Evidence map›Paper›PMID 33766936›Full record

ArticleJournal of medical genetics2022

Improving the clinical interpretation of missense variants in X linked genes using structural analysis.

Shalaw Rassul Sallah, Jamie M Ellingford, Panagiotis I Sergouniotis, Simon C Ramsden, Nicholas Lench, Simon C Lovell, Graeme C Black

Open access · hybridAbstract read
In one paragraph

Article in Journal of medical genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 20% of its field
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, 8 citations in OpenAlex.

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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

7 authors at 3 institutions in 1 country.

Shalaw Rassul SallahDivision of Evolution and Genomic Sciences, The University of Manchester Faculty of Biology, Medicine and Health, Manchester, UK.ORCID 0000-0002-3966-319X
Jamie M EllingfordDivision of Evolution and Genomic Sciences, The University of Manchester Faculty of Biology, Medicine and Health, Manchester, UK.ORCID 0000-0003-1137-9768
Panagiotis I SergouniotisManchester Centre for Genomic Medicine, St Mary's Hospital, Manchester Academic Health Sciences Centre, Manchester, UK.
Simon C RamsdenManchester Centre for Genomic Medicine, St Mary's Hospital, Manchester Academic Health Sciences Centre, Manchester, UK.
Nicholas LenchCongenica Ltd, Biodata Innovation Centre, Wellcome Genome Campus, Hinxton, London, UK.
Simon C LovellDivision of Evolution and Genomic Sciences, The University of Manchester Faculty of Biology, Medicine and Health, Manchester, UK.
Graeme C BlackDivision of Evolution and Genomic Sciences, The University of Manchester Faculty of Biology, Medicine and Health, Manchester, UK graeme.black@manchester.ac.uk.
University of Manchester · GBSt Mary's Hospital · GBEagle Genomics (United Kingdom) · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImproving the clinical interpretation of missense variants can increase the diagnostic yield of genomic testing and lead to personalised management strategies. Currently, due to the imprecision of bioinformatic tools that aim to predict variant pathogenicity, their role in clinical guidelines remains limited. There is a clear need for more accurate prediction algorithms and this study aims to improve performance by harnessing structural biology insights. The focus of this work is missense variants in a subset of genes associated with X linked disorders.

methodsWe have developed a

resultsProSper outperformed seven previously described tools, including meta-predictors, in correctly evaluating whether or not variants are pathogenic; this was the case for 11 of the 21 genes associated with X linked disorders that met the inclusion criteria for this study. We also determined gene-specific pathogenicity thresholds that improved the performance of VEST4, REVEL and ClinPred, the three best-performing tools out of the seven that were evaluated; this was the case in 11, 11 and 12 different genes, respectively.

conclusionProSper can form the basis of a molecule-specific prediction tool that can be implemented into diagnostic strategies. It can allow the accurate prioritisation of missense variants associated with X linked disorders, aiding precise and timely diagnosis. In addition, we demonstrate that gene-specific pathogenicity thresholds for a range of missense prioritisation tools can lead to an increase in prediction accuracy.

Indexed as

Genes, X-LinkedMutation, MissenseAlgorithmsComputational BiologyHumansclinical decision-makinggenetic variationmissensemutationpoint mutationproteinstructural homology

Identifiers

PMID33766936
PMCPMC8961765
OpenAlexW3138939871

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.