Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data / Olivia J. VEATCH in Journal of Neurodevelopmental Disorders, 14 (2022)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 14 (2022) Titre : Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data Type de document : texte imprimé Auteurs : Olivia J. VEATCH, Auteur ; Diego R. MAZZOTTI, Auteur ; Robert T. SCHULTZ, Auteur ; Ted ABEL, Auteur ; Jacob J. MICHAELSON, Auteur ; Edward S. BRODKIN, Auteur ; Birkan TUNC, Auteur ; Susan G. ASSOULINE, Auteur ; Thomas NICKL-JOCKSCHAT, Auteur ; Beth A. MALOW, Auteur ; James S. SUTCLIFFE, Auteur ; Allan I. PACK, Auteur Langues : Anglais (eng) Mots-clés : Adolescent  Autism Spectrum Disorder/complications/genetics  Biological Phenomena  Child  Exome/genetics  Humans  Sleep Wake Disorders/complications/genetics  Exome Sequencing  Autism spectrum disorders  Genetic risk scores  Pleiotropy  Sleep duration  Systems biology Index. décimale : PER Périodiques Résumé : BACKGROUND: Numerous genes are implicated in autism spectrum disorder (ASD). ASD encompasses a wide-range and severity of symptoms and co-occurring conditions; however, the details of how genetic variation contributes to phenotypic differences are unclear. This creates a challenge for translating genetic evidence into clinically useful knowledge. Sleep disturbances are particularly prevalent co-occurring conditions in ASD, and genetics may inform treatment. Identifying convergent mechanisms with evidence for dysfunction that connect ASD and sleep biology could help identify better treatments for sleep disturbances in these individuals. METHODS: To identify mechanisms that influence risk for ASD and co-occurring sleep disturbances, we analyzed whole exome sequence data from individuals in the Simons Simplex Collection (n = 2380). We predicted protein damaging variants (PDVs) in genes currently implicated in either ASD or sleep duration in typically developing children. We predicted a network of ASD-related proteins with direct evidence for interaction with sleep duration-related proteins encoded by genes with PDVs. Overrepresentation analyses of Gene Ontology-defined biological processes were conducted on the resulting gene set. We calculated the likelihood of dysfunction in the top overrepresented biological process. We then tested if scores reflecting genetic dysfunction in the process were associated with parent-reported sleep duration. RESULTS: There were 29 genes with PDVs in the ASD dataset where variation was reported in the literature to be associated with both ASD and sleep duration. A network of 108 proteins encoded by ASD and sleep duration candidate genes with PDVs was identified. The mechanism overrepresented in PDV-containing genes that encode proteins in the interaction network with the most evidence for dysfunction was cerebral cortex development (GO:0,021,987). Scores reflecting dysfunction in this process were associated with sleep durations; the largest effects were observed in adolescents (p = 4.65 × 10(-3)). CONCLUSIONS: Our bioinformatic-driven approach detected a biological process enriched for genes encoding a protein-protein interaction network linking ASD gene products with sleep duration gene products where accumulation of potentially damaging variants in individuals with ASD was associated with sleep duration as reported by the parents. Specifically, genetic dysfunction impacting development of the cerebral cortex may affect sleep by disrupting sleep homeostasis which is evidenced to be regulated by this brain region. Future functional assessments and objective measurements of sleep in adolescents with ASD could provide the basis for more informed treatment of sleep problems in these individuals. En ligne : https://dx.doi.org/10.1186/s11689-022-09448-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574 [article] Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data [texte imprimé] / Olivia J. VEATCH, Auteur ; Diego R. MAZZOTTI, Auteur ; Robert T. SCHULTZ, Auteur ; Ted ABEL, Auteur ; Jacob J. MICHAELSON, Auteur ; Edward S. BRODKIN, Auteur ; Birkan TUNC, Auteur ; Susan G. ASSOULINE, Auteur ; Thomas NICKL-JOCKSCHAT, Auteur ; Beth A. MALOW, Auteur ; James S. SUTCLIFFE, Auteur ; Allan I. PACK, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 14 (2022) Mots-clés : Adolescent  Autism Spectrum Disorder/complications/genetics  Biological Phenomena  Child  Exome/genetics  Humans  Sleep Wake Disorders/complications/genetics  Exome Sequencing  Autism spectrum disorders  Genetic risk scores  Pleiotropy  Sleep duration  Systems biology Index. décimale : PER Périodiques Résumé : BACKGROUND: Numerous genes are implicated in autism spectrum disorder (ASD). ASD encompasses a wide-range and severity of symptoms and co-occurring conditions; however, the details of how genetic variation contributes to phenotypic differences are unclear. This creates a challenge for translating genetic evidence into clinically useful knowledge. Sleep disturbances are particularly prevalent co-occurring conditions in ASD, and genetics may inform treatment. Identifying convergent mechanisms with evidence for dysfunction that connect ASD and sleep biology could help identify better treatments for sleep disturbances in these individuals. METHODS: To identify mechanisms that influence risk for ASD and co-occurring sleep disturbances, we analyzed whole exome sequence data from individuals in the Simons Simplex Collection (n = 2380). We predicted protein damaging variants (PDVs) in genes currently implicated in either ASD or sleep duration in typically developing children. We predicted a network of ASD-related proteins with direct evidence for interaction with sleep duration-related proteins encoded by genes with PDVs. Overrepresentation analyses of Gene Ontology-defined biological processes were conducted on the resulting gene set. We calculated the likelihood of dysfunction in the top overrepresented biological process. We then tested if scores reflecting genetic dysfunction in the process were associated with parent-reported sleep duration. RESULTS: There were 29 genes with PDVs in the ASD dataset where variation was reported in the literature to be associated with both ASD and sleep duration. A network of 108 proteins encoded by ASD and sleep duration candidate genes with PDVs was identified. The mechanism overrepresented in PDV-containing genes that encode proteins in the interaction network with the most evidence for dysfunction was cerebral cortex development (GO:0,021,987). Scores reflecting dysfunction in this process were associated with sleep durations; the largest effects were observed in adolescents (p = 4.65 × 10(-3)). CONCLUSIONS: Our bioinformatic-driven approach detected a biological process enriched for genes encoding a protein-protein interaction network linking ASD gene products with sleep duration gene products where accumulation of potentially damaging variants in individuals with ASD was associated with sleep duration as reported by the parents. Specifically, genetic dysfunction impacting development of the cerebral cortex may affect sleep by disrupting sleep homeostasis which is evidenced to be regulated by this brain region. Future functional assessments and objective measurements of sleep in adolescents with ASD could provide the basis for more informed treatment of sleep problems in these individuals. En ligne : https://dx.doi.org/10.1186/s11689-022-09448-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records / Mengge ZHAO in Journal of Neurodevelopmental Disorders, 14 (2022)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 14 (2022) Titre : Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records Type de document : texte imprimé Auteurs : Mengge ZHAO, Auteur ; James HAVRILLA, Auteur ; Jacqueline PENG, Auteur ; Madison DRYE, Auteur ; Maddie FECHER, Auteur ; Whitney GUTHRIE, Auteur ; Birkan TUNC, Auteur ; Robert SCHULTZ, Auteur ; Kai WANG, Auteur ; Yunyun ZHOU, Auteur Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis/genetics  Electronic Health Records  Humans  Natural Language Processing  Phenotype  Vocabulary  Autism  Autism spectrum disorder  Electronic health record  Natural language processing  Phenotype ontology  Terminology set Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives. METHODS: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method. RESULTS: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders. CONCLUSION: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making. En ligne : https://dx.doi.org/10.1186/s11689-022-09442-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574 [article] Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records [texte imprimé] / Mengge ZHAO, Auteur ; James HAVRILLA, Auteur ; Jacqueline PENG, Auteur ; Madison DRYE, Auteur ; Maddie FECHER, Auteur ; Whitney GUTHRIE, Auteur ; Birkan TUNC, Auteur ; Robert SCHULTZ, Auteur ; Kai WANG, Auteur ; Yunyun ZHOU, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 14 (2022) Mots-clés : Autism Spectrum Disorder/diagnosis/genetics  Electronic Health Records  Humans  Natural Language Processing  Phenotype  Vocabulary  Autism  Autism spectrum disorder  Electronic health record  Natural language processing  Phenotype ontology  Terminology set Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives. METHODS: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method. RESULTS: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders. CONCLUSION: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making. En ligne : https://dx.doi.org/10.1186/s11689-022-09442-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574

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