Russian genofond. Genogeography of surnames

Surnames are traditionally used in population genetics as "quasi-genetic" markers (i.e., analogs of genes) when studying the structure of the gene pool and the factors of its microevolution. In this study, spatial variation of Russian surnames was analyzed with the use of computer-based gene geography. Gene geography of surnames was demonstrated to be promising for population studies on the total Russian gene pool. Frequencies of surnames were studied in 64 sel'sovets (rural communities; a total of 33 thousand persons) of 52 raions (districts) of 22 oblasts (regions) of the European part of Russia. For each of 75 widespread surnames, an electronic map of its frequency was constructed. Summary maps of principal components were drawn based on all maps of individual surnames. The first 5 of 75 principal components accounted for half of the total variance, which indicates high resolving power of surnames. The map of the first principal component exhibits a trend directed from the northwestern to the eastern regions of the area studied. The trend of the second component was directed from the southwestern to the northern regions of the area studied, i.e., it was close to latitudinal. This trend almost coincided with the latitudinal trend of principal components for three sets of data (genetic, anthropological, and dermatoglyphical). Therefore, the latitudinal trend may be considered the main direction of variation of the Russian gene pool. The similarity between the main scenarios for the genetic and quasi-genetic markers demonstrates the effectiveness of the use of surnames for analysis of the Russian gene pool. In view of the dispute between R. Sokal and L.L. Cavalli-Sforza about the effects of false correlations, the maps of principal components of Russian surnames were constructed by two methods: through analysis of maps and through direct analysis of original data on the frequencies of surnames. An almost complete coincidence of these maps (correlation coefficient rho = 0.96) indicates that, taking into account the reliability of the data, the resultant maps of principal components have no errors of false correlations.     

The Russian Gene Pool: Gene Geography of Surnames

Surnames are traditionally used in population genetics as quasi-genetic markers (i.e., analogs of genes) when studying the structure of the gene pool and the factors of its microevolution. In this study, spatial variation of Russian surnames was analyzed with the use of computer-based gene geography. Gene geography of surnames was demonstrated to be promising for population studies on the total Russian gene pool. Frequencies of surnames were studied in 64 sel'sovets (rural communities; a total of 33 thousand persons) of 52 raions (districts) of 22 oblasts (regions) of the European part of Russia. For each of 75 widespread surnames, an electronic map of its frequency was constructed. Summary maps of principal components were drawn based on all maps of individual surnames. The first 5 of 75 principal components accounted for half of the total variance, which indicates high resolving power of surnames. The map of the first principal component exhibits a trend directed from the northwestern to the eastern regions of the area studied. The trend of the second component was directed from the southwestern to the northern regions of the area studied, i.e., it was close to latitudinal. This trend almost coincided with the latitudinal trend of principal components for three sets of data (genetic, anthropological, and dermatoglyphical). Therefore, the latitudinal trend may be considered the main direction of variation of the Russian gene pool. The similarity between the main scenarios for the genetic and quasi-genetic markers demonstrates the effectiveness of the use of surnames for analysis of the Russian gene pool. In view of the dispute between R. Sokal and L.L. Cavalli-Sforza about the effects of false correlations, the maps of principal components of Russian surnames were constructed by two methods: through analysis of maps and through direct analysis of original data on the frequencies of surnames. An almost complete coincidence of these maps (correlation coefficient = 0.96) indicates that, taking into account the reliability of the data, the resultant maps of principal components have no errors of false correlations.     

Gene pool and gene geography of the USSR population]

Gene pool and gene geography are discussed from the point of view of their conceptual history beginning from the original concept of A.S. Serebrovski? (1928). Difference between the present-day gene geography and gene geography of gene pool is accentuated: the former only represents a portion of the latter. Historical and territorial integrity of the USSR population gene pool, in conjunction with its huge diversity, is the main problem being analysed by various means of computerized genetic cartography. Coupled with the gene frequency mapping, following methods were also used: mapping of average heterozygosity, of interpopulation differentiation, of principal component scores and mapping of geographical trend for each mapped genetic parameter. The work is based on 100 allelic genes and haplotypes from 30 independent loci studied on the average in 225 local populations. Statistical analysis of gene geographical maps is based on 3975 nodes of regular cartographic net for the USSR territory. The wind rose of systematic changes in the USSR gene pool has three main geographic orientations: W-E, SW-NE and S-N. At the same time, there are only two main systematic forces of gene pool evolution: the force of social history with predominant W-E orientation and the force of natural history with predominant S-N orientation of their actions. The heterozygosity level of gene pool declines strictly in accordance with the resultant in the SW-NE direction.     

Genogenography of the aboriginal population of Marii El (from data on immunobiochemical polymorphism)

The geographic distribution of the frequencies of genes related to the immunological and biochemical polymorphism was studied in the Maris, who are the indigenous population of the Marii El Republic. Data on the frequencies of 33 alleles of 10 loci (ABO, TF, GC, PI, HP, AHS, F13B, ACP1, PGM1, and GLO1) in five raions (districts) of Marii El were obtained. Computer interpolation maps were constructed for all alleles. The maps allows to predict the distribution of the alleles throughout Marii El. A map of the reliability of the cartographic prediction was drawn. For the first time, the reliability of predicted gene frequencies were taken into account in constructing and interpreting the maps of gene frequencies. For the entire set of the studied genes, parameters of heterozygosity (HS) and gene diversity (GST) were estimated. Cartographic correlation analysis was performed to reveal the relationship between gene frequencies and geographic coordinates. It was found that 42% of the studied genes predominantly correlated with latitude and 9% with longitude. It was assumed that the genetic structure of Mari populations had been mainly determined by latitude-related factors. A map of Nei's genetic distances between the overall Mari gene pool and the local populations revealed a central core, which was close to the "average Mari" gene pool, and a periphery, which was genetically distant from it. Suggestions on the microevolution of the Mari gene pool were advanced. Maps of the genes with the most characteristic genetic relief (ABO*B, ACP*A, TF*D, GC*1F, PI*M2, HP*1F, and F13B*3) are shown. These maps exhibit a high correlation with the maps of principal components.     

"Synthetic" maps of the Mari gene pool (from immunobiochemical polymorphism data)]

Models of geographic distribution of 33 alleles of 10 loci (AB0, TF, GC, PI, HP, AHS, F13B, ACP1, PGM1, GLO1) in the indigenous population of five raions (districts) of Marii El Republic were analyzed by cartographic statistical methods. Based on 33 maps for individual alleles, synthetic maps were constructed; they reflected the general characteristics of the spatial variability of the Mari gene pool. A map of reliability of the synthetic maps was also obtained. This study was the first to use estimates of the reliability of the gene-geographic prognosis for constructing and interpreting the maps of principal components. Synthetic maps of principal components reveal the geography of the main factors that determine the genetic diversity of the Maris. In the map of the first principal component (accounting for 25.5% of the total variation of the Mari gene pool), isolines clearly ran in the latitudinal direction; i.e., the variability exhibited a north-south gradient. The direction of changes reflects the characteristic features of the microevolution of the Mari gene pool, because it differs from the direction of the principal components of in the total Ural gene pool. The second principal component (24.3% of variation) also exhibited a latitudinal gradient in the western part of Marii El. In the eastern part of the republic, isolines drastically change their direction and display a marked west-east gradient. This longitudinal orientation of principal components is characteristic of the Maris in the synthetic maps of the Ural region. Contributions of individual genes in the variation of principal components were analyzed. In proceeding from the geographic space to the space of principal components, it was found that Highland Maris are separated from Meadow Maris not only geographically, but also genetically.     

The Russian gene pool. Genogeography of serum genetic markers (HP, GC, PI, TF)

The study continues the series of works on the Russian gene pool. Gene geographic analysis of four serum gene markers best studied in the Russian population (HP, GC, PI, and TF) has been performed. Gene-geographic electronic maps have been constructed for 14 alleles of these loci and their correlations with geographic latitude and longitude. For all maps, statistical characteristics are presented, including the variation range and mean gene frequencies, partial and multiple correlations with latitude and longitude, and parameters of heterozygosity and interpopulation diversity. The maps of five alleles (HP*1, GC*2, GC*1S, PI*M2, and TF*C2) are shown and analyzed in detail. The genetic relief and structural elements of the maps are compared with the ecumenical trends, main variation patterns of these genes in northern Eurasia, and genetic characteristics of the indigenous populations of the Urals and Europe.     

Beta-thalassemia in the Po Delta: selection, geography, and population structure

The allele frequencies for beta-thalassemia for 51 localities in the province of Rovigo, and in 25 localities in the province of Ferrara, were studied. It was observed that in the province of Ferrara there is a significant cline of frequencies; these decrease from the coast of the Adriatic Sea toward the west. No such gradient was visible in Rovigo. It was advanced, also on the basis of geography documented by ancient maps, that in the province of Rovigo there were multiple foci of selection for the thalassemia gene, and that in the province of Ferrara selection was stronger in the Oriental part of the area. Examination of the isolation by distance model with these data showed that the Malécot-Morton model fits for the Ferrara data and geography, whereas it does not for Rovigo.     

The Russian gene pool. Genogeography of erythrocyte genetic markers (ACP1, PGM1, ESD, GLO1, 6-PGD)

The study continues the series of works on the Russian gene pool. Gene geographic analysis of five erythrocytic gene markers best studied in the Russian population (ACP1, PGM1, ESD, GLO1, and 6-PGD) has been performed. Gene-geographic electronic maps have been constructed for 13 alleles of these loci and their correlations with geographic latitude and longitude. For all maps, statistical characteristics are presented, including the variation range and mean gene frequencies, partial and multiple correlations with latitude and longitude, and parameters of heterozygosity and interpopulation diversity. The maps of eight alleles (ACP1*A, ACP1*C, PGM1*2+, PGM1*2-, PGM1*1-, ESD*1, GLO1*1, and PGD*C) are shown and analyzed in detail. The genetic relief and structural elements of the maps are compared with the ecumenical trends, main variation patterns of these genes in northern Eurasia, and genetic characteristics of the indigenous populations of the Urals and Europe.     

A Quantitative Comparison of the Similarity between Genes and Geography in Worldwide Human Populations

Multivariate statistical techniques such as principal components analysis (PCA) and multidimensional scaling (MDS) have been widely used to summarize the structure of human genetic variation, often in easily visualized two-dimensional maps. Many recent studies have reported similarity between geographic maps of population locations and MDS or PCA maps of genetic variation inferred from single-nucleotide polymorphisms (SNPs). However, this similarity has been evident primarily in a qualitative sense; and, because different multivariate techniques and marker sets have been used in different studies, it has not been possible to formally compare genetic variation datasets in terms of their levels of similarity with geography. In this study, using genome-wide SNP data from 128 populations worldwide, we perform a systematic analysis to quantitatively evaluate the similarity of genes and geography in different geographic regions. For each of a series of regions, we apply a Procrustes analysis approach to find an optimal transformation that maximizes the similarity between PCA maps of genetic variation and geographic maps of population locations. We consider examples in Europe, Sub-Saharan Africa, Asia, East Asia, and Central/South Asia, as well as in a worldwide sample, finding that significant similarity between genes and geography exists in general at different geographic levels. The similarity is highest in our examples for Asia and, once highly distinctive populations have been removed, Sub-Saharan Africa. Our results provide a quantitative assessment of the geographic structure of human genetic variation worldwide, supporting the view that geography plays a strong role in giving rise to human population structure.     

Genetic structure of the human population in the Po delta

The genetic structure of the population of Ferrara Province in the Po delta in Italy was investigated using chi 2 analysis, kinship analysis, analysis of correspondences, and geographical mapping of principal components of gene frequencies. chi 2 Analysis tests for Hardy-Weinberg equilibrium and for heterogeneity of gene and phenotype frequencies; kinship analysis tests for association between indicators of genetic and geographic proximity; analysis of correspondences relates localities and genetic systems in an eigenvectorial space; and geographic mapping displays the principal components of gene frequencies in the real space. In 1,364 adults in 26 residential units, seven presumably neutral isoenzyme systems were typed; ACP1 ESD, GLO I, GPT, PGD, PGM1 and PGP. It was found that average kinship for these neutral systems is correlated with geographic distance in this small area, but not as strongly as kinship for beta-thalassemia. A north-south gradient was observed for ESD. Analysis of correspondences indicated GPT, PGM1, and GLO I as the systems contributing most to differentiation within the province. The maps obtained from principal components of gene frequencies were consistent with the migrational history of the area.     

The Russian Gene Pool: Gene Geography of Serum Gene Markers (HP,GC,PI,and TF)

The study continues the series of works on the Russian gene pool. Gene geographic analysis of four serum gene markers best studied in the Russian population (HP, GC, PI, and TF) has been performed. Gene-geographic electronic maps have been constructed for 14 alleles of these loci and their correlations with geographic latitude and longitude. For all maps, statistical characteristics are presented, including the variation range and mean gene frequencies, partial and multiple correlations with latitude and longitude, and parameters of heterozygosity and interpopulation diversity. The maps of five alleles (HP*1, GC*2, GC*1S, PI*M2, and TF*C2) are shown and analyzed in detail. The genetic relief and structural elements of the maps are compared with the ecumenical trends, main variation patterns of these genes in northern Eurasia, and genetic characteristics of the indigenous populations of the Urals and Europe.     

Gene pool of Buryats: Clinal variability and territorial subdivision based on data of Y-chromosome markers

The structure of the Buryat gene pool has been studied based on the composition and frequency of Y-chromosome haplogroups in eight geographically distant populations. Eleven haplogroups have been found in the Buryat gene pool, two of which are the most frequent (N1c1 and C3d). The greatest difference in haplogroup frequencies was fixed between western and eastern Buryat samples. The evaluation of genetic diversity based on haplogroup frequencies revealed that it has low values in most of the samples. The evaluation of the genetic differentiation of the examined samples using an analysis of molecular variance (AMOVA) shows that the Buryat gene pool is highly differentiated by haplotype frequencies. Phylogenetic analysis within haplogroups N1c1 and C3d revealed a strong founder effect, i.e., reduced diversity and starlike phylogeny of the median network of haplotypes that form specific subclusters. The results of a phylogenetic analysis of the haplogroups identified common genetic components for Buryats and Mongols.     

At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis>

Gene expression maps for model organisms, including Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas, Arabidopsis thaliana Tiling Array Express (At-TAX), which is based on whole-genome tiling arrays. We demonstrate that tiling arrays are accurate tools for gene expression analysis and identified more than 1,000 unannotated transcribed regions. Visualizations of gene expression estimates, transcribed regions, and tiling probe measurements are accessible online at the At-TAX homepage.     

A Magnetic Resonance Image Based Atlas of the Rabbit Brain for Automatic Parcellation

Rabbit brain has been used in several works for the analysis of neurodevelopment. However, there are not specific digital rabbit brain atlases that allow an automatic identification of brain regions, which is a crucial step for various neuroimage analyses, and, instead, manual delineation of areas of interest must be performed in order to evaluate a specific structure. For this reason, we propose an atlas of the rabbit brain based on magnetic resonance imaging, including both structural and diffusion weighted, that can be used for the automatic parcellation of the rabbit brain. Ten individual atlases, as well as an average template and probabilistic maps of the anatomical regions were built. In addition, an example of automatic segmentation based on this atlas is described.     

Cautions on direct gene flow estimation in plant populations

Through simulations we have investigated the statistical properties of two of the main approaches for directly estimating pollen gene flow (m) in plant populations: genotypic exclusion and mating models. When the assumptions about accurately known background pollen pool allelic frequencies are met, both methods provide unbiased results with comparable variances across a range of true m values. However, when presumed allelic frequencies differ from actual ones, which is more likely in research practice, both estimators are biased. We demonstrate that the extent and direction of bias largely depend on the difference (measured as genetic distance) between the presumed and actual pollen pools, and on the degree of genetic differentiation between the local population and the actual background pollen sources. However, one feature of the mating model is its ability to estimate pollen gene flow simultaneously with background pollen pool allelic frequencies. We have found that this approach gives nearly unbiased pollen gene flow estimates, and is practical because it eliminates the necessity of providing independent estimates of background pollen pool allelic frequencies. Violations of the mating model assumptions of random mating within local population affect the precision of the estimates only to a limited degree.     

Genetic diversity of the Khakass gene pool: Subethnic differentiation and the structure of Y-chromosome haplogroups

The structure of Khakass gene pool has been investigated: Y-chromosome haplogroup compositions and frequencies were described in seven population samples of two basic subethnic groups, Sagai and Kachins, from three geographically separated regions of the Khakass Republic. Eight haplogroups were detected in the Khakass gene pool: C3, E, N*, N1b, N1c, R1a1a, and R1b1b1. The haplogroup spectra and the genetic diversity by haplogroups and YSTR haplotypes differed significantly between Sagai and Kachins. Kachins had a low level of gene diversity, whereas the diversity of Sagai was similar to that of other South-Siberian ethnic groups. Sagai samples from the Askizskii district were very similar to each other, and so were two Kachin samples from the Shirinskii district, while Sagai samples from the Tashtypskii district differed considerably from each other. The contribution of intergroup differences among ethnic groups was high, indicating significant genetic differentiation among native populations in Khakassia. The Khakass gene pool was strongly differentiated both by haplogroup frequencies and by YSTR haplotypes within the N1b haplogroup. The frequencies of YSTR haplotypes within the chromosome Y haplogroups N1b, N1c, and R1a1 were determined and their molecular phylogeny was investigated. Factor and cluster analysis, as well as AMOVA, suggest that the Khakass gene pool is structured by territory and subethnic groups.     

The Genetic Structure of a Tribal Population, the Yanomama Indians. Xv. Patterns Inferred by Autocorrelation Analysis

Fifteen allele frequencies have previously been determined for 50 villages of the Yanomama, an Amerindian tribe from southern Venezuela and northern Brazil. These frequencies were subjected to spatial autocorrelation analysis to investigate their population structure. There are significant spatial patterns for most allele frequencies. Clinical patterns, investigated by one-dimensional and directional spatial correlograms, were relatively few in number and were moderate in strength. Overall, however, there is a marked decline in genetic similarity with geographic distance. The results are compatible with a hierarchic population structure superimposed on the geography, and generated by a stochastic fission-fusion model of village propagation, followed by localized gene flow. Strong temporal autocorrelations of allele frequencies based on linguistic-historical distances representing time since divergence were also found. There appears to be a stronger relation between geography and linguistic-historical hierarchic subdivisions than between either feature and genetic distances. These findings confirm by different approaches the results of earlier analyses concerning the important roles of both stochastic and social factors in determining village allele frequencies and the occurrence within this tribe of some allele frequency clines most likely due to the operation of chance historical processes.     

Genogeography of human populations: computer mapping of population genetics data

Genogeography as a field of interdisciplinar investigation was introduced into science in 1928 by Russian geneticist A. S. Serebrovsky an today is well known in human genetics as gene geography. This work was undertaken to introduce a new method of computer cartography of gene frequencies in human and in any other populations. The method is different from known one of P. Menozzi, A. Piazza and L. Cavalli-Sforza. Two key moments of the method are principle of fusion-fission of gene in the homogeneous geographical space equally free-for-all human genes, and principle of local-linear (but not of the high-orders) interpolation of gene frequencies onto spheric surface of geographical space. Such procedure was used for mapping of human AB0-B gene frequencies among native populations of Central Asia.     

Estimation of haplotype frequencies,linkage-disequilibrium measures,and combination of haplotype copies in each pool by use of pooled DNA data

Inference of haplotypes is important for many genetic approaches, including the process of assigning a phenotype to a genetic region. Usually, the population frequencies of haplotypes, as well as the diplotype configuration of each subject, are estimated from a set of genotypes of the subjects in a sample from the population. We have developed an algorithm to infer haplotype frequencies and the combination of haplotype copies in each pool by using pooled DNA data. The input data are the genotypes in pooled DNA samples, each of which contains the quantitative genotype data from one to six subjects. The algorithm infers by the maximum-likelihood method both frequencies of the haplotypes in the population and the combination of haplotype copies in each pool by an expectation-maximization algorithm. The algorithm was implemented in the computer program LDPooled. We also used the bootstrap method to calculate the standard errors of the estimated haplotype frequencies. Using this program, we analyzed the published genotype data for the SAA (n=156), MTHFR (n=80), and NAT2 (n=116) genes, as well as the smoothelin gene (n=102). Our study has shown that the frequencies of major (frequency >0.1 in a population) haplotypes can be inferred rather accurately from the pooled DNA data by the maximum-likelihood method, although with some limitations. The estimated D and D' values had large variations except when the /D/ values were >0.1. The estimated linkage-disequilibrium measure rho2 for 36 linked loci of the smoothelin gene when one- and two-subject pool protocols were used suggested that the gross pattern of the distribution of the measure can be reproduced using the two-subject pool data.     

The origin of Yakuts: analysis of Y-chromosome haplotypes

Gene pool structure of Sakha Republic (Yakutia) native population has been studied: we defined composition and frequencies of Y-chromosome haplogroups for Yakuts. Six haplogroups: C3 x M77, C3c, N*, N2, N3a and R1a1 have been revealed in Yakut gene pool. A greater part of Y-chromosome in Yakut population belongs to N3a haplogroup (89%). All investigated Yakut population samples have low values of gene diversity, calculated based on haplogroup frequencies. Gene differentiation of the investigated samples estimated using the analysis of molecular variance (AMOVA) by two marker systems (haplogroup frequencies and microsatellite haplotypes of Y-chromosome) revealed a portion of interpopulation differences amounting to 0.24 and 2.85%, respectively. Frequencies and molecular phylogeny of YSTR-haplotypes were revealed for N3a haplogroup of Y-chromosome. Altogether forty haplotypes were found in Yakuts. Evenks and Yakuts are characterized by overlapping and very specific spectrum of N3a haplotypes, which is not typical for other Siberian ethnic groups. Cluster analysis of populations by N3a YSTR-haplotypes shows Yakut isolation from Turkic-speaking populations in the South Siberia. Genetic diversity generation time for a specific spectrum of Yakut haplotypes was estimated as 4.45 +/- 1.96 thousand years. As opposed to the data on mtDNA, the obtained results give an evidence for significant contribution of a local palaeolithic component into Y-chromosomal Yakut gene pool. Ethnogenetic reconstruction of the present picture of genetic diversity in N3a haplogroup in the territory of Siberia is under consideration.