Genome Wide Association Study (GWAS) Pathways

The interaction of numerous genetic and environmental factors in biological processes runs in pathways in complex diseases. Therefore, pathway information should be used to improve the results of genome-wide association studies (GWAS). Gene set analysis methods focus on the identification of entire significant pathways rather than individual markers.

By focussing on the pathway, single gene variants with only small effects in the same pathway can strengthen themselves together.

We use kernel machine learning, a methodological combination of two statistical subfields based on Reproducing Kernel Hilbert Spaces: mixed models and geostatistics. This makes it possible, for example, to investigate gene-gene interactions within the network of a pathway. Further developments for longitudinal data as well as the integration of further -omics data and Baysian approaches are also of interest.

Last updated March 2023