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PubMed · 15473313

[Multiple nonlinear statistical method of population genetic structure based on the allelic polymorphism data].

Fu-Zhong Xue·Jie-Zhen Wang·Yi-Shou Guo·Ping Hu·Xue-Sen Wu
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Abstract

The distribution and structure of the allelic polymorphism data are analyzed and it is pointed out that the distribution of allelic polymorphism data reveals the characteristic of closed data (also named as compositional data or data of constant sum). It is interpreted that the correlation structure of the allelic polymorphism data contains null correlations introduced by "closure" and the statistical distribution of the data is not normal because of its constant row sum, which resulted in great difficulties in analyzing the data with traditional multiple linear statistical methods such as principal component analysis, factor analysis, cluster analysis and canonical correlation analysis. Based on the theory of compositional data analysis proposed by Aitchison in 1982, a multiple nonlinear statistical method originating from the "logratios" approach to the statistical analysis of compositional data is put forward in this paper. As an example, the "logratios" method was used to analyze the genetic structure of TH01 polymorphic loci in Chinese population and the results were compared with those of multiple linear methods such as component principal. It is concluded that the "logratios" multiple nonlinear principle component analysis is a better method with the virtue of sensitivity and specificity for analyzing the genetic structure of population from the data of allelic polymorphism.

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AllelesGene FrequencyGenetics, PopulationHumansNonlinear DynamicsPolymorphism, GeneticEnglish AbstractJournal ArticleResearch Support, Non-U.S. Gov'tReview

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Fu-Zhong Xue, Jie-Zhen Wang, Yi-Shou Guo, Ping Hu, Xue-Sen Wu. 2004. [Multiple nonlinear statistical method of population genetic structure based on the allelic polymorphism data].. https://pubmed.ncbi.nlm.nih.gov/15473313/

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