Disentangling archaic introgression and genomic signatures of selection at human immunity genes.

Pathogens and infectious diseases have imposed exceptionally strong selective pressure on ancient and modern human genomes and contributed to the current variation in many genes. There is evidence that modern humans acquired immune variants through interbreeding with ancient hominins, but the impact of such variants on human traits is not fully understood. The main objectives of this research were to infer the genetic signatures of positive selection that may be involved in adaptation to infectious diseases and to investigate the function of Neanderthal alleles identified within a set of 50 Lithuanian genomes. Introgressed regions were identified using the machine learning tool ArchIE. Recent positive selection signatures were analysed using iHS. We detected high-scoring signals of positive selection at innate immunity genes (EMB, PARP8, HLAC, and CDSN) and evaluated their interactions with the structural proteins of pathogens. Interactions with human immunodeficiency virus (HIV) 1 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified. Overall, genomic regions introgressed from Neanderthals were shown to be enriched in genes related to immunity, keratinocyte differentiation, and sensory perception.

Microevolutionary processes analysis in the Lithuanian genome.

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.

Association of COL12A1 rs970547 Polymorphism with Elite Athlete Status.

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.

Inherited and De Novo Variation in Lithuanian Genomes: Introduction to the Analysis of the Generational Shift.

Most genetic variants are rare and specific to the population, highlighting the importance of characterizing local population genetic diversity. Many countries have initiated population-based whole-genome sequencing (WGS) studies. Genomic variation within Lithuanian families are not available in the public databases. Here, we describe initial findings of a high-coverage (an average of 36.27×) whole genome sequencing for 25 trios of the Lithuanian population. Each genome on average carried approximately 4,701,473 (±28,255) variants, where 80.6% (3,787,626) were single nucleotide polymorphisms (SNPs), and the rest 19.4% were indels. An average of 12.45% was novel according to dbSNP (build 150). The WGS structural variation (SV) analysis identified on average 9133 (±85.10) SVs, of which 95.85% were novel. De novo single nucleotide variation (SNV) analysis identified 4417 variants, where 1.1% de novo SNVs were exonic, 43.9% intronic, 51.9% intergenic, and the rest 3.13% in UTR or downstream sequence. Three potential pathogenic de novo variants in the ZSWIM8, CDC42EP1, and RELA genes were identified. Our findings provide useful information on local human population genomic variation, especially for de novo variants, and will be a valuable resource for further genetic studies, and medical implications.

Genome-Wide Landscape of North-Eastern European Populations: A View from Lithuania.

There are still several unanswered questions regarding about ancient events in the Lithuanian population. The Lithuanians, as the subject of this study, are of great interest as they represent a partially isolated population maintaining an ancient genetic composition and show genetic uniqueness in European comparisons. To elucidate the genetic relationships between the Lithuania and North-Eastern European and West Siberian populations, we analyzed the population structure, effective population size, and recent positive selection from genome-wide single nucleotide polymorphism (SNP) data. We identified the close genetic proximity of Lithuanians to neighboring populations (Latvians, Estonians, Belarusians) and in part with West and South Slavs (Poles, Slovaks, and Slovenians), however, with particular genetic distinctiveness. The estimated long-term Ne values ranged from ~5900 in the Estonian population to ~2400 in the South Russian population. The divergence times between the Lithuanian and study populations ranged from 240 to 12,871 YBP. We also found evidence of selection in 24 regions, 21 of which have not been discovered in previous analyses of selection. Undoubtedly, the genetic diversity analysis of geographically specific regions may provide new insights into microevolutionary processes affecting local human populations.

Inferring Effective Population Size and Divergence Time in the Lithuanian Population According to High-Density Genotyping Data.

The prehistory of the Lithuanian population and genetic relationship to other populations are poorly studied. Thus, the Lithuanian population, as an object of study, is interesting due to its partial isolation with genetic distinctiveness within the European context and with preserved ancient genetic composition. The main objects of this study was to infer demographic parameters, effective population size (Ne), and divergence time using high-density single nucleotide polymorphism (SNP) genotyping data generated with the Illumina HumanOmmiExpress-12v1.1 array in 295 individuals from the Lithuanian population and to compare our data with other populations from the Human Genome Cell Line Diversity Panel (HGDP-CEPH). We also aimed to reconstruct past events between the main ethnolinguistic regions-Aukstaitija and Zemaitija of Lithuania. Historically, these regions probably developed as two independent Baltic tribes. Our results of Ne in the Lithuanian population through time demonstrated a substantial reduction of Ne over the 150,000-25,000 years before present (YBP). The estimated long-term Ne of the Lithuanian population is quite low-it equals 5404, which likely is a consequence of the bottlenecks associated with the last glacial period of 25,000-12,000 YBP in Europe. The obtained divergence time estimates between the study populations are in agreement with recent studies. The reconstructed past events in Aukstaitija and Zemaitija showed significant differences between these two regions of Lithuania.

A comparative analysis of mathematical methods for homogeneity estimation of the Lithuanian population.

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.