ArticleAmerican journal of human genetics2025
Genome-wide prediction of dominant and recessive neurodevelopmental disorder-associated genes.
Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- De novo variants in NPTN cause a neurodevelopmental disorder with autism and neuroplastin-PMCA hypofunction.Genome medicine · 2026Article
- A massively parallel reporter assay ofbioRxiv : the preprint server for biology · 2026Article
- De novo variants in KDM2A cause a syndromic neurodevelopmental disorder.American journal of human genetics · 2026Article
- Article
- Using Whole Exome Sequencing to Identify Genetic Causes of Neurodevelopmental Disorders in a Cohort of 11 Patients: A Single Center Experience.International journal of molecular sciences · 2025Article
- Diverse ancestral representation improves genetic intolerance metrics.Nature communications · 2025Article
- Lethal phenotypes in Mendelian disorders.Genetics in medicine : official journal of the American College of Medical Genetics · 2024Article
- Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.Science advances · 2024Article
- Haploinsufficiency of ZFHX3, encoding a key player in neuronal development, causes syndromic intellectual disability.American journal of human genetics · 2024Article
- Lethal phenotypes in Mendelian disorders.medRxiv : the preprint server for health sciences · 2024Article
- Essential genes: a cross-species perspective.Mammalian genome : official journal of the International Mammalian Genome Society · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Despite great progress, thousands of neurodevelopmental disorder (NDD) risk genes remain to be discovered. We present a computational approach that accelerates NDD risk gene identification using machine learning. First, we demonstrate that models trained solely on single-cell RNA sequencing data can robustly predict genes implicated in autism spectrum disorder (ASD), developmental and epileptic encephalopathy (DEE), and developmental delay (DD). Notably, we find differences in gene expression patterns of genes with monoallelic and bi-allelic inheritance patterns in the developing human cortex. We then integrate expression data with 300 orthogonal features, including intolerance metrics, protein-protein interaction data, and others, in a semi-supervised machine learning framework (mantis-ml) to train inheritance-specific models for these disorders. The models have high predictive power (area under the receiver operator curves [AUCs]: 0.84-0.95), and the top-ranked genes were up to 2-fold (monoallelic models) and 6-fold (bi-allelic models) more enriched for high-confidence NDD risk genes compared to genic intolerance metrics alone. Additionally, genes ranking in the top decile were 45 to 180 times more likely to have literature support than those in the bottom decile. Collectively, this work provides robust NDD risk gene predictions that can complement large-scale gene discovery efforts and underscores the importance of considering inheritance in gene risk prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.