Open Access Highly Accessed Research article

Population substructure in Finland and Sweden revealed by the use of spatial coordinates and a small number of unlinked autosomal SNPs

Ulf Hannelius1*, Elina Salmela23, Tuuli Lappalainen3, Gilles Guillot4, Cecilia M Lindgren5, Ulrika von Döbeln6, Päivi Lahermo3 and Juha Kere127

Author Affiliations

1 Department of Biosciences and Nutrition, Karolinska Institutet, 14157 Huddinge, Sweden

2 Department of Medical Genetics, University of Helsinki, Helsinki, Finland

3 Finnish Genome Center, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

4 Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, Norway

5 Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, UK

6 Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden

7 Clinical Research Centre, Karolinska University Hospital, Stockholm, Sweden

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BMC Genetics 2008, 9:54  doi:10.1186/1471-2156-9-54

Published: 19 August 2008

Abstract

Background

Despite several thousands of years of close contacts, there are genetic differences between the neighbouring countries of Finland and Sweden. Within Finland, signs of an east-west duality have been observed, whereas the population structure within Sweden has been suggested to be more subtle. With a fine-scale substructure like this, inferring the cluster membership of individuals requires a large number of markers. However, some studies have suggested that this number could be reduced if the individual spatial coordinates are taken into account in the analysis.

Results

We genotyped 34 unlinked autosomal single nucleotide polymorphisms (SNPs), originally designed for zygosity testing, from 2044 samples from Sweden and 657 samples from Finland, and 30 short tandem repeats (STRs) from 465 Finnish samples. We saw significant population structure within Finland but not between the countries or within Sweden, and isolation by distance within Finland and between the countries. In Sweden, we found a deficit of heterozygotes that we could explain by simulation studies to be due to both a small non-random genotyping error and hidden substructure caused by immigration. Geneland, a model-based Bayesian clustering algorithm, clustered the individuals into groups that corresponded to Sweden and Eastern and Western Finland when spatial coordinates were used, whereas in the absence of spatial information, only one cluster was inferred.

Conclusion

We show that the power to cluster individuals based on their genetic similarity is increased when including information about the spatial coordinates. We also demonstrate the importance of estimating the size and effect of genotyping error in population genetics in order to strengthen the validity of the results.