Evidence map›Paper›PMID 37638449›Full record

ArticleJournal of neuromuscular diseases2023

Characterising the Genetic Landscape of Amyotrophic Lateral Sclerosis: A Catalogue and Assessment of Over 1,000 Published Genetic Variants.

Emily P McCann, Natalie Grima, Jennifer A Fifita, Sandrine Chan Moi Fat, Klaus Lehnert, Lyndal Henden, Ian P Blair, Kelly L Williams

Open access · bronzeAbstract read
In one paragraph

Article in Journal of neuromuscular diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

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    Review
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

8 authors at 2 institutions in 2 countries.

Emily P McCannMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
Natalie GrimaMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
Jennifer A FifitaMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
Sandrine Chan Moi FatMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
Klaus LehnertSchool of Biological Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand.
Lyndal HendenMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
Ian P BlairMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
Kelly L WilliamsMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
Macquarie University · AUUniversity of Auckland · NZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with genetic and phenotypic heterogeneity. Pathogenic genetic variants remain the only validated cause of disease, the majority of which were discovered in familial ALS patients. While causal gene variants are a lesser contributor to sporadic ALS, an increasing number of risk alleles (low penetrance genetic variants associated with a small increase in disease risk) and variants of uncertain significance have been reported.

objectiveTo examine the pathogenic potential of genetic variation in ALS, we sought to characterise variant- and gene-level attributes of previously reported ALS-implicated variants.

methodsA list of 1,087 genetic variants reported in ALS to March 2021 was compiled through comprehensive literature review. Individual variants were annotated using in silico tools and databases across variant features including pathogenicity scores, localisation to protein domains, evolutionary conservation, and minor allele frequencies. Gene level attributes of genic tolerance, gene expression in ALS-relevant tissues and gene ontology terms were assessed for 33 ALS genes. Statistical analysis was performed for each characteristic, and we compared the most penetrant variants found in familial cases with risk alleles exclusive to sporadic cases, to explore genetic variant features that associate with disease penetrance.

resultsWe provide spreadsheet (hg19 and GRCh38) and variant call format (GRCh38) resources for all 1,087 reported ALS-implicated variants, including detailed summaries for each attribute. We demonstrate that the characteristics of variants found exclusively in sporadic ALS cases are less severe than those observed in familial ALS.

conclusionsWe provide a comprehensive, literature-derived catalogue of genetic variation in ALS thus far and reveal crucial attributes that contribute to ALS pathogenicity. Our variant- and gene-level observations highlight the complexity of genetic variation in ALS, and we discuss important implications and considerations for novel variant interpretation.

Indexed as

Amyotrophic Lateral SclerosisNeurodegenerative DiseasesGene FrequencyHumansAmyotrophic lateral sclerosisgenetic variantmotor neuron diseasepathogenicity assessment

Identifiers

PMID37638449
PMCPMC10657717
OpenAlexW4386169048

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.