Nearly all diseases have a strong genetic component. Over 10,000 genetic risk factors have been found, but it remains a mystery how these DNA variants ultimately cause disease. It is unclear which genes, pathways and cell-types they affect.
It has always been my passion to resolve this black box, and I have done this using a functional genomics approach. My group has developed many statistical methods that use multi-omics datasets to gain insight in the molecular consequences of genetic risk factors and I have applied this approach to many different diseases. Recently we have developed new analytical strategies, enabling us to identify the key driver genes on which these genetic risk factors ultimately converge. These genes are putative targets for pharmaceutical intervention.
Since very recently hundreds of genetic risk factors have been found for breast cancer, prostate cancer and colorectal cancer, these functional genomics approaches can now be used to study heritable cancer as well. For these variants I will use eQTL and coexpression QTL mapping to identify their downstream consequences, and will use reconstructed gene networks to identify the key driver genes on which these cancer risk factors converge.