Speaker
Description
Most common diseases are polygenic; therefore, multiple even hundreds of genes out of the overall 23,000 can be responsible for a disease. The simultaneous appearance of diseases, comorbidities, like amongst neurological disorders are expected to have a common genetic background, which can be explored using network-based approaches.
Novel network-based workflows for genetic studies provide a powerful approach to investigate shared genetic factors, allowing the combination of various sources about genes and genetic networks.
We aimed to explore and identify the common genetic background of neurological comorbidities using different levels of results of the network-based analysis: at variant, gene and gene set based levels. To identify gene sets, we either used pathway databases or disease associated gene lists. To analyze the data alongside network-based techniques, we also used an approach based on regression. Polygenic risk score method uses the strength and probability of multiple gene-disease associations to create scores for each individual and given diseases.
Using these techniques, we were able to explore and create novel disease networks based on their inferred shared genetic background.
