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Auteur Marianne B. M. VAN DEN BREE |
Documents disponibles écrits par cet auteur (2)
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Copy number variants (CNVs): a powerful tool for iPSC-based modelling of ASD / Danijela DRAKULIC in Molecular Autism, 11 (2020)
[article]
Titre : Copy number variants (CNVs): a powerful tool for iPSC-based modelling of ASD Type de document : Texte imprimé et/ou numérique Auteurs : Danijela DRAKULIC, Auteur ; Srdjan DJUROVIC, Auteur ; Yasir Ahmed SYED, Auteur ; Sebastiano TRATTARO, Auteur ; Nicolò CAPORALE, Auteur ; Anna FALK, Auteur ; Rivka OFIR, Auteur ; Vivi M. HEINE, Auteur ; Samuel J. R. A. CHAWNER, Auteur ; Antonio RODRIGUEZ-MORENO, Auteur ; Marianne B. M. VAN DEN BREE, Auteur ; Giuseppe TESTA, Auteur ; Spyros PETRAKIS, Auteur ; Adrian J. HARWOOD, Auteur Article en page(s) : 42 p. Langues : Anglais (eng) Mots-clés : Autism spectrum disorders (ASD) Copy number variants (CNVs) Human iPSCs Neurodevelopmental disorders (NDD) Index. décimale : PER Périodiques Résumé : Patients diagnosed with chromosome microdeletions or duplications, known as copy number variants (CNVs), present a unique opportunity to investigate the relationship between patient genotype and cell phenotype. CNVs have high genetic penetrance and give a good correlation between gene locus and patient clinical phenotype. This is especially effective for the study of patients with neurodevelopmental disorders (NDD), including those falling within the autism spectrum disorders (ASD). A key question is whether this correlation between genetics and clinical presentation at the level of the patient can be translated to the cell phenotypes arising from the neurodevelopment of patient induced pluripotent stem cells (iPSCs).Here, we examine how iPSCs derived from ASD patients with an associated CNV inform our understanding of the genetic and biological mechanisms underlying the aetiology of ASD. We consider selection of genetically characterised patient iPSCs; use of appropriate control lines; aspects of human neurocellular biology that can capture in vitro the patient clinical phenotype; and current limitations of patient iPSC-based studies. Finally, we consider how future research may be enhanced to maximise the utility of CNV patients for research of pathological mechanisms or therapeutic targets. En ligne : http://dx.doi.org/10.1186/s13229-020-00343-4 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=427
in Molecular Autism > 11 (2020) . - 42 p.[article] Copy number variants (CNVs): a powerful tool for iPSC-based modelling of ASD [Texte imprimé et/ou numérique] / Danijela DRAKULIC, Auteur ; Srdjan DJUROVIC, Auteur ; Yasir Ahmed SYED, Auteur ; Sebastiano TRATTARO, Auteur ; Nicolò CAPORALE, Auteur ; Anna FALK, Auteur ; Rivka OFIR, Auteur ; Vivi M. HEINE, Auteur ; Samuel J. R. A. CHAWNER, Auteur ; Antonio RODRIGUEZ-MORENO, Auteur ; Marianne B. M. VAN DEN BREE, Auteur ; Giuseppe TESTA, Auteur ; Spyros PETRAKIS, Auteur ; Adrian J. HARWOOD, Auteur . - 42 p.
Langues : Anglais (eng)
in Molecular Autism > 11 (2020) . - 42 p.
Mots-clés : Autism spectrum disorders (ASD) Copy number variants (CNVs) Human iPSCs Neurodevelopmental disorders (NDD) Index. décimale : PER Périodiques Résumé : Patients diagnosed with chromosome microdeletions or duplications, known as copy number variants (CNVs), present a unique opportunity to investigate the relationship between patient genotype and cell phenotype. CNVs have high genetic penetrance and give a good correlation between gene locus and patient clinical phenotype. This is especially effective for the study of patients with neurodevelopmental disorders (NDD), including those falling within the autism spectrum disorders (ASD). A key question is whether this correlation between genetics and clinical presentation at the level of the patient can be translated to the cell phenotypes arising from the neurodevelopment of patient induced pluripotent stem cells (iPSCs).Here, we examine how iPSCs derived from ASD patients with an associated CNV inform our understanding of the genetic and biological mechanisms underlying the aetiology of ASD. We consider selection of genetically characterised patient iPSCs; use of appropriate control lines; aspects of human neurocellular biology that can capture in vitro the patient clinical phenotype; and current limitations of patient iPSC-based studies. Finally, we consider how future research may be enhanced to maximise the utility of CNV patients for research of pathological mechanisms or therapeutic targets. En ligne : http://dx.doi.org/10.1186/s13229-020-00343-4 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=427 Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach / Adam CUNNINGHAM ; Sergio Marco SALAS ; Matthew BRACHER-SMITH ; Samuel CHAWNER ; Jan STOCHL ; Tamsin FORD ; F. Lucy RAYMOND ; Valentina ESCOTT-PRICE ; Marianne B. M. VAN DEN BREE in Molecular Autism, 14 (2023)
[article]
Titre : Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach Type de document : Texte imprimé et/ou numérique Auteurs : Adam CUNNINGHAM, Auteur ; Sergio Marco SALAS, Auteur ; Matthew BRACHER-SMITH, Auteur ; Samuel CHAWNER, Auteur ; Jan STOCHL, Auteur ; Tamsin FORD, Auteur ; F. Lucy RAYMOND, Auteur ; Valentina ESCOTT-PRICE, Auteur ; Marianne B. M. VAN DEN BREE, Auteur Article en page(s) : 19 p. Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : BACKGROUND: Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. METHOD: A total of 493 individuals were included: 389 with a ND-GC, mean age=9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age=10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. RESULTS: All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. LIMITATIONS: This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. CONCLUSIONS: In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment. En ligne : http://dx.doi.org/10.1186/s13229-023-00549-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513
in Molecular Autism > 14 (2023) . - 19 p.[article] Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach [Texte imprimé et/ou numérique] / Adam CUNNINGHAM, Auteur ; Sergio Marco SALAS, Auteur ; Matthew BRACHER-SMITH, Auteur ; Samuel CHAWNER, Auteur ; Jan STOCHL, Auteur ; Tamsin FORD, Auteur ; F. Lucy RAYMOND, Auteur ; Valentina ESCOTT-PRICE, Auteur ; Marianne B. M. VAN DEN BREE, Auteur . - 19 p.
Langues : Anglais (eng)
in Molecular Autism > 14 (2023) . - 19 p.
Index. décimale : PER Périodiques Résumé : BACKGROUND: Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. METHOD: A total of 493 individuals were included: 389 with a ND-GC, mean age=9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age=10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. RESULTS: All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. LIMITATIONS: This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. CONCLUSIONS: In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment. En ligne : http://dx.doi.org/10.1186/s13229-023-00549-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513