Genetic Structure of Europeans: A View from the North–East
Using principal component (PC) analysis, we studied the genetic constitution of 3,112 individuals from Europe as portrayed by more than 270,000 single nucleotide polymorphisms (SNPs) genotyped with the Illumina Infinium platform. In cohorts where the sample size was >100, one hundred randomly cho...
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| Published in: | PloS one Vol. 4; no. 5; p. e5472 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Public Library of Science
08.05.2009
Public Library of Science (PLoS) |
| Subjects: | |
| ISSN: | 1932-6203, 1932-6203 |
| Online Access: | Get full text |
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| Abstract | Using principal component (PC) analysis, we studied the genetic constitution of 3,112 individuals from Europe as portrayed by more than 270,000 single nucleotide polymorphisms (SNPs) genotyped with the Illumina Infinium platform. In cohorts where the sample size was >100, one hundred randomly chosen samples were used for analysis to minimize the sample size effect, resulting in a total of 1,564 samples. This analysis revealed that the genetic structure of the European population correlates closely with geography. The first two PCs highlight the genetic diversity corresponding to the northwest to southeast gradient and position the populations according to their approximate geographic origin. The resulting genetic map forms a triangular structure with a) Finland, b) the Baltic region, Poland and Western Russia, and c) Italy as its vertexes, and with d) Central- and Western Europe in its centre. Inter- and intra- population genetic differences were quantified by the inflation factor lambda (lambda) (ranging from 1.00 to 4.21), fixation index (F(st)) (ranging from 0.000 to 0.023), and by the number of markers exhibiting significant allele frequency differences in pair-wise population comparisons. The estimated lambda was used to assess the real diminishing impact to association statistics when two distinct populations are merged directly in an analysis. When the PC analysis was confined to the 1,019 Estonian individuals (0.1% of the Estonian population), a fine structure emerged that correlated with the geography of individual counties. With at least two cohorts available from several countries, genetic substructures were investigated in Czech, Finnish, German, Estonian and Italian populations. Together with previously published data, our results allow the creation of a comprehensive European genetic map that will greatly facilitate inter-population genetic studies including genome wide association studies (GWAS). |
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| AbstractList | Using principal component (PC) analysis, we studied the genetic constitution of 3,112 individuals from Europe as portrayed by more than 270,000 single nucleotide polymorphisms (SNPs) genotyped with the Illumina Infinium platform. In cohorts where the sample size was >100, one hundred randomly chosen samples were used for analysis to minimize the sample size effect, resulting in a total of 1,564 samples. This analysis revealed that the genetic structure of the European population correlates closely with geography. The first two PCs highlight the genetic diversity corresponding to the northwest to southeast gradient and position the populations according to their approximate geographic origin. The resulting genetic map forms a triangular structure with a) Finland, b) the Baltic region, Poland and Western Russia, and c) Italy as its vertexes, and with d) Central- and Western Europe in its centre. Inter- and intra- population genetic differences were quantified by the inflation factor lambda (λ) (ranging from 1.00 to 4.21), fixation index (Fst) (ranging from 0.000 to 0.023), and by the number of markers exhibiting significant allele frequency differences in pair-wise population comparisons. The estimated lambda was used to assess the real diminishing impact to association statistics when two distinct populations are merged directly in an analysis. When the PC analysis was confined to the 1,019 Estonian individuals (0.1% of the Estonian population), a fine structure emerged that correlated with the geography of individual counties. With at least two cohorts available from several countries, genetic substructures were investigated in Czech, Finnish, German, Estonian and Italian populations. Together with previously published data, our results allow the creation of a comprehensive European genetic map that will greatly facilitate inter-population genetic studies including genome wide association studies (GWAS). Using principal component (PC) analysis, we studied the genetic constitution of 3,112 individuals from Europe as portrayed by more than 270,000 single nucleotide polymorphisms (SNPs) genotyped with the Illumina Infinium platform. In cohorts where the sample size was >100, one hundred randomly chosen samples were used for analysis to minimize the sample size effect, resulting in a total of 1,564 samples. This analysis revealed that the genetic structure of the European population correlates closely with geography. The first two PCs highlight the genetic diversity corresponding to the northwest to southeast gradient and position the populations according to their approximate geographic origin. The resulting genetic map forms a triangular structure with a) Finland, b) the Baltic region, Poland and Western Russia, and c) Italy as its vertexes, and with d) Central- and Western Europe in its centre. Inter- and intra- population genetic differences were quantified by the inflation factor lambda (lambda) (ranging from 1.00 to 4.21), fixation index (F(st)) (ranging from 0.000 to 0.023), and by the number of markers exhibiting significant allele frequency differences in pair-wise population comparisons. The estimated lambda was used to assess the real diminishing impact to association statistics when two distinct populations are merged directly in an analysis. When the PC analysis was confined to the 1,019 Estonian individuals (0.1% of the Estonian population), a fine structure emerged that correlated with the geography of individual counties. With at least two cohorts available from several countries, genetic substructures were investigated in Czech, Finnish, German, Estonian and Italian populations. Together with previously published data, our results allow the creation of a comprehensive European genetic map that will greatly facilitate inter-population genetic studies including genome wide association studies (GWAS). Using principal component (PC) analysis, we studied the genetic constitution of 3,112 individuals from Europe as portrayed by more than 270,000 single nucleotide polymorphisms (SNPs) genotyped with the Illumina Infinium platform. In cohorts where the sample size was >100, one hundred randomly chosen samples were used for analysis to minimize the sample size effect, resulting in a total of 1,564 samples. This analysis revealed that the genetic structure of the European population correlates closely with geography. The first two PCs highlight the genetic diversity corresponding to the northwest to southeast gradient and position the populations according to their approximate geographic origin. The resulting genetic map forms a triangular structure with a) Finland, b) the Baltic region, Poland and Western Russia, and c) Italy as its vertexes, and with d) Central- and Western Europe in its centre. Inter- and intra- population genetic differences were quantified by the inflation factor lambda ([lambda]) (ranging from 1.00 to 4.21), fixation index (F.sub.st) (ranging from 0.000 to 0.023), and by the number of markers exhibiting significant allele frequency differences in pair-wise population comparisons. The estimated lambda was used to assess the real diminishing impact to association statistics when two distinct populations are merged directly in an analysis. When the PC analysis was confined to the 1,019 Estonian individuals (0.1% of the Estonian population), a fine structure emerged that correlated with the geography of individual counties. With at least two cohorts available from several countries, genetic substructures were investigated in Czech, Finnish, German, Estonian and Italian populations. Together with previously published data, our results allow the creation of a comprehensive European genetic map that will greatly facilitate inter-population genetic studies including genome wide association studies (GWAS). Using principal component (PC) analysis, we studied the genetic constitution of 3,112 individuals from Europe as portrayed by more than 270,000 single nucleotide polymorphisms (SNPs) genotyped with the Illumina Infinium platform. In cohorts where the sample size was >100, one hundred randomly chosen samples were used for analysis to minimize the sample size effect, resulting in a total of 1,564 samples. This analysis revealed that the genetic structure of the European population correlates closely with geography. The first two PCs highlight the genetic diversity corresponding to the northwest to southeast gradient and position the populations according to their approximate geographic origin. The resulting genetic map forms a triangular structure with a) Finland, b) the Baltic region, Poland and Western Russia, and c) Italy as its vertexes, and with d) Central- and Western Europe in its centre. Inter- and intra- population genetic differences were quantified by the inflation factor lambda (lambda) (ranging from 1.00 to 4.21), fixation index (F(st)) (ranging from 0.000 to 0.023), and by the number of markers exhibiting significant allele frequency differences in pair-wise population comparisons. The estimated lambda was used to assess the real diminishing impact to association statistics when two distinct populations are merged directly in an analysis. When the PC analysis was confined to the 1,019 Estonian individuals (0.1% of the Estonian population), a fine structure emerged that correlated with the geography of individual counties. With at least two cohorts available from several countries, genetic substructures were investigated in Czech, Finnish, German, Estonian and Italian populations. Together with previously published data, our results allow the creation of a comprehensive European genetic map that will greatly facilitate inter-population genetic studies including genome wide association studies (GWAS).Using principal component (PC) analysis, we studied the genetic constitution of 3,112 individuals from Europe as portrayed by more than 270,000 single nucleotide polymorphisms (SNPs) genotyped with the Illumina Infinium platform. In cohorts where the sample size was >100, one hundred randomly chosen samples were used for analysis to minimize the sample size effect, resulting in a total of 1,564 samples. This analysis revealed that the genetic structure of the European population correlates closely with geography. The first two PCs highlight the genetic diversity corresponding to the northwest to southeast gradient and position the populations according to their approximate geographic origin. The resulting genetic map forms a triangular structure with a) Finland, b) the Baltic region, Poland and Western Russia, and c) Italy as its vertexes, and with d) Central- and Western Europe in its centre. Inter- and intra- population genetic differences were quantified by the inflation factor lambda (lambda) (ranging from 1.00 to 4.21), fixation index (F(st)) (ranging from 0.000 to 0.023), and by the number of markers exhibiting significant allele frequency differences in pair-wise population comparisons. The estimated lambda was used to assess the real diminishing impact to association statistics when two distinct populations are merged directly in an analysis. When the PC analysis was confined to the 1,019 Estonian individuals (0.1% of the Estonian population), a fine structure emerged that correlated with the geography of individual counties. With at least two cohorts available from several countries, genetic substructures were investigated in Czech, Finnish, German, Estonian and Italian populations. Together with previously published data, our results allow the creation of a comprehensive European genetic map that will greatly facilitate inter-population genetic studies including genome wide association studies (GWAS). |
| Audience | Academic |
| Author | Balaščák, Ivan Kučinskas, Vaidutis Mägi, Reedik Macek, Milan Zimprich, Fritz Antonarakis, Stylianos E. Toniolo, Daniela Deutsch, Samuel Julià, Antonio Marsal, Sara Khrunin, Andrey Attar, Homa Meitinger, Thomas Estivill, Xavier Rabionet, Raquel Karachanak, Sena Borel, Christelle Schreiber, Stefan Pfeufer, Arne Kasnauskienė, Jūratė Rehnström, Karola Gagnebin, Maryline Melegh, Béla Limborska, Svetlana Zimprich, Alexander D'Adamo, Pio Lubinski, Jan Toncheva, Draga Gasparini, Paolo Krawczak, Michael Klovins, Janis Galan, Pilar Nikitina-Zake, Liene Jakkula, Eveliina Remm, Maido Piskáčková, Tereza Debniak, Tadeusz Esko, Tõnu Lathrop, Mark Nelis, Mari Wichmann, H-Erich Peltonen, Leena Heath, Simon Polgár, Noémi Metspalu, Andres |
| AuthorAffiliation | 23 Latvian Biomedical Research and Study Center, Riga, Latvia 17 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany 21 Medical Genetics, Department of Reproductive Sciences and Development, IRCCS-Burlo Garofolo, University of Trieste, Trieste, Italy 28 Rheumatology Research group, Vall d'Hebron University Hospital Research Institute, Barcelona, Spain 29 Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland 6 Department of Medical Genetics, Medical University of Sofia, Sofia, Bulgaria 25 International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland 7 Department of Biology and Medical Genetics, Cystic Fibrosis Centre, University Hospital Motol and 2nd School of Medicine, Charles University Prague, Prague, Czech Republic 27 Center for Genomic Regulation (CRG-UPF) and CIBERESP, Barcelona, Spain 16 Institute of Human Genetics, Technische Universität Mün |
| AuthorAffiliation_xml | – name: 19 Department of Medical Genetics and Child Development, University of Pécs, Pécs, Hungary – name: 21 Medical Genetics, Department of Reproductive Sciences and Development, IRCCS-Burlo Garofolo, University of Trieste, Trieste, Italy – name: 17 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany – name: 24 Department of Human and Medical Genetics, Vilnius University, Vilnius, Lithuania – name: 2 Estonian Biocentre, Genotyping Core Facility, Tartu, Estonia – name: 15 Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany – name: 1 Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia – name: 8 Department of Neonatology, Clinic of Obstetrics and Gynecology, University Hospital Motol and 2nd School of Medicine, Charles University Prague, Prague, Czech Republic – name: 27 Center for Genomic Regulation (CRG-UPF) and CIBERESP, Barcelona, Spain – name: 11 Commissariat à l'Energie Atomique, Institut Genomique, Centre National de Génotypage, Evry, France – name: 26 Institute of Molecular Genetics, Russian Academy of Science, Moscow, Russia – name: 20 Division of Genetics and Cell Biology, San Raffaele Research Institute, Milano, Italy – name: 4 OÜ BioData, Tartu, Estonia – name: 22 Medical Genetics, Institute for Maternal and Child Health - IRCCS “Burlo Garofolo”, Trieste, Italy – name: 23 Latvian Biomedical Research and Study Center, Riga, Latvia – name: 28 Rheumatology Research group, Vall d'Hebron University Hospital Research Institute, Barcelona, Spain – name: Smithsonian Institution National Zoological Park, United States of America – name: 6 Department of Medical Genetics, Medical University of Sofia, Sofia, Bulgaria – name: 10 Institute for Molecular Medicine Finland (FIMM) and National Institute for Health and Welfare, Helsinki, Finland – name: 7 Department of Biology and Medical Genetics, Cystic Fibrosis Centre, University Hospital Motol and 2nd School of Medicine, Charles University Prague, Prague, Czech Republic – name: 13 UMR U557 Inserm, U1125 Inra, Cnam, Paris 13, Paris, France – name: 18 Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany – name: 12 Fondation Jean Dausset-CEPH, Paris, France – name: 25 International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland – name: 3 Estonian Genome Project, University of Tartu, Tartu, Estonia – name: 14 PopGen Biobank, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany – name: 16 Institute of Human Genetics, Technische Universität München, Klinikum rechts der Isar, Munich, Germany – name: 5 Department of Clinical Neurology, Medical University of Vienna, Vienna, Austria – name: 29 Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland – name: 9 Wellcome Trust Sanger Institute, Cambridge, UK and the Institute of Molecular Medicine, Biomedicum Helsinki, Helsinki, Finland |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19424496$$D View this record in MEDLINE/PubMed |
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| Title | Genetic Structure of Europeans: A View from the North–East |
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