Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability

Introduction Genomic conditions can be associated with developmental delay, intellectual disability and physical and mental health symptoms, but are individually rare and variable, which limits the use of standard clinical guidelines. A simple screening tool to identify young people with genetic conditions associated with neurodevelopmental disorders (ND-GC) who could benefit from further support would be of considerable value. We used machine learn approaches to address this question. Methods A total of 489 individuals were included: 376 with a ND-GC, mean age=9.33, 63% male) and 113 unaffected siblings; mean age=10.35, 50% male). Primary carers completed detailed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health conditions. Machine learning techniques (elastic net regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status using a limited set of variables. Exploratory Graph Analysis was used to understand associations within the final variable set. Results We identified a set of 30 variables best discriminating between ND-GC carriers and control individuals, which formed 4 dimensions: Anxiety, Motor Development, Insomnia and Depression. All methods showed high discrimination accuracy with Linear Support Vector machines outperforming other methods (AUROC between 0.959 and 0.971). 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 the structure within these measures. This work is a step toward developing of a screening instrument to select young people with ND-GCs who might benefit from further specialist assessment. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was funded by the Baily Thomas Charitable Fund (TRUST/VC/AC/SG/5196-8188; MvdB), and NIMH (U01 MH119738-01; MvdB), an NIHR clinical lectureship award (NAD), and SJRAC is funded by a Medical Research Foundation Fellowship (MRF-058-0015-F-CHAW). The IMAGINE-ID study (MvdB) was funded by Medical Research Council grants MR/L011166/1, MR/T033045/1 and MR/N022572/1. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The protocols used in this study were 107 approved by the NHS Southeast Wales Research Ethics Committee. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Code used in the project is provided in a GitHub repository https://github.com/NADonnelly/nd\_cnv\_ml and fitted models are presented as an interactive Shiny app: https://nadonnelly.shinyapps.io/cnv\_ml\_app/. Data from the IMAGINE study is available via the IMAGINE ID study website: https://imagine-id.org/healthcare-professionals/datasharing/. [https://github.com/NADonnelly/nd\_cnv\_ml][1] [https://nadonnelly.shinyapps.io/cnv\_ml\_app/][2] [1]: https://github.com/NADonnelly/nd_cnv_ml [2]: https://nadonnelly.shinyapps.io/cnv_ml_app/