Searching SNP Combinations Related to Evolutionary Information of Human Populations on HapMap Data

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• 作者：Xiaojun Ding (21)
  Haihua Gu (22)
  Zhen Zhang (21)
  Min Li (21)
  Fangxiang Wu (23)
  
• 关键词：Multi ; SNP combination ; SNP ; SNP interaction ; Evolution tree
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8492
• 期：1
• 页码：278-288
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• 作者单位：Xiaojun Ding (21)
  Haihua Gu (22)
  Zhen Zhang (21)
  Min Li (21)
  Fangxiang Wu (23)
  
  21. School of Information Science and Engineering, Central South University, Changsha, 410083, P.R. China
  22. School of Computer and Software, Nanjing College of Information Technology, Nanjing, 210023, P.R. China
  23. Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
  
• ISSN：1611-3349

文摘

The International HapMap Project is a partnership of scientists and funding agencies from different countries to develop a public resource that will help researchers find genes associated with human disease and response to pharmaceuticals. The project has collected large amounts of SNP(single-nucleotide polymorphism) data of individuals of different human populations. Many researchers have revealed evolution information from the SNP data. But how to find all the SNPs related to human evolution is still a hard work. At most time, these SNPs work together which leads to the differences between different human populations. The number of SNP combinations is very large, thus it is impossible to check all the combinations. In this paper, a novel algorithm is proposed to find the SNP combinatorial patterns whose frequencies are quite different in two different populations. The numbers of the multi-SNP combinations are regarded as the differences between each paired human populations, then a hierarchical clustering algorithm is used to construct the evolution trees for human populations. The trees from 4 chromosomes are consistent and the result can be validated by other literatures, which indicates that evolutionary information is well mined. The multi-SNP combinations found by our method can be studied further in many aspects.