Genetic Structure of Europeans: A View from the North–East

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).

Full text

Genetic Structure of Europeans: A View from the
North–East
Mari Nelis
1,2.
,To
˜nu Esko
1,2,3.
, Reedik Ma
¨gi
1,4
, Fritz Zimprich
5
, Alexander Zimprich
5
, Draga Toncheva
6
,
Sena Karachanak
6
, Tereza Piska
´c
ˇkova
´
7
, Ivan Balas
ˇc
ˇa
´k
8
, Leena Peltonen
9
, Eveliina Jakkula
10
, Karola
Rehnstro
¨m
10
, Mark Lathrop
11,12
, Simon Heath
11
, Pilar Galan
13
, Stefan Schreiber
14
, Thomas
Meitinger
15,16
, Arne Pfeufer
15,16
, H-Erich Wichmann
17,18
,Be
´la Melegh
19
, Noe
´mi Polga
´r
19
, Daniela
Toniolo
20
, Paolo Gasparini
21
, Pio D’Adamo
21
, Janis Klovins
23
, Liene Nikitina-Zake
23
, Vaidutis
Kuc
ˇinskas
24
,Ju¯ rate˙ Kasnauskiene˙
24
, Jan Lubinski
25
, Tadeusz Debniak
25
, Svetlana Limborska
26
, Andrey
Khrunin
26
, Xavier Estivill
27
, Raquel Rabionet
27
, Sara Marsal
28
, Antonio Julia
`
28
, Stylianos E.
Antonarakis
29
, Samuel Deutsch
29
, Christelle Borel
29
, Homa Attar
29
, Maryline Gagnebin
29
, Milan Macek
7
,
Michael Krawczak
14
, Maido Remm
1
, Andres Metspalu
1,2,3
*
1Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia, 2Estonian Biocentre, Genotyping Core Facility, Tartu, Estonia, 3Estonian Genome Project,
University of Tartu, Tartu, Estonia, 4OU
¨BioData, Tartu, Estonia, 5Department of Clinical Neurology, Medical University of Vienna, Vienna, Austria, 6Department of Medical
Genetics, Medical University of Sofia, Sofia, Bulgaria, 7Department of Biology and Medical Genetics, Cystic Fibrosis Centre, University Hospital Motol and 2nd School of
Medicine, Charles University Prague, Prague, Czech Republic, 8Department of Neonatology, Clinic of Obstetrics and Gynecology, University Hospital Motol and 2nd
School of Medicine, Charles University Prague, Prague, Czech Republic, 9Wellcome Trust Sanger Institute, Cambridge, UK and the Institute of Molecular Medicine,
Biomedicum Helsinki, Helsinki, Finland, 10 Institute for Molecular Medicine Finland (FIMM) and National Institute for Health and Welfare, Helsinki, Finland,
11 Commissariat a
`l’Energie Atomique, Institut Genomique, Centre National de Ge
´notypage, Evry, France, 12 Fondation Jean Dausset-CEPH, Paris, France, 13 UMR U557
Inserm, U1125 Inra, Cnam, Paris 13, Paris, France, 14 PopGen Biobank, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany, 15 Institute of Human Genetics,
Helmholtz Zentrum Mu
¨nchen, German Research Center for Environmental Health, Neuherberg, Germany, 16 Institute of Human Genetics, Technische Universita
¨t
Mu
¨nchen, Klinikum rechts der Isar, Munich, Germany, 17 Institute of Epidemiology, Helmholtz Zentrum Mu
¨nchen, German Research Center for Environmental Health,
Neuherberg, Germany, 18 Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universita
¨t, Munich, Germany, 19 Department of Medical
Genetics and Child Development, University of Pe
´cs, Pe
´cs, Hungary, 20 Division of Genetics and Cell Biology, San Raffaele Research Institute, Milano, Italy, 21 Medical
Genetics, Department of Reproductive Sciences and Development, IRCCS-Burlo Garofolo, University of Trieste, Trieste, Italy, 22 Medical Genetics, Institute for Maternal and
Child Health - IRCCS ‘‘Burlo Garofolo’’, Trieste, Italy, 23 Latvian Biomedical Research and Study Center, Riga, Latvia, 24 Department of Human and Medical Genetics, Vilnius
University, Vilnius, Lithuania, 25 International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland, 26 Institute of Molecular Genetics, Russian
Academy of Science, Moscow, Russia, 27 Center for Genomic Regulation (CRG-UPF) and CIBERESP, Barcelona, Spain, 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
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 (l) (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).
PLoS ONE | www.plosone.org 1 May 2009 | Volume 4 | Issue 5 | e5472

Citation: Nelis M, Esko T, Ma
¨gi R, Zimprich F, Zimprich A, et al. (2009) Genetic Structure of Europeans: A View from the North–East. PLoS ONE 4(5): e5472.
doi:10.1371/journal.pone.0005472
Editor: Robert C. Fleischer, Smithsonian Institution National Zoological Park, United States of America
Received January 29, 2009; Accepted March 26, 2009; Published May 8, 2009
Copyright: ß2009 Nelis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by the EU European Regional Development Fund through the Centre of Excellence in Genomics, Estonian Biocentre and
University of Tartu. MN, TE and AM were supported by Enterprise Estonia project EU19955 and by the Estonian Ministry of Education and Research grant
0180142s08. RM was financed through European Commission grant 205419 (ECOGENE) to Estonian Biocentre and EU FP6 project ‘‘LifeSpan’’. RM and MR were
also supported by the Estonian Ministry of Education and Research grant 0182649s04. Part of the financing came from the EU funded ENGAGE project (grant
201413) of the FP7. DToncheva and SK were supported by the ‘‘Characterization of the anthropo-genetic identity of Bulgarians’’ Project of the Bulgarian Ministry
of Education and Science, reference No. TK01-487/16.06.2008. LP was funded by the Center of Excellence in Common Disease Genetics of the Academy of Finland.
RR is a ‘‘Ramon y Cajal’’ postdoctoral fellow supported by the Spanish Ministry of Science and Innovation. MM was supported by grants VZFNM00064203 andNS
NS9488/3 provided by the Ministry of Health of the Czech Republic. Research on the French sample has been supported by the French Ministry of Higher
Education and Research. Research on samples from Northern Italy has been supported by the Compagnia di San Paolo Foundation and Health Ministry grant PS-
CARDIO ex 56//05/7. Research on the Spanish sample has been supported by grants from the Spanish Ministry of Science and Innovation (SAF2008-00357 to XE
and PSE-01000-2006-6 to SM), ENGAGE (FP7/2007-201413) and AnEUploidy (FP6/2006-037627) EU grants to XE. The KORA research platform was initiated and
financed by the Helmholtz Center Munich, German Research Center for Environmental Health, which is funded by the German Federal Ministry of Educationand
Research and by the State of Bavaria. The work of KORA is supported by the German Federal Ministry of Education and Research (BMBF) in the context of the
German National Genome Research Network (NGFN-2 and NGFN-plus). Swiss sample provision has been supported by grants from the National Science
Foundation, Infectigen Foundation, and AnEUploidy EU IP (037627) grants to SEA. Research on the Latvian sample has been supported by Ministry of Health,
Republic of Latvia (agreement No. 3308/IGDB-1/2008). This research was also supported by the Russian Academy of Sciences, Program ‘‘Molecular and Cell
Biology’’, and the Russian Basic Research Foundation. The study has also been supported by a research grant of the Italian Ministry of Health (Ricerca Corrente
2007 and Ricerca Finalizzata Cardiovascolare).
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: andres@ebc.ee
.These authors contributed equally to this work.
Introduction
Over the last few years, the number of genome-wide association
studies GWAS has increased markedly and, in concert, these
efforts have led to the identification of a large number of new
susceptibility loci for common multi-factorial disorders [1]. The
underlying technology is developing rapidly and is currently
moving from the use of high density SNP arrays towards medical
re-sequencing of large genomic regions. Given this development,
the availability of thoroughly phenotyped patient and control
samples is becoming even more important. Furthermore, due to
the small effect sizes that characterize susceptibility genes for
multi-factorial traits, potentially successful GWAS rely on large
sample number, with additional pressure put on the quality of
samples [2]. In reality, however, there will be only very few cohorts
comprising 10,000 or even more samples (www.p3gconsortium.
org). Exceptions include, for example, the DeCODE studies in
Iceland (www.decode.com) and the EPIC (European Prospective
Investigation into Cancer and Nutrition) cohort (http://epic.iarc.
fr). Collaborations involving diverse sample collections are
therefore essential and efforts in this field are promising, for
example the establishment of the Biobanking and BioMolecular
Resource Infrastructure (www.bbmri.eu). With cohorts from
different countries or even from different sites within the same
country being used for genetic epidemiological research, the
problem of confounding by population stratification has to be
addressed. Fortunately, with the vast amount of the genome-wide
data available, the actual extent and relevance of population
genetic differences can be clarified with high confidence for most
commonly used SNP sets.
Confounding by population stratification has been extensively
studied in the past [3]. Heterogeneity between studied samples can
give false-positive results in association studies, as the association
with the trait may by the result of the systematic ancestry
difference in allele frequencies between groups [4]. Three main
approaches have been proposed so far to capture population
genetic differences analytically, namely a) Bayesian clustering [5],
b) principal component (PC) analysis [6] and c) multidimensional
scaling (MDS) analysis based upon genome-wide identity-by-state
(IBS) distances [7]. With the recent availability of high density
SNP data, PC and MDS methodologies have become increasingly
popular because they require less computing power and have
higher discriminatory power than Bayesian analysis for closely
related (e.g. European) populations [8]. Therefore, PC analysis is
more widely used in the literature. Examples of its recent use are
provided by the analysis of high density microarray SNP data at
either a global level [9,10] or, in greater detail, for selected
European populations [11–15] or within a single country [16–18].
In Europe, PC analysis has revealed the strongest genetic
differentiation between the northwest and southeast of the
continent. The first PC accounts for approximately twice as much
of the genetic variation as PC2 [12,13,15]. In addition, Price et al.
(2008) have shown in their study of US Americans of European
descent that the consideration of three clusters of individuals,
which roughly corresponded to Northwest Europe, Southeast
Europe and Ashkenazi Jewish ancestry, may be sufficient to
correct for most of the population stratification affecting genetic
association studies. However, the extent to which the results of PC
analysis reflect the true underlying genetic map of Europe is
critically dependent upon the choice of populations analyzed.
Optimal coverage of European populations has not been achieved
so far and still represents a goal for future collaborative studies. At
present, however, it appears essential that the peripheral
populations of Europe or those with a strong founder effect in
particular must not be left out of studies aiming at the construction
of a continent-wide genetic map.
Here, we present an analysis of more than 270,000 SNPs,
genotyped with the Illumina 318K/370CNV chips, on 3,112
individuals across 16 European countries (comprising 19 different
samples). Our focus has been on the Baltic region and Eastern
Europe since these regions have not been studied in much detail
before. The results suggest that geographically adjacent popula-
tions overlap partly according to the PC analysis forming four
subgroups. Consideration of the inflation factor lambda (l) [19]
further indicates that the loss of power would be minimal when
performing and adjusting genetic association studies within these
groups.
Genetic Structure of N-E Euro
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Results
In order to investigate in detail the genetic structure of the Baltic
countries and neighbouring North-Eastern Europe, whole genome
genotyping was undertaken for over 1,000 Estonians and
additional individuals from Bulgaria, the Czech Republic,
Hungary, Latvia, Lithuania, Poland and Russia, using the
Illumina Human370CNV chip. In addition, raw genotyping data
were obtained from Scandinavia and other Western and Northern
European countries (Table 1). From samples with .100
individuals available (Table 1), a sub-set of 100 individuals was
chosen at random for subsequent analyses in order to minimize
sample size effects. In all instances, the inflation factor las
computed for the complete data set versus the random sub-set was
close to unity, indicating that the latter sets were representative of
the entire samples. In total, genotypes of 273,464 SNPs from 1,564
individuals were included in the statistical analyses.
The HapMap data was used for valuation of our results and
showing the genetic distance from other continents. The HapMap
data included four populations: CEU – U.S. Utah residents with
ancestry from Northern and Western Europe, YRI - the Yoruba
people of Ibadan, Nigeria, CHB – Han Chinese from Beijing, and
JPT – Japanese from Tokyo, in total of 203 individuals.
Minor allele frequency (MAF)
PLINK was used to compute the minor allele frequencies
(MAF) using all the 273,464 SNPs that passed the quality control
(QC) procedures. Since the Estonian biobank sample (www.
geenivaramu.ee) has been part of several previous GWAS, it was
interesting to compare the MAF spectrum seen particularly in
Estonia with that of other populations. The correlation coefficient
r
2
obtained varied markedly, from 0.9247 for Latvia and 0.8913
for Finland (Helsinki) to 0.7312 for Southern Italy. In order to
examine the extent and likely impact of MAF differences between
the studied populations in general, we next examined LD
structure, undertook PCA, and calculated fixation indexes F
st
and inflations factor l(see below).
Linkage disequilibrium (LD) structure
Pair-wise LD between SNPs was measured by means of the r
2
statistics (see Methods). Genome-wide, average r
2
ranged from
0.24 to 0.28 at smaller distances (5 kb), and decreased to between
0.05 and 0.07 at larger distances (100 kb), depending upon
population. Above 75 kb the cohorts started to diverge reflecting
the LD extinction towards the north (Figure 1), although the
difference was not statistically significant (one-tailed t-test, p-
value#0.05 was considered as statistically significant).
Principal component (PC) and multidimensional scaling
(MDS) analysis
PC analysis has been used in most previous studies of the
European genetic structure. Here, PC analyses were performed
using EIGENSOFT with default parameters. In total, 1,564
individuals plus 203 HapMap members and 266,356 autosomal
SNPs were used as the input dataset. After the removal of outliers,
1,539 individuals (or 1,742 including the HapMap members)
remained (Table S1). The first PC explains 8.7% of the genetic
variance, the second PC explains 4.9%; all other PC explained
much smaller fractions demonstrating that the Europe is genetically
quite uniform. If we add African and Asian HapMap populations to
European samples, the two first PCs describe 36.6% and 23.8% of
Table 1. Studied samples.
Country Code #of individuals
#of individuals
after QC
#randomly selected
100 individuals
Illumina genotyping
assay
Austria (Vienna) AT 88 87 87 CNV370
a
Bulgaria BG 48 47 47 CNV370
b
Czech Republic (Prague and Moravia) CZ 94 89 89 CNV370
b
Estonia EE 1090 966 100 CNV370
b
Finland (Helsinki) FI (HEL) 100 100 100 CNV370
a
Finland (Kuusamo) FI (KUU) 84 79 79 CNV370
a
France (Paris) FR 100 100 100 HumHap300
a
Northern Germany (Schleswig-Holstein) DE (N) 210 206 100 HumHap300
a
Southern Germany (Augsburg region) DE (S) 473 468 100 CNV370
a
Hungary HU 50 49 49 CNV370
b
Northern Italy (Borbera Valley) IT (N) 96 53 53 CNV370
a
Southern Italy (Region of Apulia) IT (S) 95 57 57 CNV370
a
Latvia (Riga) LV 95 87 87 CNV370
b
Lithuania LT 95 90 90 CNV370
b
Poland ((West-Pomerania) PL 48 45 45 CNV370
b
Russia (Andeapol district of Tver region) RU 96 94 94 CNV370
b
Spain ES 200 194 100 HumHap300
a
Sweden (Stockholm) SE 100 87 87 HumHap300
a
Switzerland (Geneva) CH 216 214 100 HumHap550
a
Total 3378 3112 1564
a
Raw data provided.
b
Genotyped at Estonian Biocentre.
doi:10.1371/journal.pone.0005472.t001
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the genetic variance (Figure 2). At a more detailed level, however,
several distinct regions can be distinguished within Europe: 1)
Finland, 2) the Baltic region (Estonia, Latvia and Lithuania),
Eastern Russia and Poland, 3) Central and Western Europe, and 4)
Italy, with the southern Italians being more ‘‘distant’’ (Figure 2). PC
analysis of the 1,026 Estonians revealed the fine-structure of this
population, with the first two PCs describing 1.9% and 1.5% of the
genetic variance, respectively. The spread of Estonian individuals is
relatively wide as the subregions overlap on individual level, but the
median value of PCs, calculated for each county show a remarkable
correlation with the regional map of Estonian geography (Figures 2
and S1). PC analysis of genome-wide SNP genotypes is therefore
capable of highlighting both global and minute intra-population
genetic differences (Figure 2).
As expected, MDS analyses of the data with PLINK yielded a
scatter plot of the two first dimensions that looked very similar to
that generated by PC analyses (Figure S2).
The twenty-two (11 SNPs for the first PC and 11 SNPs for the
second) most variable SNPs presented as default output of the
EIGENSOFT analysis are listed on Table S3. These SNPs have
significantly different allele frequencies between studied popula-
tions and correspond to the largest eigenvalues of the first two PCs
explaining the most variance.
Fixation index (F
st
)
Pair-wise F
st
values between samples were calculated using
EIGENSOFT. F
st
values indicate how much of the genetic
variability between individuals from different populations is due to
population affiliation. In our study, F
st
was found to correlate
considerably with geographic distances (r
2
=0.382, p-value%0.01).
Values ranged from #0.001 for neighbouring populations to 0.023
for Southern Italy and in a young subisolate of Finland (Kuusamo)
(Table S2). The F
st
distances between HapMap CEU sample and the
other samples also correlated with geographic distance (r
2
= 0.291, p-
value,0.01). The German population sample showed zero F
st
with
the CEU samplewhereas the Finns from Kuusamo and the southern
Italians were most different from them (F
st
= 0.013 and 0.008,
respectively) (Table S2). Pair-wise F
st
values for CEU and either
Latvians, Lithuanians, Estonians or western Russians were interme-
diate (0.006, 0.005, 0.004 and 0.004, respectively).
Two or more samples were available from several countries
which allowed us to measure the intra-population variability by
F
st
. Mean F
st
was 0.001 for the 14 Estonian counties, 0.005 for
Finland, 0.000 for Germany and 0.005 Italy (each with two
samples), and 0.007 for the HapMap CHB and JPT samples.
Multi-sample populations were taken from the final PC map to
demonstrate the substructure of the populations (Figure S3).
Pair-wise F
st
of the four HapMap samples (203 individuals in total)
were as follows: Europeans (CEU) – Africans (YRI) 0.153; Europeans
(CEU) – Japanese (JPT) 0.111; Europeans (CEU) – Chinese (CHB)
0.110; Africans (YRI) – Chinese (CHB) 0.190; Africans (YRI) -
Japanese (JPT) 0.192; Chinese (CHB) – Japanese (JPT) 0.007.
Using Barrier 2.2 software, we also correlated geographic and
genetic distances as measured by pair-wise F
st
and great-circle
Figure 1. Genome-wide LD pattern (based on 273,464 SNPs), measured by average r
2
, at 5 kb to 100 kb inter-marker distance.
Averages were obtained within distance categories according of size 5 kb, i.e. 0–5 kb, 5–10 kb, etc.
doi:10.1371/journal.pone.0005472.g001
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coordinates of capitals or the city where an individual population
sample had been recruited, respectively. The results overlapped
with previous findings in that the first barrier was seen between
Finland and all other samples, a second barrier separated
Southern Italy from the remainder, a third was found between
Western Russia, Poland and Lithuania on the one hand, and
Bulgaria on the other, a fourth was seen between Kuusamo and
Helsinki, and a fifth was between the Baltic region and Poland on
the one hand, and Sweden on the other (Figure S4).
Inflation factor lambda (l)
Table 2 lists the pair-wise inflation factor lbetween studied
samples. The inflation factor lwas calculated with the method of
the Genomic Control [19]. We assumed lto be constant across
the genome and lwas estimated as the median of the observed
chi-square statistics divided by the median of the central chi-
square distribution with 1 degree of freedom (i.e. 0.456). This
factor was found to range from unity (between the samples from
the same country) to 4.21 (between Spain and the Kuusamo
region). The overall average lvalue was 1.82; in separate clusters
it amounted to 1.23 (Baltic Region, Western Russia and Poland),
1.54 (Italy and Spain), 1.22 (Central and Western Europe), and
1.86 (Finland), respectively. The correlation coefficient between
geographic distance and lwas r
2
= 0.386 (p-value%0.01). This
value is probably an underestimate of the European-wide
relationship due to the inclusion of the Kuusamo and Geneva
samples. One is an isolate and the other is a highly heterogeneous
international metropolis. The lvalues between CEU and the
other samples (Table 2) were smaller than those obtained using the
Northern German sample as a reference, chosen as the nearest to
the origin of CEU sample, and the correlation between geography
and lwith CEU was only r
2
= 0.251 (p-value 0.017). Both results
probably reflect the higher genetic variability in the CEU sample.
The high level of genetic homogeneity in Europe was again
highlighted by the lvalues calculated between the four HapMap
samples (data not shown), which ranged from 21.56 (YRI vs JPT)
via 13.27 (CEU vs CHB) to 1.77 between CHB and JPT. The l
value between the African and European samples was slightly
smaller than that between the African and Asian samples.
Marker-wise significance test
Marker-wise significance test was performed in order to assess
the allelic distribution in pair-wise comparison of studied cohorts
(CEU sample was not included) (Table 2). After applying
Figure 2. The European genetic structure (based on 273,464 SNPs). Three levels of structure as revealed by PC analysis are shown: A) inter-
continental; B) intra-continental; and C) inside a single country (Estonia), where median values of the PC1&2 are shown. D) European map illustrating
the origin of sample and population size. CEU - Utah residents with ancestry from Northern and Western Europe, CHB – Han Chinese from Beijing, JPT
- Japanese from Tokyo, and YRI - Yoruba from Ibadan, Nigeria.
doi:10.1371/journal.pone.0005472.g002
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Table 2. Number of significant (p,0.05) SNPs (based on the 273,464 markers) between populations (#100 samples from every population), using Bonferroni corrected trend test
and the inflation factor lfrom the genomic control.
#of significant
SNPs/Inflation
factor lAustria Bulgaria
Czech
Republic Estonia
Finland
(Helsinki)
Finland
(Kuusamo) France
Northern
Germany
Southern
Germany Hungary
Northern
Italy
Southern
Italy Latvia Lithuania Poland Russia Spain Sweden Switzerland CEU
Austria - 0 0 2 67 468 0 0 1 0 2 25 8 8 0 2 0 1 0 1
Bulgaria 1.14 - 0 9 68 293 0 8 0 0 0 0 11 13 0 6 2 24 0 14
Czech Republic 1.08 1.21 - 1 47 498 0 0 0 0 2 32 2 2 0 0 3 4 0 1
Estonia 1.58 1.70 1.42 - 8 229 30 4 3 1 84 288 0 0 0 1 155 6 45 20
Finland (Helsinki) 2.24 2.19 2.20 1.71 - 6 190 48 73 20 253 630 85 114 4 41 515 10 230 21
Finland
(Kuusamo)
3.30 2.91 3.26 2.80 1.86 - 978 492 593 170 758 1470 598 567 109 410 1620 252 988 215
France 1.16 1.22 1.35 2.08 2.69 3.72 - 1 0 0 2 23 85 37 3 16 0 1 0 0
Northern
Germany
1.10 1.32 1.15 1.53 2.17 3.27 1.25 - 0 0 20 79 12 5 0 12 12 0 4 0
Southern
Germany
1.04 1.19 1.16 1.70 2.35 3.46 1.12 1.08 - 0 3 34 27 17 4 4 2 2 0 0
Hungary 1.04 1.10 1.06 1.41 1.87 2.68 1.16 1.11 1.08 - 0 4 7 4 0 1 2 18 0 9
Northern Italy 1.49 1.32 1.69 2.42 2.82 3.64 1.38 1.72 1.53 1.42 - 0 118 93 1 42 2 33 0 25
Southern Italy 1.79 1.38 2.04 2.93 3.37 4.18 1.68 2.14 1.85 1.63 1.54 - 337 277 22 133 3 117 3 51
Latvia 1.85 1.86 1.62 1.24 2.31 3.33 2.40 1.84 1.20 1.58 2.64 3.14 - 0 0 0 247 33 122 22
Lithuania 1.70 1.73 1.48 1.28 2.33 3.37 2.20 1.66 1.84 1.46 2.48 2.96 1.20 - 0 0 198 28 67 15
Poland 1.19 1.29 1.09 1.17 1.75 2.49 1.44 1.18 1.23 1.14 1.75 1.99 1.26 1.20 - 0 6 5 1 3
Russia 1.47 1.53 1.27 1.21 2.10 3.16 1.94 1.49 1.58 1.28 2.24 2.68 1.32 1.26 1.18 - 79 27 24 23
Spain 1.41 1.30 1.63 2.54 3.14 4.21 1.13 1.62 1.40 1.32 1.42 1.67 2.82 2.62 1.66 2.32 - 38 0 16
Sweden 1.21 1.47 1.26 1.49 1.89 2.87 1.38 1.12 1.21 1.22 1.86 2.28 1.89 1.74 1.30 1.59 1.73 - 23 0
Switzerland 1.19 1.13 1.37 2.16 2.77 3.83 1.10 1.36 1.17 1.16 1.36 1.54 2.52 2.29 1.46 1.20 1.16 1.50 - 14
CEU 1.12 1.29 1.21 1.59 1.99 2.89 1.13 1.06 1.07 1.13 1.56 1.84 1.87 1.74 1.28 1.56 1.34 1.09 1.21 -
CEU - Utah residents with ancestry from Northern and Western Europe.
doi:10.1371/journal.pone.0005472.t002
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PLoS ONE | www.plosone.org 6 May 2009 | Volume 4 | Issue 5 | e5472

Bonferroni correction (based on 273,464 markers which equals
with the p,1.8610
27
) 48 out of 171 of those modeled case-
control association analysis between current populations did not
reveal any significant hits. Although, in total 16,240 significant hits
were identified, while the highest number was 1,620 between
Kuusamo and Spain (if Finnish and Italian samples were left out,
only comparison with Spanish sample revealed more than 100
markers in single comparison). The average number was 90.4
SNPs and after exclusion of outliers comprising Southern Italy,
Kuusamo, Northern Italy and Helsinki data (as the number of
significant SNPs was times higher when comparison with Italian
and Finnish cohorts) the average decreased to 80.0; 23.0; 21.1 and
10.1 SNPs, respectively. The total number of loci that had a
‘‘significant SNP’’ was 2,263. In order to decrease the amount of
loci and identify the meaningful hits, only the loci which had at
least two significant hits in at least two pair-wise comparisons were
considered, thereby decreasing the total number to 594 loci. Only
18 of those arose from comparisons between other populations
than Italy or Finland (Table S4).
Discussion
Studies of mitochondrial DNA (mtDNA) have suggested
substantial genetic homogeneity of European populations [20],
with only a few geographic or linguistic isolates appearing to be
genetic isolates as well [21]. On the other hand, analyses of the Y
chromosome [22,23] and of autosomal diversity [24] have shown a
general gradient of genetic similarity running from the southeast to
the northwest of the continent.
In the present study using autosomal SNPs and high density
genotyping, we have focused on the genetic structure of the Baltic,
Finnish and other North-Eastern European populations, while
populations from Western and Southern Europe were included
mainly for comparison (Figure 2). Overall the samples under
investigation have a large geographic coverage, ranging from Spain
and Italy, through the Baltic to Finland and Western Russia.
Previous studies have focused upon the genetic structure in Central
and Western Europe [11–13], Northern Europe [17,25] or studied
US Americans of European and Ashkenazi-Jewish descent [14,15].
Genome-wide analyses presented here have revealed, as
expected, more extensive LD in isolated populations than in
outbred populations. It can be presumed that the average r
2
value,
particularly at larger inter-marker distances, reflects the extent of
panmixia in a population. Indeed, the Kuusamo sample, a
population isolate that was established from a small number of
founders only 300 years ago, had the highest r
2
irrespective of
distance in our and previous studies [26]. At the other end of the
scale was Geneva, one of the most cosmopolitan cities in Europe,
which yielded the lowest r
2
values. Thus, our data corroborate
earlier suggestions that the amount of LD that persists over time is
markedly reduced in more admixed populations [27]. Surprising-
ly, the Polish cohort showed a similar LD pattern as the Kuusamo
population, which is probably reflecting the homogeneity of the
Polish population. Here the similarity could be attributed to the
founder effect or admixture as the Polish sample comes from West
Pomerania, a region that was repopulated after the Second World
War, after the expulsion of the German population, with other
people from (Eastern Poland) and also some Ukrainians. Small
sample size (n = 45) does not provide a sufficient explanation for
this finding because the Hungarian and Bulgarian samples were
also similar in size (Table 1), but gave LD patterns distinct from
the Polish and Kuusamo samples (Figure 1).
PC analysis yielded a genetic map where 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. Our genetic map shows slightly
different tendency from previously published ones in that the
scatter plot takes the form of a triangle, with the Finnish, Baltic
and Italian samples as its vertexes, and with Central Europe
residing in its centre. The two PCs explain 8% and 4% of the
genetic variability in the samples, which is almost twice as much as
in previous European-based studies. This increase is likely due to
the fact that the geographic coverage in our study has been
broader and that our data captured more genetic variability
(Figure 2).
Interestingly, PC analysis was also capable of highlighting intra-
population differences, such as between the two Finnish and the
two Italian samples, respectively. A low level of intra-population
differentiation in Germany has been reported previously [18], and
was confirmed here. In addition, we detected intra-population
differences within the Czech and Estonian samples (Figure S3). In
the case of the Czech, two samples were available: Prague and
Figure 3. Impact of inflation factor lupon the required significance of disease-gene association. The graph shows the highest p-value
that would stay below 0.05 after correction using a given lin the Genomic Control approach for two scenarios: 1) the decrease of chi-square statistics
in a test with 1 degree of freedom (e.g. Allelic, Additive, Dominant, Receive), and 2) in a test with two degrees of freedom (e.g. Genotypic).
doi:10.1371/journal.pone.0005472.g003
Genetic Structure of N-E Euro
PLoS ONE | www.plosone.org 7 May 2009 | Volume 4 | Issue 5 | e5472

Moravia. Although their pair-wise F
st
was virtually zero, the
median values of PCs for the two samples sets are different. This is
explicable by the fact that Moravia has a long shared history with
the remainder of the Czech Republic, but is nevertheless separated
from the rest of the country by the Czech-Moravian highlands,
which in the past hindered stronger intermixing.
Estonia is a small country with no geographic barriers and its
Estonian population is merely one million. In order to study the
genetic structure of Estonia in more detail, all Estonian
individuals were grouped here by their county of birth. Then,
PCA was performed and the mean values of the two first PC of
the counties were plotted onto the Estonian regional map
(Figure 2). Surprisingly, the resulting genetic map correlates
almost perfectly with the geographic map, although Estonia is
only 43,400 km
2
in size, and the mean area of a county only
2,900 km
2
. Thus, fine-scale genetic difference can be revealed by
PC analysis, and the results can be useful for identification of the
distant relatives.
Barrier analysis revealed genetic barriers between Finland, Italy
and other countries, as has been described before [12].
Interestingly, barriers could be demonstrated within Finland
(between Helsinki and Kuusamo) and Italy (between northern
and southern part). Another barrier emerged between the Eastern
Baltic region and Sweden, but not between the Eastern Baltic
region and Poland (Figure S4). The barrier between Bulgaria and
Western Russia, Poland and Lithuania may have arisen due to the
fact that several populations are missing in between those
countries. It has been shown previously that the populations of
central European background are less differentiated genetically,
whereas the Finns exhibit a more homogeneous population
structure with decreased genetic diversity [17,25].
In GWAS using large numbers of markers, multiple testing
correction becomes an important issue, and a genome-wide
significance threshold of p,5610
27
has been proposed [16]. At
the same time, adjustment for population stratification can
decrease the necessary level of nominal significance even further.
This can be illustrated, for example, by adopting the Genomic
Control approach [19] where the factor lby which the chi-
squared statistic is inflated by confounding is first estimated from
the null loci and correction is then applied by dividing the actual
association chi-square statistic by l. Figure 3 illustrates the effect
that this procedure would have by showing, for each possible l,
the highest p-value that stays below 0.05 after correction. Two
scenarios are presented: 1) tests with 1 degree of freedom (Allelic,
Additive, Dominant and Receive) and 2) tests with 2 degrees of
freedom (Genotypic). When l= 1.5 (which would be common if
patients and controls came from different European countries)
(Table 2), the original p-value must be approximately three times
lower than 0.05. For geographically distant samples, the necessary
reduction may be by a factor of up to 500, as would be the case
with Kuusamo and Southern Italy. Interestingly, lvalues with
respect to other samples are smaller for CEU (originating mostly
from Northern Germany, Netherlands and Belgia [12]) than for
Northern Germany. This is probably due to the higher genetic
variability in the CEU sample, ancestry of which is from a mixture
of several different populations and therefore the CEU sample is a
better reference for European population than a single population.
It should be pointed out that any adjustment for stratification does
inflate the multiple testing correction so that, if genetically distant
case and control samples are compared in an association study, the
genome-wide significance threshold in some cases would even be
as low as p,1610
210
.
From our results, conclusions can be drawn as to which
European populations can be combined in GWAS, considering
the pair-wise calculations of inflation factor land F
st
values,
although meta-analyses may often be a more appropriate option
[28,29].
Marker-wise significance test for allelic differences in pair-wise
comparisons between the studied samples resulted in 2,263 loci. As
our sample included some genetically and geographically distant
cohorts (Finns and Italians) where the strong founder effect and
isolation driven genetic drift has changed respective allele
frequencies, therefore only loci that were present in non-Italian
and non-Finnish comparisons were considered. This step de-
creased the number of significantly different loci to 18 (Table S4).
Four genes were within LCT loci (haplotype block covering more
than 1 Mb [30]) and it has been shown, that LCT region
differentiates European populations [11], but also within a given
population [16]. Three genetically most variable SNPs revealed by
PC analysis represented the same loci also present in the
previously mentioned list of 18 loci.
In conclusion, we have described the European genetic
structure by three different measures: the inflation factor l,F
st
and PC. As a result, according to the first two PCs, individuals
from the same geographic origin cluster together and form a
genetic map where four areas could be identified: 1) Central and
Western Europe, 2) the Baltic countries, Poland and Western
Russia, 3) Finland, and 4) Italy. If not corrected for the inter-
population differences would affect the significance of disease-
gene associations. A detailed description of the European
population structure has consequences and implications for the
design of future GWAS, particularly regarding sample size and
choice of controls. As a matter of fact, the knowledge of genetic
distances between different populations is helpful in defining
which biobanks could sensibly contribute samples and data to
GWAS.
Materials and Methods
Ethics Statement
The study was approved by the Ethics Review Committee on
Human Research of the University of Tartu (166/T-21,
17.12.2007). Written informed consent for participation was
obtained from all study subjects.
Samples
Samples are described in detail in the supplementary methods
section (Text S1). The studied 3,112 individuals representing a
total of 19 cohorts (Czech Republic samples were used as one in all
analyses except from the inter-population structure analyses)
samples from 16 countries: Austria (Vienna), Bulgaria (entire
country), Czech Republic (Prague, Moravia and Silesia), Estonia
(entire country), Finland (Helsinki, and a young internal subisolate
of Kuusamo), France (Paris), Germany (Schleswig-Holstein,
Augsburg region), Hungary (entire country), Italy (Borbera Valley,
Region of Apulia), Latvia (Riga), Lithuania (entire country),
Poland (West-Pomerania), Russia (Andreapol district of the Tver
region), Spain (entire country), Sweden (Stockholm) and Switzer-
land (Geneva) (Table 1).
The HapMap data used in our study comprised four
populations, namely CEU - U.S. Utah residents with ancestry
from Northern and Western Europe, YRI - the Yoruba people of
Ibadan, Nigeria, CHB - unrelated individuals from Beijing, China,
and JPT - unrelated individuals from Tokyo, Japan. Human-
Hap300 (v1-0.0) genotypes were downloaded from Illumina
iControlDB 1.1.2 (www.illumina.com/pages.ilmn?ID = 231), com-
prising a total of 203 individuals. For the CEU and YRI samples,
only parents were used.
Genetic Structure of N-E Euro
PLoS ONE | www.plosone.org 8 May 2009 | Volume 4 | Issue 5 | e5472

Genotyping
For the samples from Bulgaria, Czech Republic, Estonia,
Hungary, Latvia, Lithuania, Poland and Russia, genotyping was
performed at the Estonian Biocentre (Tartu, Estonia) according to
the manufacturer’s instructions, using the Illumina Hu-
man370CNV-duo chips.
Additional raw genotyping data were obtained for the samples
from Austria, Finland, Southern Germany (Augsburg region) and
Italy for Illumina Human370CNV-duo, from France, Northern
Germany (Schleswig-Holstein), Spain and Sweden for Human-
Hap300-duo, and from Switzerland for HumanHap550 data.
Systematic quality control (QC) was applied to all genotypes
generated at the Estonian Biocentre. Duplicates from the Estonian
sample were used to assess genotyping reproducibility, i.e. every
40
th
individual was duplicated and the mean discordance per SNP
between pairs of individuals was found to be less than 1 in 5000
(0.0002%). The per individual call rate had to be at least 95% for
individuals to be included into subsequent analyses. The number
of individuals before and after QC is shown in detail in Table 1.
Only the genotypes for those 311,226 SNPs that were typed in
all 3,378 individuals were included in subsequent computational
analyses. Closely related individuals were identified using estima-
tion of the proportion of the genome shared identical by descent
(IBD), and the relative with the lower call rate was removed.
Inbreeding coefficient F was assessed in order to detect potential
DNA contamination. SNPs found to be out of Hardy-Weinberg
equilibrium at p,10
25
, or missing more than 1% of genotypes, or
with a minor allele frequency ,0.01 were removed from the
dataset [16]. The total rate of genotyping calls in the remaining
individuals was 0.995. After QC, 273,454 SNPs remained (from
3,112 individuals), including 203 HapMap individuals that
increased the overall sample size to 3,315. All QC procedures
were conducted with Illumina’s BeadStudio (www.illumina.com)
and the PLINK software [7].
Statistical analysis
Pair-wise LD was measured by r
2
for all SNPs less than 100 kb
apart using the Haploview software [31]. A custom Perl script was
used to categorize r
2
according to inter-marker distance (0–5 kb,
5–10 kb etc.) and mean r
2
was calculated for each category. The
significance of the mean r
2
values between cohorts was tested with
the one-tailed t-test and p-value#0.05 was considered as
statistically significant.
Principal component (PC) analysis was performed and F
st
determined between samples using EIGENSOFT [6] on three sets
of samples: 1) HapMap+Europe, 2) Europe, and 3) Estonia alone
with individual counties. All analyses were performed with the
default parameters. Multidimensional scaling (MDS) analyses were
performed with the PLINK software. The marker set was filtered
according to pair-wise LD (r
2
cut-off = 0.2) in order to remove
correlated markers. The number of remaining markers was
68,201. All PC values and MDS dimensions were multiplied by
21 to render scatter plots more similar to the geographic
distribution of individual origin.
Geographic barriers were computed with the Barrier v2.2
software [32]. For the geographic positioning of samples the great-
circle coordinates of the respective capital of the country of origin,
or the city where an individual population sample had been
recruited was used. The F
st
pair-wise comparison matrix for
genetic and geographic distance was used in barrier analyzes. The
geographic location of the CEU sample was approximated by
Northern Germany (as shown in the Lao et al. 2008 paper). The
geographic distances between the above mentioned cities were
used to calculate the correlation coefficient between geography
and statistics, like F
st
and inflation factor l. Statistical tests were
performed in R v2.8.1 (www.R-project.org).
Trend tests were performed in order to identify markers with
significant pair-wise allele frequency differences between popula-
tions. The resulting p-values were subjected to Bonferroni
correction and the significance threshold was set at p,0.05,
although the multiple testing which arises from the pair-wise
comparisons was not taken into account. The ‘‘inflation factor’’ l
of the Genomic Control method [19] was calculated using
HelixTree (Golden Helix, Inc. Bozeman, MT, USA, HelixTreeH
Software; www.goldenhelix.com).
Supporting Information
Text S1 Study subjects
Found at: doi:10.1371/journal.pone.0005472.s001 (0.04 MB
DOC)
Table S1 Sample sizes for principal component analysis of
266,356 SNPs.
Found at: doi:10.1371/journal.pone.0005472.s002 (0.05 MB
DOC)
Table S2 Pair-wise Fst between European samples.
Found at: doi:10.1371/journal.pone.0005472.s003 (0.10 MB
DOC)
Table S3 Most variable SNPs in the PC analysis.
Found at: doi:10.1371/journal.pone.0005472.s004 (0.07 MB
DOC)
Table S4 Top eighteen genetically most variable loci from the
pair-wise cohort association analysis. The locus was described by
at least two SNPs and was present in at least two pair-wise cohort
analyses.
Found at: doi:10.1371/journal.pone.0005472.s005 (0.06 MB
DOC)
Figure S1 PC map of Estonian counties. Shown are the
Estonian samples grouped by county. The great circles mark the
median PC values for each county. Counties are colour-coded as
shown in the inset.
Found at: doi:10.1371/journal.pone.0005472.s006 (0.25 MB TIF)
Figure S2 Multidimensional scaling plot of the studied Europe-
an individuals.
Found at: doi:10.1371/journal.pone.0005472.s007 (0.14 MB TIF)
Figure S3 Population structure within studied populations. The
scatter plots of the first two PCs show the level of stratification
within A) Czech Republic - north-western part (Czech lands) and
south-eastern part (Moravia), plus Austrian samples, B) Germany -
northern and southern part, C) Italy - northern and southern part,
and D) Estonia - northern and southern part.
Found at: doi:10.1371/journal.pone.0005472.s008 (0.15 MB TIF)
Figure S4 Barrier analysis of European gene pool. The analysis
was based upon great-circle coordinates of the cities where
individual population samples were recruited and pair-wise Fst.
The name of the city is indicated in the brackets in the left panel.
Lower case letters point the order of the found barriers.
Found at: doi:10.1371/journal.pone.0005472.s009 (0.15 MB TIF)
Acknowledgments
We thank Viljo Soo from the Estonian Biocentre, Genotyping Core Facility
for assistance with genotyping. We would also like to thank Dr. Miroslava
Balasˇc
ˇa´kova´ from the Department of Biology and Medical Genetics,
Charles University Prague-2, and Medical School and University Hospital
Motol for the contribution of Czech samples.
Genetic Structure of N-E Euro
PLoS ONE | www.plosone.org 9 May 2009 | Volume 4 | Issue 5 | e5472

Author Contributions
Conceived and designed the experiments: MN TE AM. Performed the
experiments: MN TE. Analyzed the data: MN TE RM MR AM.
Contributed reagents/materials/analysis tools: FZ AZ DT SK MM TP IB
LP EJ KR ML SH PG MK SS TM AP HEW BM NP DT PG PD JK LNZ
VK JK JL TD SL AK XE RR SM AJ SEA SD CB HAC MG AM. Wrote
the paper: MN TE AM.
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