ArticleJournal of neurology2023
Identifying novel genes for amyotrophic lateral sclerosis by integrating human brain proteomes with genome-wide association data.
Article in Journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 7 citations in OpenAlex.
- Identification of hub gene associated with colorectal cancer: Integrating Mendelian randomization, transcriptome analysis and experimental verification.PLoS genetics · 2025Article
- Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundGenome-Wide Association Studies (GWAS) have identified numerous risk genes for Amyotrophic Lateral Sclerosis (ALS); however, the mechanisms by which these loci confer ALS risk are uncertain. This study aims to identify novel causal proteins in the brains of patients with ALS using an integrative analytical pipeline.
methodsUsing the datasets of Protein Quantitative Trait Loci (pQTL) (N
resultsUsing PWAS, we found that the altered protein abundance of 12 genes in the brain was associated with ALS. Three genes (SCFD1, SARM1 and CAMLG) were identified as lead causal genes for ALS with solid evidence (False discovery rate < 0.05, in MR analysis; PPH4 > 80% for Bayesian colocalization). Specifically, an increased abundance of SCFD1 and CAMLG led to an increased risk of ALS, whereas a higher abundance of SARM1 led to a decreased risk of developing ALS. TWAS showed that SCFD1 and CAMLG were related to ALS at the transcriptional level.
conclusionsSCFD1, CAMLG, and SARM1 exhibited robust associations and causality with ALS. The study findings provide novel clues for identifying potential therapeutic targets in ALS. Further studies are required to explore the mechanisms underlying the identified genes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.