Browsing by Subject "Autism spectrum disorder"

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    Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism
    (Elsevier, 2020-02-06) Satterstrom, F. K.; Kosmicki, J. A.; Wang, J.; Breen, M. S.; De Rubeis, S.; An, J. - Y.; Peng, M.; Collins, R.; Grove, J.; Klei, L.; Stevens, C.; Reichert, J.; Mulhern, M. S.; Artomov, M.; Gerges, S.; Sheppard, B.; Xu, X.; Bhaduri, A.; Norman, Utku; Brand, H.; Schwartz, G.; Nguyen, R.; Guerrero, E. E.; Dias, C.; Autism Sequencing Consortium; iPSYCH-Broad Consortium; Betancur, C; Cook, E; Gallagher, L; Gill, M; Sutcliffe, J; Thurm, A; Zwick, M; State, M; Çicek, A. Ercüment; Talkowski, M; Cutler, D; Devlin, B.; Sanders, S; Roeder, K.; Daly, M; Buxbaum, J.
    We present the largest exome sequencing study ofautism spectrum disorder (ASD) to date (n = 35,584total samples, 11,986 with ASD). Using an enhancedanalytical framework to integratedenovoand case-control rare variation, we identify 102 risk genes at afalse discovery rate of 0.1 or less. Of these genes, 49show higher frequencies of disruptivedenovovari-ants in individuals ascertained to have severe neuro-developmental delay, whereas 53 show higher fre-quencies in individuals ascertained to have ASD;comparing ASD cases with mutations in thesegroups reveals phenotypic differences. Expressedearly in brain development, most risk genes haveroles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelop-mental and neurophysiological changes), and 13 fallwithin loci recurrently hit by copy number variants.In cells from the human cortex, expression of riskgenes is enriched in excitatory and inhibitoryneuronal lineages, consistent with multiple paths toan excitatory-inhibitory imbalance underlying ASD.
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    Multitask learning of gene risk for autism spectrum disorder and intellectual disability
    (2020-10) Beyreli, İlayda
    Autism Spectrum Disorder (ASD) and Intellectual Disability (ID) are comorbid neurodevelopmental disorders with complex genetic architectures. Despite largescale sequencing studies only a fraction of the risk genes were identified for both. Here, we present a novel network-based gene risk prioritization algorithm named DeepND that performs cross-disorder analysis to improve prediction power by exploiting the comorbidity of ASD and ID via multitask learning. Our model leverages information from gene co-expression networks that model human brain development using graph convolutional neural networks and learns which spatiotemporal neurodevelopmental windows are important for disorder etiologies. We show that our approach substantially improves the state-of-the-art prediction power. We observe that both disorders are enriched in transcription regulators. Despite tight regulatory links in between ASD risk genes, such is lacking across ASD and ID risk genes or within ID risk genes. Finally, we investigate frequent ASD and ID associated copy number variation regions and confident false findings to suggest several novel susceptibility gene candidates. DeepND can be generalized to analyze any combinations of comorbid disorders.
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    De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types
    (BioMed Central, 2020) Chen, S.; Wang, J.; Çiçek, Ercüment; Roeder, K.; Yu, H.; Devlin, B.
    Background: Whole-exome sequencing studies have been useful for identifying genes that, when mutated, affect risk for autism spectrum disorder (ASD). Nonetheless, the association signal primarily arises from de novo protein-truncating variants, as opposed to the more common missense variants. Despite their commonness in humans, determining which missense variants affect phenotypes and how remains a challenge. We investigate the functional relevance of de novo missense variants, specifically whether they are likely to disrupt protein interactions, and nominate novel genes in risk for ASD through integrated genomic, transcriptomic, and proteomic analyses. Methods: Utilizing our previous interactome perturbation predictor, we identify a set of missense variants that are likely disruptive to protein–protein interactions. For genes encoding the disrupted interactions, we evaluate their expression patterns across developing brains and within specific cell types, using both bulk and inferred cell-type-specific brain transcriptomes. Connecting all disrupted pairs of proteins, we construct an “ASD disrupted network.” Finally, we integrate protein interactions and cell-type-specific co-expression networks together with published association data to implicate novel genes in ASD risk in a cell-type-specific manner. Results: Extending earlier work, we show that de novo missense variants that disrupt protein interactions are enriched in individuals with ASD, often affecting hub proteins and disrupting hub interactions. Genes encoding disrupted complementary interactors tend to be risk genes, and an interaction network built from these proteins is enriched for ASD proteins. Consistent with other studies, genes identified by disrupted protein interactions are expressed early in development and in excitatory and inhibitory neuronal lineages. Using inferred gene co-expression for three neuronal cell types—excitatory, inhibitory, and neural progenitor—we implicate several hundred genes in risk (FDR ≤≤0.05), ~ 60% novel, with characteristics of genuine ASD genes. Across cell types, these genes affect neuronal morphogenesis and neuronal communication, while neural progenitor cells show strong enrichment for development of the limbic system. Limitations: Some analyses use the imperfect guilt-by-association principle; results are statistical, not functional. Conclusions: Disrupted protein interactions identify gene sets involved in risk for ASD. Their gene expression during brain development and within cell types highlights how they relate to ASD.
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    Predicting informative spatio-temporal neurodevelopmental windows and gene risk for autism spectrum disorder
    (2020-10) Karakahya, Oğuzhan
    Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Due to its intricate nature, only a fraction of the risk genes were identified despite the effort spent on large-scale sequencing studies. To perceive underlying mechanisms of ASD and predict new risk genes, a deep learning architecture is designed which processes mutational burden of genes and gene co-expression networks using graph convolutional networks. In addition, a mixture of experts model is employed to detect specific neurodevelopmental periods that are of particular importance for the etiology of the disorder. This end-to-end trainable model produces a posterior ASD risk probability for each gene and learns the importance of each network for this prediction. The results of our approach show that the ASD gene risk prediction power is improved compared to the state-of-the-art models. We identify mediodorsal nucleus of thalamus and cerebellum brain region and neonatal & early infancy to middle & late childhood period (0 month - 12 years) as the most informative neurodevelopmental window for prediction. Top predicted risk genes are found to be highly enriched in ASDassociated pathways and transcription factor targets. We pinpoint several new candidate risk genes in CNV regions associated with ASD. We also investigate confident false-positives and false negatives of the method and point to studies which support the predictions of our method.
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    Prevalence and clinical/molecular characteristics of PTEN mutations in Turkish children with autism spectrum disorders and macrocephaly
    (John Wiley & Sons Ltd., 2021-07-16) Kaymakçalan, H.; Kaya, İlyas; Kaya, İ.; Binici, N. C.; Nikerel, E.; Özbaran, B.; Aksoy, M. G.; Erbilen, S.; Özyurt, G.; Jahan, N.; Çelik, D.; Yarabaş, K.; Yalçınkaya, Leyla; Durak, S.; Köse, S.; Şençiçek, A. G. E.
    Background Phosphatase and tensin homolog (PTEN) germline mutations are associated with cancer syndromes (PTEN hamartoma tumor syndrome; PHTS) and in pediatric patients with autism spectrum disorder (ASD) and macrocephaly. The exact prevalence of PTEN mutations in patients with ASD and macrocephaly is uncertain; with prevalence rates ranging from 1% to 17%. Most studies are retrospective and contain more adult than pediatric patients, there is a need for more prospective pediatric studies. Methods We recruited 131 patients (108 males, 23 females) with ASD and macrocephaly between the ages of 3 and 18 from five child and adolescent psychiatry clinics in Turkey from July 2018 to December 2019. We defined macrocephaly as occipito-frontal HC size at or greater than 2 standard deviations (SD) above the mean for age and sex on standard growth charts. PTEN gene sequence analysis was performed using a MiSeq next generation sequencing (NGS) platform, (Illumina). Conclusion PTEN gene sequence analyses identified three pathogenic/likely pathogenic mutations [NM_000314.6; p.(Pro204Leu), (p.Arg233*) and novel (p.Tyr176Cys*8)] and two variants of uncertain significance (VUS) [NM_000314.6; p.(Ala79Thr) and c.*10del]. We also report that patient with (p.Tyr176Cys*8) mutation has Grade 1 hepatosteatosis, a phenotype not previously described. This is the first PTEN prevalence study of patients with ASD and macrocephaly in Turkey and South Eastern Europe region with a largest homogenous cohort. The prevalence of PTEN mutations was found 3.8% (VUS included) or 2.29% (VUS omitted). We recommend testing for PTEN mutations in all patients with ASD and macrocephaly.
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    Spatio-temporal gene discovery for autism spectrum disorder
    (2018-05) Norman, Utku
    Whole Exome Sequencing (WES) studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes to date, because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited. Here, we present a spatiotemporal gene discovery algorithm for ASD, which leverages information from evolving gene coexpression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest based problem on coexpression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on WES data of 3,871 samples and identify risk clusters using BrainSpan coexpression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: Predicted clusters are hit more (i.e. they contain genes with more disruptive mutations on them) and show higher enrichment in ASD-related functions compared to the state of the art.