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Genetic Epidemiology aims at identifying and quantifying genetic risk factors for complex diseases as well as their interaction with non-genetic factors. Genome association studies (GWAS) are one of the most successful approach to unravel the genetic regions that are associated with various diseases and disease-relevant parameters. This research requires expertise not only in statistical methodology and knowledge of the human genetic architecture, but also the means to analyse high-dimensional "-omics" data via bioinformatic approaches. The quantity and depth of the data is rapidly increasing by technological advancements. Thus, the respective methodology requires a constant update and further development. A particular challenge is the understanding of the biological pathways and specific genes, by which the genetic variants associated with the disease exert their influence on the disease occurrence or progression. This requires statistical modelling, bioinformatic methods, gene expression data analyses, and the cooperation with research partners working on functional studies. Beyond the identification of the genetic make-up of diseases, Genetic Epidemiology also addresses questions of quantifying genetic risk and disease risk stratification that are relevant for prevention, diagnosis, and therapy.

Our main research focus includes:

  • genome-wide association studies (GWAS) for various diseases and disease-relevant parameters
  • bioinformatic pipelines for managing and analyzing high-dimensional data
  • regression models, methodology of genetic statistics, and meta-analyses
  • sex-specific genetic effects and other gene-by-environment interaction (GxE)

Our research platforms include

  • Genetics for kidney function decline (SFB-1350/1 C6)
  • Genetics of AMD (International AMD Genomics Consortium, NIH-RES511967 and NIH-RES516564)
  • GPS - an approach for GenPrioritiSation for genes in GWAS-loci by fine-mapping and functional annotation
  • GWAMA Center - Regensburg Analysis Center for Meta-Analyses of genome-wide association studies for research partners and international consortia (e.g. for lipids, anthropometric parameters, kidney function, age-related macular degeneration)
  • Regenburg GEM Platform - development of genetic-epidemiological methods (GEM) and implementation into software (GWAS data quality control, interaction analyses, stratified approaches, Imputation)
  • AugUR study - a study to evaluate genetic and non-genetic risk factors for diseases in the elderly (DFG-HE 3690/7-1, DFG-BR 6028/2-1, BMBF 01ER1206, BMBF 01ER1507)

These projects and platforms are funded by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF), the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), and the National Institutes of Health (NIH)

We foster numerous collaborations with colleagues from Epidemiology, Medical Sociology, Human Genetics, and clinical partners (e.g. Nephrology, Cardiology, Ophthalmology, Virology).