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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Insights from Spatial Measures of Intolerance to Identifying Pathogenic Variants in Developmental and Epileptic
Michael Silk1,2,3, Alex de Sá1,2,3,4, Moshe Olshansky1,2,3
1Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia.
Abstract:
Developmental and epileptic encephalopathies (DEEs) are a group of epilepsies with early onset and severe symptoms that sometimes lead to death. Although previous work successfully discovered several genes implicated in disease outcomes, it remains challenging to identify causative mutations within these genes from the background variation present in all individuals due to disease heterogeneity. Nevertheless, our ability to detect possible pathogenic variants has continued to improve as in silico predictors of deleteriousness have advanced. We investigate their use in prioritising likely pathogenic variants in epileptic encephalopathy patients' whole exome sequences. We showed that the inclusion of structure-based predictors of intolerance improved upon previous attempts to demonstrate enrichment within epilepsy genes.
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