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  • Genome-wide Association Studies and candidate gene analyses

    We perform genome-wide association analyses for both, quantitative traits for different phenotypes and case-control designs for different disease entities. This comprises data management, quality control, state of the art data analysis, replication studies, meta-analyses and pipeline development. The studies are performed in close co-operations with our clinical partners.

  • Molecular Epidemiology

    We design, establish, conduct and analyse observational studies with molecular-genetic focus. Examples are PROGRESS, CAPSyS, LIFE-Adult and LIFE-Heart.

  • Integrative Genome Analyses

    We study the relations of different molecular-genetic markers (e.g. SNPs, expression profiles, metabolomics) and (disease) phenotypes. This comprises data management and mapping, high-throughput computations, management of high-dimensional analysis results and systems-biological modelling. As a new and hot topic, we study, develop and apply methods of causal inference regarding relationships of molecular-genetic layers.

  • Comparison and Development of Statistical Methods

    In order to ensure state of the art analyses methods, we are routinely concerned with comparisons and evaluations of newly developed methods or software tools. We also aim to develop own standards regarding different types of analysis.

  • Systems-medicine

    In order to improve our understanding of both, physiological processes and pathomechanisms and in order to enable predictions, we aim to construct statistical or continuous mathematical models. Examples are hierarchical modelling of pathways or disease and therapy models based on ordinary differential equations. Major fields of applications are atherosclerosis, respiratory tract infections and haematological cancers. 

  • Epidemiologic modelling

    We develop mechanistic models of the spread of respiratory tract infections considering age-dependent contact patterns, non-pharmaceutical interventions, vaccination and variant replacement effects. We apply Bayesian methods to parametrize models based on observational data also considering their stochastic and systematic biases. Major fields of applications are Pneumococcal lung disease and COVID-19.