Browsing by Author "Asif, M."
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- Adapting the early life exposure assessment tool (ELEAT) to Portugal: a pilot study to tackle gene-environment interactions in autism spectrum disorderPublication . Rasga, C.; Santos, J.; Lopes, A.L.; Marques, A.R.; Vilela, J.; Asif, M.; Walker, C.K.; Schmidt, R.J.; Vicente, A.M.The objective was to pilot a Portuguese version of the Early Life Exposure Assessment Tool (ELEAT) for the assessment of the role of environmental exposures in a population of Portuguese children with ASD.
- An integrative system biology approach for dissecting Autism Spectrum DisorderPublication . Asif, M.; Rasga, C.; Martiniano, H.; Santos, J.X.; Marques, A.R.; Couto, F.M .; Vicente, A.M.Autism Spectrum Disorder (ASD) is characterized by a wide spectrum of behavioral presentation. Many genetic factors are implicated in ASD, however their role in the heterogeneous ASD phenotype remains elusive. Using data mining-based integrative approaches, we seek to identify patterns of association between ASD phenotypic subgroups and altered biological processes inferred from CNVs targeting brain genes.
- An integrative system biology approach to delineate complex genotype-phenotype associations in Autism Spectrum DisorderPublication . Asif, M.; Martiniano, Hugo F.; Rasga, Célia; Marques, Ana R.; Santos, João X.; Couto, Francisco M.; Vicente, Astrid M.Objectives: The global objective of the study is to improve the current knowledge on ASD prognosis and diagnosis by delineating the complex genotype-phenotype associations using an integrative systems biology approach. For this purpose three specific objectives were pursued: - To identify clinically similar subgroups of individuals with ASD; - To find biological processes disrupted by rare CNVs targeting brain genes in ASD subjects; - To train a machine learning classifier for the clinical prediction of disease progression from genetic information in very young children.
- Autism Spectrum Disorder (ASD): genetic, epigenetic and environmental issuesPublication . Marques, Ana Rita; Martiniano, Hugo; Xavier-Santos, João; Asif, M.; Oliveira, Guiomar; Romão, Luísa; Vicente, Astrid M.Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication/interaction and by unusual repetitive and restricted behaviors and interests. ASD often co-occurs in the same families with other neuropsychiatric diseases (NPD), such as intellectual disability, schizophrenia, depression and attention deficit hyperactivity disorder. Genetic factors have an important role in ASD etiology. Multiple copy number variants (CNVs) and single nucleotide variants (SNVs) in candidate genes have been associated with an increased risk to develop ASD [8-10]. Nevertheless, recent heritability estimates and the high genotypic and phenotypic heterogeneity characteristic of ASD indicate a role of environmental and epigenetic factors, such as long noncoding RNA (lncRNA) and microRNA (miRNA), as modulators of genetic expression and clinical presentation. The aim of this project is to understand the role of lncRNA, miRNA and other epigenetic factors in ASD. For this purpose we are, in a first approach, screening for CNVs and SNVs encompassing lncRNA and miRNA loci in two large datasets: the Autism Genome Project (AGP), with CNV data from 2611 autism trios and the ARRA Autism Sequencing Collaboration, with whole exome sequencing data (WES) from 3056 autism trios. These datasets include data from Portuguese ASD probands recruited by our team. Thus far we have explored the variant call format files that contain all WES variants called by GATK. We started by testing different annotation tools and databases to obtain the best subset of variants that will be filtered according to their genomic coordinates and their pathogenic status. We are also selecting the CNVs from the AGP file that contain lncRNA and miRNA loci. The goal is to identify individuals with potential mutations in lncRNA and miRNA loci that may be disrupting their function upon target genes. Experimental validation will be carried out by measuring gene expression in these patients. A second approach will involve exploring available multiplex families in which ASD co-occurs with other NPDs. Segregation analysis will allow us to define patterns of NPD transmission, identify common gene variants and explore the role of modulating epigenetic factors that lead to differential disease expression.
- Autism Spectrum Disorder (ASD): genetic, epigenetic and environmental issuesPublication . Marques, Ana Rita; Martiniano, Hugo; Xavier-Santos, João; Asif, M.; Oliveira, Guiomar; Luísa, Romão; M. Vicente, AstridAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder which affects the brain structure and the proper establishment of the neuronal connectivity.
- Autism Spectrum Disorder: gene variants involved in the nonsense-mediated mRNA decay pathwayPublication . Marques, Ana Rita; Martiniano, Hugo; Santos, J.X.; Vilela, Joana; Asif, M.; Rasga, C.; Oliveira, G.; Romão, Luísa; Vicente, AstridGenetic factors account for 50-80% of the familial risk of Autism Spectrum Disorder (ASD), but most of the genetic determinants are still unknown and a role for other regulatory mechanisms is likely. The nonsense-mediated decay (NMD) pathway is essential to control mRNA quality and has an important role in the regulation of the transcriptome. Mutations in genes involved in the NMD pathway, such as the UPF3B gene, a core component of this pathway, were previously linked to ASD. In this study we explored the potential role of NMD factors in ASD. We generated a list of 153 genes involved in the NMD pathway using AmiGO, Reactome and a systematic literature review. To identify potentially pathogenic variants in the NMD genes, we analyzed whole exome sequencing data (WES) data from 1338 ASD subjects. We also searched for Copy Number Variants (CNVs) targeting NMD genes in ASD patients (n=3570) and checked their frequency in controls (n=9649). We identified 43 high impact variants in 28 NMD genes, including the UPF3B and ACE, two genes previously implicated in ASD. Importantly, 11 were novel candidate genes that carry loss-of-function and missense (deleterious and damaging) variants with a frequency of 1 to 5% in this ASD dataset. Additionally, 5 NMD genes were found to be targeted by CNVs in 12 ASD subjects but none of the controls. The discovery of 33 NMD genes that are intriguing candidates for ASD in large patient genomic datasets provides evidence supporting the involvement of the NMD pathway in ASD pathophysiology.
- CNVs targeting genes that regulate exposure to toxicants in Autism Spectrum Disorder (ASD): a role for gene-environment interactionsPublication . Santos, J.X.; Rasga, C.; Asif, M.; Marques, A.R.; Vicente, A.M.Objective: Our overall goal is to identify genes involved in detoxification and regulation of barrier permeability processes that can mediate the effect of exposure to toxicants in individuals with ASD. For this purpose, we screened large ASD and control datasets for CNVs targeting selected detoxification and barrier permeability genes.
- Evidence for a role of nonsense-mediated mRNA decay pathway genes in Autism Spectrum DisorderPublication . Marques, Ana Rita; Martiniano, H.; Santos, J.X.; Vilela, J.; Asif, M.; Oliveira, G.; Romão, L.; Vicente, A.M.Autism Spectrum Disorder (ASD): is a neurodevelopmental disorder characterized by deficits in social communication/interaction and by unusual repetitive and restricted behaviors and interests.
- Evidence for a role of nonsense-mediated mRNA decay pathway genes in Autism Spectrum DisorderPublication . Marques, Ana; Martiniano, Hugo; Santos, J.X.; Vilela, J.; Asif, M.; Oliveira, G.; Romão, Luísa; Vicente, AstridIntroduction: Autism Spectrum Disorder (ASD) is a highly heterogeneous neurodevelopmental disorder with an unclear etiology. Genetic factors are estimated to account for 50 to 80% of the familial ASD risk, but most of the genetic determinants are still not known and a role for other regulatory mechanisms is likely. The nonsense-mediated decay (NMD) pathway controls mRNA quality and plays an important role in the regulation of the transcriptome. Mutations in genes involved in the NMD pathway have been linked to neurodevelopmental disorders, with intriguing evidence for an involvement of mutations in the UPF3B gene, a core component of the NMD pathway, in ASD.
- Identifying disease genes using machine learning and gene functional similarities, assessed through Gene OntologyPublication . Asif, M.; Martiniano, H.F.M.C.M.; Vicente, A.M.; Couto, F.M.Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.
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