Advertisement

Improved Classification Method for Detecting Potential Interactions Between Genes

  • Li-Yeh Chuang
  • Yu-Da LinEmail author
  • Cheng-Hong YangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

Multifactor dimensionality reduction (MDR) constitutes a highly accurate classification algorithm for gene–gene interaction (GGI) identification. GGI detection quality is commonly assessed using the correct classification rate (CCR). Nevertheless, the CCR alone might not be suitable for assessing the detection of some GGIs due to various model preferences and disease complexities. Accordingly, we developed an MDR-based multiple-objective method that combines the CCR and chi-squared measures (called MDR–Cχ2) for GGI detection. In the proposed method, a Pareto set operation is executed to ensure the combination of the CCR and chi-squared measures in the MDR process for GGI detection. The most significant GGIs within the Pareto sets are determined using cross-validation consistency values. Herein, we report the MDR and MDR–Cχ2 detection success rates in a simulated environment and demonstrate that the proposed MDR–Cχ2 provides superior GGI detection success rates.

Keywords

Classification Multifactor dimensionality reduction Multiple objective 

Notes

Acknowledgment

The Ministry of Science and Technology, R.O.C., partially supported this study (Grants 105-2221-E-151-053-MY2 and 106-2811-E-151-002).

References

  1. 1.
    Steen, K.V.: Travelling the world of gene-gene interactions. Brief. Bioinform. 13, 1–19 (2012)CrossRefGoogle Scholar
  2. 2.
    Sun, J.W., Bi, J.B., Kranzler, H.R.: Multiview comodeling to improve subtyping and genetic association of complex diseases. IEEE J. Biomed. Health Inform. 18, 548–554 (2014)CrossRefGoogle Scholar
  3. 3.
    Kourou, K., Papaloukas, C., Fotiadis, D.I.: Integration of pathway knowledge and dynamic bayesian networks for the prediction of oral cancer recurrence. IEEE J. Biomed. Health Inform. 21, 320–327 (2017)CrossRefGoogle Scholar
  4. 4.
    Moore, J.H., Asselbergs, F.W., Williams, S.M.: Bioinformatics challenges for genome-wide association studies. Bioinformatics 26, 445–455 (2010)CrossRefGoogle Scholar
  5. 5.
    Mackay, T.F.C.: Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nat. Rev. Genet. 15, 22–33 (2014)CrossRefGoogle Scholar
  6. 6.
    Mackay, T.F.C., Moore, J.H.: Why epistasis is important for tackling complex human disease genetics. Genome Med. 6, 42 (2014)CrossRefGoogle Scholar
  7. 7.
    Kooperberg, C., Ruczinski, I.: Identifying interacting SNPs using Monte Carlo logic regression. Genet. Epidemiol. 28, 157–170 (2005)CrossRefGoogle Scholar
  8. 8.
    Zhang, Y., Liu, J.S.: Bayesian inference of epistatic interactions in case-control studies. Nat. Genet. 39, 1167–1173 (2007)CrossRefGoogle Scholar
  9. 9.
    Chuang, L.Y., Moi, S.H., Lin, Y.D., Yang, C.H.: A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes. Artif. Intell. Med. 73, 23–33 (2016)CrossRefGoogle Scholar
  10. 10.
    Yang, C.-H., Lin, Y.-D., Chuang, L.-Y., Chang, H.-W.: Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility. J. Biomed. Inform. 63, 112–119 (2016)CrossRefGoogle Scholar
  11. 11.
    Yang, C.-H., Lin, Y.-D., Chuang, L.-Y., Chen, J.-B., Chang, H.-W.: Joint analysis of SNP–SNP-Environment interactions for chronic dialysis by an improved branch and bound algorithm. J. Comput. Biol. 24(12), 1212–1225 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hahn, L.W., Ritchie, M.D., Moore, J.H.: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19, 376–382 (2003)CrossRefGoogle Scholar
  13. 13.
    Zhang, X., Huang, S.P., Zou, F., Wang, W.: TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics 26, i217–i227 (2010)CrossRefGoogle Scholar
  14. 14.
    Li, J.H., Dan, J., Li, C.L., Wu, R.L.: A model-free approach for detecting interactions in genetic association studies. Brief. Bioinform. 15, 1057–1068 (2014)CrossRefGoogle Scholar
  15. 15.
    Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., et al.: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69, 138–147 (2001)CrossRefGoogle Scholar
  16. 16.
    Yang, C.H., Lin, Y.D., Yen, C.Y., Chuang, L.Y., Chang, H.W.: A systematic gene-gene and gene-environment interaction analysis of DNA repair genes XRCC1, XRCC2, XRCC3, XRCC4, and oral cancer risk. OMICS-A J. Integr. Biol. 19, 238–247 (2015)CrossRefGoogle Scholar
  17. 17.
    Yang, C.H., Lin, Y.D., Wu, S.J., Chuang, L.Y., Chang, H.W.: High order gene-gene interactions in eight single nucleotide polymorphisms of renin-angiotensin system genes for hypertension association study. Biomed Res. Int. 2015 (2015). Article ID 454091Google Scholar
  18. 18.
    Fu, O.Y., Chang, H.W., Lin, Y.D., Chuang, L.Y., Hou, M.F., Yang, C.H.: Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER). Oncol. Rep. 36, 1739–1747 (2016)CrossRefGoogle Scholar
  19. 19.
    Gola, D., John, J.M.M., van Steen, K., Konig, I.R.: A roadmap to multifactor dimensionality reduction methods. Brief. Bioinform. 17, 293–308 (2016)CrossRefGoogle Scholar
  20. 20.
    Gui, J., Moore, J.H., Williams, S.M., Andrews, P., Hillege, H.L., van der Harst, P., et al.: A simple and computationally efficient approach to multifactor dimensionality reduction analysis of gene-gene interactions for quantitative traits. PLoS One 8, e66545 (2013)CrossRefGoogle Scholar
  21. 21.
    Lee, S., Kwon, M.S., Oh, J.M., Park, T.: Gene-gene interaction analysis for the survival phenotype based on the Cox model. Bioinformatics 28, I582–I588 (2012)CrossRefGoogle Scholar
  22. 22.
    Chung, Y.J., Lee, S.Y., Elston, R.C., Park, T.: Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions. Bioinformatics 23, 71–76 (2007)CrossRefGoogle Scholar
  23. 23.
    Lee, S.Y., Chung, Y., Elston, R.C., Kim, Y., Park, T.: Log-linear model-based multifactor dimensionality reduction method to detect genegene interactions. Bioinformatics 23, 2589–2595 (2007)CrossRefGoogle Scholar
  24. 24.
    Yang, C.H., Lin, Y.D., Chuang, L.Y., Chang, H.W.: Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes. IEEE ACM Trans. Comput. Biol. Bioinform. 10, 361–371 (2013)CrossRefGoogle Scholar
  25. 25.
    Yu, W., Lee, S., Park, T.: A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions. Bioinformatics 32, 605–610 (2016)CrossRefGoogle Scholar
  26. 26.
    Greene, C.S., Sinnott-Armstrong, N.A., Himmelstein, D.S., Park, P.J., Moore, J.H., Harris, B.T.: Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS. Bioinformatics 26, 694–695 (2010)CrossRefGoogle Scholar
  27. 27.
    Yang, C.H., Lin, Y.D., Yang, C.S., Chuang, L.Y.: An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction. BMC Genom. 16, 489 (2015)Google Scholar
  28. 28.
    Yang, C.-H., Chuang, L.-Y., Lin, Y.-D.: CMDR based differential evolution identify the epistatic interaction in genome-wide association studies. Bioinformatics 33, 2354–2362 (2017)CrossRefGoogle Scholar
  29. 29.
    Greco, S., Figueira, J., Ehrgott, M.: Multiple Criteria Decision Analysis, vol. 233. Springer, New York, Dordrecht, Heidelberg, London (2005)zbMATHGoogle Scholar
  30. 30.
    Deb, K., Sindhya, K., Hakanen, J.: Multi-objective optimization. In: Decision Sciences: Theory and Practice. CRC Press, pp. 145–184 (2016)Google Scholar
  31. 31.
    Wang, J., Zhu, S., Zhang, W., Lu, H.: Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 35, 1671–1678 (2010)CrossRefGoogle Scholar
  32. 32.
    Shang, J.L., Zhang, J.Y., Lei, X.J., Zhao, W.Y., Dong, Y.F.: EpiSIM: simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis. Genes Genomics 35, 305–316 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Chemical EngineeringI-Shou UniversityKaohsiungTaiwan
  2. 2.Department of Electronic EngineeringNational Kaohsiung University of Science and TechnologyKaohsiungTaiwan
  3. 3.Graduate Institute of Clinical MedicineKaohsiung Medical UniversityKaohsiungTaiwan

Personalised recommendations