Genetic Structure of the Armenian Population

Koban culture genome-wide and archeological data open the bridge between Bronze and Iron Ages in the North Caucasus

The North Caucasus played a key role during the ancient colonization of Eurasia and the formation of its cultural and genetic ancestry. Previous archeogenetic studies described a relative genetic and cultural continuity of ancient Caucasus societies, since the Eneolithic period. The Koban culture, which formed in the Late Bronze Age on the North Caucasian highlands, is considered as a cultural "bridge" between the ancient and modern autochthonous peoples of the Caucasus. Here, we discuss the place of this archeological culture and its representatives in the genetic orbit of Caucasian cultures using genome-wide SNP data from five individuals of the Koban culture and one individual of the early Alanic culture as well as previously published genomic data of ancient and modern North Caucasus individuals. Ancient DNA analysis shows that an ancient individual from Klin-Yar III, who was previously described as male, was in fact a female. Additional studies on well-preserved ancient human specimens are necessary to determine the level of local mobility and kinship between individuals in ancient societies of North Caucasus. Further studies with a larger sample size will allow us gain a deeper understanding of this topic.

Portability of Polygenic Risk Scores for Sleep Duration, Insomnia and Chronotype in 33,493 Individuals

Polygenic risk scores (PRSs) estimate genetic liability for diseases and traits. However, the portability of PRSs in sleep traits has remained elusive. We generated PRSs for self-reported insomnia, chronotype and sleep duration using summary data from genome-wide association studies (GWASs) performed in 350,000 to 697,000 European-ancestry individuals. We then projected the scores in two independent Finnish population cohorts (N = 33,493) and tested whether the PRSs were associated with their respective sleep traits. We observed that all the generated PRSs were associated with their corresponding traits (p < 0.05 in all cases). Furthermore, we found that there was a 22.2 min difference in reported sleep between the 5% tails of the PRS for sleep duration (p < 0.001). Our findings indicate that sleep-related PRSs show portability across cohorts. The findings also demonstrate that sleep measures using PRSs for sleep behaviors may provide useful instruments for testing disease and trait associations in cohorts where direct sleep parameters have not yet been measured.

The Bronze and Iron Age populations of the Armenian Highland in the genetic history of Armenians

OBJECTIVES

To investigate the biological diversity of the late Bronze and Iron Age populations in the Armenian Highland by nonmetric cranial traits, evaluate the genetic continuity in the development of the modern Armenian gene pool, and compare the results obtained with genetic data.

MATERIALS AND METHODS

Twenty-eight nonmetric cranial traits were scored on 498 adult crania from different late Bronze and Iron Age cemeteries, as well as from modern Armenians and other European populations. We carried out a biodistance analysis between populations using the mean measure of divergence (MMD) statistics, tested the spatial-temporal model of population structure, and assessed the diversity within the late Bronze and early Iron Ages by using the values of variability index (Fst).

RESULTS

The biodistance analysis revealed a close relationship among different ancient Armenian populations and between the average frequencies of the three sequential periods (late Bronze Age, early Iron Age I and II) and modern Armenians. A gradual increase of variability (Fst) within the three successive periods was observed.

DISCUSSION

The analysis of nonmetric trait data reflects deep roots and continuity in the formation of the Armenian population. Since at least the Late Bronze Age, owing to permanent isolation, no significant changes have occurred in the Armenian gene pool. An increase in variability over the successive periods reflects the process of population differentiation from a single gene pool while maintaining average trait frequencies. The congruence of the results obtained with the genetic data confirms, once more, the possibility of using nonmetric cranial traits as a proxy for genetic markers.

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians - A Machine Learning Approach

Background: During the last decades a number of genome-wide association studies (GWASs) has identified numerous single nucleotide polymorphisms (SNPs) associated with different complex diseases. However, associations reported in one population are often conflicting and did not replicate when studied in other populations. One of the reasons could be that most GWAS employ a case-control design in one or a limited number of populations, but little attention was paid to the global distribution of disease-associated alleles across different populations. Moreover, the majority of GWAS have been performed on selected European, African, and Chinese populations and the considerable number of populations remains understudied. Aim: We have investigated the global distribution of so far discovered disease-associated SNPs across worldwide populations of different ancestry and geographical regions with a special focus on the understudied population of Armenians. Data and Methods: We have used genotyping data from the Human Genome Diversity Project and of Armenian population and combined them with disease-associated SNP data taken from public repositories leading to a final dataset of 44,234 markers. Their frequency distribution across 1039 individuals from 53 populations was analyzed using self-organizing maps (SOM) machine learning. Our SOM portrayal approach reduces data dimensionality, clusters SNPs with similar frequency profiles and provides two-dimensional data images which enable visual evaluation of disease-associated SNPs landscapes among human populations. Results: We find that populations from Africa, Oceania, and America show specific patterns of minor allele frequencies of disease-associated SNPs, while populations from Europe, Middle East, Central South Asia, and Armenia mostly share similar patterns. Importantly, different sets of SNPs associated with common polygenic diseases, such as cancer, diabetes, neurodegeneration in populations from different geographic regions. Armenians are characterized by a set of SNPs that are distinct from other populations from the neighboring geographical regions. Conclusion: Genetic associations of diseases considerably vary across populations which necessitates health-related genotyping efforts especially for so far understudied populations. SOM portrayal represents novel promising methods in population genetic research with special strength in visualization-based comparison of SNP data.