ArticleEuropean journal of human genetics : EJHG2015
A gene-based information gain method for detecting gene-gene interactions in case-control studies.
Article in European journal of human genetics : EJHG, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transferring entropy to the realm of GxG interactions.Briefings in bioinformatics · 2018Pooled it
- cLD: Rare-variant linkage disequilibrium between genomic regions identifies novel genomic interactions.PLoS genetics · 2023Article
- A gene-based association test of interactions for maternal-fetal genotypes identifies genes associated with nonsyndromic congenital heart defects.Genetic epidemiology · 2023Article
- Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.Frontiers in genetics · 2021Article
- Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies.Frontiers in cell and developmental biology · 2021Article
- Integrate Molecular Phenome and Polygenic Interaction to Detect the Genetic Risk of Ischemic Stroke.Frontiers in cell and developmental biology · 2020Article
- Article
- A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia.Journal of neuroscience methods · 2018Article
- Is the Sequence of SuperAlarm Triggers More Predictive Than Sequence of the Currently Utilized Patient Monitor Alarms?IEEE transactions on bio-medical engineering · 2017Article
- Genetics of Common Antipsychotic-Induced Adverse Effects.Molecular neuropsychiatry · 2016Review
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Authors and funding
10 authors.
Funding
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Abstract
Currently, most methods for detecting gene-gene interactions (GGIs) in genome-wide association studies are divided into SNP-based methods and gene-based methods. Generally, the gene-based methods can be more powerful than SNP-based methods. Some gene-based entropy methods can only capture the linear relationship between genes. We therefore proposed a nonparametric gene-based information gain method (GBIGM) that can capture both linear relationship and nonlinear correlation between genes. Through simulation with different odds ratio, sample size and prevalence rate, GBIGM was shown to be valid and more powerful than classic KCCU method and SNP-based entropy method. In the analysis of data from 17 genes on rheumatoid arthritis, GBIGM was more effective than the other two methods as it obtains fewer significant results, which was important for biological verification. Therefore, GBIGM is a suitable and powerful tool for detecting GGIs in case-control studies.
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