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Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach

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jats:titleAbstract</jats:title>jats:sec jats:titleBackground</jats:title> jats:pGenomic 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.</jats:p> </jats:sec>jats:sec jats:titleMethod</jats:title> jats:pA 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.</jats:p> </jats:sec>jats:sec jats:titleResults</jats:title> jats:pAll 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.</jats:p> </jats:sec>jats:sec jats:titleLimitations</jats:title> jats:pThis 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.</jats:p> </jats:sec>jats:sec jats:titleConclusions</jats:title> jats:pIn 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.</jats:p> </jats:sec>


Acknowledgements: We are extremely grateful to all the families that participated in this study as well as to support charities Max Appeal, The 22Crew and Unique for their help and support. We thank all members of the IMAGINE-ID consortium for their contributions.

Funder: National Institute for Health and Care Research; doi:


5202 Biological Psychology, 52 Psychology, Genetics, Brain Disorders, Intellectual and Developmental Disabilities (IDD), Prevention, Autism, Behavioral and Social Science, Mental Health, Mental health, 3 Good Health and Well Being

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Molecular Autism

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Springer Science and Business Media LLC
Baily Thomas Charitable Fund (TRUST/VC/AC/SG/5196-8188)
Medical Research Foundation (MRF-058-0015-F-CHAW)
Medical Research Council (MR/L011166/1)
National Institute of Mental Health (U01 MH119738-01)