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      Development of GMDR-GPU for Gene-Gene Interaction Analysis and Its Application to WTCCC GWAS Data for Type 2 Diabetes

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          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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          Many sequence variants affecting diversity of adult human height.

          Adult human height is one of the classical complex human traits. We searched for sequence variants that affect height by scanning the genomes of 25,174 Icelanders, 2,876 Dutch, 1,770 European Americans and 1,148 African Americans. We then combined these results with previously published results from the Diabetes Genetics Initiative on 3,024 Scandinavians and tested a selected subset of SNPs in 5,517 Danes. We identified 27 regions of the genome with one or more sequence variants showing significant association with height. The estimated effects per allele of these variants ranged between 0.3 and 0.6 cm and, taken together, they explain around 3.7% of the population variation in height. The genes neighboring the identified loci cluster in biological processes related to skeletal development and mitosis. Association to three previously reported loci are replicated in our analyses, and the strongest association was with SNPs in the ZBTB38 gene.
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            Prioritizing GWAS results: A review of statistical methods and recommendations for their application.

            Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach. 2010 The American Society of Human Genetics. Published by Elsevier Inc.
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              Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.

              Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                23 April 2013
                : 8
                : 4
                : e61943
                Affiliations
                [1 ]Institute of Bioinformatics, Zhejiang University, Hangzhou, China
                [2 ]State Key Laboratory for Diagnosis and Treatment of Infection Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
                [3 ]Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, Virginia, United States of America
                Yale University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MDL JZ. Analyzed the data: Zhixiang Zhu XT Zhihong Zhu ML WC KS. Wrote the paper: Zhixiang Zhu WC MDL JZ.

                Article
                PONE-D-12-36724
                10.1371/journal.pone.0061943
                3633958
                23626757
                fdb1af47-cd7d-4be2-99c6-96a55d7d73a1
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 21 November 2012
                : 15 March 2013
                Page count
                Pages: 9
                Funding
                This work was supported in part by National Basic Research Program of China (2009CB118404), Key Technologies R&D Program in China (2009ZX08009-004B), National Natural Science Foundation of China grant 81273223, Microsoft Research Asia, Microsoft Server and Tools Business Department, and NIH grant DA-012844. Funding for the Wellcome Trust Case Control Consortium project was provided by the Wellcome Trust under award 076113. The funders had no rule in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Computational Biology
                Genomics
                Genome Analysis Tools
                Genome-Wide Association Studies
                Genetics
                Epigenetics
                Genetics of Disease
                Genome-Wide Association Studies
                Genomics
                Genome Analysis Tools
                Genome-Wide Association Studies
                Medicine
                Endocrinology
                Diabetic Endocrinology
                Diabetes Mellitus Type 2

                Uncategorized
                Uncategorized

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