Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Identify Major Gene-Gene Interaction Effects Using SNPHarvester

SNPHarvester를 활용한 주요 유전자 상호작용 효과 감명

  • Received : 20091000
  • Accepted : 20091100
  • Published : 2009.11.30
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Abstract

The gene which is related in the disease of the human has been searched among numerous genes in GWA(Genome-Wide Association) research. However, most current statistical methods used to detect gene-gene interactions in disease association studies cannot be easily applied to handle the whole genome association study(GWAS) due to heavy computing. Therefore SNPHarvester is developed to find the main gene group among numerous genes. This research finds the superior gene groups which are related with the economic traits of the Korean beef cattle, not that of human, among sets of SNPs by using SNPHarvester, and also finds the superior genotypes which can enhance various qualities of Korean beef among SNP groups.

광범위 유전자 연관(genome-wide association) 연구에서는 무수히 많은 유전자들 중에 인간의 질병에 관련된 유전자를 찾아왔다. 기존의 인간 질병에 관련된 유전자를 찾는 방법에서 이렇게 많은 유전자들 중에서 우수한 유전자를 찾는데 직접 이용할 시에는 계산이 복잡해지고 비용이 많이 들어가며 시간이 오래 걸린다는 단점이 생긴다. 따라서 이번 수많은 유전자들 중 주요 유전자 그룹을 찾는 방법으로 SNPHarvester가 개발되였다. 본 연구에서는 인간의 질병이 아닌 한우의 여러 경제형질에 관련된 우수 유전자를 SNPHarvester를 이용하여 17 개의 SNP들 중에서 우수한 유전자 그룹을 찾았고 의사결정나무(decision tree)를 이용하여 한우의 여러 경제형질을 높일 수 있는 SNP 그룹 내의 우수 유전자형도 함께 규명할 수 있었다.

Keywords

References

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