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Genome-Wide Analysis of Single Nucleotide 
Polymorphisms Uncovers Population Structure in 
Northern Europe 


Elina Salmela’?”®, Tuuli Lappalainen?”®, Ingegerd Fransson?, Peter M. Andersen’, Karin Dahlman- 
Wright?, Andreas Fiebig®, Pertti Sistonen®, Marja-Liisa Savontaus’, Stefan Schreiber®, Juha Kere’’””?, 
Paivi Lahermo7* 


1 Department of Medical Genetics, University of Helsinki, Helsinki, Finland, 2 Finnish Genome Center, Institute for Molecular Medicine Finland, University of Helsinki, 
Helsinki, Finland, 3 Department of Biosciences and Nutrition, Karolinska Institutet, and Clinical Research Centre, Karolinska University Hospital, Huddinge, Sweden, 
4 Department of Neurology, Umea University Hospital, University of Umea, Umea, Sweden, 5 Popgen Biobank, Institute for Clinical Molecular Biology, Christian-Albrechts- 
University, Kiel, Germany, 6 Finnish Red Cross Blood Transfusion Center, Helsinki, Finland, 7 Department of Medical Genetics, University of Turku, Turku, Finland, 
8 Department of General Internal Medicine, Institute for Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany, 9 Folkhalsan Institute of Genetics, 
Biomedicum Helsinki, Helsinki, Finland 


Abstract 


Background: Genome-wide data provide a powerful tool for inferring patterns of genetic variation and structure of human 
populations. 


Principal Findings: \n this study, we analysed almost 250,000 SNPs from a total of 945 samples from Eastern and Western 
Finland, Sweden, Northern Germany and Great Britain complemented with HapMap data. Small but statistically significant 
differences were observed between the European populations (Fst = 0.0040, p<10 “), also between Eastern and Western 
Finland (Fst = 0.0032, p<107°). The latter indicated the existence of a relatively strong autosomal substructure within the 
country, similar to that observed earlier with smaller numbers of markers. The Germans and British were less differentiated 
than the Swedes, Western Finns and especially the Eastern Finns who also showed other signs of genetic drift. This is likely 
caused by the later founding of the northern populations, together with subsequent founder and bottleneck effects, and a 
smaller population size. Furthermore, our data suggest a small eastern contribution among the Finns, consistent with the 
historical and linguistic background of the population. 


Significance: Our results warn against a priori assumptions of homogeneity among Finns and other seemingly isolated 
populations. Thus, in association studies in such populations, additional caution for population structure may be necessary. 
Our results illustrate that population history is often important for patterns of genetic variation, and that the analysis of 


hundreds of thousands of SNPs provides high resolution also for population genetics. 


Citation: Salmela E, Lappalainen T, Fransson |, Andersen PM, Dahlman-Wright K, et al. (2008) Genome-Wide Analysis of Single Nucleotide Polymorphisms 
Uncovers Population Structure in Northern Europe. PLoS ONE 3(10): e3519. doi:10.1371/journal.pone.0003519 


Editor: Neil John Gemmell, University of Otago, New Zealand 
Received June 25, 2008; Accepted October 1, 2008; Published October 24, 2008 


Copyright: © 2008 Salmela 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: Emil Aaltonen foundation (PL, TL), Research Foundation of the University of Helsinki (TL), Graduate School in Computational Biology, Bioinformatics, 
and Biometry (ES), Sigrid Juselius Foundation (JK), Academy of Finland (JK), Swedish Research Council (JK), Finnish Cultural Foundation (TL, PL), National Genome 
Research Network (NGFN) and the popgen biobank, both through the German Ministry of Education and Science (AF, SS), DFG excellence cluster "inflammation at 
interfaces" (SS). Funding for the WTCCC project was provided by the Wellcome Trust under award 076113. The funders had no role in study design, data collection 
and analysis, decision to publish, or preparation of the manuscript. 


Competing Interests: Prof. Schreiber has been a member of Applied Biosystems scientific advisory board. No Applied Biosystems product or services were used 
in this study. 


* E-mail: paivi.lahermo@helsinki.fi 


3 These authors contributed equally to this work. 





Introduction 


Emerging genome-wide data are a powerful resource for 
analysis of population genetic variation, including population 
history and structure. These studies are of importance not only for 
researchers with historical interests, but also as a baseline for 
population-based studies of human disease, most notably associ- 
ation analyses of complex diseases where unknown population 
structure may introduce bias [1,2]. Compared to previous 
methodology of human population genetics, the analysis of 


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hundreds of thousands of loci across the genome allows a whole 
new level of accuracy and power without the constraint of having 
to use only a few loci as a proxy for the whole genome. This has 
already been demonstrated by a number of studies [e.g. 3-11]. 
We employed genome-wide SNP data to characterize genetic 
variation in Finland and Sweden in comparison with two reference 
populations from Germany and Great Britain, which have a 
Central European background and are larger, older and more 
admixed. Additionally, we also compared these data to the three 
HapMap populations from Europe, Africa and Asia [12]. 


October 2008 | Volume 3 | Issue 10 | e3519 


The population history of Northern Europe has been reviewed 
earlier by several authors [13-20]. The settlement of the Baltic Sea 
region advanced rapidly after the Ice Age, beginning about 14,000 
BC in Northern Germany and 10,000 BC in Finland. All the 
populations have their roots mainly in Central Europe, although 
some eastern influence has been observed among the Finns [21— 
23]. The early settlement in Finland covered almost exclusively the 
coastal and southwestern regions until a major settlement wave 
starting from central eastern Finland (the province of South Savo) 
led to the settlement of northern and eastern Finland from the 16" 
century onwards. Even then, the population size throughout the 
country remained small, causing extensive genetic drift which, 
together with local and regional founder and bottleneck effects, led 
to the characteristic features of historical settlement of Finland: 
heavily drifted and isolated small breeding units. ‘The results of this 
process have been seen in both common and especially rare 
autosomal alleles [13,17]. Y-chromosomal studies have shown a 
strong genetic borderline between Western Finland and Eastern 
Finland [23-25], also supported by some studies of autosomal 
variation [26,27]. Several studies have shown a longer range of 
linkage disequilibrium among the Finns, especially among the late 
settlement population of Eastern Finland, compared to the more 
outbred European populations [28-30]. 

Genetic variation in Sweden, Germany and Great Britain has 
been characterized less extensively than in Finland, and there is 
little evidence of strong population structure. In Sweden, 
mitochondrial DNA and Y-chromosomal studies indicate some 
geographical gradients [31,32], and a pattern of local isolation has 
also been observed in northern parts of the country [33]; linkage 
disequilibrium studies indicate a lower extent of LD than among 
the Finns [34]. In Germany, only a minor degree of population 
structure between the northern and southern parts of the country 
has been detected by studies of autosomal markers [35], and some 
local differences by Y-chromosomal analysis [36]. Additionally, 
the German province of Schleswig-Holstein analyzed in this study 
has Y-chromosomal evidence [36] as well as historical records [37] 
indicating substantial admixture with the Danes. Genome-wide 
analysis of the British population has indicated only a slight genetic 
gradient from Southeast to Northwest, and the lack of strong 
substructure has been considered to be consistent with the multiple 
migrations that have affected the population [4]. 

The aim of this study was to characterize the genetic variation 
of Finland, Sweden, Northern Germany and Great Britain 
together with the HapMap data (Fig. 1) on a finer level than 
previously possible, using 250,000 SNPs. In addition to analysing 
patterns of population differentiation, diversity and admixture in 
North Europe, we had a special interest on elucidating population 
structure within Finland. The populations of Central European 
background showed signs of only minor population differentiation, 
whereas the Swedes and Finns exhibited a stronger population 
structure—also within Finland—and decreased genetic diversity, 
both of which suggested a pronounced genetic drift among North 
Europeans. 





Results 


Analyses between populations 

After genotyping on Affymetrix 250K Sty SNP arrays (see 
Methods and Table S1 for success rates and quality criteria), the 
data from 1003 European individuals were first compared without 
prior population assignment in the analyses of pairwise identities 
by state (IBS) and calculations with the Structure software. In 
multidimensional scaling of the IBS distances, there were four 
clusters: Eastern Finns, Western Finns, Swedes, and a group 


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SNP Variation in North Europe 









3 


CEU 58 
YRI 57 
CHB + JPT 87 








Figure 1. The map of Northern Europe (a) and Finland (b), and 
the sample sizes. The studied (sub)populations and their geograph- 
ical ranges are shown in white. Abbreviations for the populations: 
Western Finland (FIW); Eastern Finland (FIE); Sweden (SWE); Germany 
(GER); Great Britain (BRI); Utah residents with ancestry from northern 
and western Europe (CEU); Yoruba from Ibadan, Nigeria (YRI); Han 
Chinese from Beijing, China (CHB); and Japanese from Tokyo, Japan 
(PT). Abbreviations within Finland: Southwest Finland (SWF); Satakunta 
(SAT); Hame (HAM); Southern Ostrobothnia (SOB); Swedish-speaking 
Ostrobothnia (SSOB); Savo (SAV); Northern Karelia (NKAR); Kainuu (KAI); 
Northern Ostrobothnia (NOB); Miscellaneous (MISC). 
doi:10.1371/journal.pone.0003519.g001 


including the Germans, British and CEU (from now on called 
“Central Europeans”; Fig. 2a,b, Fig. Sla). The median IBSs 
between the European population pairs (Table 1), which are free 
of the potential bias caused by multidimensional scaling, indicated 
a closer relationship of Eastern v. Western Finns and Germans v. 
British, and largest differences between the Eastern Finns v. British 
and Eastern Finns v. Germans (p<1o7!* for all population pairs 
except between Sweden v. Western Finland, Germany and Great 
Britain). The Structure analysis (Fig. 3, Fig. S2a,b) found most 
support for three or four clusters, one dominated by the Eastern 
Finns, one by the Swedes, and one by the Central Europeans; 
increasing the number of clusters did not bring out further 
differences. When only the Finnish samples were analysed with 
Structure, they formed two clusters (Fig. S2c), consisting of the 
Eastern and Western Finns, with only 1.8% of the samples 
associating more strongly to the cluster not corresponding to their 
geographic origin (data not shown). A Structure analysis of the 
three Central European populations combined found only one 
cluster. 

When data from HapMap Han Chinese+Japanese and Yoruba 
individuals was included in the analysis, the MDS plot of IBS 
formed a triangle of the three continents in the first two 
dimensions, with the third dimension separating the European 
populations clinally from each other (Fig. $3). In the histograms of 
IBS between the five European populations and each HapMap 
population (Fig. 4a), the studied populations were most similar 
with the CEU and least similar with YRI. Interestingly, the 
similarity with the Asians varied between populations, being 
higher for Eastern Finns, Western Finns and Swedes than for the 
Germans and British (p<107'* for all comparisons except for 
GER and BRI whose distributions did not differ). The same 
pattern was also observed when comparing the allele frequencies 
in the study populations and in CEU and CHB+JPT: the Eastern 
Finns had the largest proportion of SNPs deviating towards the 
Asian frequencies (Table $2; p<10~°), also when markers with 
smallest differences were excluded (data not shown). 


October 2008 | Volume 3 | Issue 10 | e3519 


SNP Variation in North Europe 





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1st dimension (0.97%) 





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© 
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5 © 
9 S 
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-0.04 0.00 0.04 0.08 
1st dimension (0.97%) 
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FIW FIE SWE GER BRI CEU 


Figure 2. Multidimensional scaling plots of the identity by state 





-0.05 0.00 0.05 
1st dimension (0.96%) 

oO ie Q @ (2) 
SWF SAT SSOB SOB HAM 
(eC) g H ® e 
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matrices. Plots for the Europeans in the 1** and 2"4 dimensions (a), and the 


15 and 3" dimensions (b), and the Finnish samples in the 1** and 2"4 dimensions (0), and the 1° and 3 dimensions (d). The label of each axis shows 


the proportion of the dimension’s eigenvalue to the sum of absolute ei 
for three-dimensional animations. 
doi:10.1371/journal.pone.0003519.g002 


Quantile-quantile plots of pairwise allele frequency differences 
(Fig. 5) and Fsgy calculations (Table 1) showed a pattern of the 
largest differences being between Eastern Finland versus Great 
Britam, Germany and Sweden (Fgy=0.0072—0.0094) and the 
smallest between the British and Germans (Igy = 0.0005). All the 
FSTs differed from zero (p<10~*), and most of them also differed 
from each other (the range of 95% confidence intervals =0.0005 
or less). The Fsr over all populations was 0.0040 (p<10~*). 
Notably, there was no indication of the closer relationship of the 
two Finnish populations that was observed in the IBS analysis of 
individuals (Fig. S4a). The relationships between populations 
could also be measured by the number of shared monomorphic 


7. PLoS ONE | www.plosone.org 


genvalues of all dimensions. Abbreviations as in Figure 1. See also Figure $1 


markers in Finland, Sweden and Germany (Fig. 6). There, the 
total number of monomorphic and uniquely monomorphic 
markers were highest in Eastern Finland, pairwise sharing was 
highest between Eastern and Western Finland, and three-way 
sharing between the two Finnish populations and Swedes. A total 
of 19088 markers were monomorphic in all four populations and 
an additional 2231 when the populations were sampled to equal 
size, and these were excluded from the analysis. 


Variation within populations 


The IBS between individuals within populations (Fig. 4b) was 
highest for Eastern Finland and lowest in Germany 


October 2008 | Volume 3 | Issue 10 | e3519 


Table 1. Pairwise F<7’s (lower diagonal) and the median IBS 
(upper diagonal) between population pairs. 








SWE FIW FIE GER BRI 
SWE 0.7997 0.7990 0.7997 0.7997 
FIW 0.0030 0.8005 0.7994 0.7993 
FIE 0.0072 0.0032 0.7985 0.7982 
GER 0.0021 0.0033 0.0084 0.8002 
BRI 0.0024 0.0042 0.0094 0.0005 





All Fs7’'s differ from zero (p<1073), and their 95% confidence intervals are 
+0.0005 or narrower. For the IBS, p<107 “* for all population pairs except 
between Sweden v. Western Finland, Germany and Great Britain. 
doi:10.1371/journal.pone.0003519.t001 


(p<4.6 107‘). Differences in the extent of linkage disequilibrium 
were highly significant (p<6.2x10~'°) for all population pairs 
except Germans and British (Fig. 7): LD was highest in Eastern 
Finns and lowest in Germans and British. Marker and sample 
heterozygosities, inbreeding coefficients and minor allele frequen- 


SWE FIW FIE 


AUG HMI HUG LA dat IL 
* daa iil My Li iain 





SNP Variation in North Europe 


cy distributions had only very small, although mostly significant, 
differences between the populations (Table $3). When the 
European populations were analysed separately in Structure, 
none showed evidence of a substructure. 

The information about the grandparental birthplaces of the 
Finnish samples enabled a more detailed analysis of population 
structure within Finland. In the multidimensional scaling plot of IBS 
within Finland (Fig. 2c,d, Fig. S1b), the first dimension showed the 
division to Eastern and Western Finland; the Hame samples settled 
between the clusters. The second dimension showed a north-south 
gradient within Eastern and the third dimension withm Western 
Finland. Here the Swedish-speaking Ostrobothnians showed no 
separation from their Finnish-speaking neighbours, whereas in the 
MDS plot of the European populations, the Finnish samples closest 
to the Swedes were almost exclusively Swedish-speakers (data not 
shown), and in the Structure analysis the Swedish-speaking Finns 
showed twice as large an admixture with the Sweden-dominated 
cluster as the other Western Finnish samples did (48.9% versus 
24.6%, data not shown). In the analysis of isolation by distance (Fig. 
$5), the correlation of genetic and geographic distances between 
pairs of Finnish individuals was 0.31 (p<10~°). 





\ | | 
i T I }, bi 


GER CEU 


Mi lll Me RE Uhl 
H h sl AAR itl 


R 


Figure 3. The Structure results for two, three and four clusters. Each individual is represented by a thin vertical line, and colours denote the 
clusters. Abbreviations as in Figure 1. The probabilities of the different clusterings are given in Supplementary Figure 2b. 


doi:10.1371/journal.pone.0003519.g003 


YRI CHB+JPT 


0.15 > 


0.10 


0.05 


0.74 0.76 0.78 
IBS 


CEU 


0.15 W 


0.10 


0.05 





0.80 0.795 


0.800 
IBS 


0.805 0.810 


@ © & © Oo 
FIW FIE SWE GER BRI 


Figure 4. Distributions of pairwise identities by state. IBS between the five studied populations and each HapMap population (a) and within 
the populations (b). Within the four groups of comparisons, all distribution pairs differed significantly (p<4.6x10 * for comparisons within the 
populations, p<10° '4 with CEU and with CHB+JPT, and p<0.025 with YRI) except that in the comparisons with Asians, Germany and Great Britain did 
not differ. Abbreviations as in Figure 1. 
doi:10.1371/journal.pone.0003519.g004 


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FIE v. BRI FIE v. GER 





7. = 3.09 2 = 2.93 





FIE v. SWE 






FIW v. BRI 


2 = 2.61 2 = 1.94 





FIW v. GER 
2. = 1,81 - 


FIW v. SWE 
7. = 1.69 


15 


10 


FIW v. FIE SWE v. BRI 





7. = 1.68 2. = 1.57 


t+) 10 20 3 4 50 x 0 5 10 


SWE v. GER GER v. BRI 





2 = 1.54 R= 111 


Figure 5. Quantile-quantile plots of allele frequencies between 
population pairs. ) denotes the overdispersion factor. One SNP with 
an observed value of ~120 has been left out from all the plots with the 
Germans. Note the two different scales of the axes. Abbreviations as in 
Figure 1. 

doi:10.1371/journal.pone.0003519.g005 


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SNP Variation in North Europe 


1508 


FiE 
7403 





aes 


306 


594 


FIW 
5831 





744 
SWE 
6028 


Figure 6. The number of monomorphic markers. The total 
number of monomorphic markers within each population is given in 
bold, and the markers that are monomorphic exclusively in one 
population are in underlined italics. The edges of the tetrahedron 
denote the markers that are monomorphic only in two populations, and 
the faces correspond to monomorphy shared between three popula- 
tions. 21 319 SNPs that were monomorphic in all the four populations 
are not included in the figure. Abbreviations as in Figure 1. 
doi:10.1371/journal.pone.0003519.g006 


Discussion 


Analysing large numbers of autosomal markers has advantages 
over the traditional tools of population genetic studies. Mitochon- 
drial DNA and Y-chromosomal markers represent only two loci 
and thus do not fully capture the evolutionary history throughout 
the whole genome, and limited numbers of autosomal loci may 
lack the power to detect differences especially between closely 
related populations. In this study, we used 250,000 SNPs to 
elucidate the population structure and differentiation in Northern 


D’ 
0.4 06 08 1.0 





20 40 60 80 
distance between SNP pairs (kb) 


100 


Figure 7. Linkage disequilibrium as a function of distance 
between marker pairs. Median D’ in overlapping 10 kb windows at 5 
kb intervals is plotted for each population. All differences were 
significant (p<6.2x107 '°), except between Germany and Great Britain. 
Abbreviations as in Figure 1. 

doi:10.1371/journal.pone.0003519.g007 


October 2008 | Volume 3 | Issue 10 | e3519 


Europe by analyzing carefully ascertained samples from Eastern 
and Western Finland, Sweden, Germany and Great Britain. Our 
results revealed a relatively strong population structure within 
Finland, and a small but significant differentiation between all the 
populations, although especially the Germans and British 
appeared genetically very homogeneous. 

The F sy values showed a pattern of very small yet statistically 
significant differences between the populations. The overall Fsr 
(0.0040) was equal to the Fg; between European regions calculated 
from a similar set of markers [9]. The population structure among 
Eastern and Western Finland (Fsy= 0.0032) was similar to that 
between the Icelandic subpopulations (0.0034) [38], but much 
stronger than what has been observed between Northern and 
Southern Germany (0.00017) [35], and stronger than between some 
of the countries in our data, despite the shorter geographic distance. 
A comparable structure within Finland has been observed earlier 
with Y-chromosomal and autosomal markers [23,27]. The differ- 
ences between populations detected with Fsy and other measures 
accounted for such a small proportion of the total genetic variation 
that large numbers of SNPs are needed to observe them, once again 
illustrating how most of the human genetic variation is found 
between individuals instead of populations [39]. Even small 
differences between populations can be interesting regarding 
population history, but elucidating their phenotypic significance will 
require further studies. 

The MDS plot of the European populations showed a pattern of 
population differences that was consistent with our other analyses 
and earlier observations of a greater degree of differentiation in the 
geographical extremes of Europe [3,5,7,9-11]. Our German, 
British and CEU samples formed a single cluster, possibly due to a 
lack of neighbouring reference populations, and contrary to studies 
with a more comprehensive sampling from Central Europe [7,9]. 
The Swedes showed a wider spreading than the other populations, 
but this was supported neither by diversity calculations nor by a 
more detailed comparison of the IBS and MDS distance matrices 
(results not shown). Thus, the differential spread was at least partly 
an artefact of the MDS, where the representation in a few 
dimensions likely fails to capture all aspects of complex data. Thus, 
as visually attractive as the MDS plots are, they must be 
interpreted with caution and, if sample sizes allow, be accompa- 
nied with analyses based on allele frequencies. 

The MDS analysis of Finns showed a pattern resembling their 
geographic origins, although with some overlap of the provinces. A 
similar regional clustering of individuals has been seen in the Swiss 
[9], but not in Great Britain [4]. The increased Swedish 
contribution among the Swedish-speaking Finns agrees with 
earlier findings [27,40], as well as with their medieval Swedish 
origin [14]. Interestingly, in the MDS plots the Finnish-Swedes 
stood out from the rest of Western Finland only when Sweden was 
included in the analysis, which highlights the importance of 
relevant reference populations also when detecting patterns of 
variation within a country. 

The extreme features of Eastern Finland-high linkage disequi- 
librium, high similarity within the population, increased number of 
monomorphic markers and divergence from the other popula- 
tions-are in accordance with earlier studies [20-30]. They are 
likely caused by population history: the young age of the 
population, founder and bottleneck effects, and substantial genetic 
drift attributable to small population size. The settlement of 
Eastern Finland from the province of South Savo beginning in the 
16th century led to serial founder effects, and genetic drift 
remained strong in the small and isolated breeding units during 
the following centuries [17,18]. These local processes were also 
reflected in the regional MDS clustering of individuals within 


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SNP Variation in North Europe 


Eastern and Western Finland. Similar processes, although much 
less extreme in magnitude, have probably caused the slight 
decrease in diversity observed in Sweden and Western Finland. 
Conversely, the Germans and British showed much less diver- 
gence, and their LD was significantly lower and diversity higher 
than among the Nordic populations. 

Another factor behind the outlier status of Finland could be 
admixture with other populations outside the studied region. 
Indeed, the comparison to the Asian HapMap samples revealed 
interesting differences between the studied populations, with the 
Nordic populations and especially Eastern Finns appearing to 
harbour a significantly stronger Asian affinity than Central 
Europeans. A similar eastern influence has been observed in Y- 
chromosomal, mitochondrial DNA and autosomal studies of the 
Finns [5,20—23], consistently with archaeological and linguistic 
data. A small degree of Saami admixture has been observed 
among the Finns [41] and could also contribute to the 
differentiation observed in this study, but it could not be detected 
in the absence of reference data. Thus, the possible eastern 
contribution observed among the Finns supports the earlier studies 
done with a more limited number of markers, although a full 
synthesis of past migration waves is beyond the scope of this study 
and would require additional data. 

In this study, the potential bias caused by limited sample size 
should not be a major problem, since the sample sizes were 
similar or larger than those commonly used in population genetic 
studies. Another putative source of error, genotyping centre 
artefacts between datasets, is difficult to exclude completely. 
However, the data for Finland and Sweden comes from a single 
genotyping centre, and thus analyses within the dataset are free 
from this potential bias. The genetic differences between the 
German and British datasets are small (Fs; = 0.0005, 4= 1.11) 
despite being genotyped in different laboratories, and thus these 
datasets seem comparable. Additionally, the bias in SNP 
ascertainment for the chips and in the LD-based formation of 
smaller datasets (Table Sl) may affect the sensitivity of the 
markers to detect population structure, and thus the exact values 
of e.g. Fsp [42]. A further important factor in population genetic 
research is the geographical scale of sampling. Indeed, our 
German sample is from a region with considerable Scandinavian 
admixture [37]. Consequently, the German sample presumably 
captures neither the full extent of diversity and variability within 
Germany nor unbiased relationships between the whole 
populations. Within Finland, the observed sharp genetic 
borderline is probably partly explained by the gap between 
Western and Eastern Finland in our sampling, and a geograph- 
ically continuous sampling could have yielded a more clinal 
pattern of genetic variation. Nonetheless, the extent of the 
differences between the areas now sampled would obviously not 
change. 

In the analysis of differences between populations, the patterns 
observed in individual-based analysis and in calculations based on 
allele frequencies usually correlated well. However, in the IBS 
analysis the Eastern and Western Finns appeared relatively closer 
to each other than in the quantile-quantile plots or Fsy (Fig. S4a). 
Figures S4b,c show the expected values of mean markerwise IBS 
and chi-square test statistic for all combinations of allele 
frequencies in two populations, and demonstrate that the measures 
behave differently with respect to allele frequencies. This 
difference explains why two population pairs could show disparate 
distances with one measure and similar with the other. The 
measures could also vary in their sensitivity to various patterns of 
allele frequency differences and thus to the population genetic 
processes that have caused the patterns. 


October 2008 | Volume 3 | Issue 10 | e3519 


Population isolates are easily considered homogeneous without 
further evaluation. Many of the advantages of using population 
isolates in gene mapping [15,43] are a consequence of factors that 
also make the population subunits vulnerable to genetic drift and 
may lead to population stratification. Our results show that these 
factors have had a substantial effect in the patterns of genetic 
variation in Northern Europe, where the populations show a 
greater degree of differentiation than the more stable and admixed 
Central European populations. Because the detected structure 
within the Finnish population is of the magnitude that has been 
suggested to be a potential source of bias in association studies 
[1,2,38], our results suggest that attention to population 
substructure may be needed to ensure the quality of association 
studies that are performed using Finnish samples. In fact, the 
differences between Eastern and Western Finns were of the same 
magnitude as the differences between Swedes and British, and 
much stronger than those between British and Germans. Thus, 
relevant units of genetic variation often do not correspond to 
preconceived political, linguistic or even cultural borders. 


Materials and Methods 


We genotyped 139 genomic DNA samples from Eastern 
Finland, 141 samples from Western Finland and 113 samples 
from eastern Sweden with the Affymetrix 250K Sty SNP array 
(Santa Clara, CA) (Fig. 1). All the sample donors were males. The 
geographical origin of the Finnish samples was assessed according 
to grandparental birthplace, but no detailed ancestry information 
was available for the Swedes. Additionally, we used data for 256 
male control samples from the PopGen cohort from Kiel area in 
Schleswig-Holstem im Northern Germany [44]. All the samples 
were collected with informed consent according to the principles 
of the Declaration of Helsinki, and the project was approved by 
the ethics committees of the Finnish Red Cross, Umea University, 
and the Kiel Medical Faculty. We also used data from 296 male 
controls of the 1958 birth cohort kindly provided by the Wellcome 
Trust Gase Control Consortium [4] and sampled according to the 
region information to cover the entire Great Britain. Furthermore, 
we obtained 250K Sty array genotypes of the unrelated HapMap 
[12] individuals from Affymetrix: 58 Utah residents with ancestry 
from northern and western Europe (CEU), 57 Yoruba from 
Ibadan, Nigeria (YRI), 42 Japanese from Tokyo, Japan (JPT) and 
45 Han Chinese from Beijing, China (CHB). 

The genotype calling was done by the BRLMM algorithm in 
the Affymetrix GeneChip Genotyping Analysis Software 
(GTYPE) version 4.1, and the quality control procedures 
followed for the most part the Wellcome Trust Case Control 
Consortium standards [4] (Table $1). Samples with success rate 
below 97% were excluded. For markers, the exclusion limits 
were 95% for success rate, p<0.001 for deviation from Hardy- 
Weinberg equilibrium in any of the populations, and 0.005 for 
minor allele frequency. This yielded a total of 201 011 SNPs and 
1147 samples that passed the quality control. Additionally, two 
smaller marker sets were constructed by LD-based SNP pruning: 
68469 SNPs with r?<0.2, and 6369 SNPs with minor allele 
frequency >0.1 and r?<0.02. The former set was used for the 
IBS and inbreeding analyses and the latter for Structure and F sr 
analyses. Many of the analyses were performed without the 
HapMap populations in order to avoid extensive sampling or 
possible bias due to their lower sample sizes. We performed most 
of the analyses in parallel in Plink version 1.00 (http://pngu. 
megh.harvard.edu/purcell/plink/) [45] and the R 2.6.2 (www. 
R-project.org) [46] package GenABEL 1.3—5 [47] to eliminate 
human and software errors. 





7. PLoS ONE | www.plosone.org 


SNP Variation in North Europe 


We calculated pairwise identities by state (IBS) for all samples, 
and performed classical multidimensional scaling (MDS) on the 
identity matrices for the total data and for the European and 
Finnish datasets separately. The informativeness of the presented 
dimensions was assessed by calculating the proportion of their 
respective eigenvalues to the sum of absolute eigenvalues. 
Distributions of IBS in sample pairs within and _ between 
populations, as well as marker and sample heterozygosities and 
inbreeding coefficients were calculated in GenABEL, together 
with distributions of minor allele frequencies in the populations. 
Geographic coordinates for each Finnish individual were deter- 
mined as the mean of grandparental birthplace coordinates, and 
the geographic distances between all the individuals were 
calculated as great-circle distances in R package fields [48]. The 
correlation between the geographic and genetic distances (1-IBS) 
was measured by Mantel test as implemented in R package ade4 
[49]. We estimated the extent of linkage disequilibrium (LD) in 
each population by calculating D’ between all marker pairs within 
100 SNPs from each other, using for each marker pair the median 
result of the values based on the frequency estimates of all four 
haplotypes calculated with the E-M algorithm in Plink. Population 
structure was assessed also by Structure 2.2 software [50] with the 
admixture model and 10000 burn-ins and iterations, doing four 
separate runs for each K. Estimation of the correct K was based on 
visual inspection of the respective probabilities and of the 
distribution of the populations among the inferred clusters. No 
substructure was inferred when the probability was largest for 
K= 1. For Fsgy calculations we used Arlequin 3.11 [51]; the p- 
values and 95% confidence intervals are based on 10100 
permutations. The allele frequency differences in population pairs 
were tested with markerwise 1-df chi-square tests in Plink, and the 
deviation from expected chi-square distribution was visualized in 
quantile-quantile plots. Their overdispersion factor (A) was 
calculated as a ratio of the means of the lowest 90% of the 
observed and expected chi-square values as in [52]. Additionally, 
we calculated the number and distribution of markers that were 
monomorphic in at least one of the populations; this analysis was 
performed only for the Finns, Swedes and Germans due to the 
difficulty of visualising multiple population comparisons. 

To study the extent of eastern influence, we counted in each of 
the five European populations the number of markers where the 
population’s allele frequency and the CHB+JPT allele frequency 
deviated from the CEU allele frequency to the same direction, and 
the number of markers where the allele frequencies deviated in 
opposite directions. We then compared the numbers to the null 
hypothesis that all the five populations stem from the same proto- 
European population (approximated by the CEU frequencies) 
from which they have subsequently diverged via genetic drift in the 
absence of admixture. In such a case, one would expect the 
number of markers drifting into a given direction (e.g. towards the 
Asian frequencies) to be similar across the populations, whereas a 
varying degree of eastern admixture in each population would 
result in disparate marker proportions. Using the number of 
deviating markers instead of the absolute size of the deviations 
should even out some of the effects of differing extent of drift in the 
populations. 

The statistical significancies of the differences between the 
distributions of each analysis were tested in R by first assessing 
their normality by a Shapiro-Wilk test. As all were strongly non- 
normal, the pairwise analyses (LD, marker heterozygosities) were 
done with a sign test; in the independent analyses (allele 
frequencies, sample heterozygosities, IBS distributions, inbreeding 
coefficients), an overall significance of the difference was first 
calculated from a Kruskal-Wallis one-way analysis of variance, 





October 2008 | Volume 3 | Issue 10 | e3519 


and if that was significant, the differences were further located by 
pairwise comparisons with a Mann-Whitney U test. ‘The medians 
given in Tables 1 and S3 are calculated from the datasets listed in 
Table $1, but to avoid possible effects of sample size, the 
significance testing of marker heterozygosities, inbreeding and 
allele frequencies was done on populations sampled to n= 113. 
The statistical significance of differences in the number of SNPs 
whose frequencies deviated towards or away from the Asian 
frequencies was assessed by a 2X5 chi-square test. A Bonferroni 
correction has been applied to the reported significance levels to 
correct for the number of pairwise comparisons within each 
analysis. 


Supporting Information 


Table $1 Quality control parameters and the different datasets 
used in analyses 

Found at: dot:10.1371/journal.pone.0003519.s001 
XLS) 


(0.04 MB 


Table $2 The number of SNPs per population that have a 
frequency deviation from CEU to the same or opposite direction 
as Asia (CHB+JPT). The markers with identical frequencies in 
either CEU and the studied population or CEU and CHB+JPT 
have been excluded. The proportions differ significantly (p< 10-5). 
Found at: doi:10.1371/journal.pone.0003519.s002 (0.02 MB 
XLS) 


Table $3. Summary table of population statistics 


Found at: dot:10.1371/journal.pone.0003519.s003 (0.02 MB 
XLS) 
Figure $1 Animation of the three-dimensional multidimensional 


scaling plot of the identity by state matrix of the Europeans (a), and 
the Finnish samples (b), with the legend in (c). The file can be 
opened e.g. in most internet browsers. Abbreviations as in Figure 1. 
Found at: do1:10.1371/journal.pone.0003519.s004 (20.95 MB 
GIF) 


Figure $2. Admixture proportions of the European individuals in 
a Structure analysis of K = 3 (a); and the probabilities of different 
numbers of clusters in the Structure analysis of the European 
dataset (b), and the Finnish dataset (c). 

Found at: doi:10.1371/journal.pone.0003519.s005 (0.54 MB TIF) 


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on 


&. PLoS ONE | www.plosone.org 


SNP Variation in North Europe 


Figure $3 Multidimensional scaling plots of the identity by state 
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Figure $4 Median IBS and overdispersion factor (lambda) of the 
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Found at: doi:10.1371/journal.pone.0003519.s008 (0.15 MB TIF) 


Acknowledgments 


This study makes use of data generated by the Wellcome Trust Case 
Control Consortium. A full list of the investigators who contributed to the 
generation of the data is available from www.wtccc.org.uk. Furthermore, 
we would like to express our gratitude to David Brodin, Huberta von Eller- 
Eberstein, Ulf Hannelius, Riitta Lehtinen, Timo Miettinen, Anu Puomila, 
Jouni K. Seppanen, and the personnel at Bioinformatics and Expression 
Analysis core facility at Karolinska Institutet for technical support. 


Author Contributions 


Conceived and designed the experiments: ES TL MLS JK PL. Performed 
the experiments: ES TL IF AF. Analyzed the data: ES TL. Contributed 
reagents/materials/analysis tools: PMA KDW AF PS SS. Wrote the paper: 
ES TL. 


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