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- Expression Profiling and Population Structure Analysis of Copy Number Variants (CNVs) in Autism Spectrum Disorder (ASD): 1st Triennium Post Doc Fellowship ReportPublication . Pires Carreira da Conceição, Inês SusanaAutism Spectrum Disorder (ASD) is a common complex behavioral disorder with significant clinical heterogeneity and unclear etiology. Assessment protocols and diagnostic instruments are complex to implement, and not widely used by most clinical practitioners or pediatricians worldwide. It is well known that educational interventions, language and behavioral therapies may significantly improve the patient’s quality of life, and are particularly beneficial when initiated at a young age. While the contribution of genetic factors for ASD etiology has been clearly established by family and twin studies, common risk genes accounting for a high proportion of autism heritability have not yet been identified1. At the same time, the increased burden of rare variants in ASD is increasingly recognized from genomic screening in large population samples. The latest genome wide association studies (GWAS) have failed to find common risk variants with a significant impact, however all have shown an excess of rare and heterogeneous structural variants designated Copy Number Variants (CNV). The significance of most of these rare CNVs for etiological diagnosis of ASD still needs to be properly addressed. The importance of rare variation in autism has also been supported by the many reports of rare mutations in synaptic candidate genes (such as SHANK, MET, NLGN, NRNX amongst others). It seems likely that both common low risk genes and rare, high penetrance, variants will converge in a restricted number of biological pathways. Our group is part of the international consortium Autism Genome Project (AGP), which gathers more than 100 scientists in 13 investigation centers. Recently it has been conducting a Genome Wide Association Study (GWAS), with more than 3000 families of autistic patients, including 342 Portuguese families. In this GWAS analysis, almost 40.000 CNVs were identified in patients and parents, the functional consequences and relevance of these CNVs is being analyzed. Also, the correlation between genetic and clinical data in a large scale sample is uncommon, but very important for a better characterization of the pathology. Our sample has the major advantage of a detailed clinical evaluation following the Autism Speaks project funded The Autism Trio Collection (TASC) project.
- Postdoctoral Fellowship Final Report, SFRH/BPD/74739/2010Publication . Conceição, Inês Susana Pires Carreira daAutism Spectrum Disorder (ASD) is a common complex behavioural disorder with significant clinical heterogeneity and unclear etiology. Assessment protocols and diagnostic instruments are complex to implement, and not widely used by most clinical practitioners or pediatricians worldwide. It is well known that educational interventions, language and behavioural therapies may significantly improve the patient’s quality of life, and are particularly beneficial when initiated at a young age. While the contribution of genetic factors for ASD etiology has been clearly established by family and twin studies, common risk genes accounting for a high proportion of autism heritability have not yet been identified. At the same time, the increased burden of rare variants in ASD is increasingly recognized from genomic screening in large population samples. The latest genome wide association studies (GWAS) have failed to find common risk variants with a significant impact, however all have shown an excess of rare and heterogeneous structural variants designated Copy Number Variants (CNV). The significance of most of these rare CNVs for etiological diagnosis of ASD still needs to be properly addressed. The importance of rare variation in autism has also been supported by the many reports of rare mutations in synaptic candidate genes (such as SHANK, MET, NLGN, NRNX amongst others). It seems likely that both common low risk genes and rare, high penetrance, variants will converge in a restricted number of biological pathways. Our group is part of the international consortium Autism Genome Project (AGP), which gathers more than 100 scientists in 13 investigation centers. Recently it has been conducting a Genome Wide Association Study (GWAS), with more than 3000 families of autistic patients, including 342 Portuguese families. In this GW analysis, almost 40.000 CNVs were identified in patients and parents. The functional consequences and relevance of these CNVs have been analyzed, namely for ANXA1, PARK2 and DSC3 genes, and novel mutations in candidate genes identified by a PPI-based approach are being validated and targeted exome sequencing is being performed. Also, the correlation between genetic and clinical data, using bioinformatic-based data mining approaches, in a large scale sample is very important for a better characterization of the pathology. Our sample has the major advantage of a detailed clinical evaluation following the Autism Speaks project funded The Autism Trio Collection (TASC) project protocol. Finally, the fact that common risk genes for ASD have not yet been identified, indicates that different hypothesis should be addressed, mainly the possibility that epigenetic factors, such deregulated microRNAs (miRNAs) expression. We have been characterizing the expression profile of circulating miRNAs in plasma samples, as putative novel biomarkers for ASD.
- Sinais precoces de autismo - integração de informação comportamental e genética para deteção precoce de Perturbações do Espectro do Autismo numa população em risco: relatório finalPublication . Moura Vicente, AstridAutism Spectrum Disorder (ASD) is a common neurodevelopmental disorder (NDD) characterized by impairments in social interaction and reciprocal communication, as well as by patterns of repetitive and stereotyped behaviours. Educational interventions, language and behavioral therapies may significantly improve the patient’s prognosis, especially when initiated at a young age. A clinical diagnosis of autism, however, is a complex and lengthy process and, particularly for younger children, may be very difficult to attain even using complex diagnostic instruments by medical experts. Furthermore, diagnostic instruments generally perform well at discriminating between ASD and typically developing children, but not ASD from other neurodevelopmental disabilities. Distinguishing between ASD and other NDDs is frequently difficult through behavioural assessment at an early age, when children would most benefit from specific early intervention. Finding reliable methods for early and specific detection of ASD is thus a priority. Over the last decade much progress has been made regarding the genetic etiology of ASD. Recent studies from large research consortia showed that ASD in many cases results from a large number of highly penetrant rare variants that likely converge in a small number of affected pathways. The clinical significance of rare but recurrent submicroscopic deletions and duplications, or Copy Number Variants (CNVs), as well as single nucleotide variants (SNVs), is under study in large population samples, to establish frequencies in ASD vs control datasets, recurrence rates in ASD, segregation in families and gene content. This knowledge is slowly advancing towards translation into clinical practice, and CNV analysis is nowadays widely used for etiological diagnosis. However, questions frequently arise regarding the clinical significance of many of the variants identified, and there seems to be an extensive overlap between ASD and other NDDs. This issue has not been fully explored. A main objective of this project was therefore to understand whether there is a significant gain in diagnostic yield by adding genomic data, namely data from Copy Number Variant analysis, to the normally extensive clinical assessment required for the clinical diagnosis of Autism Spectrum Disorder. We further seek to understand whether genomic data could improve early differentiation between ASD and other NDDs, specifically targeting very young children. A final aim would be to define a screening method for early detection of ASD, integrating behavioural assessment instruments and genomic CNV analysis that could be a useful tool for clinicians.
- System medicine approach to improve diagnosis and prognosis in Autism Spectrum Disorders (ASD), based on extensive genomic, biochemical and clinical dataPublication . Asif, Muhammad; Moura, Astrid; Couto, Francisco M.Autism spectrum disorder (ASD) is a neurodevelopmental disorder of well known complexity. ASD is characterized by impaired social interaction and communication and by stereotyped behaviors, and a high heterogeneity in clinical and genetic presentation. It is hypothesized that such complex heterogeneous phenotypic behaviors are associated with genetic factors. To dissect the complex correlations between phenotype and genotype in ASD, in the current study we will use powerful machine learning and data mining algorithms, like decision trees. We will integrate clinical information (from the diagnostic instruments ADI-R: Autism Diagnostic Interview-Revised and ADOS: Autism Diagnostic Observation Schedule, as well as adaptive behavior scale VABS: Vineland Adaptive Behavior Scale and cognitive scales adapted to age and cognitive level) and genetic data (Copy Number Variants, CNVs) of 3000 ASD individuals with ASD. Data on this patient cohort was obtained by the Autism Genome Project international consortium, which included 335 Portuguese patients from our dataset. This analysis will identify autism behavior associations with genetic risk factors, and eventually allow categorization of patients and prognosis according to genotype. We will initially assess the effect of deletion and duplication events and de novo and transmitted CNVs in disease clinical presentation, and progress to analyze the association of CNVs containing candidate genes for ASD with disease phenotype. So far, the etiology of autism is not well understood due to interactions between multiple factors. Genetic, metabolic, gastrointestinal, immunological and neurobiological factors have been associated with ASD etiology. Therefore, we will use a system biology based approach for ASD analysis, which will integrate genetic, miRNA, neurobiology and clinical data to determine how multiple factors can influence the autism heterogeneity. This work will improve the accuracy of data mining techniques, by building specialized classifiers based on a machine learning approach), and by applying semantic enrichment analysis. These classifiers will help in rapid diagnosis of ASD. Moreover, we will provide a framework for autism analysis with knowledge graph based data organization. This framework will enlist classifiers, feature selection and cross validation methods for ASD analysis. We will also provide a comparative and testing phase to cross check the accuracy of framework. ASD is a complex disorder, therefore enhanced understanding of associations at multiple levels (genetic, miRNA, neurobiology, clinical and behavioral), will be useful to assist in ASD diagnosis and prognosis.