Evidence map›Paper›PMID 36422108›Full record

ReviewJournal of personalized medicine2022

Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis.

Christina Vasilopoulou, Stephanie Duguez, William Duddy

Open access · goldAbstract readReview
In one paragraph

Review in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 42% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
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

3 authors at 1 institution in 1 country.

Christina VasilopoulouPersonalised Medicine Centre, School of Medicine, Ulster University, Londonderry BT47 6SB, UK.ORCID 0000-0002-5351-0130
Stephanie DuguezPersonalised Medicine Centre, School of Medicine, Ulster University, Londonderry BT47 6SB, UK.ORCID 0000-0001-6510-5426
William DuddyPersonalised Medicine Centre, School of Medicine, Ulster University, Londonderry BT47 6SB, UK.ORCID 0000-0003-2239-9094
University of Ulster · GB

Funding

Department for the Economy No number
6 · The paper itself

Abstract

The rapid increase in the number of genetic variants identified to be associated with Amyotrophic Lateral Sclerosis (ALS) through genome-wide association studies (GWAS) has created an emerging need to understand the functional pathways that are implicated in the pathology of ALS. Gene-set analysis (GSA) is a powerful method that can provide insight into the associated biological pathways, determining the joint effect of multiple genetic markers. The main contribution of this review is the collection of ALS GSA studies that employ GWAS or individual-based genotype data, investigating their methodology and results related to ALS-associated molecular pathways. Furthermore, the limitations in standard single-gene analyses are summarized, highlighting the power of gene-set analysis, and a brief overview of the statistical properties of gene-set analysis and related concepts is provided. The main aims of this review are to investigate the reproducibility of the collected studies and identify their strengths and limitations, in order to enhance the experimental design and therefore the quality of the results of future studies, deepening our understanding of this devastating disease.

Indexed as

ALS pathologyamyotrophic lateral sclerosisfunctional genomicsgene-set analysisgenome-wide association studies

Identifiers

PMID36422108
PMCPMC9699154
OpenAlexW4309738250

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.