Cross-disorder comparative analysis of comorbid conditions reveals novel autism candidate genes (original) (raw)
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Comparative analysis of neurological disorders focuses genome-wide search for autism genes
2009
The behaviors of autism overlap with a diverse array of other neurological disorders, suggesting common molecular mechanisms. We conducted a large comparative analysis of the network of genes linked to autism with those of 432 other neurological diseases to circumscribe a multi-disorder subcomponent of autism. We leveraged the biological process and interaction properties of these multi-disorder autism genes to overcome the across-the-board multiple hypothesis corrections that a purely data-driven approach requires.
Journal of Molecular Neuroscience, 2015
Autism spectrum disorder (ASD) is a neurodevelopmental syndrome known to have a significant but complex genetic etiology. Hundreds of diverse genes have been implicated in ASD; yet understanding how many genes, each with disparate function, can all be linked to a single clinical phenotype remains unclear. We hypothesized that understanding functional relationships between autism candidate genes during normal human brain development may provide convergent mechanistic insight into the genetic heterogeneity of ASD. We analyzed the co-expression relationships of 455 genes previously implicated in autism using the BrainSpan human transcriptome database, across 16 anatomical brain regions spanning prenatal life through adulthood. We discovered modules of ASD candidate genes with biologically relevant temporal coexpression dynamics, which were enriched for functional ontologies related to synaptogenesis, apoptosis, and GABAergic neurons. Furthermore, we also constructed coexpression networks from the entire transcriptome and found that ASD candidate genes were enriched in modules related to mitochondrial function, protein translation, and ubiquitination. Hub genes central to these ASD-enriched modules were further identified, and their functions supported these ontological findings. Overall, our multi-dimensional co-expression analysis of ASD candidate genes in the normal developing human brain suggests the heterogeneous set of ASD candidates share transcriptional networks related to synapse formation and elimination, protein turnover, and mitochondrial function.
Gene, 2012
Autism spectrum disorders (ASDs) are a group of diseases exhibiting impairment in social drive, communication/language skills and stereotyped behaviors. Though an increased number of candidate genes and molecular interactions have been identified by various approaches, the pathogenesis remains elusive. Based on clinical observations, data from accessible GWAS and expression datasets we identified ASDs gene candidates. Integrative gene network and a novel CNV-centric Node Network (CNN) analysis method highlighted ASDs-associated key elements and biological processes. Functional analysis identified neurological functions including synaptic cholinergic receptor (CHRNA) families, dopamine receptor (DRD2), and correlations between social behavior and oxytocin related pathways. CNN analysis of genome-wide genetic and expression data identified inheritance-related clusters related to PTEN/TSC1/FMR1 and mTor/ PI3K regulation. Integrative analysis identified potential regulators of networks, specifically TNF and beta-estradiol, suggesting a potential central role in ASDs. Our data provides information on potential disease mechanisms, and key regulators that may generate novel postulations, and diagnostic molecular biomarkers.
The discovery of integrated gene networks for autism and related disorders
Genome Research, 2014
Despite considerable genetic heterogeneity underlying neurodevelopmental diseases, there is compelling evidence that many disease genes will map to a much smaller number of biological subnetworks. We developed a computational method, termed MAGI (Merging Affected Genes into Integrated-networks), that simultaneously integrates protein-protein interaction and RNAseq expression profiles during brain development to discover "modules" enriched for de novo mutations in probands. We applied this method
Frontiers in genetics, 2015
The substantial progress in the last few years toward uncovering genetic causes and risk factors for autism spectrum disorders (ASDs) has opened new experimental avenues for identifying the underlying neurobiological mechanism of the condition. The bounty of genetic findings has led to a variety of data-driven exploratory analyses aimed at deriving new insights about the shared features of these genes. These approaches leverage data from a variety of different sources such as co-expression in transcriptomic studies, protein-protein interaction networks, gene ontologies (GOs) annotations, or multi-level combinations of all of these. Here, we review the recurrent themes emerging from these analyses and highlight some of the challenges going forward. Themes include findings that ASD associated genes discovered by a variety of methods have been shown to contain disproportionate amounts of neurite outgrowth/cytoskeletal, synaptic, and more recently Wnt-related and chromatin modifying gen...
Autworks: a cross-disease network biology application for Autism and related disorders
BMC Medical Genomics, 2012
Background The genetic etiology of autism is heterogeneous. Multiple disorders share genotypic and phenotypic traits with autism. Network based cross-disorder analysis can aid in the understanding and characterization of the molecular pathology of autism, but there are few tools that enable us to conduct cross-disorder analysis and to visualize the results. Description We have designed Autworks as a web portal to bring together gene interaction and gene-disease association data on autism to enable network construction, visualization, network comparisons with numerous other related neurological conditions and disorders. Users may examine the structure of gene interactions within a set of disorder-associated genes, compare networks of disorder/disease genes with those of other disorders/diseases, and upload their own sets for comparative analysis. Conclusions Autworks is a web application that provides an easy-to-use resource for researchers of varied backgrounds to analyze the autism...
Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder
2016
Autism spectrum disorder (ASD) is a range of major neurodevelopmental disabilities with a strong genetic basis. Yet, owing to extensive genetic heterogeneity, multiple modes of inheritance and limited study sizes, sequencing and quantitative genetics approaches have had limited success in characterizing the complex genetics of ASD. Currently, only a small fraction of potentially causal genes -about 65 genes out of an estimated several hundred- are known based on strong genetic evidence. Hence, there is a critical need for complementary approaches to further characterize the genetic basis of ASD, enabling development of better screening and therapeutics. Here, we use a machine-learning approach based on a human brain-specific functional gene interaction network to present a genome-wide prediction of autism-associated genes, including hundreds of candidate genes for which there is minimal or no prior genetic evidence. Our approach is validated in an independent case-control sequencing...
Toward a Pathway-Driven Clinical-Molecular Framework for Classifying Autism Spectrum Disorders
Pediatric Neurology, 2019
Background: The current classification system of neurodevelopmental disorders is based on clinical criteria; however, this method alone fails to incorporate what is now known about genomic similarities and differences between closely related clinical neurodevelopmental disorders. Here we present an alternative clinical molecular classification system of neurodevelopmental disorders based on shared molecular and cellular pathways, using syndromes with autistic features as examples. Methods: Using the Online Mendelian Inheritance in Man database, we identified 83 syndromes that had "autism" as a feature of disease, which in combination were associated with 69 autism disease-causing genes. Using annotation terms generated from the DAVID annotation tool, we grouped each gene and its associated autism syndrome into three biological pathways: ion transport, cellular synaptic function, and transcriptional regulation. Results: The majority of the autism syndromes we analyzed (54 of 83) enriched for processes related to transcriptional regulation and were associated with more non-neurologic symptoms and co-morbid psychiatric disease when compared with the other two pathways studied. Disorders with disrupted cellular synaptic function had significantly more motor-related symptoms when compared with the other groups of disorders. Conclusion: Our pathway-based classification system identified unique clinical characteristics within each group that may help guide clinical diagnosis, prognosis, and treatment. These results suggest that shifting current clinical classification of autism disorders toward molecularly driven, pathway-related diagnostic groups such as this may more precisely guide clinical decision making and may be informative for future clinical trial and drug development approaches.
Enrichment of genomic variation in pathways linked to autism
2020
The genetic heterogeneity of autism has stymied the search for causes and cures. Even whole-genomic studies on large numbers of families have yielded results of relatively little impact. In the present work, we analyze two genomic databases using a novel strategy that takes prior knowledge of genetic relationships into account and that was designed to boost signal important to our understanding of the molecular basis of autism. Our strategy was designed to identify significant genomic variation within a priori defined biological concepts and improves signal detection while lessening the severity of multiple test correction seen in standard analysis of genome-wide association data. Upon application of our approach using 3,244 biological concepts, we detected genomic variation in 68 biological concepts with significant association to autism in comparison to family-based controls. These concepts clustered naturally into a total of 19 classes, principally including cell adhesion, cancer...