ArticlePloS one2013
Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach.Journal of the neurological sciences · 2017Pooled it
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- Genome-wide association studies revealed complex genetic architecture and breeding perspective of maize ear traits.BMC plant biology · 2022Article
- Polymorphisms and Gene-Gene Interaction in AGER/IL6 Pathway Might Be Associated with Diabetic Ischemic Heart Disease.Journal of personalized medicine · 2022Article
- Association analysis between the interaction of RAS family genes mutations and papillary thyroid carcinoma in the Han Chinese population.International journal of medical sciences · 2021Observational
- Genetic Modifiers and Rare Mendelian Disease.Genes · 2020Review
- Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets.Frontiers in genetics · 2020Article
- Robustification of Linear Regression and Its Application in Genome-Wide Association Studies.Frontiers in genetics · 2020Article
- Genetic Susceptibility to Gestational Diabetes Mellitus in a Chinese Population.Frontiers in endocrinology · 2020Article
- Single-cell transcriptomics unveils gene regulatory network plasticity.Genome biology · 2019Article
- Statistical Methods and Software for Substance Use and Dependence Genetic Research.Current genomics · 2019Review
- Interaction between TP53 and XRCC1 increases susceptibility to cervical cancer development: a case control study.BMC cancer · 2019Article
- Genome-wide association study of maize plant architecture using FPlant molecular biology · 2019Article
- Association and cis-mQTL analysis of variants in serotonergic genes associated with nicotine dependence in Chinese Han smokers.Translational psychiatry · 2018Article
- Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions.Scientific reports · 2018Article
- Evolutionary footprint of epistasis.PLoS computational biology · 2018Article
- Dissection of complicate genetic architecture and breeding perspective of cottonseed traits by genome-wide association study.BMC genomics · 2018Article
- Association and cis-mQTL analysis of variants in CHRNA3-A5, CHRNA7, CHRNB2, and CHRNB4 in relation to nicotine dependence in a Chinese Han population.Translational psychiatry · 2018Article
- Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population.Frontiers in plant science · 2018Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Although genome-wide association studies (GWAS) have identified a significant number of single-nucleotide polymorphisms (SNPs) associated with many complex human traits, the susceptibility loci identified so far can explain only a small fraction of the genetic risk. Among other possible explanations, the lack of a comprehensive examination of gene-gene interaction (G×G) is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR) approach for detecting G×G in both dichotomous and quantitative phenotypes. However, the computational burden and less efficient implementation of the original programs make them impossible to use for GWAS. In this study, we developed a graphics processing unit (GPU)-based GMDR program (named GWAS-GPU), which is able not only to analyze GWAS data but also to run much faster than the earlier version of the GMDR program. As a demonstration of the program, we used the GMDR-GPU software to analyze a publicly available GWAS dataset on type 2 diabetes (T2D) from the Wellcome Trust Case Control Consortium. Through an exhaustive search of pair-wise interactions and a selected search of three- to five-way interactions conditioned on significant pair-wise results, we identified 24 core SNPs in six genes (FTO: rs9939973, rs9940128, rs9922047, rs1121980, rs9939609, rs9930506; TSPAN8: rs1495377; TCF7L2: rs4074720, rs7901695, rs4506565, rs4132670, rs10787472, rs11196205, rs10885409, rs11196208; L3MBTL3: rs10485400, rs4897366; CELF4: rs2852373, rs608489; RUNX1: rs445984, rs1040328, rs990074, rs2223046, rs2834970) that appear to be important for T2D. Of these core SNPs, 11 in FTO, TSPAN8, and TCF7L2 have been reported to be associated with T2D, obesity, or both, providing an independent replication of previously reported SNPs. Importantly, we identified three new susceptibility genes; i.e., L3MBTL3, CELF4, and RUNX1, for T2D, a finding that warrants further investigation with independent samples.
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