelingfunctional screeningnoise reductionprecision medicinestatistical modelingvariant assessmentvariants of uncertain significance

Identifiers

PMID39389016
PMCPMC11602636

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.