Sirvi Autor "Nelis, Mari" järgi

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Genetic structure of the Estonian population and genetic distance from other populations of European descent
(Tartu University Press, 2010-03-10T08:24:00Z) Nelis, Mari
The popularity to find the genes causing the common complex diseases has increased markedly in recent years. The complex diseases such as cardiovascular disorders, hypertension, various cancers, diabetes or asthma, are difficult to study since many genes contribute to the disease. One way to study the complex diseases is to use the population based association studies, in case the allele frequency of genetic markers is compared between the cases (diseases carrying individuals) and controls (disease non-carriers). Often, variations in a single population are of insufficient frequency to provide an adequate number of individuals for a study, and individuals from various nationalities are used in one study. As the differences in allele frequencies between cases and controls may be caused by systematic differences in ancestry, rather than by the association of genes with the disease, the population stratification should be tested carefully before data analysis. In the current Ph.D. thesis I have studied three aspects that influence the performance of the whole-genome association studies, such as marker selection, the informativeness of the used commercial genotyping chips, and also characterized the genetic structure inside Estonian population and between other European populations. HapMap database, initially based on genotyping data of four populations (Europe, China, Japan and Africa), is a good start to select the markers for the association study. The analyses showed that markers (tagSNPs) selected from the HapMap European sample, capture most of the variation in the Estonian sample (90-95% of the common SNPs). Still, it is possible to use the commercial chips in association studies. Two main companies, Affymetrix and Illumina produce whole-genome genotyping chips. As the strategy of marker selection is different between these companies, the Illumina chips perform better in Estonian sample as the markers are selected from the HapMap European population dataset. Further, the genetic structure of Estonian population and the distance from other European populations was studied using the principal component analysis of genotype data of more than 270,000 SNP markers of 3112 individuals from Europe. The analysis yielded a genetic structure map of Europe in which two first principal components highlight genetic diversity corresponding to a northwest to southeast gradient, and position the populations according to their approximate geographic origin. The results of this thesis demonstrate that Estonian samples can be analyzed with most other European samples, with the exception of the isolates (Kuusamo) identified here and the southernmost Europeans, without great loss of study power.