Genomics & Informatics

  • 제14권4호
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  • Pages.160-165
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  • 2016
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  • 1598-866X(pISSN)
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  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
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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)
  • 투고 : 2016.08.29
  • 심사 : 2016.11.20
  • 발행 : 2016.12.31
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초록

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.

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참고문헌

  1. Ueki M, Cordell HJ. Improved statistics for genome-wide interaction analysis. PLoS Genet 2012;8:e1002625. https://doi.org/10.1371/journal.pgen.1002625
  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. https://doi.org/10.1002/gepi.21779
  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. https://doi.org/10.1002/gepi.21786
  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. https://doi.org/10.1086/321276
  6. Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat Genet 2007;39:1167-1173. https://doi.org/10.1038/ng2110
  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. https://doi.org/10.1093/bioinformatics/btq257
  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. https://doi.org/10.1086/519795
  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. https://doi.org/10.1016/j.ajhg.2010.07.021
  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. https://doi.org/10.1093/bioinformatics/btr114
  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. https://doi.org/10.4093/dmj.2011.35.5.431
  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. https://doi.org/10.1038/jhg.2012.16
  13. Park KS. The search for genetic risk factors of type 2 diabetes mellitus. Diabetes Metab J 2011;35:12-22. https://doi.org/10.4093/dmj.2011.35.1.12
  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. https://doi.org/10.1038/ng.357
  15. Agresti A. Categorical Data Analysis. 2nd ed. New York: Wiley-Interscience, 2002.
  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. https://doi.org/10.1038/ng1847
  19. 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. https://doi.org/10.1186/1741-7015-9-12
  20. 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. https://doi.org/10.1111/ajt.13426