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Field Synopsis and Re-analysis of Systematic Meta-analyses of Genetic Association Studies in Multiple Sclerosis: a Bayesian Approach

Overview of attention for article published in Molecular Neurobiology, October 2017
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Title
Field Synopsis and Re-analysis of Systematic Meta-analyses of Genetic Association Studies in Multiple Sclerosis: a Bayesian Approach
Published in
Molecular Neurobiology, October 2017
DOI 10.1007/s12035-017-0773-2
Pubmed ID
Authors

Jae Hyon Park, Joo Hi Kim, Kye Eun Jo, Se Whan Na, Michael Eisenhut, Andreas Kronbichler, Keum Hwa Lee, Jae Il Shin

Abstract

To provide an up-to-date summary of multiple sclerosis-susceptible gene variants and assess the noteworthiness in hopes of finding true associations, we investigated the results of 44 meta-analyses on gene variants and multiple sclerosis published through December 2016. Out of 70 statistically significant genotype associations, roughly a fifth (21%) of the comparisons showed noteworthy false-positive rate probability (FPRP) at a statistical power to detect an OR of 1.5 and at a prior probability of 10(-6) assumed for a random single nucleotide polymorphism. These associations (IRF8/rs17445836, STAT3/rs744166, HLA/rs4959093, HLA/rs2647046, HLA/rs7382297, HLA/rs17421624, HLA/rs2517646, HLA/rs9261491, HLA/rs2857439, HLA/rs16896944, HLA/rs3132671, HLA/rs2857435, HLA/rs9261471, HLA/rs2523393, HLA-DRB1/rs3135388, RGS1/rs2760524, PTGER4/rs9292777) also showed a noteworthy Bayesian false discovery probability (BFDP) and one additional association (CD24 rs8734/rs52812045) was also noteworthy via BFDP computation. Herein, we have identified several noteworthy biomarkers of multiple sclerosis susceptibility. We hope these data are used to study multiple sclerosis genetics and inform future screening programs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 35%
Student > Bachelor 2 12%
Student > Master 2 12%
Other 1 6%
Professor > Associate Professor 1 6%
Other 0 0%
Unknown 5 29%
Readers by discipline Count As %
Neuroscience 6 35%
Medicine and Dentistry 4 24%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Unknown 6 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 October 2017.
All research outputs
#20,449,496
of 23,005,189 outputs
Outputs from Molecular Neurobiology
#2,819
of 3,486 outputs
Outputs of similar age
#283,335
of 324,848 outputs
Outputs of similar age from Molecular Neurobiology
#45
of 66 outputs
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