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http://dx.doi.org/10.5808/GI.2016.14.4.160

Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes  

Li, Donghe (Interdisciplinary Program of Bioinformatics, Seoul National University)
Wo, Sungho (Interdisciplinary Program of Bioinformatics, Seoul National University)
Abstract
Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.
Keywords
epistasis; gene-gene interaction; genome-wide association study; type 2 diabetes mellitus;
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1 Ueki M, Cordell HJ. Improved statistics for genome-wide interaction analysis. PLoS Genet 2012;8:e1002625.   DOI
2 Won S, Kwon MS, Mattheisen M, Park S, Park C, Kihara D, et al. Efficient strategy for detecting gene x gene joint action and its application in schizophrenia. Genet Epidemiol 2014;38:60-71.   DOI
3 Wang X, Elston RC, Zhu X. Statistical interaction in human genetics: how should we model it if we are looking for biological interaction? Nat Rev Genet 2011;12:74.
4 Hu JK, Wang X, Wang P. Testing gene-gene interactions in genome wide association studies. Genet Epidemiol 2014;38:123-134.   DOI
5 Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001;69:138-147.   DOI
6 Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat Genet 2007;39:1167-1173.   DOI
7 Schwarz DF, Konig IR, Ziegler A. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data. Bioinformatics 2010;26:1752-1758.   DOI
8 Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-575.   DOI
9 Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NL, et al. BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet 2010;87: 325-340.   DOI
10 Yung LS, Yang C, Wan X, Yu W. GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. Bioinformatics 2011;27:1309-1310.   DOI
11 Ko SH, Kim SR, Kim DJ, Oh SJ, Lee HJ, Shim KH, et al. 2011 clinical practice guidelines for type 2 diabetes in Korea. Diabetes Metab J 2011;35:431-436.   DOI
12 Park SE, Lee WY, Oh KW, Baek KH, Yoon KH, Kang MI, et al. Impact of common type 2 diabetes risk gene variants on future type 2 diabetes in the non-diabetic population in Korea. J Hum Genet 2012;57:265-268.   DOI
13 Park KS. The search for genetic risk factors of type 2 diabetes mellitus. Diabetes Metab J 2011;35:12-22.   DOI
14 Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534.   DOI
15 Ross KA. Evidence for somatic gene conversion and deletion in bipolar disorder, Crohn's disease, coronary artery disease, hypertension, rheumatoid arthritis, type-1 diabetes, and type-2 diabetes. BMC Med 2011;9:12.   DOI
16 Matsuda H. Physical nature of higher-order mutual information: intrinsic correlations and frustration. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 2000;62(3 Pt A): 3096-3102.
17 Howie B, Marchini J, Stephens M. Genotype imputation with thousands of genomes. G3 (Bethesda) 2011;1:457-470.
18 Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38:904-909.   DOI
19 Giri A, Sanders M, Velez Edwards D, Ikizler T, Roden D, Birdwell K. A genome wide association study of new onset diabetes after transplant in kidney transplantation. Am J Transplant 2016;16(Suppl 3):B235.   DOI
20 Agresti A. Categorical Data Analysis. 2nd ed. New York: Wiley-Interscience, 2002.