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Genetic Epidemiology Unit

  • In Genetic Epidemiology, we investigate the influence of genetic variants and their interaction with lifestyle and environmental factors for diseases or disease-relevant parameter in humans.
  • At the Department of Genetic Epdidemiology at the University of Regensburg, we conduct and analyze molecular epidemiological studies. We develop methododology and software for the analyses of high-dimensional data.
  • We work in large international consortia for genome-wide association studies (GWAS) for various diseases. GWAS are one of the most successful approaches to identify the genetics of complex diseases. The genes in GWAS association signals for diseases and disease-relevant parameters are more than twice as likely successful in drug development pipelines. Genes with evidence for human genetic association are thus considered promising drug targets for the respective diseases. GWAS analyses imply large, high-dimensional data ("big data"). Big data requires specific biostatistical and bioinformatic methodology for integrating various data sources to help unravel the physiological and clinical relevance.
  • Our genetic epidemiological research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF), the Bavarian State Ministry for Science and Art (Bay. Staatsministerium für Wissenschaft und Kunst) and the National Institutes of Health (NIH).

Selected research projects

  • Genetics of kidney function decline (SFB-1350/C6)
  • Genetics of AMD (International AMD Genomics Consortium, NIH-RES511967 and NIH-RES516564)
  • the AugUR study (DFG-HE 3690/7-1, DFG-BR 6028/2-1, BMBF 01ER1206, BMBF 01ER1507)
  • Meta- versus mega-approaches for genotype-imputation (DFG-HE 3690/5-1)

Selected publications

  • Gorski M, Rasheed H, Teumer A, Thomas LF, … , Pattaro C, Köttgen A, Kronenberg F, Heid IM. Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies. Kidney international 2022 link.
  • Stanzick KJ, Li Y, Schlosser P, Gorski M, ..., Pattaro C, Köttgen A, Stark KJ, Heid IM,Winkler TW. Discovery and prioritization of variants and genes for kidney function in 1.2 million individuals. Nature communications 2021. 1(12). 4350. link
  • Fritsche LG, Igl W, ..., Abecasis GR,Heid IM. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nature genetics 2016. 2(48). 134–143. link
  • Guenther F, Brandl C, Winkler TW, Wanner V, Stark K, Kuechenhoff H, Heid IM. Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degeneration. Genetic epidemiology 2020. 7(44). 759–777. link
  • Winkler TW, Günther F, Höllerer S, Zimmermann M, Loos RJ, Kutalik Z, Heid IM. A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications. Nature communications 2018. 1(9). 1946. link