Reference : Discovery and pathogenicity assessment of neuropathology-associated gene variants
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Life sciences : Genetics & genetic processes
Human health sciences : Neurology
Systems Biomedicine
Discovery and pathogenicity assessment of neuropathology-associated gene variants
Neupert, Lisa-Marie []
May, Patrick mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Kobow, Katja []
Nothnagel, Michael []
Nürnberg, Peter []
Freiman, Thomas []
Harter, Patrick []
Klein, Karl-Martin []
Weber, Yvonne []
Blümcke, Ingmar []
Lal, Dennis []
Blackwell Science
Yes (verified by ORBilu)
32nd International Epilepsy Congress Barcelona
02-09-2017 5o 06-09-2017
[en] Neuropathology ; Epilepsy ; Variants
[en] Germline and brain-specific somatic variants have been reported as an underlying cause in patients with epilepsy-associated neuropathologies, including focal cortical dysplasias (FCDs) and long-term epilepsy associated tumors (LEAT). However, evaluation of identified neuropathology associated
variants in genetic screens is complex since not all observed variants contribute to the etiology of neuropathologies not even in genuinely disease-associated genes. Here, we critically reevaluated the pathogenicity of 12 previously published disease-related genes and of 79 neuropathology-associated missense variants listed in the PubMed and ClinVar databases. We (1) assessed the evolutionary gene constraint using the pLI and the missense z score, (2) used the latest American College of Medical Genetics and Genomics (ACMG) guidelines, and (3) performed bioinformatic variant pathogenicity prediction analyses using PolyPhen-2, CADD and GERP. Constraint analysis classified only seven out of 12 genes to be likely disease-associated. Furthermore, 78 (89%) of 88 neuropathology-associated missense variants were classified as being of unknown significance
(VUS) and only 10 (11%) as being likely pathogenic (LPII). Pathogenicity prediction yielded a discrimination between LPII variants and a discrimination for VUS compared with rare variant scores from individuals present in the Genome Aggregation Database (gnomAD). In summary, our results demonstrate that interpretation of variants associated with neuropathologies is complex while the application of current ACMG guidelines including bioinformatic pathogenicity prediction can help improving variant evaluation. Furthermore, we will augment this set of literature-identified variants at the conference by results from our variant screen using self-generated deep sequencing data in >150 candidate genes in >50 patients not yet analyzed.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)

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