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. 2023 Jul 5;7(1):e001966. doi: 10.1136/bmjpo-2023-001966

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© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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The flow chart of neuroCNVscore development and evaluation in this study. In data sets, the sources of training set and test set are listed. The training set was derived from four neurodevelopmental disorders (NDDs) studies under the case–control design, while the validation set was from ClinVar and GnomAD. The numbers of raw and cleaned CNVs in the brackets are indicated. In neurofeatures, comprehensive neuro/brain-related features were gathered at gene, sequence and functional/genomic segments levels. In prediction and validation, biological validations were performed in two ways: (1) correlation analyses between phyloP46way and the pathogenic scores generated by the new model where phyloP46way was excluded from the feature matrix; (2) utilisation of an independent set of NDD-related gene lists including PSD genes to cognition, CHD8 targets and ASD risk genes. CNV, copy number variantl LP, likely pathogenic; PSD, postsynaptic density.