Biomedical subjects

Jie-Zhen Wang

Publications and source records attributed to Jie-Zhen Wang.

4 recordsLinked to original sources

PubMed2005

The "Kriging" model of spatial genetic structure in human population genetics.

This paper presents the application of Kriging technique in the field of human population genetics for quantifying the spatial genetic heterogeneity of HLA-A locus in the area of China,and for mapping its spatial genetic structure using the measurement of synthetic genetic structure (SPC) and the principal components (PC). Both principles of the method and the basic equations are given. The Kriging model has several advantages over other interpolation and smoothing methods. Firstly, it relies on the structure of the spatial genetic semivariogram model, which can be used to quantify the spatial genetic heterogeneity of the locus (loci) before mapping its spatial genetic structure. Secondly, it is virtually unbiased in the interpolation situation,where the location to be estimated is surrounded by data on all sides and is influenced within the range of these data. Thirdly, it allows of estimative error of interpolation, which can be used to appraise the predicting effect for the spatial estimation,and the error maps can be used to decide where to introduce new sampling population genetic data. However, the "Kriging" model also has some disadvantages. Firstly,when the theoretical spatial genetic semivariogram can not be fitted by any models, the "Kriging" model can not be set up. Secondly, if the Kriging model was built by a poor spatial genetic semivariogram,the Kriging estimation standard deviation is remarkably high in the whole area, hence the Kriging model can not be suitable to estimating the distribution of spatial genetic structure. In these situations,the interpolation algorithm, whose assumption is spatial random rather than spatial autocorrelation,such as the Cavalli-Sforza method in Genography, inverse distance-weighted methods, splines, should be used to estimate or map the distribution of spatial genetic structure.

Genetics, Population↗

PubMed2005

Spatial genetic structure of two HIV-I-resistant polymorphisms (CCR2-64 I and SDF1-3'A) alleles in population of Shandong Province, China.

OBJECTIVE: To explore the spatial genetic structure of two HIV-I-resistant polymorphisms (CCR2-64 I and SDF1-3'A) alleles in the population of Shandong Province, China. METHODS: Using the techniques of spatial stratified sampling and spatial statistics, the spatial genetic structure of the locus (CCR2-64 I and SDF1-3'A), which was shown to be important co-receptor for HIV infection, was quantified from the populations of 36 sampled counties of Shandong Province, and a total of 3147 and 3172 samples were taken for testing CCR2-64I and SDF1-3'A respectively from individuals without known history of HIV-I infection and AIDS symptoms. RESULTS: There were significantly spatial genetic structures of the two alleles at different spatial distance classes on the scale of populations, but on the scale of individuals, no spatial structure was found in either the whole area of Shandong Province or the area of each sampled county. Although the change of frequencies of the two alleles with geographic locations in Shandong Province both showed gradual increase trends, their changing directions were inverse. The frequency of CCR2-64I allele gradually increased from the southwest to the northeast, while the frequency of SDF1-3'A allele gradually increased from the northeast to the southwest. However the RH to AIDS of combined types of their different genotypes did not represent obvious geographic diversity on the whole area of the Province. CONCLUSION: The frequency of allele usually has some spatial genetic structures or spatial autocorrelation with different spatial distance classes, but the genotypes of individuals have random distribution in the same geographic area. Evaluating spatial distribution of the genetic susceptibility of HIV (AIDS) to CCR2-64I and SDF1-3'A alleles, should focus on the frequencies of combined genotypes of CCR2 and SDF1 based on the two-locus genotypes of each individual rather than the frequencies of CCR2-64I and SDF1-3'A alleles.

Acquired Immunodeficiency Syndrome↗

PubMed2005

[The "horse-shoe effect"of correspondence analysis for human population genetic structure and its population genetic explanation].

At present study, the reasons of "horse-shoe effect" in correspondence analysis for analyzing human population genetic structure was explained. Based on the structure of gene frequency matrix, we displaye the different patterns of Scallergram of correspondent analysis from different types of loci (HLA-A locus, and STR- CSF1PO locus in Chinese Han populations). The results indicate that different types of loci showed different patterns of Scallergram of correspondent analysis. When some alleles have very low frequency in the gene frequency matrix, there would be "horse-shoe effect" in the Scallergram of correspondent analysis. The reason is that the c2 distance measurement in correspondent analysis usually overrates the effect of the genes with low frequencies. To carry out the correspondent analysis of human population genetic structure, when the Scallergram presents "horse-shoe effect", one should examine the structure of gene frequency matrix, and confirm whether the "horse-shoe effect" shows the real pattern of population genetic structure. Only in this way, one can explain the "horse-shoe effect" correctly.

Alleles↗

PubMed2004

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

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.

Alleles↗