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

Hannelius U, Salmela E, Lappalainen T, Guillot G, Lindgren CM, von Döbeln U, Lahermo P, Kere J - BMC Genet. (2008)

Bottom Line: However, some studies have suggested that this number could be reduced if the individual spatial coordinates are taken into account in the analysis.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biosciences and Nutrition, Karolinska Institutet, 14157 Huddinge, Sweden. ulf.hannelius@ki.se

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.

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Geographic location of the Swedish and Finnish counties. The division of counties into five regions is denoted by their colour. Sample sizes are given in Additional file 1.
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Figure 1: Geographic location of the Swedish and Finnish counties. The division of counties into five regions is denoted by their colour. Sample sizes are given in Additional file 1.

Mentions: A map of the locations of the Finnish and Swedish counties is presented in Figure 1 [for sample sizes see Additional file 1]. For county-level analyses, the Swedish counties of Gotland and Kalmar, as well as Kronoberg and Blekinge were pooled in order to reach adequate sample sizes, and 13 Finnish samples were excluded from the analyses due to lacking county-level information of origin. Since the cities of Malmö and Gothenburg harbour large immigrant populations (in 2003, 33% and 26% of inhabitants had a foreign background, respectively; Statistics Sweden, ), some analyses, as indicated in the text, were performed also on a data set where these cities were excluded, in order to investigate whether the large percentage of immigrants affected the results.


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

Hannelius U, Salmela E, Lappalainen T, Guillot G, Lindgren CM, von Döbeln U, Lahermo P, Kere J - BMC Genet. (2008)

Geographic location of the Swedish and Finnish counties. The division of counties into five regions is denoted by their colour. Sample sizes are given in Additional file 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2527025&req=5

Figure 1: Geographic location of the Swedish and Finnish counties. The division of counties into five regions is denoted by their colour. Sample sizes are given in Additional file 1.
Mentions: A map of the locations of the Finnish and Swedish counties is presented in Figure 1 [for sample sizes see Additional file 1]. For county-level analyses, the Swedish counties of Gotland and Kalmar, as well as Kronoberg and Blekinge were pooled in order to reach adequate sample sizes, and 13 Finnish samples were excluded from the analyses due to lacking county-level information of origin. Since the cities of Malmö and Gothenburg harbour large immigrant populations (in 2003, 33% and 26% of inhabitants had a foreign background, respectively; Statistics Sweden, ), some analyses, as indicated in the text, were performed also on a data set where these cities were excluded, in order to investigate whether the large percentage of immigrants affected the results.

Bottom Line: However, some studies have suggested that this number could be reduced if the individual spatial coordinates are taken into account in the analysis.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biosciences and Nutrition, Karolinska Institutet, 14157 Huddinge, Sweden. ulf.hannelius@ki.se

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.

Show MeSH
Related in: MedlinePlus