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GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data

Cited 5 time in Web of Science Cited 2 time in Scopus
Authors
Kwon, Minseok; Leem, Sangseob; Yoon, Joon; Park, Taesung
Issue Date
2018-03-19
Publisher
BioMed Central
Citation
BMC Systems Biology, 12(Suppl 2):19
Keywords
Gene-gene interactionRare variantMultifactor dimensionality reduction
Abstract
Background
With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants.

Results
Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes.

Conclusion
The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at
http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.
ISSN
1752-0509
Language
English
URI
https://hdl.handle.net/10371/139760
DOI
https://doi.org/10.1186/s12918-018-0543-4
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Journal Papers (저널논문_통계학과)
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