Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

  • Lee, Sungyoung (Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Kwon, Min-Seok (Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Park, Taesung (Interdisciplinary Program in Bioinformatics, Seoul National University)
  • Received : 2012.10.31
  • Accepted : 2012.11.16
  • Published : 2012.12.31
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Abstract

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene ($G{\times}G$) interactions. However, the biological interpretation of $G{\times}G$ interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified $G{\times}G$ interactions. The proposed network graph analysis consists of three steps. The first step is for performing $G{\times}G$ interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified $G{\times}G$ interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform $G{\times}G$ interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified $G{\times}G$ interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of $G{\times}G$ interactions.

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References

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