Today, technologies exist able to screen complete human genomes for genetic defects, hereby producing massive amounts of data. These techniques include microarrays for the detection of duplicated or missing genomic material and next-generation sequencing for the detection of variation at the nucleotide level. In parallel, extensive public resources contain additional biological information on the observed variation to aid in interpretation of the data. We develop intuitive interfaces to efficiently identify and interpret the variation observed in the genome. All our programs, including CNV-Webstore for CNV- and VariantDB for sequence analysis are freely available for non-profit usage.
While some variants show full penetrance, others can be present in both seemingly healthy and severely impaired family members, indicating that disease modifying variants play a role in the clinical presentation. This led to the formulation of a ‘many genes, common pathways’ paradigm. To study genetic variation under this paradigm, novel models placing interpretation of individual results in a context of multiple patients are mandatory. Searching for common patterns over large patient cohorts might identify recurrently affected pathways with a critical role in the studied disease. Simultaneously considering multiple variants affecting such a pathway will thus help to explain both the observed phenotype and combined with pedigree information, the interfamilial variability. We investigate how we can apply state-of-the-art data mining methods to reveal hidden relationships between variants, with the goal of gaining new insights in the molecular pathology of cognitive disorders.