Research article
Population-genetic comparison of the Sorbian isolate population in Germany with the German KORA population using genome-wide SNP arrays
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* Corresponding author: Markus Scholz markus.scholz@imise.uni-leipzig.de
1 Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany
2 LIFE Center (Leipzig Interdisciplinary Research Cluster of Genetic Factors, Phenotypes and Environment), University of Leipzig, Philipp-Rosenthal Strasse 27, 04103 Leipzig, Germany
3 Department of Medicine, University of Leipzig, Liebigstrasse 18, 04103 Leipzig, Germany
4 IFB Adiposity Diseases, University of Leipzig, Stephanstrasse 9c, 04103 Leipzig, Germany
5 Interdisciplinary Center for Clinical Research, University of Leipzig, Liebigstrasse 21, 04103 Leipzig, Germany
6 Dept Eco & Evo Biol, Interdepartmental Program in Bioinformatics, University of California, 621 Charles E. Young Dr South, Box 951606, Los Angeles, Los Angeles, CA 90095-1606 USA
7 Center for Society and Genetics. University of California, 1323 Rolfe Hall, Box 957221, Los Angeles, Los Angeles, CA 90095-7221, USA
8 Dept of History, University of California, 6265 Bunche Hall, Box 951473, Los Angeles, Los Angeles, CA 90095-1473, USA
9 Helmholtz Centre Munich, German Research Center for Environmental Health, Institute of Epidemiology, Ingolstaedter Landstraße 1, 85764 Neuherberg, Germany
10 Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany
11 Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-University, Marchioninistraße 15, 81377 Munich, Germany
12 Klinikum Grosshadern, Ludwig Maximilians University, Marchioninistraße 15, 81377 Munich, Germany
BMC Genetics 2011, 12:67 doi:10.1186/1471-2156-12-67
Published: 28 July 2011Additional files
Additional file 1:
Workflow of data pre-processing. The workflow of data pre-processing is presented. We start with the autosomal SNP data of four different populations (KORA, Sorbs, HapMap CEU, HapMap TSI). Numbers of remaining markers at each step of pre-processing are presented in bold.
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Additional file 2:
Derivation of the formula for 
.
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Additional file 3:
Comparisons of power for Sorbs977 for minimal and maximal heritability of phenotypes. Simulation results of the power for minimal (
) and maximal (100%) heritability. For the minimal heritability, we present the results
of our analytical formula. The values presented in Tables 3 and 4 are displayed in bold.
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Additional file 4:
Variance inflation under relatedness. Comparison of the theoretical variance of the β1-estimator assuming uncorrelated phenotypes (analytical formula 
) with the averaged variances over all SNPs of chromosome 22 under a heritability
of 100% assuming correlated phenotypes. The standard error of this estimate and the
inflation factor are also provided. Sorbs977 are presented in bold due to high inflation of variances of β1-estimates.
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Additional file 5:
Simulation results for power under assumption of correlated phenotypes. Heritability was modified between and 100%. Explained variances of the SNP are 2% or 5% with corresponding p-value
thresholds of 10-5 and 10-7, respectively. All simulations were performed for KORA977, Sorbs977, KORA532, and Sorbs532. Power distribution is derived using the results of all SNPs of Chromosome 22.
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Additional file 6:
Additional inbreeding and co-ancestry coefficients. Estimates and standard errors (SE) of inbreeding coefficients FIS and co-ancestry coefficients FST for KORA and Sorbs and different levels of relatedness: without filtering for relatedness (KORA977, Sorbs977), filtering for relatedness > 0.2 (KORA532, Sorbs532), filtering for relatedness > 0.1 (KORA414, Sorbs414). Indices refer to resulting numbers of cases.
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