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Genome-wide prediction of dominant and recessive neurodevelopmental disorder risk genes

Ryan S. Dhindsa, Blake Weido, View ORCID ProfileJustin S. Dhindsa, View ORCID ProfileArya J. Shetty, Chloe Sands, Slavé Petrovski, View ORCID ProfileDimitrios Vitsios, View ORCID ProfileAnthony W. Zoghbi
doi: https://doi.org/10.1101/2022.11.21.517436
Ryan S. Dhindsa
1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030
2Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030
3Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA
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  • For correspondence: ryan.dhindsa@bcm.edu anthony.zoghbi@bcm.edu
Blake Weido
1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030
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Justin S. Dhindsa
2Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030
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Arya J. Shetty
2Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030
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Chloe Sands
2Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030
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Slavé Petrovski
4Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
5Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
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Dimitrios Vitsios
4Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
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Anthony W. Zoghbi
1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030
3Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA
6Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030
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  • For correspondence: ryan.dhindsa@bcm.edu anthony.zoghbi@bcm.edu
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Abstract

Despite great progress in the identification of neurodevelopmental disorder (NDD) risk genes, there are thousands that remain to be discovered. Computational tools that provide accurate gene-level predictions of NDD risk can significantly reduce the costs and time needed to prioritize and discover novel NDD risk genes. Here, we first demonstrate that machine learning models trained solely on single-cell RNA-sequencing data from the developing human cortex can robustly predict genes implicated in autism spectrum disorder (ASD), developmental and epileptic encephalopathy (DEE), and developmental delay (DD). Strikingly, we find differences in gene expression patterns of genes with monoallelic and biallelic inheritance patterns. We then integrate these expression data with 300 orthogonal features in a semi-supervised machine learning framework (mantis-ml) to train inheritance-specific models for ASD, DEE, and DD. The models have high predictive power (AUCs: 0.84 to 0.95) and top-ranked genes were up to two-fold (monoallelic models) and six-fold (biallelic models) more enriched for high-confidence NDD risk genes than genic intolerance metrics. Across all models, genes in the top decile of predicted risk genes were 60 to 130 times more likely to have publications strongly linking them to the phenotype of interest in PubMed compared to the bottom decile. Collectively, this work provides highly robust novel NDD risk gene predictions that can complement large-scale gene discovery efforts and underscores the importance of incorporating inheritance into gene risk prediction tools (https://nddgenes.com).

Competing Interest Statement

R.S.D., S.P., D.V., and A.W.Z. are current employees and/or stockholders of AstraZeneca. B.W., J.S.D., A.J.S., and C.S. declare no competing interests.

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  • https://nddgenes.com

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 21, 2022.
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Genome-wide prediction of dominant and recessive neurodevelopmental disorder risk genes
Ryan S. Dhindsa, Blake Weido, Justin S. Dhindsa, Arya J. Shetty, Chloe Sands, Slavé Petrovski, Dimitrios Vitsios, Anthony W. Zoghbi
bioRxiv 2022.11.21.517436; doi: https://doi.org/10.1101/2022.11.21.517436
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Genome-wide prediction of dominant and recessive neurodevelopmental disorder risk genes
Ryan S. Dhindsa, Blake Weido, Justin S. Dhindsa, Arya J. Shetty, Chloe Sands, Slavé Petrovski, Dimitrios Vitsios, Anthony W. Zoghbi
bioRxiv 2022.11.21.517436; doi: https://doi.org/10.1101/2022.11.21.517436

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