Genetic Geostatistical Framework for Spatial Analysis of Fine-Scale Genetic Heterogeneity in Modern Populations: Results from the KORA Study.

A N Diaz-Lacava, M Walier, D Holler, M Steffens, C Gieger, C Furlanello, C Lamina, H E Wichmann, T Becker

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Article in International journal of genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

A N Diaz-LacavaInstitute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, 53127 Bonn, Germany ; Cologne Center for Genomics, University of Cologne, 50931 Cologne, Germany ; DNA Analysis Unit, Official College of Pharmacists and Biochemists, C1184ABA Buenos Aires, Argentina.

M WalierInstitute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, 53127 Bonn, Germany.

D HollerInstitute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, 53127 Bonn, Germany.

M SteffensInstitute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, 53127 Bonn, Germany.

C GiegerResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany ; Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany.

C FurlanelloFBK, 38122 Trento, Italy.

C LaminaDivision of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, 6020 Innsbruck, Austria.

H E WichmannInstitute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-University, 81377 Munich, Germany ; Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany ; Institute of Medical Statistics and Epidemiology, Technical University Munich, 81675 Munich, Germany.

T BeckerInstitute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, 53127 Bonn, Germany ; German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aiming to investigate fine-scale patterns of genetic heterogeneity in modern humans from a geographic perspective, a genetic geostatistical approach framed within a geographic information system is presented. A sample collected for prospective studies in a small area of southern Germany was analyzed. None indication of genetic heterogeneity was detected in previous analysis. Socio-demographic and genotypic data of German citizens were analyzed (212 SNPs; n = 728). Genetic heterogeneity was evaluated with observed heterozygosity (H O ). Best-fitting spatial autoregressive models were identified, using socio-demographic variables as covariates. Spatial analysis included surface interpolation and geostatistics of observed and predicted patterns. Prediction accuracy was quantified. Spatial autocorrelation was detected for both socio-demographic and genetic variables. Augsburg City and eastern suburban areas showed higher H O values. The selected model gave best predictions in suburban areas. Fine-scale patterns of genetic heterogeneity were observed. In accordance to literature, more urbanized areas showed higher levels of admixture. This approach showed efficacy for detecting and analyzing subtle patterns of genetic heterogeneity within small areas. It is scalable in number of loci, even up to whole-genome analysis. It may be suggested that this approach may be applicable to investigate the underlying genetic history that is, at least partially, embedded in geographic data.

Identifiers

PMID26258132

DOI10.1155/2015/693193

PMCPMC4519554

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