ArticleGenes and immunity2011
A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility.
Article in Genes and immunity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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.
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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.
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Who cites it
19 citing papers in PubMed, 34 citations in OpenAlex.
- The Motility of Mouse Spermatozoa Changes Differentially After 30-Minute Exposure Under Simulating Weightlessness and Hypergravity.International journal of molecular sciences · 2024Article
- Embedding electronic health records onto a knowledge network recognizes prodromal features of multiple sclerosis and predicts diagnosis.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study.PloS one · 2020Article
- Protease-activated receptor-1 activation by granzyme B causes neurotoxicity that is augmented by interleukin-1β.Journal of neuroinflammation · 2017Article
- Identifying gene-gene interactions that are highly associated with four quantitative lipid traits across multiple cohorts.Human genetics · 2017Article
- Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network.Genetic epidemiology · 2015Article
- Novel genes in Human Asthma Based on a Mouse Model of Allergic Airway Inflammation and Human Investigations.Allergy, asthma & immunology research · 2014Article
- Pathway-guided identification of gene-gene interactions.Annals of human genetics · 2014Article
- Analysis pipeline for the epistasis search - statistical versus biological filtering.Frontiers in genetics · 2014Review
- Analysis of gene-gene interactions using gene-trait similarity regression.Human heredity · 2014Article
- Novel somatic single nucleotide variants within the RNA binding protein hnRNP A1 in multiple sclerosis patients.F1000Research · 2014Article
- Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development.BioData mining · 2013Article
- A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design.PloS one · 2013Article
- Gene-based testing of interactions in association studies of quantitative traits.PLoS genetics · 2013Article
- Review
- Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS.BioData mining · 2012Article
- Th17-related genes and celiac disease susceptibility.PloS one · 2012Article
- Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data.Nature reviews. Genetics · 2011Review
- Six Degrees of Epistasis: Statistical Network Models for GWAS.Frontiers in genetics · 2011Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 3 countries.
Funding
Abstract
Gene-gene interactions are proposed as an important component of the genetic architecture of complex diseases, and are just beginning to be evaluated in the context of genome-wide association studies (GWAS). In addition to detecting epistasis, a benefit to interaction analysis is that it also increases power to detect weak main effects. We conducted a knowledge-driven interaction analysis of a GWAS of 931 multiple sclerosis (MS) trios to discover gene-gene interactions within established biological contexts. We identify heterogeneous signals, including a gene-gene interaction between CHRM3 (muscarinic cholinergic receptor 3) and MYLK (myosin light-chain kinase) (joint P=0.0002), an interaction between two phospholipase C-β isoforms, PLCβ1 and PLCβ4 (joint P=0.0098), and a modest interaction between ACTN1 (actinin alpha 1) and MYH9 (myosin heavy chain 9) (joint P=0.0326), all localized to calcium-signaled cytoskeletal regulation. Furthermore, we discover a main effect (joint P=5.2E-5) previously unidentified by single-locus analysis within another related gene, SCIN (scinderin), a calcium-binding cytoskeleton regulatory protein. This work illustrates that knowledge-driven interaction analysis of GWAS data is a feasible approach to identify new genetic effects. The results of this study are among the first gene-gene interactions and non-immune susceptibility loci for MS. Further, the implicated genes cluster within inter-related biological mechanisms that suggest a neurodegenerative component to MS.
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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.