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Genetic predisposition for blood cancer

Computational analysis of large genomic data sets and advanced experimental approaches

We study how genetic variation influences blood cell formation and blood cancer risk. We work in the interface between human genetics, bioinformatics, and hematology/immunology. Methodologically, we combine large-scale genomics and high-throughput phenotyping with advanced computational and experimental approaches.

In our genome-wide association studies (GWAS) we combine unique sample materials with sets of advanced phenotypes. We make use of high-resolution, high-throughput flow cytometry, advanced imaging techniques, gene expression data as well as crosslinking data from population based registries. Our data analysis work involves mathematical modeling, and high-performance computing and we develop computational methods for large scale genetic data. To characterize identified variants and genes functionally, we rely on a broad repertoire of functional genomics methods, including CRISPR-Cas9, synthetic oligonucleotide libraries, and massively parallel reporter assays (MPRA).


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