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Project

Deciphering hidden inheritance patterns using advanced data mining techniques on high throughput genomic data.

Today, technologies exist to screen complete human genomes for genetic defects, hereby producing vast 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 but distributed public resources contain biological information on the observed variation, to aid in interpretation of the data. To study genetic variation under a "many genes, common pathways" paradigm, or under oligogenic inheritance, interpretation of individual results in a context of multiple patients is mandatory. To overcome genetic heterogeneity, this context must be lifted from gene level to higher biological orfunctional levels. Searching for common patterns of affected pathways over large patient cohorts might thus identify recurrently affected pathways with a critical role in the disease. Simultaneously considering multiple variants affecting such a pathway will thus help to explain both the observed phenotype and combined with pedigree information, the intrafamilial variability.Here, we will generate a performant and scalable framework of patient, variant, and annotation information. This framework is mandatory for applying state-of-the-art data mining methods to reveal hidden relationships between the variants in relation to patient phenotype, with the goal of gaining new insights in the molecular pathology of heritable diseases.
Date:1 Jan 2015 →  31 Dec 2017
Keywords:HERITABLE DISEASE, DATA MINING
Disciplines:Scientific computing, Bioinformatics and computational biology, Public health care, Public health services