89 research outputs found
THE VARIATION OF GENOME SITES ASSOCIATED WITH SEVERE COVID-19 ACROSS POPULATIONS: THE WORLDWIDE AND NATIONAL PATTERNS
No full textSUMMARY Background The knowledge of clinically relevant markers distribution might become a useful tool in COVID-19 therapy using personalized approach in the lack of unified recommendations for COVID-19 patients management during pandemic. We aimed to identify the frequencies and distribution patterns of rs11385942 and rs657152 polymorphic markers, associated with severe COVID-19, among populations of the world, as well at the national level within Russia. The study was also dedicated to reveal whether population frequencies of both polymorphic markers are associated with COVID-19 cases, recovery and death rates. Methods We genotyped 1883 samples from 91 ethnic populations from Russia and neighboring countries by rs11385942 and rs657152 markers. Local populations which were geographically close and genetically similar were pooled into 28 larger groups. In the similar way we compiled a dataset on the other regions of the globe using genotypes extracted or imputed from the available datasets (32 populations worldwide). The differences in alleles frequencies between groups were estimated and the frequency distribution geographic maps have been constructed. We run the correlation analysis of both markers frequencies in various populations with the COVID-19 epidemiological data on the same populations. Findings The cartographic analysis revealed that distribution of rs11385942 follows the West Eurasian pattern: it is frequent in Europeans, West Asians, and particularly in South Asians but rare or absent in all other parts of the globe. Notably, there is no abrupt changes in frequency across Eurasia but the clinal variation instead. The distribution of rs657152 is more homogeneous. Higher population frequencies of both risk alleles correlated positively with the death rate. For the rs11385942 we can state the tendency only (r=0,13, p=0.65), while for rs657152 the correlation was significantly high (r=0,59, p=0,02). These reasonable correlations were obtained on the Russian dataset, but not on the world dataset. Interpretation Using epidemiological statistics on Russia and neighboring countries we revealed the evident correlation of the risk alleles frequencies with the death rate from COVID-19. The lack of such correlations at the world level should be attributed to the differences in the ways epidemiological data have been counted in different countries. So that, we believe that genetic differences between populations make small but real contribution into the heterogeneity of the pandemic worldwide. New studies on the correlations between COVID-19 recovery/mortality rates and population’s gene pool are urgently needed
Recombination gives a new insight in the effective population size and history of the Old World human populations
No full textChristina J. Adler, Alan Cooper, Clio S. I. Der Sarkissian and Wolfgang Haak are members of the Genographic ConsortiumThe information left by recombination in our genomes can be used to make inferences on our recent evolutionary history. Specifically, the number of past recombination events in a population sample is a function of its effective population size (Ne). We have applied a method, Identifying Recombination in Sequences (IRiS), to detect specific past recombination events in 30 Old World populations to infer their Ne. We have found that sub-Saharan African populations have an Ne that is approximately four times greater than those of non-African populations and that outside of Africa, South Asian populations had the largest Ne. We also observe that the patterns of recombinational diversity of these populations correlate with distance out of Africa if that distance is measured along a path crossing South Arabia. No such correlation is found through a Sinai route, suggesting that anatomically modern humans first left Africa through the Bab-el-Mandeb strait rather than through present Egypt.Marta Melé, Asif Javed, Marc Pybus, Pierre Zalloua, Marc Haber, David Comas, Mihai G. Netea, Oleg Balanovsky, Elena Balanovska, Li Jin, Yajun Yang, R. M. Pitchappan, G. Arunkumar, Laxmi Parida, Francesc Calafell, Jaume Bertranpetit, and the Genographic Consortiu
Parallel evolution of genes and languages in the Caucasus region
No full textWe analyzed 40 single nucleotide polymorphism and 19 short tandem repeat Y-chromosomal markers in a large sample of 1,525 indigenous individuals from 14 populations in the Caucasus and 254 additional individuals representing potential source populations. We also employed a lexicostatistical approach to reconstruct the history of the languages of the North Caucasian family spoken by the Caucasus populations. We found a different major haplogroup to be prevalent in each of four sets of populations that occupy distinct geographic regions and belong to different linguistic branches. The haplogroup frequencies correlated with geography and, even more strongly, with language. Within haplogroups, a number of haplotype clusters were shown to be specific to individual populations and languages. The data suggested a direct origin of Caucasus male lineages from the Near East, followed by high levels of isolation, differentiation, and genetic drift in situ. Comparison of genetic and linguistic reconstructions covering the last few millennia showed striking correspondences between the topology and dates of the respective gene and language trees and with documented historical events. Overall, in the Caucasus region, unmatched levels of gene–language coevolution occurred within geographically isolated populations, probably due to its mountainous terrain.Oleg Balanovsky, Khadizhat Dibirova, Anna Dybo, Oleg Mudrak, Svetlana Frolova, Elvira Pocheshkhova, Marc Haber, Daniel Platt, Theodore Schurr, Wolfgang Haak, Marina Kuznetsova, Magomed Radzhabov, Olga Balaganskaya, Alexey Romanov, Tatiana Zakharova, David F. Soria Hernanz, Pierre Zalloua, Sergey Koshel, Merritt Ruhlen, Colin Renfrew, R. Spencer Wells, Chris Tyler-Smith, Elena Balanovska and The Genographic Consortiu
Recombination networks as genetic markers in a human variation study of the Old World
No full textChristina J. Adler, Alan Cooper, Clio S. I. Der Sarkissian and Wolfgang Haak are members of The Genographic ConsortiumWe have analyzed human genetic diversity in 33 Old World populations including 23 populations obtained through Genographic Project studies. A set of 1,536 SNPs in five X chromosome regions were genotyped in 1,288 individuals (mostly males). We use a novel analysis employing subARG network construction with recombining chromosomal segments. Here, a subARG is constructed independently for each of five gene-free regions across the X chromosome, and the results are aggregated across them. For PCA, MDS and ancestry inference with STRUCTURE, the subARG is processed to obtain feature vectors of samples and pairwise distances between samples. The observed population structure, estimated from the five short X chromosomal segments, supports genome-wide frequency-based analyses: African populations show higher genetic diversity, and the general trend of shared variation is seen across the globe from Africa through Middle East, Europe, Central Asia, Southeast Asia, and East Asia in broad patterns. The recombinational analysis was also compared with established methods based on SNPs and haplotypes. For haplotypes, we also employed a fixed-length approach based on information-content optimization. Our recombinational analysis suggested a southern migration route out of Africa, and it also supports a single, rapid human expansion from Africa to East Asia through South Asia.Asif Javed, Marta Melé, Marc Pybus, Pierre Zalloua, Marc Haber, David Comas, Mihai G. Netea, Oleg Balanovsky, Elena Balanovska, Li Jin, Yajun Yang, GaneshPrasad ArunKumar, Ramasamy Pitchappan, Jaume Bertranpetit, Francesc Calafell, Laxmi Parida, The Genographic Consortiu
Phylogeography of human Y-chromosome haplogroup Q3-L275 from an academic/citizen science collaboration
No full textBackground: The Y-chromosome haplogroup Q has three major branches: Q1, Q2, and Q3. Q1 is found in both Asia
and the Americas where it accounts for about 90% of indigenous Native American Y-chromosomes; Q2 is found in
North and Central Asia; but little is known about the third branch, Q3, also named Q1b-L275. Here, we combined the
efforts of population geneticists and genetic genealogists to use the potential of full Y-chromosome sequencing for
reconstructing haplogroup Q3 phylogeography and suggest possible linkages to events in population history.
Results: We analyzed 47 fully sequenced Y-chromosomes and reconstructed the haplogroup Q3 phylogenetic tree in
detail. Haplogroup Q3-L275, derived from the oldest known split within Eurasian/American haplogroup Q, most likely
occurred in West or Central Asia in the Upper Paleolithic period. During the Mesolithic and Neolithic epochs, Q3 remained
a minor component of the West Asian Y-chromosome pool and gave rise to five branches (Q3a to Q3e), which spread
across West, Central and parts of South Asia. Around 3–4 millennia ago (Bronze Age), the Q3a branch underwent a rapid
expansion, splitting into seven branches, some of which entered Europe. One of these branches, Q3a1, was acquired by a
population ancestral to Ashkenazi Jews and grew within this population during the 1st millennium AD, reaching up to 5%
in present day Ashkenazi.
Conclusions: This study dataset was generated by a massive Y-chromosome genotyping effort in the genetic genealogy
community, and phylogeographic patterns were revealed by a collaboration of population geneticists and genetic
genealogists. This positive experience of collaboration between academic and citizen science provides a model for further
joint projects. Merging data and skills of academic and citizen science promises to combine, respectively, quality and
quantity, generalization and specialization, and achieve a well-balanced and careful interpretation of the paternal-side
history of human populations
Image3_Determining the Area of Ancestral Origin for Individuals From North Eurasia Based on 5,229 SNP Markers.JPEG
No full textCurrently available genetic tools effectively distinguish between different continental origins. However, North Eurasia, which constitutes one-third of the world’s largest continent, remains severely underrepresented. The dataset used in this study represents 266 populations from 12 North Eurasian countries, including most of the ethnic diversity across Russia’s vast territory. A total of 1,883 samples were genotyped using the Illumina Infinium Omni5Exome-4 v1.3 BeadChip. Three principal components were computed for the entire dataset using three iterations for outlier removal. It allowed the merging of 266 populations into larger groups while maintaining intragroup homogeneity, so 29 ethnic geographic groups were formed that were genetically distinguishable enough to trace individual ancestry. Several feature selection methods, including the random forest algorithm, were tested to estimate the number of genetic markers needed to differentiate between the groups; 5,229 ancestry-informative SNPs were selected. We tested various classifiers supporting multiple classes and output values for each class that could be interpreted as probabilities. The logistic regression was chosen as the best mathematical model for predicting ancestral populations. The machine learning algorithm for inferring an ancestral ethnic geographic group was implemented in the original software “Homeland” fitted with the interface module, the prediction module, and the cartographic module. Examples of geographic maps showing the likelihood of geographic ancestry for individuals from different regions of North Eurasia are provided. Validating methods show that the highest number of ethnic geographic group predictions with almost absolute accuracy and sensitivity was observed for South and Central Siberia, Far East, and Kamchatka. The total accuracy of prediction of one of 29 ethnic geographic groups reached 71%. The proposed method can be employed to predict ancestries from the populations of Russia and its neighbor states. It can be used for the needs of forensic science and genetic genealogy.</p
Image2_Determining the Area of Ancestral Origin for Individuals From North Eurasia Based on 5,229 SNP Markers.JPEG
No full textCurrently available genetic tools effectively distinguish between different continental origins. However, North Eurasia, which constitutes one-third of the world’s largest continent, remains severely underrepresented. The dataset used in this study represents 266 populations from 12 North Eurasian countries, including most of the ethnic diversity across Russia’s vast territory. A total of 1,883 samples were genotyped using the Illumina Infinium Omni5Exome-4 v1.3 BeadChip. Three principal components were computed for the entire dataset using three iterations for outlier removal. It allowed the merging of 266 populations into larger groups while maintaining intragroup homogeneity, so 29 ethnic geographic groups were formed that were genetically distinguishable enough to trace individual ancestry. Several feature selection methods, including the random forest algorithm, were tested to estimate the number of genetic markers needed to differentiate between the groups; 5,229 ancestry-informative SNPs were selected. We tested various classifiers supporting multiple classes and output values for each class that could be interpreted as probabilities. The logistic regression was chosen as the best mathematical model for predicting ancestral populations. The machine learning algorithm for inferring an ancestral ethnic geographic group was implemented in the original software “Homeland” fitted with the interface module, the prediction module, and the cartographic module. Examples of geographic maps showing the likelihood of geographic ancestry for individuals from different regions of North Eurasia are provided. Validating methods show that the highest number of ethnic geographic group predictions with almost absolute accuracy and sensitivity was observed for South and Central Siberia, Far East, and Kamchatka. The total accuracy of prediction of one of 29 ethnic geographic groups reached 71%. The proposed method can be employed to predict ancestries from the populations of Russia and its neighbor states. It can be used for the needs of forensic science and genetic genealogy.</p
Image4_Determining the Area of Ancestral Origin for Individuals From North Eurasia Based on 5,229 SNP Markers.JPEG
No full textCurrently available genetic tools effectively distinguish between different continental origins. However, North Eurasia, which constitutes one-third of the world’s largest continent, remains severely underrepresented. The dataset used in this study represents 266 populations from 12 North Eurasian countries, including most of the ethnic diversity across Russia’s vast territory. A total of 1,883 samples were genotyped using the Illumina Infinium Omni5Exome-4 v1.3 BeadChip. Three principal components were computed for the entire dataset using three iterations for outlier removal. It allowed the merging of 266 populations into larger groups while maintaining intragroup homogeneity, so 29 ethnic geographic groups were formed that were genetically distinguishable enough to trace individual ancestry. Several feature selection methods, including the random forest algorithm, were tested to estimate the number of genetic markers needed to differentiate between the groups; 5,229 ancestry-informative SNPs were selected. We tested various classifiers supporting multiple classes and output values for each class that could be interpreted as probabilities. The logistic regression was chosen as the best mathematical model for predicting ancestral populations. The machine learning algorithm for inferring an ancestral ethnic geographic group was implemented in the original software “Homeland” fitted with the interface module, the prediction module, and the cartographic module. Examples of geographic maps showing the likelihood of geographic ancestry for individuals from different regions of North Eurasia are provided. Validating methods show that the highest number of ethnic geographic group predictions with almost absolute accuracy and sensitivity was observed for South and Central Siberia, Far East, and Kamchatka. The total accuracy of prediction of one of 29 ethnic geographic groups reached 71%. The proposed method can be employed to predict ancestries from the populations of Russia and its neighbor states. It can be used for the needs of forensic science and genetic genealogy.</p
Image5_Determining the Area of Ancestral Origin for Individuals From North Eurasia Based on 5,229 SNP Markers.JPEG
No full textCurrently available genetic tools effectively distinguish between different continental origins. However, North Eurasia, which constitutes one-third of the world’s largest continent, remains severely underrepresented. The dataset used in this study represents 266 populations from 12 North Eurasian countries, including most of the ethnic diversity across Russia’s vast territory. A total of 1,883 samples were genotyped using the Illumina Infinium Omni5Exome-4 v1.3 BeadChip. Three principal components were computed for the entire dataset using three iterations for outlier removal. It allowed the merging of 266 populations into larger groups while maintaining intragroup homogeneity, so 29 ethnic geographic groups were formed that were genetically distinguishable enough to trace individual ancestry. Several feature selection methods, including the random forest algorithm, were tested to estimate the number of genetic markers needed to differentiate between the groups; 5,229 ancestry-informative SNPs were selected. We tested various classifiers supporting multiple classes and output values for each class that could be interpreted as probabilities. The logistic regression was chosen as the best mathematical model for predicting ancestral populations. The machine learning algorithm for inferring an ancestral ethnic geographic group was implemented in the original software “Homeland” fitted with the interface module, the prediction module, and the cartographic module. Examples of geographic maps showing the likelihood of geographic ancestry for individuals from different regions of North Eurasia are provided. Validating methods show that the highest number of ethnic geographic group predictions with almost absolute accuracy and sensitivity was observed for South and Central Siberia, Far East, and Kamchatka. The total accuracy of prediction of one of 29 ethnic geographic groups reached 71%. The proposed method can be employed to predict ancestries from the populations of Russia and its neighbor states. It can be used for the needs of forensic science and genetic genealogy.</p
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