Determining the Area of Ancestral Origin for Individuals From North Eurasia Based on 5,229 SNP Markers.

Currently available genetic tools effectively distinguish between different continental origins. However, North Eurasia, which constitutes one-third of the world's largest continent, remains severely underrepresented. The dataset used in this study represents 266 populations from 12 North Eurasian countries, including most of the ethnic diversity across Russia's vast territory. A total of 1,883 samples were genotyped using the Illumina Infinium Omni5Exome-4 v1.3 BeadChip. Three principal components were computed for the entire dataset using three iterations for outlier removal. It allowed the merging of 266 populations into larger groups while maintaining intragroup homogeneity, so 29 ethnic geographic groups were formed that were genetically distinguishable enough to trace individual ancestry. Several feature selection methods, including the random forest algorithm, were tested to estimate the number of genetic markers needed to differentiate between the groups; 5,229 ancestry-informative SNPs were selected. We tested various classifiers supporting multiple classes and output values for each class that could be interpreted as probabilities. The logistic regression was chosen as the best mathematical model for predicting ancestral populations. The machine learning algorithm for inferring an ancestral ethnic geographic group was implemented in the original software "Homeland" fitted with the interface module, the prediction module, and the cartographic module. Examples of geographic maps showing the likelihood of geographic ancestry for individuals from different regions of North Eurasia are provided. Validating methods show that the highest number of ethnic geographic group predictions with almost absolute accuracy and sensitivity was observed for South and Central Siberia, Far East, and Kamchatka. The total accuracy of prediction of one of 29 ethnic geographic groups reached 71%. The proposed method can be employed to predict ancestries from the populations of Russia and its neighbor states. It can be used for the needs of forensic science and genetic genealogy.

Variation of Genomic Sites Associated with Severe Covid-19 Across Populations: Global and National Patterns.

BACKGROUND: Information about the distribution of clinically significant genetic markers in different populations may be helpful in elaborating personalized approaches to the clinical management of COVID-19 in the absence of consensus guidelines. AIM: Analyze frequencies and distribution patterns of two markers associated with severe COVID-19 (rs11385942 and rs657152) and look for potential correlations between these markers and deaths from COVID-19 among populations in Russia and across the world. METHODS: We genotyped 1883 samples from 91 ethnic groups pooled into 28 populations representing Russia and its neighbor states. We also compiled a dataset on 32 populations from other regions using genotypes extracted or imputed from the available databases. Geographic maps showing the frequency distribution of the analyzed markers were constructed using the obtained data. RESULTS: The cartographic analysis revealed that rs11385942 distribution follows the West Eurasian pattern: the marker is frequent among the populations of Europe, West Asia and South Asia but rare or absent in all other parts of the globe. Notably, the transition from high to low rs11385942 frequencies across Eurasia is not abrupt but follows the clinal variation pattern instead. The distribution of rs657152 is more homogeneous. The analysis of correlations between the frequencies of the studied markers and the epidemiological characteristics of COVID-19 in a population revealed that higher frequencies of both risk alleles correlated positively with mortality from this disease. For rs657152, the correlation was especially strong (r = 0.59, p = 0.02). These reasonable correlations were observed for the "Russian" dataset only: no such correlations were established for the "world" dataset. This could be attributed to the differences in methodology used to collect COVID-19 statistics in different countries. CONCLUSION: Our findings suggest that genetic differences between populations make a small yet tangible contribution to the heterogeneity of the pandemic worldwide.

Medieval Super-Grandfather founder of Western Kazakh Clans from Haplogroup C2a1a2-M48.

Western Kazakhstan is populated by three clans totaling 2 million people. Since the clans are patrilineal, the Y-chromosome is the most informative genetic system for tracing their origin. We genotyped 40 Y-SNP and 17 Y-STR markers in 330 Western Kazakhs. High phylogenetic resolution within haplogroup C2a1a2-M48 was achieved by using additional SNPs. Three lines of evidence indicate that the Alimuly and Baiuly clans (but not the Zhetiru clan) have a common founder placed 700 ± 200 years back by the STR data and 500 ± 200 years back by the sequencing data. This supports traditional genealogy claims about the descent of these clans from Emir Alau, who lived 650 years ago and whose lineage might be carried by two-thirds of Western Kazakhs. There is accumulation of specific haplogroups in the subclans representing other lineages, confirming that the clan structure corresponds with the paternal genetic structure of the steppe population.

Optimizing the genetic prediction of the eye and hair color for North Eurasian populations.

BACKGROUND: Predicting the eye and hair color from genotype became an established and widely used tool in forensic genetics, as well as in studies of ancient human populations. However, the accuracy of this tool has been verified on the West and Central Europeans only, while populations from border regions between Europe and Asia (like Caucasus and Ural) also carry the light pigmentation phenotypes. RESULTS: We phenotyped 286 samples collected across North Eurasia, genotyped them by the standard HIrisPlex-S markers and found that predictive power in Caucasus/Ural/West Siberian populations is reasonable but lower than that in West Europeans. As these populations have genetic ancestries different from that of West Europeans, we hypothesized they may carry a somewhat different allele spectrum. Thus, for all samples we performed the exome sequencing additionally enriched with the 53 genes and intergenic regions known to be associated with the eye/hair color. Our association analysis replicated the importance of the key previously known SNPs but also identified five new markers whose eye color prediction power for the studied populations is compatible with the two major previously well-known SNPs. Four out of these five SNPs lie within the HERС2 gene and the fifth in the intergenic region. These SNPs are found at high frequencies in most studied populations. The released dataset of exomes from Russian populations can be further used for population genetic and medical genetic studies. CONCLUSIONS: This study demonstrated that precision of the established systems for eye/hair color prediction from a genotype is slightly lower for the populations from the border regions between Europe and Asia that for the West Europeans. However, this precision can be improved if some newly revealed predictive SNPs are added into the panel. We discuss that the replication of these pigmentation-associated SNPs on the independent North Eurasian sample is needed in the future studies.

The genetic history of admixture across inner Eurasia.

The indigenous populations of inner Eurasia-a huge geographic region covering the central Eurasian steppe and the northern Eurasian taiga and tundra-harbour tremendous diversity in their genes, cultures and languages. In this study, we report novel genome-wide data for 763 individuals from Armenia, Georgia, Kazakhstan, Moldova, Mongolia, Russia, Tajikistan, Ukraine and Uzbekistan. We furthermore report additional damage-reduced genome-wide data of two previously published individuals from the Eneolithic Botai culture in Kazakhstan (~5,400 BP). We find that present-day inner Eurasian populations are structured into three distinct admixture clines stretching between various western and eastern Eurasian ancestries, mirroring geography. The Botai and more recent ancient genomes from Siberia show a decrease in contributions from so-called 'ancient North Eurasian' ancestry over time, which is detectable only in the northern-most 'forest-tundra' cline. The intermediate 'steppe-forest' cline descends from the Late Bronze Age steppe ancestries, while the 'southern steppe' cline further to the south shows a strong West/South Asian influence. Ancient genomes suggest a northward spread of the southern steppe cline in Central Asia during the first millennium BC. Finally, the genetic structure of Caucasus populations highlights a role of the Caucasus Mountains as a barrier to gene flow and suggests a post-Neolithic gene flow into North Caucasus populations from the steppe.

The Connection of the Genetic, Cultural and Geographic Landscapes of Transoxiana.

We have analyzed Y-chromosomal variation in populations from Transoxiana, a historical region covering the southwestern part of Central Asia. We studied 780 samples from 10 regional populations of Kazakhs, Uzbeks, Turkmens, Dungans, and Karakalpaks using 35 SNP and 17 STR markers. Analysis of haplogroup frequencies using multidimensional scaling and principal component plots, supported by an analysis of molecular variance, showed that the geographic landscape of Transoxiana, despite its distinctiveness and diversity (deserts, fertile river basins, foothills and plains) had no strong influence on the genetic landscape. The main factor structuring the gene pool was the mode of subsistence: settled agriculture or nomadic pastoralism. Investigation of STR-based clusters of haplotypes and their ages revealed that cultural and demic expansions of Transoxiana were not closely connected with each other. The Arab cultural expansion introduced Islam to the region but did not leave a significant mark on the pool of paternal lineages. The Mongol expansion, in contrast, had enormous demic success, but did not impact cultural elements like language and religion. The genealogy of Muslim missionaries within the settled agricultural communities of Transoxiana was based on spiritual succession passed from teacher to disciple. However, among Transoxianan nomads, spiritual and biological succession became merged.

Human Y Chromosome Haplogroup N: A Non-trivial Time-Resolved Phylogeography that Cuts across Language Families.

The paternal haplogroup (hg) N is distributed from southeast Asia to eastern Europe. The demographic processes that have shaped the vast extent of this major Y chromosome lineage across numerous linguistically and autosomally divergent populations have previously been unresolved. On the basis of 94 high-coverage re-sequenced Y chromosomes, we establish and date a detailed hg N phylogeny. We evaluate geographic structure by using 16 distinguishing binary markers in 1,631 hg N Y chromosomes from a collection of 6,521 samples from 56 populations. The more southerly distributed sub-clade N4 emerged before N2a1 and N3, found mostly in the north, but the latter two display more elaborate branching patterns, indicative of regional contrasts in recent expansions. In particular, a number of prominent and well-defined clades with common N3a3'6 ancestry occur in regionally dissimilar northern Eurasian populations, indicating almost simultaneous regional diversification and expansion within the last 5,000 years. This patrilineal genetic affinity is decoupled from the associated higher degree of language diversity.

Deep phylogenetic analysis of haplogroup G1 provides estimates of SNP and STR mutation rates on the human Y-chromosome and reveals migrations of Iranic speakers.

Y-chromosomal haplogroup G1 is a minor component of the overall gene pool of South-West and Central Asia but reaches up to 80% frequency in some populations scattered within this area. We have genotyped the G1-defining marker M285 in 27 Eurasian populations (n= 5,346), analyzed 367 M285-positive samples using 17 Y-STRs, and sequenced ~11 Mb of the Y-chromosome in 20 of these samples to an average coverage of 67X. This allowed detailed phylogenetic reconstruction. We identified five branches, all with high geographical specificity: G1-L1323 in Kazakhs, the closely related G1-GG1 in Mongols, G1-GG265 in Armenians and its distant brother clade G1-GG162 in Bashkirs, and G1-GG362 in West Indians. The haplotype diversity, which decreased from West Iran to Central Asia, allows us to hypothesize that this rare haplogroup could have been carried by the expansion of Iranic speakers northwards to the Eurasian steppe and via founder effects became a predominant genetic component of some populations, including the Argyn tribe of the Kazakhs. The remarkable agreement between genetic and genealogical trees of Argyns allowed us to calibrate the molecular clock using a historical date (1405 AD) of the most recent common genealogical ancestor. The mutation rate for Y-chromosomal sequence data obtained was 0.78×10-9 per bp per year, falling within the range of published rates. The mutation rate for Y-chromosomal STRs was 0.0022 per locus per generation, very close to the so-called genealogical rate. The "clan-based" approach to estimating the mutation rate provides a third, middle way between direct farther-to-son comparisons and using archeologically known migrations, whose dates are subject to revision and of uncertain relationship to genetic events.