Identifying dosage sensitive genes causing mental retardation

General background
Advancements in genomic microarray technologies have recently demonstrated that copy number variation (CNV) contributes significantly to human genomic variation. Due to this variation, any two randomly chosen genomes differ by millions of bases potentially containing thousands of protein-coding genes and other functionally important sequences. Importantly, many of these functional elements are dosage sensitive: If these elements are present in genomic regions showing affected by a CNV this can have severe phenotypic consequences. Our research has shown that CNVs often cause a significant proportion of mental retardation (MR) and therefore we have implemented microarray-based CNV analysis as our primary diagnostic screening tool in this disorder.

The goal of this project is to identify the dosage sensitive genes in overlapping MR associated CNVs regions causing MR. For this we will perform in depth bioinformatic analyses of CNV data from MR patients as well as CNV data from control individuals.

We will apply these approaches in a two-step approach:
  1. We will develop probabilistic models that predict the phenotypic effects of CNVs The best model will be developed into a CNV classifier that will be implemented in routine MR diagnostics and, subsequently, will be applied to other diseases as well.
  2. We will develop novel approaches to identify dosage sensitive genes within the MR-causing CNVs. These genes will be screened for causative mutations in large patient cohorts using novel high-throughput resequencing procedures.
This project will take genomic copy number studies from the collection phase to the functional phase and allow us to distinguish CNVs with a dosage-dependent phenotypic effect from those without a phenotypic effect. This novel approach will be of major importance for disease-gene identification studies in MR. Moreover, this work will generate an invaluable tool for the clinical diagnosis of patients with MR.

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