Evidence map›Paper›PMID 38940340›Full record

ArticleDisease models & mechanisms2024

High-throughput assays to assess variant effects on disease.

Kaiyue Ma, Logan O Gauthier, Frances Cheung, Shushu Huang, Monkol Lek

Abstract read
In one paragraph

Article in Disease models & mechanisms, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. 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

5 authors.

Kaiyue MaDepartment of Genetics, Yale School of Medicine, New Haven, CT 06510, USA.ORCID 0009-0001-4302-3371
Logan O GauthierDepartment of Genetics, Yale School of Medicine, New Haven, CT 06510, USA.ORCID 0009-0003-9847-6735
Frances CheungDepartment of Genetics, Yale School of Medicine, New Haven, CT 06510, USA.ORCID 0009-0008-2991-7388
Shushu HuangDepartment of Genetics, Yale School of Medicine, New Haven, CT 06510, USA.ORCID 0000-0002-0220-4395
Monkol LekDepartment of Genetics, Yale School of Medicine, New Haven, CT 06510, USA.ORCID 0000-0003-1227-6293

Funding

Muscular Dystrophy Association 629095
6 · The paper itself

Abstract

Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.

Indexed as

High-Throughput Screening AssaysAnimalsDiseaseGenetic VariationHumansMutationDeep mutational scanningHigh-throughput functional assaysVariant interpretation

Identifiers

PMID38940340
PMCPMC11225591

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.