03413nam 2200661z- 450 991036775130332120231214133433.03-03921-611-2(CKB)4100000010106204(oapen)https://directory.doabooks.org/handle/20.500.12854/45005(EXLCZ)99410000001010620420202102d2019 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierDiagnosis of neurogenetic disorders contribution of next generation sequencing and deep phenotyping /special issue editor, Alisdair McNeillMDPI - Multidisciplinary Digital Publishing Institute20191 electronic resource (94 p.)3-03921-610-4 The contribution of genomic variants to the aetiopathogenesis of both paediatric and adult neurological disease is being increasingly recognized. The use of next-generation sequencing has led to the discovery of novel neurodevelopmental disorders, as exemplified by the deciphering developmental disorders (DDD) study, and provided insight into the aetiopathogenesis of common adult neurological diseases. Despite these advances, many challenges remain. Correctly classifying the pathogenicity of genomic variants from amongst the large number of variants identified by next-generation sequencing is recognized as perhaps the major challenge facing the field. Deep phenotyping (e.g., imaging, movement analysis) techniques can aid variant interpretation by correctly classifying individuals as affected or unaffected for segregation studies. The lack of information on the clinical phenotype of novel genetic subtypes of neurological disease creates limitations for genetic counselling. Both deep phenotyping and qualitative studies can capture the clinical and patient’s perspective on a disease and provide valuable information. This Special Issue aims to highlight how next-generation sequencing techniques have revolutionised our understanding of the aetiology of brain disease and describe the contribution of deep phenotyping studies to a variant interpretation and understanding of natural history.Diagnosis of Neurogenetic DisordersNeurogeneticspolymicrogyrianeurodegenerative diseasenext generation sequencing (NGS)inborn error of metabolismgenetic biomarkerdeep learningTUBA1AAlzheimer's disease (AD)ataxiarisk predictionp.(Arg2His)movement sciencetubulinR2Hdiagnosismachine learningmetal storage disordersamyotrophic lateral sclerosis (ALS)glucocerebrosidaseParkinsonismcerebellar hypoplasiaGaucher diseasedisease phenotypingtubulinopathyParkinson's disease (PD)dementiaParkinson's diseaseNeurogenetics.612.8McNeill AlisdairBOOK9910367751303321Diagnosis of neurogenetic disorders3401997UNINA