Abstract
Population genetic structure is one of themost important population genetic parameters revealing its demographic features. Theaim of this study was to evaluate thehomogeneity of the Lithuanian population on the basis of the genome-wide genotyping data. Thecomparative analysis of three methods-multidimensional scaling, principal components, and principal coordinates analysis-to visualize multidimensional genetics data was performed. Theresults of visualization (mapping images) are also presented. The data set consisted of 425 samples from six ethnolinguistic groups of theLithuanian population. Genomic DNA was extracted from whole venous blood using either thephenol-chloroform extraction method or theautomated DNA extraction platform TECAN Freedom EVO. Genotyping was performed at theDepartment of Human and Medical Genetics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Lithuania, with theIllumina HumanOmniExpress-12 v1.1 and theInfinium OmniExpress-24. For theestimation of homogeneity of theLithuanian population, PLINKdata file was obtained using PLINK v1.07 program. The Past3 software was used to visualize thegenotype data with multidimensional scaling and principal coordinates methods. The SmartPCA from EIGENSOFT 7.2.1 program was used in theprincipal component analysis to determine thepopulation structure. Methods of multidimensional scaling, principal coordinate, and principal component for thegenetic structure of the Lithuanian population were investigated and compared. Theprincipal coordinate and principal component methods can be used for genotyping data visualization, since any essential differences in theresults obtained were not observed and compared to multidimensional scaling. The Lithuanian population is homogenous whereas thepoints are strongly close when we use theprincipal coordinates or principal component methods.
Highlights
Nowadays population genetic structure is one of the most important parameters in analysing pop ulation research
Different genetic models based on genetic markers are used to evaluate the pop ulation structure
Genotyping was performed at the Department of Human and Medical Genetics, Institute of Bio medical Sciences, Faculty of Medicine, Vilnius University, Lithuania, with Illumina HumanOm niExpress-12v1.1 (296 samples) and the Infinium OmniExpress-24 (129 samples) arrays (Illumina, San Diego, CA, USA), with overlap of 707,138 single nucleotide poly morphisms (SNPs) genome-wide distributed
Summary
Nowadays population genetic structure is one of the most important parameters in analysing pop ulation research. Different genetic models based on genetic markers are used to evaluate the pop ulation structure. Appropriate mathematical me thods have been developed for different genetic models to obtain information from genetic mark er data to explore the population structure. There is a large class of methods that have been developed for multidimensional data visualiza tion [1, 2]. The visual presentation of the data en ables seeing the data structure, clusters, outliers, and other properties of multidimensional data. The comparative analysis of three methods – multi dimensional scaling, principal components, and principal coor dinates analysis – to visualize multidimensional genetics data was performed.
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