Population genetics of Lithuanians

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—Aukštaitija and Žemaitija 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 Aukštaitija and Žemaitija showed significant differences between these two regions of Lithuania.

Patterns of genetic structure and adaptive positive selection in the Lithuanian population from high-density SNP data

The analysis of geographically specific regions and the characterization of fine-scale patterns of genetic diversity may facilitate a much better understanding of the microevolutionary processes affecting local human populations. Here we generated genome-wide high-density SNP genotype data in 425 individuals from six geographical regions in Lithuania and combined our dataset with available ancient and modern data to explore genetic population structure, ancestry components and signatures of natural positive selection in the Lithuanian population. Our results show that Lithuanians are a homogenous population, genetically differentiated from neighbouring populations but within the general expected European context. Moreover, we not only confirm that Lithuanians preserve one of the highest proportions of western, Scandinavian and eastern hunter-gather ancestry components found in European populations but also that of an steppe Early to Middle Bronze Age pastoralists, which together configure the genetic distinctiveness of the Lithuanian population. Finally, among the top signatures of positive selection detected in Lithuanians, we identified several candidate genes related with diet (PNLIP, PPARD), pigmentation (SLC24A5, TYRP1 and PPARD) and the immune response (BRD2, HLA-DOA, IL26 and IL22).

CNV analysis in the Lithuanian population

Background

Although copy number variation (CNV) has received much attention, knowledge about the characteristics of CNVs such as occurrence rate and distribution in the genome between populations and within the same population is still insufficient. In this study, Illumina 770 K HumanOmniExpress-12 v1.0 (and v1.1) arrays were used to examine the diversity and distribution of CNVs in 286 unrelated individuals from the two main ethnolinguistic groups of the Lithuanian population (Aukštaičiai and Žemaičiai) (see Additional file 3). For primary data analysis, the Illumina GenomeStudio™ Genotyping Module v1.9 and two algorithms, cnvPartition 3.2.0 and QuantiSNP 2.0, were used to identify high-confidence CNVs.

Results

A total of 478 autosomal CNVs were detected by both algorithms, and those were clustered in 87 copy number variation regions (CNVRs), spanning ~12.5 Mb of the genome (see Table 1). At least 8.6 % of the CNVRs were unique and had not been reported in the Database of Genomic Variants. Most CNVRs (57.5 %) were rare, with a frequency of <1 %, whereas common CNVRs with at least 5 % frequency made up only 1.1 % of all CNVRs identified. About 49 % of non-singleton CNVRs were shared between Aukštaičiai and Žemaičiai, and the remaining CNVRs were specific to each group. Many of the CNVs detected (66 %) overlapped with known UCSC gene regions.

Conclusions

The ethnolinguistic groups of the Lithuanian population could not be differentiated based on CNV profiles, which may reflect their geographical proximity and suggest the homogeneity of the Lithuanian population. In addition, putative novel CNVs unique to the Lithuanian population were identified. The results of our study enhance the CNV map of the Lithuanian population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0373-6) contains supplementary material, which is available to authorized users.

Y-Chromosomal Lineages of Latvians in the Context of the Genetic Variation of the Eastern-Baltic Region

Variations of the nonrecombining Y-chromosomal region were investigated in 159 unrelated Baltic-speaking ethnic Latvians from four different geographic regions, using 28 biallelic markers and 12 short tandem repeats. Eleven different haplogroups (hgs) were detected in a regionally homogeneous Latvian population, among which N1c, R1a, and I1 cover more than 85% of its paternal lineages. When compared its closest geographic neighbors, the composition of the Latvian Y-chromosomal gene pool was found to be very similar to those of Lithuanians and Estonians. Despite the comparable frequency distribution of hg N1c in Latvians and Lithuanians with the Finno-Ugric-speaking populations from the Eastern coast of the Baltic Sea, the observed differences in allelic variances of N1c haplotypes between these two groups are in concordance with the previously stated hypothesis of different dispersal ways of this lineage in the region. More than a third of Latvian paternal lineages belong specifically to a recently defined R1a-M558 hg, indicating an influence from a common source within Eastern Slavic populations on the formation of the present-day Latvian Y-chromosome gene pool.

Mitochondrial DNA Portrait of Latvians: Towards the Understanding of the Genetic Structure of Baltic-Speaking Populations

Mitochondrial DNA (mtDNA) variation was investigated in a sample of 299 Latvians, a Baltic-speaking population from Eastern Europe. Sequencing of the first hypervariable segment (HVS-I) in combination with analysis of informative coding region markers revealed that the vast majority of observed mtDNAs belong to haplogroups (hgs) common to most European populations. Analysis of the spatial distribution of mtDNA haplotypes found in Latvians, as well as in Baltic-speaking populations in general, revealed that they share haplotypes with all neighbouring populations irrespective of their linguistic affiliation. Hence, the results of our mtDNA analysis show that the previously described sharp difference between the Y-chromosomal hg N3 distribution in the paternally inherited gene pool of Baltic-speaking populations and of other European Indo-European speakers does not have a corresponding maternal counterpart.

Y chromosome and mitochondrial DNA variation in Lithuanians

The genetic composition of the Lithuanian population was investigated by analysing mitochondrial DNA hypervariable region 1, RFLP polymorphisms and Y chromosomal biallelic and STR markers in six ethnolinguistic groups of Lithuanians, to address questions about the origin and genetic structure of the present day population. There were no significant genetic differences among ethnolinguistic groups, and an analysis of molecular variance confirmed the homogeneity of the Lithuanian population. MtDNA diversity revealed that Lithuanians are close to both Slavic (Indo-European) and Finno-Ugric speaking populations of Northern and Eastern Europe. Y-chromosome SNP haplogroup analysis showed Lithuanians to be closest to Latvians and Estonians. Significant differences between Lithuanian and Estonian Y chromosome STR haplotypes suggested that these populations have had different demographic histories. We suggest that the observed pattern of Y chromosome diversity in Lithuanians may be explained by a population bottleneck associated with Indo-European contact. Different Y chromosome STR distributions in Lithuanians and Estonians might be explained by different origins or, alternatively, be the result of some period of isolation and genetic drift after the population split.