Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation.
The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 53; no. 3; pp. 963 - 977 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162233048&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233048 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Mar2023 vid: 53 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233048 156965842 162233048 162233048 10.1007/s10803-022-05586-z 162233048 ppf: 963 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation. aug: au: Chow, Julie C. Hormozdiari, Fereydoun affil: UC Davis Genome Center, University of California, 95616, Davis, CA, USA sug: subj: Mental Disorders Diagnosed in Childhood Mutation Early Diagnosis Genes Human Outcomes (Health Care) Phenotype Prediction Models Funding Source ab: The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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