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Abstract Differences in the relative fitness of genomic variants are foundational, without these, neither natural selection nor adaption can exist. This research analyzed two microevolutionary forces, mutations, and positive selection, using whole genome sequencing data from Lithuanians across three generations: newborns (generation I), their parents (generation II), 60 years old Lithuanians, and the root ancestors (generation III). The main objective was to determine the frequency of mutations under selection in modern humans and how allele frequencies change across generations. Our results show that going through all the landscapes of the relative fitness on each chromosome, the general relative fitness background pattern remains the same in analysed generations. However, the tendency of relative fitness to decrease, in general, is noted. We hypothesize that the de novo genome variants or genome variants with a very low frequency that formed in the previous generation did not have time to be as affected by natural selection, thus, in the following generation, the force of natural selection acting on them is greater and their cumulative relative fitness also decreases. The strong natural selection pressure on the genetic regions that encode the NEGR1 and PTPN1/PTNP21 genes were also identified, highlighting the evolution of the Lithuanian population’s genome over generations, and possible genomic “deficiencies” for better adaptation.

In this paper the multidimensional scaling, the principal coordinate and principal component methods for the Lithuanian population structure have investigated, taken that the proximity measures are Euclid, Gower, Bray-Curtis, Kulczynski, Jaccard and Morisita. The genome-wide single nucleotide polymorphism genetic data analyzed. A comparative analysis of proximity measures performed. The results of visualization are also presented.

The role of genetics, as an intrinsic factor, in research of sports performance increases with every passing year. The polymorphism rs970547 of the COL12A1 gene is one of the most promising genetic markers linked to soft-tissue injuries. This study aimed to investigate whether COL12A1 rs970547 genotypes are associated with elite Lithuanian athletes from high-risk various sports, such as running, throwing, jumping, and football. The study involved 293 Lithuanian elite athletes and 287 healthy untrained individuals from the Lithuanian population. The results of this study suggest that the rs970547 T allele and TT genotype were significantly over-represented in the total athlete group compared to controls (p < 0.05). There was a significantly lower C allele frequency in the sprint/power group (16.9%) as well as in footballers (19.4%) compared to controls (33.3%, p < 0.05). Positive selection analysis results showed that the derived allele experiences selection pressure within the general population of Lithuanians. Taken together, the findings of this study suggested that COL12A1 rs970547 (T allele and TT genotype) is associated with elite athlete status, especially with sprint/power athlete and footballer`s performance. However, larger-scale studies within different ethnic backgrounds are still warranted to confirm the findings of our study.

Background. Population genetic structure is one of the most important population genetic parameters revealing its demographic features. The aim of this study was to evaluate the homogeneity of the Lithuanian population on the basis of the genome-wide genotyping data. The comparative analysis of three methods – multidimensional scaling, principal components, and principal coordinates analysis – to visualize multidimensional genetics data was performed. The results of visualization (mapping images) are also presented. Materials and methods. The data set consisted of 425 samples from six ethnolinguistic groups of the Lithuanian population. Genomic DNA was extracted from whole venous blood using either the phenol-chloroform extraction method or the automated DNA extraction platform TECAN Freedom EVO. Genotyping was performed at the Department of Human and Medical Genetics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Lithuania, with the Illumina HumanOmniExpress-12 v1.1 and the Infinium OmniExpress-24. For the estimation of homogeneity of the Lithuanian population, PLINK data file was obtained using PLINK v1.07 program. The Past3 software was used to visualize the genotype data with multidimensional scaling and principal coordinates methods. The SmartPCA from EIGENSOFT 7.2.1 program was used in the principal component analysis to determine the population structure. Conclusions. Methods of multidimensional scaling, principal coordinate, and principal component for the genetic structure of the Lithuanian population were investigated and compared. The principal coordinate and principal component methods can be used for genotyping data visualization, since any essential differences in the results obtained were not observed and compared to multidimensional scaling. The Lithuanian population is homogenous whereas the points are strongly close when we use the principal coordinates or principal component methods.

In this paper two mathematical methods, McEvoy and Mezzavilla–Ghirotto, were investigated, they are devoted to determine the effective population size. Comparative analysis of two methods, for estimating effective population size, was performed.