Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping
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 diso...
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| Formato: | Online |
| Lenguaje: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Acceso en línea: | 42594 |
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| _version_ | 1869526856655437824 |
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| author | McNeill, Alisdair |
| author_browse | McNeill, Alisdair |
| author_facet | McNeill, Alisdair |
| author_sort | McNeill, Alisdair |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-45005 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-450052024-03-31T13:09:49Z Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping McNeill, Alisdair R5-920 RC346-429 polymicrogyria n/a neurodegenerative disease next generation sequencing (NGS) inborn error of metabolism genetic biomarker deep learning TUBA1A Alzheimer’s disease (AD) ataxia risk prediction p.(Arg2His) movement science tubulin R2H diagnosis machine learning metal storage disorders amyotrophic lateral sclerosis (ALS) glucocerebrosidase Parkinsonism cerebellar hypoplasia Gaucher disease disease phenotyping tubulinopathy Parkinson’s disease (PD) dementia Parkinson’s disease thema EDItEUR::M Medicine and Nursing 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. 2021-02-11T11:21:40Z 2021-02-11T11:21:40Z 2019-12-09 11:49:16 2019 book 42594 9783039216109 9783039216116 https://directory.doabooks.org/handle/20.500.12854/45005 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/1735 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03921-611-6 10.3390/books978-3-03921-611-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039216109 9783039216116 94 open access |
| spellingShingle | R5-920 RC346-429 polymicrogyria n/a neurodegenerative disease next generation sequencing (NGS) inborn error of metabolism genetic biomarker deep learning TUBA1A Alzheimer’s disease (AD) ataxia risk prediction p.(Arg2His) movement science tubulin R2H diagnosis machine learning metal storage disorders amyotrophic lateral sclerosis (ALS) glucocerebrosidase Parkinsonism cerebellar hypoplasia Gaucher disease disease phenotyping tubulinopathy Parkinson’s disease (PD) dementia Parkinson’s disease thema EDItEUR::M Medicine and Nursing McNeill, Alisdair Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping |
| title | Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping |
| title_full | Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping |
| title_fullStr | Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping |
| title_full_unstemmed | Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping |
| title_short | Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping |
| title_sort | diagnosis of neurogenetic disorders contribution of next generation sequencing and deep phenotyping |
| topic | R5-920 RC346-429 polymicrogyria n/a neurodegenerative disease next generation sequencing (NGS) inborn error of metabolism genetic biomarker deep learning TUBA1A Alzheimer’s disease (AD) ataxia risk prediction p.(Arg2His) movement science tubulin R2H diagnosis machine learning metal storage disorders amyotrophic lateral sclerosis (ALS) glucocerebrosidase Parkinsonism cerebellar hypoplasia Gaucher disease disease phenotyping tubulinopathy Parkinson’s disease (PD) dementia Parkinson’s disease thema EDItEUR::M Medicine and Nursing |
| topic_facet | R5-920 RC346-429 polymicrogyria n/a neurodegenerative disease next generation sequencing (NGS) inborn error of metabolism genetic biomarker deep learning TUBA1A Alzheimer’s disease (AD) ataxia risk prediction p.(Arg2His) movement science tubulin R2H diagnosis machine learning metal storage disorders amyotrophic lateral sclerosis (ALS) glucocerebrosidase Parkinsonism cerebellar hypoplasia Gaucher disease disease phenotyping tubulinopathy Parkinson’s disease (PD) dementia Parkinson’s disease thema EDItEUR::M Medicine and Nursing |
| url | 42594 |
| work_keys_str_mv | AT mcneillalisdair diagnosisofneurogeneticdisorderscontributionofnextgenerationsequencinganddeepphenotyping |