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Oleg Balanovsky,1– 3 Valeria Petrushenko,1,4 Karin Mirzaev,2,5 Sherzod Abdullaev,5 Igor Gorin,1,4 Denis Chernevskiy,2 Anastasiya Agdzhoyan,1,2 Elena Balanovska,2,3 Alexander Kryukov,5 Ilyas Temirbulatov,2,5 Dmitriy Sychev5 1Laboratory of Genome Geography, Vavilov Institute of General Genetics, Moscow, Russia; 2Laboratory of Human Population Genetics, Research Centre for Medical Genetics, Moscow, Russia; 3Biobank of North Eurasia, Moscow, Russia; 4Department of Bioinformatics Moscow Institute of Physics and Technology, Moscow, Russia; 5Department of Clinical Pharmacology and Therapeutics, Russian Medical Academy of Continuous Professional Education, Moscow, RussiaCorrespondence: Elena BalanovskaLaboratory of Human Population Genetics, Research Centre for Medical Genetics, Moscow, RussiaTel +7 499 612-81-79Email balanovska@mail.ruDmitriy SychevDepartment of Clinical Pharmacology and Therapeutics, Russian Medical Academy of Continuous Professional Education, Moscow, RussiaTel +7 495 680-05-99Email dmitry.alex.sychev@gmail.comBackground: 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.Keywords: severe COVID-19, genetic markers, AB0, rs11385942, rs657152, gene geography

We studied the Y-chromosome pool of the ethnic Russian population of Novgorod oblast (Russia) by 49 SNP and 17 STR markers. The total sample ( N = 191) consists of four populations of the Novgorod region, including its southwestern (Shelon Pyatina) and eastern (Bezhetsk Pyatina) parts. Altogether, these four populations represent both the area of the Sopki archaeological culture (supposedly linked with the Novgorod Slovens tribe known from the chronicles) and the area of the Long Barrows culture (supposedly linked with the Krivichi Slavic tribe or with Balts). The pronounced genetic differences between southern and northern Russian populations are well known from previous studies; however, the Novgorod gene pool turned out to be neither northern nor southern, but a representative of the intermediate buffer zone. This zone was identified in this study and included a set of regional Russian populations from Pskov in the west to Kostroma in the east. All four studied populations of Novgorod region are genetically similar. The minor differences among them might represent the medieval Slavic migrations along the rivers, which survived despite the massive demographic shifts during the following history. Haplogroup N3 comprises one-fifth of the Novgorod pool of paternal lineages, with conditionally 'Finnic' N3a4 and conditionally 'East Baltic Sea Coast' N3a3 clades being almost equally frequent. The N3a3 phylogenetic network revealed the specific 'Balto-Slavic' cluster of STR haplotypes, which is frequent in Baltic-speaking Lithuanians but infrequent in Finno-Ugric speaking Estonians. The Novgorod haplotypes lie outside this cluster, indicating that the Novgorod population received both N3a3 and N3a4 from Finno-Ugric speaking populations of the region, which, in turn, acquired the Mesolithic gene pool of the Northeastern Europe. [ABSTRACT FROM AUTHOR]

The mutation rate on the Y-chromosome matters for estimating the time-to-the-most-recent-common-ancestor (TMRCA, i.e. haplogroup age) in population genetics, as well as for forensic, medical, and genealogical studies. Large-scale sequencing efforts have produced several independent estimates of Y-SNP mutation rates. Genealogical, or pedigree, rates tend to be slightly faster than evolutionary rates obtained from ancient DNA or calibrations using dated (pre)historical events. It is, therefore, suggested to report TMRCAs using an envelope defined by the average aDNA-based rate and the average pedigree-based rate. The current estimate of the 'envelope rate' is 0.75-0.89 substitutions per billion base pairs per year. The available Y-SNP mutation rates can be applied to high-coverage data from the entire X-degenerate region, but other datasets may demand recalibrated rates. While a consensus on Y-SNP rates is approaching, the debate on Y-STR rates has continued for two decades, because multiple genealogical rates were consistent with each other but three times faster than the single evolutionary estimate. Applying Y-SNP and Y-STR rates to the same haplogroups recently helped to clarify the issue. Genealogical and evolutionary STR rates typically provide lower and upper bounds of the 'true' (SNP-based) age. The genealogical rate often-but not always-works well for haplogroups less than 7000 years old. The evolutionary rate, although calibrated using recent events, inflates ages of young haplogroups and deflates the age of the entire Y-chromosomal tree, but often provides reasonable estimates for intermediate ages (old haplogroups). Future rate estimates and accumulating case studies should further clarify the Y-SNP rates. [ABSTRACT FROM AUTHOR]

Allele and genotype frequencies of the − 174G/ C polymorphism (rs1800795) in the regulatory region of the IL6 gene, which encode anti-inflammatory cytokine interleukin 6, were determined in seven populations representing five ethnic groups from the European part of Russia (440 individuals), as well as in small cohorts that represent populations from 24 countries of Africa and Eurasia (365 individuals). The maps of the geographic distribution of the − 174G/ C allele frequencies were constructed based on personal (22 populations) and the literature data (66 populations), and the data from dbSNP database obtained by the HapMap project (10 populations). The frequency of the − 174G allele varied from 45 to 100% and was characterized by nonrandom geographic distribution. These data could reflect the adaptive load of the alleles examined, which was different in different regions of the world. It is suggested that the level of pathogen prevalence is one of the environmental factors that determine different adaptive values of the IL6*-174G/ C alleles. This suggestion is supported by a positive correlation between the − 174G allele frequency and level of pathogen prevalence calculated based on historical data ( R = 0.768; p < 0.0001). [ABSTRACT FROM AUTHOR]