ArticlePhysiological genomics2019
Breakdown of multiple sclerosis genetics to identify an integrated disease network and potential variant mechanisms.
Article in Physiological genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Polymorphisms in CLOCK and ARNTL/BMAL-1 genes in multiple sclerosis: a systematic review and meta-analysis.European journal of medical research · 2026Pooled it
- Genetic background shapes SEZ6L2 autoimmunity and reveals coordinated immune responses linked to neurological dysfunction.bioRxiv : the preprint server for biology · 2026Article
- Sez6L2 autoimmunity induces cerebellar ataxia in mice.Journal of neuroinflammation · 2025Article
- The Multifaceted Roles of BACH1 in Disease: Implications for Biological Functions and Therapeutic Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Unraveling the RKIP-YY1 axis: immune crosstalk in the pathogenesis of metabolic disorders.Frontiers in immunology · 2025Review
- Heat shock protein 70 gene polymorphisms in Iranian patients with Multiple sclerosis.American journal of clinical and experimental immunology · 2024Article
- Shared Molecular Signatures Across Zika Virus Infection and Multiple Sclerosis Highlight AP-1 Transcription Factor as a Potential Player in Post-ZIKV MS-Like Phenotypes.Molecular neurobiology · 2023Article
- Cerebrospinal Fluid-Basic Concepts Review.Biomedicines · 2023Review
- Risk Factors from Pregnancy to Adulthood in Multiple Sclerosis Outcome.International journal of molecular sciences · 2022Review
- Combination of Genomic and Transcriptomic Approaches Highlights Vascular and Circadian Clock Components in Multiple Sclerosis.International journal of molecular sciences · 2021Article
- Contribution of Human Pluripotent Stem Cell-Based Models to Drug Discovery for Neurological Disorders.Cells · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Genetics of multiple sclerosis (MS) are highly polygenic with few insights into mechanistic associations with pathology. In this study, we assessed MS genetics through linkage disequilibrium and missense variant interpretation to yield a MS gene network. This network of 96 genes was taken through pathway analysis, tissue expression profiles, single cell expression segregation, expression quantitative trait loci (eQTLs), genome annotations, transcription factor (TF) binding profiles, structural genome looping, and overlap with additional associated genetic traits. This work revealed immune system dysfunction, nerve cell myelination, energetic control, transcriptional regulation, and variants that overlap multiple autoimmune disorders. Tissue-specific expression and eQTLs of MS genes implicate multiple immune cell types including macrophages, neutrophils, and T cells, while the genes in neural cell types enrich for oligodendrocyte and myelin sheath biology. There are eQTLs in linkage with lead MS variants in 25 genes including the multitissue eQTL, rs9271640, for
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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.