Evidence map›Paper›PMID 25308886›Full record

ArticleImmunogenetics2015

Functional relevance for multiple sclerosis-associated genetic variants.

Xiang Lin, Fei-Yan Deng, Xing-Bo Mo, Long-Fei Wu, Shu-Feng Lei

Abstract read
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In one paragraph

Article in Immunogenetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

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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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Elevated EBNA-1 IgG in MS is associated with genetic MS risk variants.Neurology(R) neuroimmunology & neuroinflammation · 2017
    Article
  5. Article
  6. Article
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

5 authors.

Xiang LinCenter for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Fei-Yan Deng
Xing-Bo Mo
Long-Fei Wu
Shu-Feng Lei

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple sclerosis (MS) is an inflammatory and demyelinating disease of central nervous system. Many genetic variants associated with MS have been identified by genome-wide association studies, but functional mechanism underlying the associations is largely unclear. Utilizing the publically available datasets, we carried out gene relationships among implicated loci (GRAIL) analyses to search for MS-associated SNPs/genes. Expression quantitative trait loci (eQTLs) analyses were conducted to identify eQTL SNPs/target genes. Further, functional prediction for SNP, differential gene expression, and functional annotation clustering analyses for gene were conducted to explore their functional relevance to MS. Among the 284 identified MS-associated SNPs (P < 10(-4)), eQTL analysis showed that 45 SNPs act as cis-effect regulators on 19 MS-associated genes. Among the 19 eQTL target genes, 14 showed significantly differential expressions in MS-related cells. Among the 45 SNPs, 15 were predicted most likely located in transcription factor (TF) binding sites, and five predicted SNPs (rs3095329 of TUBB, rs9469220/rs2647046 of HLA-DQB1, rs11154801 of AHI1, and rs1062158 of NDFIP1) have corresponding target genes with significantly differential expressions in multiple cell groups, while rs7194 of HLA-DRA was predicted in the has-miR-6507-3p binding site. The functional evidence, taken together, highlighted the functional relevance of the six SNPs to MS. The present findings provide novel insights into the functional mechanisms underlying the MS-associated genetic variants, which improve our understanding of the genetic association for MS.

Indexed as

Genome-Wide Association StudyBinding SitesCentral Nervous SystemChromosome MappingGene Expression RegulationGenetic Predisposition to DiseaseHLA-DQ beta-ChainsHumansMultiple SclerosisPolymorphism, Single NucleotideQuantitative Trait LociHLA-DQB1 antigenHLA-DQ beta-Chains

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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.