Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 Diabetes

Zhu, Zhixiang, Tong, Xiaoran, Zhu, Zhihong, Liang, Meimei, Cui, Wenyan, Su, Kunkai, Li, Ming D. and Zhu, Jun (2013) Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 Diabetes. PLoS One, 8 4: e61943.1-e61943.9. doi:10.1371/journal.pone.0061943

Author Zhu, Zhixiang
Tong, Xiaoran
Zhu, Zhihong
Liang, Meimei
Cui, Wenyan
Su, Kunkai
Li, Ming D.
Zhu, Jun
Title Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 Diabetes
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2013-04-23
Sub-type Article (original research)
DOI 10.1371/journal.pone.0061943
Open Access Status DOI
Volume 8
Issue 4
Start page e61943.1
End page e61943.9
Total pages 9
Editor Huiping Zhang
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Language eng
Subject 1100 Agricultural and Biological Sciences
1300 Biochemistry, Genetics and Molecular Biology
2700 Medicine
Formatted 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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Brain Institute Publications
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Citation counts: TR Web of Science Citation Count  Cited 15 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 01 Aug 2014, 23:22:32 EST by Zhihong Zhu on behalf of Queensland Brain Institute