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Query: UMLS:C0004352 (autism)
 32,579 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Recently, several studies have reported an association between anxiety traits, affective disorders and autism and alleles of a functional promoter polymorphism (5HTT-LPR) in the human serotonin transporter (5HTT, SERT).1-3 The mechanistic basis for allelic differences in transporter transcription are presently unknown. To explore this issue, we cloned the human 5HTT promoter region from a PAC genomic library and now describe an unreported 381-bp insert between the polymorphic region and the transcription start site. We verified the presence of this novel sequence by Southern hybridization of genomic digests and PCR amplifications from multiple unrelated individuals. Sequence analysis of the novel region reveals a number of canonical transcription factor binding sites (eg AP1, Elk1, NFkappaB) that may be important in controlling the response of the 5HTT gene to regulatory factors. PCR studies of genomic templates reveal a low level of amplification of a deleted template matching the size of the originally reported 5HTT promoter. This deleted template is absent from PAC amplifications, suggesting that the human 5HTT promoter may exhibit in vivo instability. Molecular Psychiatry (2000) 5, 110-115.
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PMID:Modified structure of the human serotonin transporter promoter. 1067 78

Autism is a neuropsychiatric disorder characterized by impairments in social interaction, restricted and stereotypic pattern of interest with onset by 3 years of age. The results of genetic linkage studied for autistic disorder (AD) have suggested a susceptibility locus for the disease on the long arm of chromosome 7. We report a girl with AD and a balanced reciprocal translocation t(5;7)(q14;q32). The mother carries the translocation but do not express the disease. Fluorescent in situ hybridization (FISH) analysis with chromosome 7-specific YAC clones showed that the breakpoint coincides with the candidate region for AD. We identified a PAC clone that spans the translocation breakpoint and the breakpoint was mapped to a 2 kb region. Mutation screening of the genes SSBP and T2R3 located just centromeric to the breakpoint was performed in a set of 29 unrelated autistic sibling pairs who shared at least one chromosome 7 haplotype. We found no sequence variations, which predict amino acid alterations. Two single nucleotide polymorphisms were identified in the T2R3 gene, and associations between allele variants and AD in our population were not found. The methylation pattern of different chromosome 7 regions in the patient's genomic DNA appears normal. Here we report the clinical presentation of the patient with AD and the characterization of the genomic organization across the breakpoint at 7q32. The precise localization of the breakpoint on 7q32 may be relevant for further linkage studies and molecular analysis of AD in this region.
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PMID:A balanced reciprocal translocation t(5;7)(q14;q32) associated with autistic disorder: molecular analysis of the chromosome 7 breakpoint. 1180 21

Searching for a mechanism underlying autoimmunity in autism, we postulated that gliadin peptides, heat shock protein 60 (HSP-60), and streptokinase (SK) bind to different peptidases resulting in autoantibody production against these components. We assessed this hypothesis in patients with autism and in those with mixed connective tissue diseases. Associated with antigliadin and anti-HSP antibodies, children with autism and patients with autoimmune disease developed anti-dipeptidylpeptidase I (DPP I), anti-dipeptidylpeptidase IV (DPP IV [or CD26]) and anti-aminopeptidase N (CD13) autoantibodies. A significant percentage of autoimmune and autistic sera were associated with elevated immunoglobulin G (IgG), IgM, or IgA antibodies against three peptidases, gliadin, and HSP-60. These antibodies are specific, since immune absorption demonstrated that only specific antigens (e.g., DPP IV absorption of anti-DPP IV), significantly reduced IgG, IgM, and IgA antibody levels. For direct demonstration of SK, HSP-60, and gliadin peptide binding to DPP IV, microtiter wells coated with DPP IV were reacted with SK, HSP-60, and gliadin. They were then reacted with anti-DPP IV or anti-SK, anti-HSP, and antigliadin antibodies. Adding SK, HSP-60, and gliadin peptides to DPP IV resulted in 27 to 43% inhibition of the DPP IV-anti-DPP IV reaction, but DPP IV-positive peptides caused 18 to 20% enhancement of antigen-antibody reactions. We propose that (i) superantigens (e.g., SK and HSP-60) and dietary proteins (e.g., gliadin peptides) in individuals with predisposing HLA molecules bind to aminopeptidases and (ii) they induce autoantibodies to peptides and tissue antigens. Dysfunctional membrane peptidases and autoantibody production may result in neuroimmune dysregulation and autoimmunity.
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PMID:Heat shock protein and gliadin peptide promote development of peptidase antibodies in children with autism and patients with autoimmune disease. 1513 76

Until fairly recently, it was believed that essentially all human cells harbor two copies of each locus in the autosomal genome. However, studies have now shown that there are segments of the genome that are polymorphic with regard to genomic copy number. These copy number variations (CNVs) have a role in various diseases such as Alzheimer disease, Crohn's disease, autism and schizophrenia. In the effort to scan the entire genome for these gains and losses of DNA, single nucleotide polymorphism (SNP) arrays have emerged as an important tool. As such, CNV identification from SNP array data is attracting considerable attention as an algorithmic problem, and many methods have been published over the last few years. However, many of the existing model-based methods train their models based on common variations and are therefore less successful in the identification of rare CNVs, detection of which may be very important in personalized genomics applications. In this paper, we formulate CNV identification explicitly as an optimization problem with an objective function that is characterized by several adjustable parameters. These parameters can be configured based on the characteristics of the experimental platform and target application, so that the solution to the optimization problem is the most accurate set of CNV calls. Our method, termed COKGEN, efficiently solves this problem using a variant of the well-known heuristic simulated annealing. We apply COKGEN to data from hundreds of samples, and demonstrate its ability to detect known CNVs at a high level of sensitivity without sacrificing specificity, not only for common but also rare CNVs. Furthermore, we show that it performs better than other publicly-available methods. The configurability of COKGEN, its computational efficiency, and its accuracy in calling rare CNVs make it particularly useful for personalized genomics applications. COKGEN is implemented as an R package and is freely available at http://mendel.gene.cwru.edu/laframboiselab/software.php.
Pac Symp Biocomput 2010
PMID:COKGEN: a software for the identification of rare copy number variation from SNP microarrays. 1990 89

This review summarizes the published work on the prevalence and incidence rates of autism spectrum disorder (ASD) in Chinese populations. The authors searched MEDLINE, Web of Science and the PsycINFO database and identified seven studies that were published in the English language. In mainland China, Li and colleagues reported an autism prevalence rate of 2.38/10,000 but admitted the possibility of underestimation. A higher prevalence of 11/10,000 was reported by Zhang and Ji based on a survey that was conducted in Tianjin, China. In Taiwan, Chien and colleagues reported that the cumulative prevalence of ASD increased from 1.79 to 28.72/10,000 from 1996 to 2005 and the annual incidence rate increased from 0.91 to 4.41/10,000 per year from 1997 to 2005. Another study based on the Taiwan national health insurance database reported a high prevalence rate of 122.8/10,000 for the year 2007. Two studies based on the Taiwan national disability registry data reported an increasing trend of ASD for the period 2000-2007 and 2004-2010, respectively. In Hong Kong, Wong and colleagues estimated that the incidence of ASD was 5.49/10,000 and the average prevalence over the 1986-2005 period was 16.1/10,000. We identified 12 studies through the searching of Chinese databases. The prevalences among these studies varied from 2.8 to 29.5/10,000. While existing data appear to suggest, it remains unclear whether there is a true rise in the prevalence of ASD in ethnic Chinese population across geographic sites. More collaborative research on this topic should be conducted in the future.
Asia Pac Psychiatry 2013 Jun
PMID:Autism spectrum disorder in Chinese populations: a brief review. 2385 5

A clear and predictive understanding of the etiology of autism spectrum disorders (ASD), a group of neurodevelopmental disorders characterized by varying deficits in social interaction and communication as well as repetitive behaviors, has not yet been achieved. There remains active debate about the origins of autism, and the degree to which genetic and environmental factors, and their interplay, produce the range and heterogeneity of cognitive, developmental, and behavioral features seen in children carrying a diagnosis of ASD. Unlocking the causes of these complex developmental disorders will require a collaboration of experts in many disciplines, including clinicians, environmental exposure experts, bioinformaticists, geneticists, and computer scientists. For this workshop we invited prominent researchers in the field of autism, covering a range of topics from genetic and environmental research to ethical considerations. The goal of this workshop: provide an introduction to the current state of autism research, highlighting the potential for multi-disciplinary collaborations that rigorously evaluate the many potential contributors to ASD. It is further anticipated that approaches that successfully advance the understanding of ASD can be applied to the study of other common, complex disorders. Herein we provide a short review of ASD and the work of the invited speakers.
Pac Symp Biocomput 2014
PMID:Uncovering the etiology of autism spectrum disorders: genomics, bioinformatics, environment, data collection and exploration, and future possibilities. 2429 68

The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets.
Pac Symp Biocomput 2016
PMID:DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS. 2677 81

Recent studies on copy number variation (CNV) have suggested that an increasing burden of CNVs is associated with susceptibility or resistance to disease. A large number of genes or genomic loci contribute to complex diseases such as autism. Thus, total genomic copy number burden, as an accumulation of copy number change, is a meaningful measure of genomic instability to identify the association between global genetic effects and phenotypes of interest. However, no systematic annotation pipeline has been developed to interpret biological meaning based on the accumulation of copy number change across the genome associated with a phenotype of interest. In this study, we develop a comprehensive and systematic pipeline for annotating copy number variants into genes/genomic regions and subsequently pathways and other gene groups using Biofilter - a bioinformatics tool that aggregates over a dozen publicly available databases of prior biological knowledge. Next we conduct enrichment tests of biologically defined groupings of CNVs including genes, pathways, Gene Ontology, or protein families. We applied the proposed pipeline to a CNV dataset from the Marshfield Clinic Personalized Medicine Research Project (PMRP) in a quantitative trait phenotype derived from the electronic health record - total cholesterol. We identified several significant pathways such as toll-like receptor signaling pathway and hepatitis C pathway, gene ontologies (GOs) of nucleoside triphosphatase activity (NTPase) and response to virus, and protein families such as cell morphogenesis that are associated with the total cholesterol phenotype based on CNV profiles (permutation p-value < 0.01). Based on the copy number burden analysis, it follows that the more and larger the copy number changes, the more likely that one or more target genes that influence disease risk and phenotypic severity will be affected. Thus, our study suggests the proposed enrichment pipeline could improve the interpretability of copy number burden analysis where hundreds of loci or genes contribute toward disease susceptibility via biological knowledge groups such as pathways. This CNV annotation pipeline with Biofilter can be used for CNV data from any genotyping or sequencing platform and to explore CNV enrichment for any traits or phenotypes. Biofilter continues to be a powerful bioinformatics tool for annotating, filtering, and constructing biologically informed models for association analysis - now including copy number variants.
Pac Symp Biocomput 2016
PMID:BIOFILTER AS A FUNCTIONAL ANNOTATION PIPELINE FOR COMMON AND RARE COPY NUMBER BURDEN. 2677

Autism has been shown to have a major genetic risk component; the architecture of documented autism in families has been over and again shown to be passed down for generations. While inherited risk plays an important role in the autistic nature of children, de novo (germline) mutations have also been implicated in autism risk. Here we find that autism de novo variants verified and published in the literature are Bonferroni-significantly enriched in a gene set implicated in synaptic elimination. Additionally, several of the genes in this synaptic elimination set that were enriched in protein-protein interactions (CACNA1C, SHANK2, SYNGAP1, NLGN3, NRXN1, and PTEN) have been previously confirmed as genes that confer risk for the disorder. The results demonstrate that autism-associated de novos are linked to proper synaptic pruning and density, hinting at the etiology of autism and suggesting pathophysiology for downstream correction and treatment.
Pac Symp Biocomput 2017
PMID:DE NOVO MUTATIONS IN AUTISM IMPLICATE THE SYNAPTIC ELIMINATION NETWORK. 2789 3

Despite mounting evidence for the strong role of genetics in the phenotypic manifestation of Autism Spectrum Disorder (ASD), the specific genes responsible for the variable forms of ASD remain undefined. ASD may be best explained by a combinatorial genetic model with varying epistatic interactions across many small effect mutations. Coalitional or cooperative game theory is a technique that studies the combined effects of groups of players, known as coalitions, seeking to identify players who tend to improve the performance--the relationship to a specific disease phenotype--of any coalition they join. This method has been previously shown to boost biologically informative signal in gene expression data but to-date has not been applied to the search for cooperative mutations among putative ASD genes. We describe our approach to highlight genes relevant to ASD using coalitional game theory on alteration data of 1,965 fully sequenced genomes from 756 multiplex families. Alterations were encoded into binary matrices for ASD (case) and unaffected (control) samples, indicating likely gene-disrupting, inherited mutations in altered genes. To determine individual gene contributions given an ASD phenotype, a "player" metric, referred to as the Shapley value, was calculated for each gene in the case and control cohorts. Sixty seven genes were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Using network and cross-study analysis, we found that these genes are involved in biological pathways known to be affected in the autism cases and that a subset directly interact with several genes known to have strong associations to autism. These findings suggest that coalitional game theory can be applied to large-scale genomic data to identify hidden yet influential players in complex polygenic disorders such as autism.
Pac Symp Biocomput 2018
PMID:Coalitional game theory as a promising approach to identify candidate autism genes. 2921 3


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