Chinese Journal of Tissue Engineering Research ›› 2012, Vol. 16 ›› Issue (33): 6206-6210.doi: 10.3969/j.issn.2095-4344.2012.33.025

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Exploring population structure using principal components analysis for common variants

Yang Zheng, Hua Lin, Liu Hong   

  1. Capital University of Medical Sciences, Beijing 100069, China
  • Received:2011-12-12 Revised:2012-02-23 Online:2012-08-12 Published:2012-08-12
  • Contact: Hua Lin, Lecturer, Capital University of Medical Sciences, Beijing 100069, China Liu Hong, Associate professor, Capital University of Medical Sciences, Beijing 100069, China
  • About author:Yang Zheng★, Studying for master’s degree, Capital University of Medical Sciences, Beijing 100069, China yzh31350@163.com

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Abstract:

BACKGROUND: Common variants play important roles in the population stratification and population structure studies.
OBJECTIVE: To explore population structure using principal components analysis for common variants.
METHODS: In this study, we extracted the first two principal components from the common variants, and performed classification to seven populations using random forest algorithm. In addition, we mined gene function by performing KEGG pathway and Gene Ontology enrichment analysis to those genes showing the highest loading in the first two principal components, respectively.
RESULTS AND CONCLUSION: The results showed that combining principal components analysis and random forest could improve the classification correct rate to 99.6%, suggesting that allele frequency differences between populations can be used to identify population structure. In addition, we also found the genes extracted by principal components analysis showed a certain functional aggregation, which approved that the methods of the present study could be used to direct molecule biology research and explore gene function.

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