ArticleAnnals of clinical and translational neurology2023
Brain proteome-wide association study linking-genes in multiple sclerosis pathogenesis.
Article in Annals of clinical and translational neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Deciphering cell type-specific causal genetic effects on brain imaging-derived phenotypes and disorders with single-cell Mendelian randomization.PLoS computational biology · 2026Article
- G‑Quadruplex and i‑Motif Structures in theACS omega · 2026Article
- Integrated multi-omics and machine learning prioritize key immune genes for multiple sclerosis risk prediction.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Lenvatinib resistance mechanism and potential ways to conquer.Frontiers in pharmacology · 2023Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo identify genes that confer MS risk via the alteration of cis-regulated protein abundance and verify their aberrant expression in human brain.
methodsUtilizing a two-stage proteome-wide association study (PWAS) design, MS GWAS data (N = 41,505) was respectively integrated with two distinct human brain proteomes from the dorsolateral prefrontal cortex, including ROSMAP (N = 376) in the discovery stage and Banner (N = 152) in the confirmation stage. In the following, Bayesian colocalization analysis was conducted for GWAS and protein quantitative trait loci signals to prioritize candidate genes. Differential expression analysis was then used to verify the dysregulation of risk genes in white matter and gray matter for evidence at the transcription level.
resultsA total of 51 genes whose protein abundance had association with the MS risk were identified, of which 18 genes overlapped in the discovery and confirmation PWAS. Bayesian colocalization indicated six causal genes with genetic risk variants for the MS risk. The differential expression analysis of SHMT1 (P
interpretationDysregulation of SHMT1, FAM120B and ICA1L may confer MS risk. Our findings shed new light on the pathogenesis of MS and prioritized promising targets for future therapy research.
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Registered trials
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