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Hovhannisyan A, Delser PM, Hakobyan A, Jones ER, Schraiber JG, Antonosyan M, Margaryan A, Xue Z, Jeon S, Bhak J, Hrechdakian P, Sahakyan H, Saag L, Khachatryan Z, Yepiskoposyan L, Manica A. Demographic history and genetic variation of the Armenian population. Am J Hum Genet 2025; 112:11-27. [PMID: 39591962 PMCID: PMC11739871 DOI: 10.1016/j.ajhg.2024.10.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 10/29/2024] [Accepted: 10/30/2024] [Indexed: 11/28/2024] Open
Abstract
We introduce a sizable (n = 34) whole-genome dataset on Armenians, a population inhabiting the region in West Asia known as the Armenian highlands. Equipped with this genetic data, we conducted a whole-genome study of Armenians and deciphered their fine-scale population structure and complex demographic history. We demonstrated that the Armenian populations from western, central, and eastern parts of the highlands are relatively homogeneous. The Sasun, a population in the south that had been argued to have received a major genetic contribution from Assyrians, was instead shown to have derived its slightly divergent genetic profile from a bottleneck that occurred in the recent past. We also investigated the debated question on the genetic origin of Armenians and failed to find any significant support for historical suggestions by Herodotus of their Balkan-related ancestry. We checked the degree of continuity of modern Armenians with ancient inhabitants of the eastern Armenian highlands and detected a genetic input into the region from a source linked to Neolithic Levantine Farmers at some point after the Early Bronze Age. Additionally, we cataloged an abundance of new mutations unique to the population, including a missense mutation predicted to cause familial Mediterranean fever, an autoinflammatory disorder highly prevalent in Armenians. Thus, we highlight the importance of further genetic and medical studies of this population.
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Affiliation(s)
- Anahit Hovhannisyan
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland; Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK; Institute of Molecular Biology, National Academy of Sciences, Yerevan 0014, Armenia; Estonian Biocentre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia.
| | - Pierpaolo Maisano Delser
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland; Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Anna Hakobyan
- Institute of Molecular Biology, National Academy of Sciences, Yerevan 0014, Armenia; Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, 1030 Vienna, Austria
| | - Eppie R Jones
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland; Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Joshua G Schraiber
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90018, USA
| | - Mariya Antonosyan
- Institute of Molecular Biology, National Academy of Sciences, Yerevan 0014, Armenia; Max Planck Institute of Geoanthropology, 07745 Jena, Germany
| | - Ashot Margaryan
- Institute of Molecular Biology, National Academy of Sciences, Yerevan 0014, Armenia; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350 Copenhagen, Denmark
| | - Zhe Xue
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Sungwon Jeon
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Uslan 44919, Republic of Korea; Clinomics Inc., Ulsan 44919, Republic of Korea
| | - Jong Bhak
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Uslan 44919, Republic of Korea; Clinomics Inc., Ulsan 44919, Republic of Korea
| | | | - Hovhannes Sahakyan
- Institute of Molecular Biology, National Academy of Sciences, Yerevan 0014, Armenia; Estonian Biocentre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Lehti Saag
- Estonian Biocentre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Zaruhi Khachatryan
- Institute of Molecular Biology, National Academy of Sciences, Yerevan 0014, Armenia
| | - Levon Yepiskoposyan
- Institute of Molecular Biology, National Academy of Sciences, Yerevan 0014, Armenia.
| | - Andrea Manica
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
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2
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Schurr TG, Shengelia R, Shamoon-Pour M, Chitanava D, Laliashvili S, Laliashvili I, Kibret R, Kume-Kangkolo Y, Akhvlediani I, Bitadze L, Mathieson I, Yardumian A. Genetic Analysis of Mingrelians Reveals Long-Term Continuity of Populations in Western Georgia (Caucasus). Genome Biol Evol 2023; 15:evad198. [PMID: 37935112 PMCID: PMC10665041 DOI: 10.1093/gbe/evad198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/26/2023] [Accepted: 10/28/2023] [Indexed: 11/09/2023] Open
Abstract
To elucidate the population history of the Caucasus, we conducted a survey of genetic diversity in Samegrelo (Mingrelia), western Georgia. We collected DNA samples and genealogical information from 485 individuals residing in 30 different locations, the vast majority of whom being Mingrelian speaking. From these DNA samples, we generated mitochondrial DNA (mtDNA) control region sequences for all 485 participants (female and male), Y-short tandem repeat haplotypes for the 372 male participants, and analyzed all samples at nearly 590,000 autosomal single nucleotide polymorphisms (SNPs) plus around 33,000 on the sex chromosomes, with 27,000 SNP removed for missingness, using the GenoChip 2.0+ microarray. The resulting data were compared with those from populations from Anatolia, the Caucasus, the Near East, and Europe. Overall, Mingrelians exhibited considerable mtDNA haplogroup diversity, having high frequencies of common West Eurasian haplogroups (H, HV, I, J, K, N1, R1, R2, T, U, and W. X2) and low frequencies of East Eurasian haplogroups (A, C, D, F, and G). From a Y-chromosome standpoint, Mingrelians possessed a variety of haplogroups, including E1b1b, G2a, I2, J1, J2, L, Q, R1a, and R1b. Analysis of autosomal SNP data further revealed that Mingrelians are genetically homogeneous and cluster with other modern-day South Caucasus populations. When compared with ancient DNA samples from Bronze Age archaeological contexts in the broader region, these data indicate that the Mingrelian gene pool began taking its current form at least by this period, probably in conjunction with the formation of a distinct linguistic community.
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Affiliation(s)
- Theodore G Schurr
- Department of Anthropology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ramaz Shengelia
- Department of the History of Medicine, Tbilisi State Medical University, Tbilisi, Georgia
| | - Michel Shamoon-Pour
- First-year Research Immersion, Binghamton University, Binghamton, New York, USA
| | - David Chitanava
- Laboratory for Anthropologic Studies, Ivane Javakhishvili Institute of History and Ethnology, Tbilisi, Georgia
| | - Shorena Laliashvili
- Laboratory for Anthropologic Studies, Ivane Javakhishvili Institute of History and Ethnology, Tbilisi, Georgia
| | - Irma Laliashvili
- Laboratory for Anthropologic Studies, Ivane Javakhishvili Institute of History and Ethnology, Tbilisi, Georgia
| | - Redate Kibret
- Department of History and Social Science, Bryn Athyn College, Bryn Athyn, Pennsylvania, USA
| | - Yanu Kume-Kangkolo
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Lia Bitadze
- Laboratory for Anthropologic Studies, Ivane Javakhishvili Institute of History and Ethnology, Tbilisi, Georgia
| | - Iain Mathieson
- Department of Genetics, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Aram Yardumian
- Department of History and Social Science, Bryn Athyn College, Bryn Athyn, Pennsylvania, USA
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3
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Žak R, Navasardyan L, Hunák J, Martinů J, Heneberg P. PTPN22 intron polymorphism rs1310182 (c.2054-852T>C) is associated with type 1 diabetes mellitus in patients of Armenian descent. PLoS One 2023; 18:e0286743. [PMID: 37315092 DOI: 10.1371/journal.pone.0286743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/23/2023] [Indexed: 06/16/2023] Open
Abstract
Protein tyrosine phosphatase, nonreceptor type 22 (PTPN22), is an archetypal non-HLA autoimmunity gene. It is one of the most prominent genetic contributors to type 1 diabetes mellitus outside the HLA region, and prevalence of its risk variants is subject to enormous geographic variability. Here, we address the genetic background of patients with type 1 diabetes mellitus of Armenian descent. Armenia has a population that has been genetically isolated for 3000 years. We hypothesized that two PTPN22 polymorphisms, rs2476601 and rs1310182, are associated with type 1 diabetes mellitus in persons of Armenian descent. In this association study, we genotyped the allelic frequencies of two risk-associated PTPN22 variants in 96 patients with type 1 diabetes mellitus and 100 controls of Armenian descent. We subsequently examined the associations of PTPN22 variants with the manifestation of type 1 diabetes mellitus and its clinical characteristics. We found that the rs2476601 minor allele (c.1858T) frequency in the control population was very low (q = 0.015), and the trend toward increased frequency of c.1858CT heterozygotes among patients with type 1 diabetes mellitus was not significant (OR 3.34, 95% CI 0.88-12.75; χ2 test p > 0.05). The control population had a high frequency of the minor allele of rs1310182 (q = 0.375). The frequency of c.2054-852TC heterozygotes was significantly higher among the patients with type 1 diabetes mellitus (OR 2.39, 95% CI 1.35-4.24; χ2 test p < 0.001), as was the frequency of the T allele (OR 4.82, 95% CI 2.38-9.76; χ2 test p < 0.001). The rs2476601 c.1858CT genotype and the T allele correlated negatively with the insulin dose needed three to six months after diagnosis. The rs1310182 c.2054-852CC genotype was positively associated with higher HbA1c at diagnosis and 12 months after diagnosis. We have provided the first information on diabetes-associated polymorphisms in PTPN22 in a genetically isolated Armenian population. We found only a limited contribution of the prototypic gain-of-function PTPN22 polymorphism rs2476601. In contrast, we found an unexpectedly close association of type 1 diabetes mellitus with rs1310182.
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Affiliation(s)
- Robert Žak
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Lusine Navasardyan
- Department of Endocrinology, Yerevan State Medical University after Mkhitar Heratsi, Yerevan, Armenia
| | - Ján Hunák
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jiřina Martinů
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Petr Heneberg
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
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Németh S, Kriegshäuser G, Hovhannesyan K, Hayrapetyan H, Oberkanins C, Sarkisian T. Very low frequency of the lactase persistence allele LCT-13910T in the Armenian population. Ann Hum Biol 2022; 49:260-262. [PMID: 36129808 DOI: 10.1080/03014460.2022.2126887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Primary lactose malabsorption is characterised by a down-regulation of lactase activity after weaning and inability to digest lactose in adulthood. It has been suggested that the historical introduction of dairying led to a positive selection for lactase persistence variants in a regulatory region upstream of the LCT gene. Here, we genotyped 202 Armenian subjects for LCT-13910T, a lactase persistence variant which is widespread in Europeans. The homozygous C/C genotype associated with primary hypolactasia, the heterozygous C/T and the homozygous T/T lactase persistence genotypes were found in 191 (94.6%), 11 (5.4%), and 0 (0.0%) samples, respectively. The frequency for the LCT-13910*T allele was 2.7%. The observed allele frequency of 2.7% for LCT-13910T is even lower than previously reported and supports current phenotypic data about lactose malabsorption in Armenia.
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Affiliation(s)
| | | | | | - Hasmik Hayrapetyan
- Center of Medical Genetics and Primary Health Care, Yerevan, Armenia.,Department of Medical Genetics, Yerevan State Medical University, Yerevan, Armenia
| | | | - Tamara Sarkisian
- Center of Medical Genetics and Primary Health Care, Yerevan, Armenia.,Department of Medical Genetics, Yerevan State Medical University, Yerevan, Armenia
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Alladio E, Poggiali B, Cosenza G, Pilli E. Multivariate statistical approach and machine learning for the evaluation of biogeographical ancestry inference in the forensic field. Sci Rep 2022; 12:8974. [PMID: 35643723 PMCID: PMC9148302 DOI: 10.1038/s41598-022-12903-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 04/13/2022] [Indexed: 11/24/2022] Open
Abstract
The biogeographical ancestry (BGA) of a trace or a person/skeleton refers to the component of ethnicity, constituted of biological and cultural elements, that is biologically determined. Nowadays, many individuals are interested in exploring their genealogy, and the capability to distinguish biogeographic information about population groups and subgroups via DNA analysis plays an essential role in several fields such as in forensics. In fact, for investigative and intelligence purposes, it is beneficial to inference the biogeographical origins of perpetrators of crimes or victims of unsolved cold cases when no reference profile from perpetrators or database hits for comparative purposes are available. Current approaches for biogeographical ancestry estimation using SNPs data are usually based on PCA and Structure software. The present study provides an alternative method that involves multivariate data analysis and machine learning strategies to evaluate BGA discriminating power of unknown samples using different commercial panels. Starting from 1000 Genomes project, Simons Genome Diversity Project and Human Genome Diversity Project datasets involving African, American, Asian, European and Oceania individuals, and moving towards further and more geographically restricted populations, powerful multivariate techniques such as Partial Least Squares-Discriminant Analysis (PLS-DA) and machine learning techniques such as XGBoost were employed, and their discriminating power was compared. PLS-DA method provided more robust classifications than XGBoost method, showing that the adopted approach might be an interesting tool for forensic experts to infer BGA information from the DNA profile of unknown individuals, but also highlighting that the commercial forensic panels could be inadequate to discriminate populations at intra-continental level.
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Affiliation(s)
- Eugenio Alladio
- Department of Chemistry, University of Turin, Turin, Italy.,Centro Regionale Antidoping e di Tossicologia "A. Bertinaria", Orbassano, Torino, Italy
| | - Brando Poggiali
- Department of Biology, Forensic Molecular Anthropology Laboratory, University of Florence, Florence, Italy
| | - Giulia Cosenza
- Department of Biology, Forensic Molecular Anthropology Laboratory, University of Florence, Florence, Italy
| | - Elena Pilli
- Department of Biology, Forensic Molecular Anthropology Laboratory, University of Florence, Florence, Italy.
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Balanovsky O, Petrushenko V, Mirzaev K, Abdullaev S, Gorin I, Chernevskiy D, Agdzhoyan A, Balanovska E, Kryukov A, Temirbulatov I, Sychev D. Variation of Genomic Sites Associated with Severe Covid-19 Across Populations: Global and National Patterns. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:1391-1402. [PMID: 34764675 PMCID: PMC8575442 DOI: 10.2147/pgpm.s320609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 10/04/2021] [Indexed: 01/10/2023]
Abstract
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.
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Affiliation(s)
- Oleg Balanovsky
- Laboratory of Genome Geography, Vavilov Institute of General Genetics, Moscow, Russia.,Laboratory of Human Population Genetics, Research Centre for Medical Genetics, Moscow, Russia.,Biobank of North Eurasia, Moscow, Russia
| | - Valeria Petrushenko
- Laboratory of Genome Geography, Vavilov Institute of General Genetics, Moscow, Russia.,Department of Bioinformatics Moscow Institute of Physics and Technology, Moscow, Russia
| | - Karin Mirzaev
- Laboratory of Human Population Genetics, Research Centre for Medical Genetics, Moscow, Russia.,Department of Clinical Pharmacology and Therapeutics, Russian Medical Academy of Continuous Professional Education, Moscow, Russia
| | - Sherzod Abdullaev
- Department of Clinical Pharmacology and Therapeutics, Russian Medical Academy of Continuous Professional Education, Moscow, Russia
| | - Igor Gorin
- Laboratory of Genome Geography, Vavilov Institute of General Genetics, Moscow, Russia.,Department of Bioinformatics Moscow Institute of Physics and Technology, Moscow, Russia
| | - Denis Chernevskiy
- Laboratory of Human Population Genetics, Research Centre for Medical Genetics, Moscow, Russia
| | - Anastasiya Agdzhoyan
- Laboratory of Genome Geography, Vavilov Institute of General Genetics, Moscow, Russia.,Laboratory of Human Population Genetics, Research Centre for Medical Genetics, Moscow, Russia
| | - Elena Balanovska
- Laboratory of Human Population Genetics, Research Centre for Medical Genetics, Moscow, Russia.,Biobank of North Eurasia, Moscow, Russia
| | - Alexander Kryukov
- Department of Clinical Pharmacology and Therapeutics, Russian Medical Academy of Continuous Professional Education, Moscow, Russia
| | - Ilyas Temirbulatov
- Laboratory of Human Population Genetics, Research Centre for Medical Genetics, Moscow, Russia.,Department of Clinical Pharmacology and Therapeutics, Russian Medical Academy of Continuous Professional Education, Moscow, Russia
| | - Dmitriy Sychev
- Department of Clinical Pharmacology and Therapeutics, Russian Medical Academy of Continuous Professional Education, Moscow, Russia
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Moutsouri I, Keravnou A, Manoli P, Bertoncini S, Michailidou K, Christofi V, Xenophontos S, Cariolou MA, Bashiardes E. Comparative Y-chromosome analysis among Cypriots in the context of historical events and migrations. PLoS One 2021; 16:e0255140. [PMID: 34424929 PMCID: PMC8382168 DOI: 10.1371/journal.pone.0255140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/12/2021] [Indexed: 11/19/2022] Open
Abstract
Y-chromosome analysis provides valuable information regarding the migration patterns of male ancestors, ranging from the Paleolithic age to the modern humans. STR and SNP genotyping analysis provides data regarding the genetic and geographical ancestry of the populations studied. This study focused on the analysis of the Y-chromosome in Maronite Cypriots and Armenian Cypriots, who came to the island as a result of different historical events. The aim was to provide information on the paternal genetic ancestry of Maronites and Armenians of Cyprus and investigate any affinity with the Greek Cypriots and Turkish Cypriots of the island. Since there is limited information in the current literature, we proceeded and used 23 Y-chromosome STRs and 28 Y-chromosome SNPs to genotype 57 Maronite Cypriots and 56 Armenian Cypriots, which were then compared to data from 344 Greek Cypriots and 380 Turkish Cypriots. All samples were assigned to eight major Y-haplogroups but the most frequent haplogroup among all Cypriots is haplogroup J in the major subclade J2a-L559. The calculated pairwise genetic distances between the populations show that Armenian Cypriots are genetically closer to Greek and Turkish Cypriots compared to Maronite Cypriots. Median Joining Network analysis in 17 Y-STR haplotypes of all Cypriots assigned to J2a-L559, revealed that Cypriots share a common paternal ancestor, prior to the migration of the Armenians and Maronites to Cyprus, estimated in the Late Bronze Age and Early Iron Age.
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Affiliation(s)
- Irene Moutsouri
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Anna Keravnou
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Department of Cardiovascular Genetics and The Laboratory of Forensic Genetics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Panayiotis Manoli
- Department of Cardiovascular Genetics and The Laboratory of Forensic Genetics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | | | - Kyriaki Michailidou
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Vasilis Christofi
- Department of Cardiovascular Genetics and The Laboratory of Forensic Genetics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Stavroulla Xenophontos
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Department of Cardiovascular Genetics and The Laboratory of Forensic Genetics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marios A. Cariolou
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Department of Cardiovascular Genetics and The Laboratory of Forensic Genetics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Evy Bashiardes
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Department of Cardiovascular Genetics and The Laboratory of Forensic Genetics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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Balanovska EV, Petrushenko VS, Koshel SM, Pocheshkhova EA, Chernevskiy DK, Mirzaev KB, Abdullaev S, Balanovsky OP. Cartographic atlas of frequency variation for 45 pharmacogenetic markers in populations of Russia and its neighbor states. BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY 2020. [DOI: 10.24075/brsmu.2020.080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The lack of information about the frequency of pharmacogenetic markers in Russia impedes the adoption of personalized treatment algorithms originally developed for West European populations. The aim of this paper was to study the distribution of some clinically significant pharmacogenetic markers across Russia. A total of 45 pharmacogenetic markers were selected from a few population genetic datasets, including ADME, drug target and hemostasis-controlling genes. The total number of donors genotyped for these markers was 2,197. The frequencies of these markers were determined for 50 different populations, comprised of 137 ethnic and subethnic groups. A comprehensive pharmacogenetic atlas was created, i.e. a systematic collection of gene geographic maps of frequency variation for 45 pharmacogenetic DNA markers in Russia and its neighbor states. The maps revealed 3 patterns of geographic variation. Clinal variation (a gradient change in frequency along the East-West axis) is observed in the pharmacogenetic markers that follow the main pattern of variation for North Eurasia (13% of the maps). Uniform distribution singles out a group of markers that occur at average frequency in most Russian regions (27% of the maps). Focal variation is observed in the markers that are specific to a certain group of populations and are absent in other regions (60% of the maps). The atlas reveals that the average frequency of the marker and its frequency in individual populations do not indicate the type of its distribution in Russia: a gene geographic map is needed to uncover the pattern of its variation.
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Affiliation(s)
- EV Balanovska
- Bochkov Research Center for Medical Genetics, Moscow, Russia; Biobank of North Eurasia, Moscow, Russia
| | - VS Petrushenko
- Bochkov Research Center for Medical Genetics, Moscow, Russia; Vavilov Institute of General Genetics, Moscow, Russia
| | - SM Koshel
- Bochkov Research Center for Medical Genetics, Moscow, Russia; Lomonosov Moscow State University, Moscow, Russia
| | - EA Pocheshkhova
- Bochkov Research Center for Medical Genetics, Moscow, Russia; Kuban State Medical Institute, Krasnodar, Russia
| | - DK Chernevskiy
- Bochkov Research Center for Medical Genetics, Moscow, Russia
| | - KB Mirzaev
- Russian Medical Academy of Continuous Professional Education, Moscow, Russia
| | - ShP Abdullaev
- Russian Medical Academy of Continuous Professional Education, Moscow, Russia
| | - OP Balanovsky
- Bochkov Research Center for Medical Genetics, Moscow, Russia; Biobank of North Eurasia, Moscow, Russia; Vavilov Institute of General Genetics, Moscow, Russia
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Chen Y, Li X, Ge L, Pan B, Bing Z, Ying X, Yang K, Han X. Comparison of life quality in older adults living in traditional family versus nursing home: a systematic review and meta-analysis. PSYCHOL HEALTH MED 2020; 27:1072-1083. [PMID: 33315480 DOI: 10.1080/13548506.2020.1847303] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
To assess and compare the QoL of the older people dwelling in traditional family versus nursing home/institution. A comprehensive literature search was performed on 10 January 2018 to identify studies that investigated the QoL of older adults dwelling in family versus nursing home settings. Analyses were run using random-effects meta-analyses. A total of six cross-sectional studies with 1623 people were included. The quality of included studies was moderate. Meta-analysis showed that compared with nursing home support, the family support could significantly improve the physical health (6 studies, SMD = 0.50, 95%CI: 0.32-0.68, p < 0.05), mental status (6 studies, SMD = 0.45, 95%CI: 0.26-0.65, p < 0.05), and social relationship (5 studies, SMD = 0.51, 95%CI: 0.19-0.83, p < 0.05). Traditional family support model demonstrated a significant improvement in the physical health, psychological status and social relationships among older adults. The conclusions were driven by cross-sectional studies, Larger, adequately powered RCTs are required to confirm our finding.
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Affiliation(s)
- Yajing Chen
- School of Public Health, Fudan University, Shanghai, China.,Evidence-Based Social Science Center, School of Public Health, Lanzhou University, Lanzhou Gansu, China
| | - Xiuxia Li
- Evidence-Based Social Science Center, School of Public Health, Lanzhou University, Lanzhou Gansu, China.,Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Long Ge
- Evidence-Based Social Science Center, School of Public Health, Lanzhou University, Lanzhou Gansu, China.,Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Bei Pan
- Evidence-Based Social Science Center, School of Public Health, Lanzhou University, Lanzhou Gansu, China.,Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Zhitong Bing
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.,Evidence-Based Medicine Center, School of Basic Medical Science, Lanzhou University, Lanzhou Gansu, China
| | - Xiaohua Ying
- School of Public Health, Fudan University, Shanghai, China
| | - Kehu Yang
- Evidence-Based Social Science Center, School of Public Health, Lanzhou University, Lanzhou Gansu, China.,Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.,Evidence-Based Medicine Center, School of Basic Medical Science, Lanzhou University, Lanzhou Gansu, China
| | - Xuemei Han
- Evidence-Based Social Science Center, School of Public Health, Lanzhou University, Lanzhou Gansu, China
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Nikoghosyan M, Schmidt M, Margaryan K, Loeffler-Wirth H, Arakelyan A, Binder H. SOMmelier-Intuitive Visualization of the Topology of Grapevine Genome Landscapes Using Artificial Neural Networks. Genes (Basel) 2020; 11:genes11070817. [PMID: 32709105 PMCID: PMC7397337 DOI: 10.3390/genes11070817] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/26/2020] [Accepted: 07/15/2020] [Indexed: 01/02/2023] Open
Abstract
Background: Whole-genome studies of vine cultivars have brought novel knowledge about the diversity, geographical relatedness, historical origin and dissemination, phenotype associations and genetic markers. Method: We applied SOM (self-organizing maps) portrayal, a neural network-based machine learning method, to re-analyze the genome-wide Single Nucleotide Polymorphism (SNP) data of nearly eight hundred grapevine cultivars. The method generates genome-specific data landscapes. Their topology reflects the geographical distribution of cultivars, indicates paths of cultivar dissemination in history and genome-phenotype associations about grape utilization. Results: The landscape of vine genomes resembles the geographic map of the Mediterranean world, reflecting two major dissemination paths from South Caucasus along a northern route via Balkan towards Western Europe and along a southern route via Palestine and Maghreb towards Iberian Peninsula. The Mediterranean and Black Sea, as well as the Pyrenees, constitute barriers for genetic exchange. On the coarsest level of stratification, cultivars divide into three major groups: Western Europe and Italian grapes, Iberian grapes and vine cultivars from Near East and Maghreb regions. Genetic landmarks were associated with agronomic traits, referring to their utilization as table and wine grapes. Pseudotime analysis describes the dissemination of grapevines in an East to West direction in different waves of cultivation. Conclusion: In analogy to the tasks of the wine waiter in gastronomy, the sommelier, our ‘SOMmelier’-approach supports understanding the diversity of grapevine genomes in the context of their geographic and historical background, using SOM portrayal. It offers an option to supplement vine cultivar passports by genome fingerprint portraits.
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Affiliation(s)
- Maria Nikoghosyan
- Research Group of Bioinformatics, Institute of Molecular Biology of National Academy of Sciences RA, Yerevan 0014, Armenia; (M.N.); (A.A.)
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan 0051, Armenia
| | - Maria Schmidt
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
| | - Kristina Margaryan
- Research Group of Plant Genetics and Immunology, Institute of Molecular Biology of National Academy of Sciences RA, Yerevan 0014, Armenia;
- Department of Genetics and Cytology, Yerevan State University, Yerevan 0025, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
| | - Arsen Arakelyan
- Research Group of Bioinformatics, Institute of Molecular Biology of National Academy of Sciences RA, Yerevan 0014, Armenia; (M.N.); (A.A.)
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan 0051, Armenia
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
- Correspondence:
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11
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Tay GK, Henschel A, Daw Elbait G, Al Safar HS. Genetic Diversity and Low Stratification of the Population of the United Arab Emirates. Front Genet 2020; 11:608. [PMID: 32595703 PMCID: PMC7304494 DOI: 10.3389/fgene.2020.00608] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 05/19/2020] [Indexed: 01/09/2023] Open
Abstract
With high consanguinity rates on the Arabian Peninsula, it would not have been unexpected if the population of the United Arab Emirates (UAE) was shown to be relatively homogenous. However, this study of 1000 UAE nationals provided a contrasting perspective, one of a relatively heterogeneous population. Located at the apex of Europe, Asia, and Africa, the observed diversity could be explained by a plethora of migration patterns since the first Out-of-Africa movement. A strategy to explore the extent of genetic variation of the population of the UAE is presented. The first step involved a comprehensive population stratification study that was instructive for subsequent whole genome sequencing (WGS) of suitable representatives (which is described elsewhere). When these UAE data were compared to previous smaller studies from the region, the findings were consistent with a population that is a diverse and admixed group of people. However, rather than sharp and distinctive clusters, cluster analysis reveals low levels of stratification throughout the population. UAE emirates exhibit high within-Emirate-distance/among-Emirate distance ratios. Supervised admixture analysis showed a continuous gradient of ancestral populations, suggesting that admixture on the south eastern tip of the Arabian Peninsula occurred gradually. When visualized using a unique technique that combined admixture ratios and principal component analysis (PCA), unappreciated diversity was revealed while mitigating projection bias of conventional PCA. We observe low population stratification in the UAE in terms of homozygosity versus separation cluster coefficients. This holds for the UAE in a global context as well as for isolated cluster analysis of the Emirati birthplaces. However, the subtle clustering observed in the Emirates reflects geographic proximity and historic migration events. The analytical strategy used here highlights the complementary nature of data from genotype array and WGS for anthropological studies. Specifically, genotype array data were instructive to select representative subjects for WGS. Furthermore, from the 2.3 million allele frequencies obtained from genotype arrays, we identified 46,481 loci with allele frequencies that were significantly different with respect to other world populations. This comparison of allele frequencies facilitates variant prioritization in common diseases. In addition, these loci bear great potential as biomarkers in anthropological and forensic studies.
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Affiliation(s)
- Guan K Tay
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Faculty of Health and Medical Sciences, UWA Medical School, The University of Western Australia, Crawley, WA, Australia.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Andreas Henschel
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Gihan Daw Elbait
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Habiba S Al Safar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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12
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Movsesian AA, Mkrtchyan RA, Simonyan HG. The Bronze and Iron Age populations of the Armenian Highland in the genetic history of Armenians. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2020; 173:158-167. [PMID: 32274801 DOI: 10.1002/ajpa.24060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/16/2020] [Accepted: 03/21/2020] [Indexed: 11/10/2022]
Abstract
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.
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Affiliation(s)
- Alla A Movsesian
- Department of Anthropology, Lomonosov State University, Moscow, Russian Federation
| | - Rusan A Mkrtchyan
- Department of Cultural Studies, Yerevan State University, Yerevan, Republic of Armenia
| | - Hasmik G Simonyan
- Department of Archeology and Ethnography, Yerevan State University, Yerevan, Republic of Armenia
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13
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Zakharyan R, Ghazaryan H, Kocourkova L, Chavushyan A, Mkrtchyan A, Zizkova V, Arakelyan A, Petrek M. Association of Genetic Variants of Dopamine and Serotonin In Schizophrenia. Arch Med Res 2020; 51:13-20. [PMID: 32086104 DOI: 10.1016/j.arcmed.2019.12.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 11/29/2019] [Accepted: 12/16/2019] [Indexed: 01/03/2023]
Abstract
BACKGROUND Several studies indicated that antipsychotic treatment response and side effect manifestation can be different due to inter-individual variability in genetic variations. AIM OF THE STUDY Here we perform a case-control study to explore a potential association between schizophrenia and variants within the antipsychotic drug molecular targets (DRD1, DRD2, DRD3, HTR2A, HTR6) and metabolizing enzymes (CYP2D6, COMT) genes in Armenian population including also analysis of their possible relationship with disease clinical symptoms. METHODS A total of 18 SNPs was studied in patients with schizophrenia (n = 78) and healthy control subjects (n = 77) using MassARRAY genotyping. RESULTS We found that two studied genetic variants, namely DRD2 rs4436578*C and HTR2A rs6314*A are underrepresented in the group of patients compared to healthy subjects. After the correction for multiple testing, the rs4436578*C variant remained significant while the rs6314*A reported borderline significance. No significant differences in minor allele frequencies for other studied variants were identified. Also, a relationship between the genotypes and age of onset as well as disease duration has been detected. CONCLUSIONS The DRD2 rs4436578*C genetic variant might have protective role against schizophrenia, at least in Armenians.
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Affiliation(s)
- Roksana Zakharyan
- Institute of Molecular Biology NAS RA, Yerevan, Armenia; Russian-Armenian, University, Yerevan, Armenia.
| | - Hovsep Ghazaryan
- Andranik Chavushyan, Institute of Molecular Biology NAS RA, Yerevan, Armenia
| | - Lenka Kocourkova
- Department of Pathological Physiology, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | - Andranik Chavushyan
- Andranik Chavushyan, Institute of Molecular Biology NAS RA, Yerevan, Armenia
| | - Artur Mkrtchyan
- Department of Psychiatry, National Institute of Health, MH RA, Yerevan, Armenia
| | - Veronika Zizkova
- Department of Pathological Physiology, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | - Arsen Arakelyan
- Institute of Molecular Biology NAS RA, Yerevan, Armenia; Russian-Armenian, University, Yerevan, Armenia
| | - Martin Petrek
- Department of Pathological Physiology, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
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14
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Grugni V, Raveane A, Colombo G, Nici C, Crobu F, Ongaro L, Battaglia V, Sanna D, Al-Zahery N, Fiorani O, Lisa A, Ferretti L, Achilli A, Olivieri A, Francalacci P, Piazza A, Torroni A, Semino O. Y-chromosome and Surname Analyses for Reconstructing Past Population Structures: The Sardinian Population as a Test Case. Int J Mol Sci 2019; 20:E5763. [PMID: 31744094 PMCID: PMC6888588 DOI: 10.3390/ijms20225763] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/11/2019] [Accepted: 11/14/2019] [Indexed: 11/17/2022] Open
Abstract
Many anthropological, linguistic, genetic and genomic analyses have been carried out to evaluate the potential impact that evolutionary forces had in shaping the present-day Sardinian gene pool, the main outlier in the genetic landscape of Europe. However, due to the homogenizing effect of internal movements, which have intensified over the past fifty years, only partial information has been obtained about the main demographic events. To overcome this limitation, we analyzed the male-specific region of the Y chromosome in three population samples obtained by reallocating a large number of Sardinian subjects to the place of origin of their monophyletic surnames, which are paternally transmitted through generations in most of the populations, much like the Y chromosome. Three Y-chromosome founding lineages, G2-L91, I2-M26 and R1b-V88, were identified as strongly contributing to the definition of the outlying position of Sardinians in the European genetic context and marking a significant differentiation within the island. The present distribution of these lineages does not always mirror that detected in ancient DNAs. Our results show that the analysis of the Y-chromosome gene pool coupled with a sampling method based on the origin of the family name, is an efficient approach to unravelling past heterogeneity, often hidden by recent movements, in the gene pool of modern populations. Furthermore, the reconstruction and comparison of past genetic isolates represent a starting point to better assess the genetic information deriving from the increasing number of available ancient DNA samples.
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Affiliation(s)
- Viola Grugni
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Alessandro Raveane
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Giulia Colombo
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Carmen Nici
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Francesca Crobu
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), 09042 Monserrato, Italy
| | - Linda Ongaro
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
- Estonian Biocentre, Institute of Genomics, Riia 23, 51010 Tartu, Estonia
- Department of Evolutionary Biology, Institute of Molecular and Cell Biology, Riia 23, 51010 Tartu, Estonia
| | - Vincenza Battaglia
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Daria Sanna
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
- Dipartimento di Scienze Biomediche, Università di Sassari, 07100 Sassari, Italy
| | - Nadia Al-Zahery
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Ornella Fiorani
- Istituto di Genetica Molecolare “L.L. Cavalli-Sforza”, Consiglio Nazionale delle Ricerche (CNR), 27100 Pavia, Italy; (O.F.); (A.L.)
| | - Antonella Lisa
- Istituto di Genetica Molecolare “L.L. Cavalli-Sforza”, Consiglio Nazionale delle Ricerche (CNR), 27100 Pavia, Italy; (O.F.); (A.L.)
| | - Luca Ferretti
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Alessandro Achilli
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Anna Olivieri
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Paolo Francalacci
- Dipartimento di Scienza della Vita e dell’Ambiente, Università di Cagliari, 09123 Cagliari, Italy;
| | - Alberto Piazza
- Dipartimento di Scienze Mediche, Scuola di Medicina, Università di Torino, 10124 Torino, Italy;
| | - Antonio Torroni
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
| | - Ornella Semino
- Dipartimento di Biologia e Biotecnologie “L. Spallanzani”, Università di Pavia, 27100 Pavia, Italy; (V.G.); (A.R.); (G.C.); (C.N.); (F.C.); (L.O.); (V.B.); (D.S.); (N.A.-Z.); (L.F.); (A.A.); (A.O.); (A.T.)
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15
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Distinct genetic variation and heterogeneity of the Iranian population. PLoS Genet 2019; 15:e1008385. [PMID: 31550250 PMCID: PMC6759149 DOI: 10.1371/journal.pgen.1008385] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 08/27/2019] [Indexed: 02/07/2023] Open
Abstract
Iran, despite its size, geographic location and past cultural influence, has largely been a blind spot for human population genetic studies. With only sparse genetic information on the Iranian population available, we pursued its genome-wide and geographic characterization based on 1021 samples from eleven ethnic groups. We show that Iranians, while close to neighboring populations, present distinct genetic variation consistent with long-standing genetic continuity, harbor high heterogeneity and different levels of consanguinity, fall apart into a cluster of similar groups and several admixed ones and have experienced numerous language adoption events in the past. Our findings render Iran an important source for human genetic variation in Western and Central Asia, will guide adequate study sampling and assist the interpretation of putative disease-implicated genetic variation. Given Iran's internal genetic heterogeneity, future studies will have to consider ethnic affiliations and possible admixture.
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16
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Insights into matrilineal genetic structure, differentiation and ancestry of Armenians based on complete mitogenome data. Mol Genet Genomics 2019; 294:1547-1559. [DOI: 10.1007/s00438-019-01596-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 07/22/2019] [Indexed: 01/01/2023]
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17
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Haber M, Doumet-Serhal C, Scheib CL, Xue Y, Mikulski R, Martiniano R, Fischer-Genz B, Schutkowski H, Kivisild T, Tyler-Smith C. A Transient Pulse of Genetic Admixture from the Crusaders in the Near East Identified from Ancient Genome Sequences. Am J Hum Genet 2019; 104:977-984. [PMID: 31006515 PMCID: PMC6506814 DOI: 10.1016/j.ajhg.2019.03.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 03/18/2019] [Indexed: 11/12/2022] Open
Abstract
During the medieval period, hundreds of thousands of Europeans migrated to the Near East to take part in the Crusades, and many of them settled in the newly established Christian states along the Eastern Mediterranean coast. Here, we present a genetic snapshot of these events and their aftermath by sequencing the whole genomes of 13 individuals who lived in what is today known as Lebanon between the 3rd and 13th centuries CE. These include nine individuals from the “Crusaders’ pit” in Sidon, a mass burial in South Lebanon identified from the archaeology as the grave of Crusaders killed during a battle in the 13th century CE. We show that all of the Crusaders’ pit individuals were males; some were Western Europeans from diverse origins, some were locals (genetically indistinguishable from present-day Lebanese), and two individuals were a mixture of European and Near Eastern ancestries, providing direct evidence that the Crusaders admixed with the local population. However, these mixtures appear to have had limited genetic consequences since signals of admixture with Europeans are not significant in any Lebanese group today—in particular, Lebanese Christians are today genetically similar to local people who lived during the Roman period which preceded the Crusades by more than four centuries.
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18
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Nikoghosyan M, Hakobyan S, Hovhannisyan A, Loeffler-Wirth H, Binder H, Arakelyan A. Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians - A Machine Learning Approach. Front Genet 2019; 10:394. [PMID: 31105750 PMCID: PMC6498285 DOI: 10.3389/fgene.2019.00394] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 04/11/2019] [Indexed: 12/25/2022] Open
Abstract
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.
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Affiliation(s)
- Maria Nikoghosyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan, Armenia
- Research Group of Bioinformatics, Institute of Molecular Biology NAS RA, Yerevan, Armenia
| | - Siras Hakobyan
- Research Group of Bioinformatics, Institute of Molecular Biology NAS RA, Yerevan, Armenia
| | - Anahit Hovhannisyan
- Laboratory of Ethnogenomics, Institute of Molecular Biology NAS RA, Yerevan, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
| | - Arsen Arakelyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan, Armenia
- Research Group of Bioinformatics, Institute of Molecular Biology NAS RA, Yerevan, Armenia
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Telomere shortening in blood leukocytes of patients with posttraumatic stress disorder. J Psychiatr Res 2019; 111:83-88. [PMID: 30685566 DOI: 10.1016/j.jpsychires.2019.01.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/14/2019] [Accepted: 01/21/2019] [Indexed: 01/09/2023]
Abstract
Telomeres are protective fragments on chromosome ends involved in maintaining genome stability, preventing chromosomal fusions, regulation of cell division. It was shown that telomere attrition rate is accelerated in age-related diseases, as well as in response to physiological and psychosocial stress. The aim of this study was to evaluate relative leukocyte telomere length (LTL) in patients with post traumatic stress disorder (PTSD), as well as to investigate association of functional SNPs of telomerase TERC and TERT genes with LTL and PTSD. The relative LTL was measured by multiplex quantitative PCR method; genotyping of TERC rs12696304, TERT rs7726159 and rs2736100 was performed by PCR with sequence specific primers. Comparison of LTL in diseased and healthy subjects showed that PTSD patients had shorter average LTL than controls. Also, the frequency and the carriage rate of the TERT rs2736100*T allele was higher in PTSD patients compared to controls. Overall our results are in line with previous research in different populations. Furthermore, we have demonstrated that rs2736100 of TERT gene was significantly associated with PTSD and the minor allele of this polymorphism may be considered as a risk factor for PTSD in the Armenian population.
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Stepanyan A, Zakharyan R, Simonyan A, Tsakanova G, Arakelyan A. Involvement of polymorphisms of the nerve growth factor and its receptor encoding genes in the etiopathogenesis of ischemic stroke. BMC MEDICAL GENETICS 2018; 19:33. [PMID: 29499660 PMCID: PMC5834891 DOI: 10.1186/s12881-018-0551-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 02/23/2018] [Indexed: 11/25/2022]
Abstract
Background Despite the important role of the nerve growth factor in the survival and maintenance of neurons in ischemic stroke, data regarding the relationships between variations in the encoding gene and stroke are lacking. In the present study, we evaluated the association of the functional polymorphisms in NGF (rs6330) and NGFR (rs2072446 and rs734194) genes with ischemic stroke in an Armenian population. Methods In total, 370 unrelated individuals of Armenian nationality were enrolled in this study. Genomic DNA samples of patients and healthy controls were genotyped using polymerase chain reaction with sequence-specific primers. Results The results obtained indicate that the minor allele of rs6330 (Pcorr = 2.4E-10) and rs2072446 (Pcorr = 0.02) are significantly overrepresented in stroke group, while the minor allele of rs734194 (Pcorr = 8.5E-10) was underrepresented in diseased subjects. Single nucleotide polymorphisms in NGF gene (rs6330) and NGFR gene (rs2072446 and rs734194) are associated with the disease. Furthermore, it was shown that the carriage of the NGF rs6330*T minor allele is associated with increased infarct volume and higher risk of recurrent stroke. Conclusions In conclusion, our findings suggest that the NGF rs6330*T and NGFR rs2072446*T minor alleles might be nominated as a risk factor for developing ischemic stroke and NGFR rs734194*G minor allele as a protective against this disease at least in Armenian population.
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Affiliation(s)
- Ani Stepanyan
- Institute of Molecular Biology NAS RA, 7 Hasratyan Str, 0014, Yerevan, Armenia.
| | - Roksana Zakharyan
- Institute of Molecular Biology NAS RA, 7 Hasratyan Str, 0014, Yerevan, Armenia
| | - Arsen Simonyan
- Hospital and Polyclinic №2 CJSC, 54 Aram Str, 0002, Yerevan, Armenia
| | - Gohar Tsakanova
- Institute of Molecular Biology NAS RA, 7 Hasratyan Str, 0014, Yerevan, Armenia
| | - Arsen Arakelyan
- Institute of Molecular Biology NAS RA, 7 Hasratyan Str, 0014, Yerevan, Armenia
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Thouzeau V, Mennecier P, Verdu P, Austerlitz F. Genetic and linguistic histories in Central Asia inferred using approximate Bayesian computations. Proc Biol Sci 2018; 284:rspb.2017.0706. [PMID: 28835553 DOI: 10.1098/rspb.2017.0706] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 07/17/2017] [Indexed: 11/12/2022] Open
Abstract
Linguistic and genetic data have been widely compared, but the histories underlying these descriptions are rarely jointly inferred. We developed a unique methodological framework for analysing jointly language diversity and genetic polymorphism data, to infer the past history of separation, exchange and admixture events among human populations. This method relies on approximate Bayesian computations that enable the identification of the most probable historical scenario underlying each type of data, and to infer the parameters of these scenarios. For this purpose, we developed a new computer program PopLingSim that simulates the evolution of linguistic diversity, which we coupled with an existing coalescent-based genetic simulation program, to simulate both linguistic and genetic data within a set of populations. Applying this new program to a wide linguistic and genetic dataset of Central Asia, we found several differences between linguistic and genetic histories. In particular, we showed how genetic and linguistic exchanges differed in the past in this area: some cultural exchanges were maintained without genetic exchanges. The methodological framework and the linguistic simulation tool developed here can be used in future work for disentangling complex linguistic and genetic evolutions underlying human biological and cultural histories.
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Affiliation(s)
- Valentin Thouzeau
- CNRS, MNHN, Université Paris Diderot, UMR 7206 Eco-Anthropologie et Ethnobiologie, Paris 75016, France
| | - Philippe Mennecier
- CNRS, MNHN, Université Paris Diderot, UMR 7206 Eco-Anthropologie et Ethnobiologie, Paris 75016, France
| | - Paul Verdu
- CNRS, MNHN, Université Paris Diderot, UMR 7206 Eco-Anthropologie et Ethnobiologie, Paris 75016, France
| | - Frédéric Austerlitz
- CNRS, MNHN, Université Paris Diderot, UMR 7206 Eco-Anthropologie et Ethnobiologie, Paris 75016, France
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Triska P, Chekanov N, Stepanov V, Khusnutdinova EK, Kumar GPA, Akhmetova V, Babalyan K, Boulygina E, Kharkov V, Gubina M, Khidiyatova I, Khitrinskaya I, Khrameeva EE, Khusainova R, Konovalova N, Litvinov S, Marusin A, Mazur AM, Puzyrev V, Ivanoshchuk D, Spiridonova M, Teslyuk A, Tsygankova S, Triska M, Trofimova N, Vajda E, Balanovsky O, Baranova A, Skryabin K, Tatarinova TV, Prokhortchouk E. Between Lake Baikal and the Baltic Sea: genomic history of the gateway to Europe. BMC Genet 2017; 18:110. [PMID: 29297395 PMCID: PMC5751809 DOI: 10.1186/s12863-017-0578-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The history of human populations occupying the plains and mountain ridges separating Europe from Asia has been eventful, as these natural obstacles were crossed westward by multiple waves of Turkic and Uralic-speaking migrants as well as eastward by Europeans. Unfortunately, the material records of history of this region are not dense enough to reconstruct details of population history. These considerations stimulate growing interest to obtain a genetic picture of the demographic history of migrations and admixture in Northern Eurasia. RESULTS We genotyped and analyzed 1076 individuals from 30 populations with geographical coverage spanning from Baltic Sea to Baikal Lake. Our dense sampling allowed us to describe in detail the population structure, provide insight into genomic history of numerous European and Asian populations, and significantly increase quantity of genetic data available for modern populations in region of North Eurasia. Our study doubles the amount of genome-wide profiles available for this region. We detected unusually high amount of shared identical-by-descent (IBD) genomic segments between several Siberian populations, such as Khanty and Ket, providing evidence of genetic relatedness across vast geographic distances and between speakers of different language families. Additionally, we observed excessive IBD sharing between Khanty and Bashkir, a group of Turkic speakers from Southern Urals region. While adding some weight to the "Finno-Ugric" origin of Bashkir, our studies highlighted that the Bashkir genepool lacks the main "core", being a multi-layered amalgamation of Turkic, Ugric, Finnish and Indo-European contributions, which points at intricacy of genetic interface between Turkic and Uralic populations. Comparison of the genetic structure of Siberian ethnicities and the geography of the region they inhabit point at existence of the "Great Siberian Vortex" directing genetic exchanges in populations across the Siberian part of Asia. Slavic speakers of Eastern Europe are, in general, very similar in their genetic composition. Ukrainians, Belarusians and Russians have almost identical proportions of Caucasus and Northern European components and have virtually no Asian influence. We capitalized on wide geographic span of our sampling to address intriguing question about the place of origin of Russian Starovers, an enigmatic Eastern Orthodox Old Believers religious group relocated to Siberia in seventeenth century. A comparative reAdmix analysis, complemented by IBD sharing, placed their roots in the region of the Northern European Plain, occupied by North Russians and Finno-Ugric Komi and Karelian people. Russians from Novosibirsk and Russian Starover exhibit ancestral proportions close to that of European Eastern Slavs, however, they also include between five to 10 % of Central Siberian ancestry, not present at this level in their European counterparts. CONCLUSIONS Our project has patched the hole in the genetic map of Eurasia: we demonstrated complexity of genetic structure of Northern Eurasians, existence of East-West and North-South genetic gradients, and assessed different inputs of ancient populations into modern populations.
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MESH Headings
- Algorithms
- Asia
- DNA
- Datasets as Topic
- Emigration and Immigration/history
- Ethnicity/genetics
- Europe
- Female
- Genetic Variation
- Genetics, Population
- Genotyping Techniques
- History, 15th Century
- History, 16th Century
- History, 17th Century
- History, 18th Century
- History, 19th Century
- History, 20th Century
- History, 21st Century
- History, Ancient
- History, Medieval
- Humans
- Male
- Russia
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Affiliation(s)
- Petr Triska
- Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Nikolay Chekanov
- Federal State Institution "Federal Research Centre «Fundamentals of Biotechnology» of the Russian Academy of Sciences", Moscow, Russia
- "Genoanalytica" CJSC, Moscow, Russia
| | - Vadim Stepanov
- Institute of Medical Genetics, Tomsk National Medical Research Center, Russian Academy of Sciences, Siberian Branch, Tomsk, Russia
| | - Elza K Khusnutdinova
- Institute of Biochemistry and Genetics, Russian Academy of Sciences, Ufa Scientific Centre of Russian Academy of Sciences, Ufa, Russia
- Bashkir State University, Ufa, Russia
| | | | - Vita Akhmetova
- Institute of Biochemistry and Genetics, Russian Academy of Sciences, Ufa Scientific Centre of Russian Academy of Sciences, Ufa, Russia
| | - Konstantin Babalyan
- Moscow Institute of Physics and Technology, Department of Molecular and Bio-Physics, Moscow, Russia
| | | | - Vladimir Kharkov
- Institute of Medical Genetics, Tomsk National Medical Research Center, Russian Academy of Sciences, Siberian Branch, Tomsk, Russia
| | - Marina Gubina
- Institute of Cytology and Genetics, Russian Academy of Sciences, Siberian Branch, Novosibirsk, Russia
| | - Irina Khidiyatova
- Institute of Biochemistry and Genetics, Russian Academy of Sciences, Ufa Scientific Centre of Russian Academy of Sciences, Ufa, Russia
- Bashkir State University, Ufa, Russia
| | - Irina Khitrinskaya
- Institute of Medical Genetics, Tomsk National Medical Research Center, Russian Academy of Sciences, Siberian Branch, Tomsk, Russia
| | - Ekaterina E Khrameeva
- "Genoanalytica" CJSC, Moscow, Russia
- Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Moscow, Russia
| | - Rita Khusainova
- Institute of Biochemistry and Genetics, Russian Academy of Sciences, Ufa Scientific Centre of Russian Academy of Sciences, Ufa, Russia
- Bashkir State University, Ufa, Russia
| | | | - Sergey Litvinov
- Institute of Biochemistry and Genetics, Russian Academy of Sciences, Ufa Scientific Centre of Russian Academy of Sciences, Ufa, Russia
| | - Andrey Marusin
- Institute of Medical Genetics, Tomsk National Medical Research Center, Russian Academy of Sciences, Siberian Branch, Tomsk, Russia
| | - Alexandr M Mazur
- Federal State Institution "Federal Research Centre «Fundamentals of Biotechnology» of the Russian Academy of Sciences", Moscow, Russia
| | - Valery Puzyrev
- Institute of Medical Genetics, Tomsk National Medical Research Center, Russian Academy of Sciences, Siberian Branch, Tomsk, Russia
| | - Dinara Ivanoshchuk
- Institute of Cytology and Genetics, Russian Academy of Sciences, Siberian Branch, Novosibirsk, Russia
| | - Maria Spiridonova
- Institute of Medical Genetics, Tomsk National Medical Research Center, Russian Academy of Sciences, Siberian Branch, Tomsk, Russia
| | - Anton Teslyuk
- Moscow Institute of Physics and Technology, Department of Molecular and Bio-Physics, Moscow, Russia
| | - Svetlana Tsygankova
- Moscow Institute of Physics and Technology, Department of Molecular and Bio-Physics, Moscow, Russia
| | - Martin Triska
- Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Natalya Trofimova
- Institute of Biochemistry and Genetics, Russian Academy of Sciences, Ufa Scientific Centre of Russian Academy of Sciences, Ufa, Russia
| | - Edward Vajda
- Department of Modern and Classical Languages, Western Washington University, Bellingham, WA, USA
| | - Oleg Balanovsky
- Research Centre for Medical Genetics, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow, Russia
| | - Ancha Baranova
- Research Centre for Medical Genetics, Moscow, Russia
- School of Systems Biology, George Mason University, Fairfax, VA, USA
- Atlas Biomed Group, Moscow, Russia
| | - Konstantin Skryabin
- Federal State Institution "Federal Research Centre «Fundamentals of Biotechnology» of the Russian Academy of Sciences", Moscow, Russia
- Russian Scientific Centre "Kurchatov Institute", Moscow, Russia
- Department of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Tatiana V Tatarinova
- Vavilov Institute of General Genetics, Moscow, Russia.
- School of Systems Biology, George Mason University, Fairfax, VA, USA.
- Atlas Biomed Group, Moscow, Russia.
- Department of Biology, University of La Verne, La Verne, CA, USA.
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.
| | - Egor Prokhortchouk
- Federal State Institution "Federal Research Centre «Fundamentals of Biotechnology» of the Russian Academy of Sciences", Moscow, Russia.
- Department of Biology, Lomonosov Moscow State University, Moscow, Russia.
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Margaryan A, Derenko M, Hovhannisyan H, Malyarchuk B, Heller R, Khachatryan Z, Avetisyan P, Badalyan R, Bobokhyan A, Melikyan V, Sargsyan G, Piliposyan A, Simonyan H, Mkrtchyan R, Denisova G, Yepiskoposyan L, Willerslev E, Allentoft ME. Eight Millennia of Matrilineal Genetic Continuity in the South Caucasus. Curr Biol 2017; 27:2023-2028.e7. [DOI: 10.1016/j.cub.2017.05.087] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 04/25/2017] [Accepted: 05/26/2017] [Indexed: 01/27/2023]
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Genetic differentiation between upland and lowland populations shapes the Y-chromosomal landscape of West Asia. Hum Genet 2017; 136:437-450. [DOI: 10.1007/s00439-017-1770-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 02/20/2017] [Indexed: 12/22/2022]
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25
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Yepiskoposyan L, Hovhannisyan A, Khachatryan Z. Genetic Structure of the Armenian Population. Arch Immunol Ther Exp (Warsz) 2017; 64:113-116. [DOI: 10.1007/s00005-016-0431-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 10/20/2016] [Indexed: 11/25/2022]
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26
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Atshemyan S, Chavushyan A, Berberian N, Sahakyan A, Zakharyan R, Arakelyan A. Characterization of BRCA1/2 mutations in patients with family history of breast cancer in Armenia. F1000Res 2017; 6:29. [PMID: 28357044 PMCID: PMC5357036 DOI: 10.12688/f1000research.10434.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/29/2016] [Indexed: 12/20/2022] Open
Abstract
Background. Breast cancer is one of the most common cancers in women worldwide. The germline mutations of the BRCA1 and BRCA2 genes are the most significant and well characterized genetic risk factors for hereditary breast cancer. Intensive research in the last decades has demonstrated that the incidence of mutations varies widely among different populations. In this study we attempted to perform a pilot study for identification and characterization of mutations in BRCA1 and BRCA2 genes among Armenian patients with family history of breast cancer and their healthy relatives. Methods. We performed targeted exome sequencing for BRCA1 and BRCA2 genes in 6 patients and their healthy relatives. After alignment of short reads to the reference genome, germline single nucleotide variation and indel discovery was performed using GATK software. Functional implications of identified variants were assessed using ENSEMBL Variant Effect Predictor tool. Results. In total, 39 single nucleotide variations and 4 indels were identified, from which 15 SNPs and 3 indels were novel. No known pathogenic mutations were identified, but 2 SNPs causing missense amino acid mutations had significantly increased frequencies in the study group compared to the 1000 Genome populations. Conclusions. Our results demonstrate the importance of screening of BRCA1 and BRCA2 gene variants in the Armenian population in order to identity specifics of mutation spectrum and frequencies and enable accurate risk assessment of hereditary breast cancers.
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Affiliation(s)
- Sofi Atshemyan
- Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
| | - Andranik Chavushyan
- Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
| | | | - Arthur Sahakyan
- ARTMED Medical Rehabilitation Center (CJSC), Yerevan, Armenia
| | - Roksana Zakharyan
- Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
| | - Arsen Arakelyan
- Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
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Abstract
The Druze are an aggregate of communities in the Levant and Near East living almost exclusively in the mountains of Syria, Lebanon and Israel whose ~1000 year old religion formally opposes mixed marriages and conversions. Despite increasing interest in genetics of the population structure of the Druze, their population history remains unknown. We investigated the genetic relationships between Israeli Druze and both modern and ancient populations. We evaluated our findings in light of three hypotheses purporting to explain Druze history that posit Arabian, Persian or mixed Near Eastern-Levantine roots. The biogeographical analysis localised proto-Druze to the mountainous regions of southeastern Turkey, northern Iraq and southeast Syria and their descendants clustered along a trajectory between these two regions. The mixed Near Eastern–Middle Eastern localisation of the Druze, shown using both modern and ancient DNA data, is distinct from that of neighbouring Syrians, Palestinians and most of the Lebanese, who exhibit a high affinity to the Levant. Druze biogeographic affinity, migration patterns, time of emergence and genetic similarity to Near Eastern populations are highly suggestive of Armenian-Turkish ancestries for the proto-Druze.
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28
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Broushaki F, Thomas MG, Link V, López S, van Dorp L, Kirsanow K, Hofmanová Z, Diekmann Y, Cassidy LM, Díez-del-Molino D, Kousathanas A, Sell C, Robson HK, Martiniano R, Blöcher J, Scheu A, Kreutzer S, Bollongino R, Bobo D, Davudi H, Munoz O, Currat M, Abdi K, Biglari F, Craig OE, Bradley DG, Shennan S, Veeramah K, Mashkour M, Wegmann D, Hellenthal G, Burger J. Early Neolithic genomes from the eastern Fertile Crescent. Science 2016; 353:499-503. [PMID: 27417496 PMCID: PMC5113750 DOI: 10.1126/science.aaf7943] [Citation(s) in RCA: 153] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 07/05/2016] [Indexed: 01/06/2023]
Abstract
We sequenced Early Neolithic genomes from the Zagros region of Iran (eastern Fertile Crescent), where some of the earliest evidence for farming is found, and identify a previously uncharacterized population that is neither ancestral to the first European farmers nor has contributed substantially to the ancestry of modern Europeans. These people are estimated to have separated from Early Neolithic farmers in Anatolia some 46,000 to 77,000 years ago and show affinities to modern-day Pakistani and Afghan populations, but particularly to Iranian Zoroastrians. We conclude that multiple, genetically differentiated hunter-gatherer populations adopted farming in southwestern Asia, that components of pre-Neolithic population structure were preserved as farming spread into neighboring regions, and that the Zagros region was the cradle of eastward expansion.
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Affiliation(s)
- Farnaz Broushaki
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
| | - Mark G Thomas
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Vivian Link
- Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Saioa López
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Lucy van Dorp
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Karola Kirsanow
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
| | - Zuzana Hofmanová
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
| | - Yoan Diekmann
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Lara M. Cassidy
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland
| | - David Díez-del-Molino
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, SE-10405, Stockholm, Sweden
| | - Athanasios Kousathanas
- Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015 Paris, France
| | - Christian Sell
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
| | - Harry K. Robson
- BioArCh, Department of Archaeology, University of York, York, YO10 5YW, UK
| | - Rui Martiniano
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland
| | - Jens Blöcher
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
| | - Amelie Scheu
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
| | - Susanne Kreutzer
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
| | - Ruth Bollongino
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
| | - Dean Bobo
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, 11794- 5245, USA
| | - Hossein Davudi
- Department of Archaeology, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
| | - Olivia Munoz
- UMR 7041 ArScAn -VEPMO, Maison de l’Archéologie et de l’Ethnologie, 21 allée de l’Université, 92023 Nanterre, France
| | - Mathias Currat
- Department of Genetics & Evolution-Anthropology Unit, University of Geneva, 1211 Geneva, Switzerland
| | - Kamyar Abdi
- Samuel Jordan Center for Persian Studies and Culture, University of California-lrvine, Irvine, CA 92697-3370, USA
| | - Fereidoun Biglari
- Paleolithic Department, National Museum of Iran, 113617111, Tehran, Iran
| | - Oliver E. Craig
- BioArCh, Department of Archaeology, University of York, York, YO10 5YW, UK
| | - Daniel G Bradley
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland
| | - Stephen Shennan
- Institute of Archaeology, University College London, London WC1H 0PY, UK
| | - Krishna Veeramah
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, 11794- 5245, USA
| | - Marjan Mashkour
- CNRS/MNHN/SUs – UMR 7209, Archéozoologie et Archéobotanique, Sociétés, Pratiques et Environnements, Département Ecologie et Gestion de la Biodiversité, 55 rue Buffon, 75005 Paris, France
| | - Daniel Wegmann
- Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Garrett Hellenthal
- Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Joachim Burger
- Palaeogenetics Group, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
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Voskarides K, Mazières S, Hadjipanagi D, Di Cristofaro J, Ignatiou A, Stefanou C, King RJ, Underhill PA, Chiaroni J, Deltas C. Y-chromosome phylogeographic analysis of the Greek-Cypriot population reveals elements consistent with Neolithic and Bronze Age settlements. INVESTIGATIVE GENETICS 2016; 7:1. [PMID: 26870315 PMCID: PMC4750176 DOI: 10.1186/s13323-016-0032-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 02/02/2016] [Indexed: 12/15/2022]
Abstract
Background The archeological record indicates that the permanent settlement of Cyprus began with pioneering agriculturalists circa 11,000 years before present, (ca. 11,000 y BP). Subsequent colonization events followed, some recognized regionally. Here, we assess the Y-chromosome structure of Cyprus in context to regional populations and correlate it to phases of prehistoric colonization. Results Analysis of haplotypes from 574 samples showed that island-wide substructure was barely significant in a spatial analysis of molecular variance (SAMOVA). However, analyses of molecular variance (AMOVA) of haplogroups using 92 binary markers genotyped in 629 Cypriots revealed that the proportion of variance among the districts was irregularly distributed. Principal component analysis (PCA) revealed potential genetic associations of Greek-Cypriots with neighbor populations. Contrasting haplogroups in the PCA were used as surrogates of parental populations. Admixture analyses suggested that the majority of G2a-P15 and R1b-M269 components were contributed by Anatolia and Levant sources, respectively, while Greece Balkans supplied the majority of E-V13 and J2a-M67. Haplotype-based expansion times were at historical levels suggestive of recent demography. Conclusions Analyses of Cypriot haplogroup data are consistent with two stages of prehistoric settlement. E-V13 and E-M34 are widespread, and PCA suggests sourcing them to the Balkans and Levant/Anatolia, respectively. The persistent pre-Greek component is represented by elements of G2-U5(xL30) haplogroups: U5*, PF3147, and L293. J2b-M205 may contribute also to the pre-Greek strata. The majority of R1b-Z2105 lineages occur in both the westernmost and easternmost districts. Distinctively, sub-haplogroup R1b- M589 occurs only in the east. The absence of R1b- M589 lineages in Crete and the Balkans and the presence in Asia Minor are compatible with Late Bronze Age influences from Anatolia rather than from Mycenaean Greeks. Electronic supplementary material The online version of this article (doi:10.1186/s13323-016-0032-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Konstantinos Voskarides
- Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Kallipoleos 75, 1678 Nicosia, Cyprus
| | - Stéphane Mazières
- Aix Marseille Université, ADES UMR7268, CNRS, EFS-AM, Marseille, France
| | - Despina Hadjipanagi
- Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Kallipoleos 75, 1678 Nicosia, Cyprus
| | | | - Anastasia Ignatiou
- Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Kallipoleos 75, 1678 Nicosia, Cyprus
| | - Charalambos Stefanou
- Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Kallipoleos 75, 1678 Nicosia, Cyprus
| | - Roy J King
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA USA
| | - Peter A Underhill
- Department of Genetics, Stanford University, Stanford, California 94305 USA
| | - Jacques Chiaroni
- Aix Marseille Université, ADES UMR7268, CNRS, EFS-AM, Marseille, France
| | - Constantinos Deltas
- Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Kallipoleos 75, 1678 Nicosia, Cyprus
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