Evidence map›Paper›PMID 36711907›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Joint estimation and imputation of variant functional effects using high throughput assay data.

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

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Tian YuDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
James D FifeDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Ivan AdzhubeyDepartment of Biomedical Informatics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts.
Richard SherwoodDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Christopher A CassaDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Brigham and Women's Hospital · USHarvard University · US

Funding

Integrated pathogenicity assessment of clinically actionable genetic variantsR01HG010372 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI CASSA, CHRISTOPHER · 2018 to 2022
$3.5M
Medical Research Council MC_PC_17228NHGRI NIH HHS R01 HG010372
6 · The paper itself

Abstract

Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE (

Indexed as

Base editingClinical risk assessmentFunctional screening assaysGenome editing/CRISPRPrecision medicineStatistical modelingVariant assessment

Identifiers

PMID36711907
PMCPMC9882428
OpenAlexW4313701341

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.