Detecting Genetic Association and Gene-Gene Interaction using Network Analysis in Case-Control Study

  • Received : 2012.04.10
  • Accepted : 2012.06.05
  • Published : 2012.08.31


Various methods of analysis have been proposed to understand the gene-disease relation and gene-gene interaction effect for a disease through comparison of genotype in case-control study. In this study, we proposed the method to detect a genetic association and gene-gene interaction through the use of a network graph and centrality measures that are used in social network analysis. The applicability of the proposed method was studied through an analysis of real genetic data.


Network analysis;genetic association;gene-gene interaction;centrality measures


  1. Balding, D. J. (2006). A tutorial on statistical methods for population association studies, Nature Reviews Genetics, 7, 781-791.
  2. Butts, C. T. (2008). Social network analysis with sna, Journal of Statistical Software, 24.
  3. Cordell, H. J. (2009). Detecting gene-gene interaction that underlies human diseases, Nature Reviews Genetics, 10, 392-403.
  4. Davis, N., Crowe, J., Pajewski, N. and McKinney, B. (2010). Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine, Genes and Immunity, 11, 630-636.
  5. Fortes, M., Reverter, A., Zhang, Y., Collis, E., Nagaraj, S. H., Jonsson, N., Prayaga, K., Barris, W. and Hawken, R. (2010). Association weight matrix for the genetic dissection of puberty in beef cattle, PNAS, 107, 13642-13647.
  6. Fruchterman, T. M. J. and Reingold, E. M. (1991). Graph drawing by force-directed placement, Software-Practice and Experience, 21, 1129-1164.
  7. Hanneman, R. A. and Riddle, M. (2005). Introduction to social network methods, Software-Practice and Experience.
  8. Huh, M. (2010). Introduction to Social Network Analysis Using R, Free Academy, Kyunggi.
  9. Jung, J., Yee, J., Lee, S. and Park, M. (2011). Exploration of the gene-gene interactions using the relative risks in distinct genotypes, The Korean Journal of Applied Statistics, 24, 861-869.
  10. Kamada, T. and Kawai, S. (1989). An algorithm for drawing general undirected graphs, Information Processing Letters, 31, 7-15.
  11. Lee, H., Kim, M. and Park, M. (2010). A review of genetic association analyses in population and family based data: Methods and software, The Korean Journal of Applied Statistics, 23, 95-111.
  12. Lindlof, A. and Olsson, B. (2002). Could correlation-based methods be used to derive genetic association networks?, Information Sciences, 146, 103-113.
  13. Namkung, J. H., Kim, K., Yi, S., Chung, W., Kwon, M. S. and Park, T. (2009). New evaluation measures for multifactor dimensionality reduction classifiers in gene-gene interaction analysis, Bioinformatics, 25, 338-345.
  14. Namkung, J. H., Lee, J., Kim, E., Cho, H. J., Kim, S., Shin, E. S., Cho, E. Y. and Yang, J. M. (2007). IL-5 and IL-5 receptor alpha polymorphisms are associated with atopic dermatitis in Koreans, Allergy, 62, 934-942.
  15. Ritchie, M. D., Hahn, L., Roodi, L., Bailey, L., Dupont, W., Parl, F. and Moore, J. H. (2001). Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer, American Journal of Human Genetics, 69, 138-147.
  16. Snel, B. and Huynen, M. (2002). The identification of functional modules from the genomic association of genes, PNAS, 99, 5890-5895.
  17. Sohn, D. (2002). Social Network Analysis, Kyungmunsa, Seoul.
  18. Yanai, I. adn DeLisi, C. (2002). The society of genes: networks of functional links between genes from comparative genomics, Genome Biology, 3, research0064.1-0064.12.
  19. Zhu, X., Gerstein, M. and Snyder, M. (2007). Getting connected: analysis and principles of biological networks, Genes and Development, 21, 1010-1024.


Supported by : National Research Foundation of Korea (NRF)