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Wu CS, Tsai TH, Chen WL, Tsai HJ, Chien YL. Ophthalmologic diagnoses in youths with autism spectrum disorder: Prevalence and clinical correlates. Autism Res 2023; 16:2008-2020. [PMID: 37632715 DOI: 10.1002/aur.3019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/10/2023] [Indexed: 08/28/2023]
Abstract
Autism spectrum disorder (ASD) is associated with a high prevalence of visual dysfunction. This study aimed to investigate the rates of amblyopia, refractive errors, and strabismus, as well as their clinical correlates in ASD. This population-based matched-cohort study used data from the Taiwan National Health Insurance Research Database. A total of 3,551 youths with ASD and 35,510 non-autistic control participants matched by age and sex were included. All the participants were followed-up until they were 18 years old. The prevalence of amblyopia, refractive errors, and strabismus was compared between the ASD and control groups. Effect modifiers, including sex, ASD subgroup, and co-diagnosis of intelligence disability, were examined. Compared to the control group, youths with ASD had a significantly increased risk of amblyopia (adjusted odds ratio [aOR] = 1.75), anisometropia (aOR = 1.66), astigmatism (aOR = 1.51), hypermetropia (aOR = 2.08), exotropia (aOR = 2.86), and esotropia (aOR = 2.63), but a comparable likelihood of myopia according to age. Males with ASD had a significantly lower likelihood of exotropia, but a higher likelihood of myopia than females with ASD. The autism subgroup had a higher OR for hypermetropia, but a lower OR for myopia than the other ASD subgroups. ASD youths with intelligence disabilities demonstrated significantly higher ORs for amblyopia, hypermetropia, and all types of strabismus and lower OR for myopia than those without intelligence disabilities. In conclusion, the rates of amblyopia, refractive errors, and strabismus were higher in youths with ASD. Ocular abnormalities in youths with ASD require a comprehensive assessment and management.
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Affiliation(s)
- Chi-Shin Wu
- National Center for Geriatrics and Welfare Research, National Health Research Institutes, Yunlin, Taiwan
- Department of Psychiatry, National Taiwan University Hospital, Yunlin Branch, Yunlin, Taiwan
| | - Tzu-Hsun Tsai
- Department of Ophthalmology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Wei-Li Chen
- Department of Ophthalmology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
- Advanced Ocular Surface and Corneal Nerve Regeneration Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Hui-Ju Tsai
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Yi-Ling Chien
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
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Matta J, Dobrino D, Yeboah D, Howard S, EL-Manzalawy Y, Obafemi-Ajayi T. Connecting phenotype to genotype: PheWAS-inspired analysis of autism spectrum disorder. Front Hum Neurosci 2022; 16:960991. [PMID: 36310845 PMCID: PMC9605200 DOI: 10.3389/fnhum.2022.960991] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/14/2022] [Indexed: 04/13/2024] Open
Abstract
Autism Spectrum Disorder (ASD) is extremely heterogeneous clinically and genetically. There is a pressing need for a better understanding of the heterogeneity of ASD based on scientifically rigorous approaches centered on systematic evaluation of the clinical and research utility of both phenotype and genotype markers. This paper presents a holistic PheWAS-inspired method to identify meaningful associations between ASD phenotypes and genotypes. We generate two types of phenotype-phenotype (p-p) graphs: a direct graph that utilizes only phenotype data, and an indirect graph that incorporates genotype as well as phenotype data. We introduce a novel methodology for fusing the direct and indirect p-p networks in which the genotype data is incorporated into the phenotype data in varying degrees. The hypothesis is that the heterogeneity of ASD can be distinguished by clustering the p-p graph. The obtained graphs are clustered using network-oriented clustering techniques, and results are evaluated. The most promising clusterings are subsequently analyzed for biological and domain-based relevance. Clusters obtained delineated different aspects of ASD, including differentiating ASD-specific symptoms, cognitive, adaptive, language and communication functions, and behavioral problems. Some of the important genes associated with the clusters have previous known associations to ASD. We found that clusters based on integrated genetic and phenotype data were more effective at identifying relevant genes than clusters constructed from phenotype information alone. These genes included five with suggestive evidence of ASD association and one known to be a strong candidate.
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Affiliation(s)
- John Matta
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, United States
| | - Daniel Dobrino
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, United States
| | - Dacosta Yeboah
- Department of Computer Science, Missouri State University, Springfield, MO, United States
| | - Swade Howard
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, United States
| | - Yasser EL-Manzalawy
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, United States
| | - Tayo Obafemi-Ajayi
- Engineering Program, Missouri State University, Springfield, MO, United States
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Ahmed H, Soliman H, Elmogy M. Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree. Comput Biol Med 2022; 146:105622. [PMID: 35751201 DOI: 10.1016/j.compbiomed.2022.105622] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 11/18/2022]
Abstract
Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.
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Affiliation(s)
- Hala Ahmed
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt
| | - Hassan Soliman
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt
| | - Mohammed Elmogy
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt.
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Matta J, Dobrino D, Howard S, Yeboah D, Kopel J, El-Manzalawy Y, Obafemi-Ajayi T. A PheWAS Model of Autism Spectrum Disorder. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2110-2114. [PMID: 34891705 DOI: 10.1109/embc46164.2021.9629533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Children with Autism Spectrum Disorder (ASD) exhibit a wide diversity in type, number, and severity of social deficits as well as communicative and cognitive difficulties. It is a challenge to categorize the phenotypes of a particular ASD patient with their unique genetic variants. There is a need for a better understanding of the connections between genotype information and the phenotypes to sort out the heterogeneity of ASD. In this study, single nucleotide polymorphism (SNP) and phenotype data obtained from a simplex ASD sample are combined using a PheWAS-inspired approach to construct a phenotype-phenotype network. The network is clustered, yielding groups of etiologically related phenotypes. These clusters are analyzed to identify relevant genes associated with each set of phenotypes. The results identified multiple discriminant SNPs associated with varied phenotype clusters such as ASD aberrant behavior (self-injury, compulsiveness and hyperactivity), as well as IQ and language skills. Overall, these SNPs were linked to 22 significant genes. An extensive literature search revealed that eight of these are known to have strong evidence of association with ASD. The others have been linked to related disorders such as mental conditions, cognition, and social functioning.Clinical relevance- This study further informs on connections between certain groups of ASD phenotypes and their unique genetic variants. Such insight regarding the heterogeneity of ASD would support clinicians to advance more tailored interventions and improve outcomes for ASD patients.
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Ahmed H, Alarabi L, El-Sappagh S, Soliman H, Elmogy M. Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles. PeerJ Comput Sci 2021; 7:e697. [PMID: 34616886 PMCID: PMC8459785 DOI: 10.7717/peerj-cs.697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 08/05/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. METHODS We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. RESULTS It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. CONCLUSIONS Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.
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Affiliation(s)
- Hala Ahmed
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Louai Alarabi
- Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Hassan Soliman
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
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Turkmen AS, Lin S. Detecting X-linked common and rare variant effects in family-based sequencing studies. Genet Epidemiol 2020; 45:36-45. [PMID: 32864779 DOI: 10.1002/gepi.22352] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 06/26/2020] [Accepted: 08/03/2020] [Indexed: 11/08/2022]
Abstract
The breakthroughs in next generation sequencing have allowed us to access data consisting of both common and rare variants, and in particular to investigate the impact of rare genetic variation on complex diseases. Although rare genetic variants are thought to be important components in explaining genetic mechanisms of many diseases, discovering these variants remains challenging, and most studies are restricted to population-based designs. Further, despite the shift in the field of genome-wide association studies (GWAS) towards studying rare variants due to the "missing heritability" phenomenon, little is known about rare X-linked variants associated with complex diseases. For instance, there is evidence that X-linked genes are highly involved in brain development and cognition when compared with autosomal genes; however, like most GWAS for other complex traits, previous GWAS for mental diseases have provided poor resources to deal with identification of rare variant associations on X-chromosome. In this paper, we address the two issues described above by proposing a method that can be used to test X-linked variants using sequencing data on families. Our method is much more general than existing methods, as it can be applied to detect both common and rare variants, and is applicable to autosomes as well. Our simulation study shows that the method is efficient, and exhibits good operational characteristics. An application to the University of Miami Study on Genetics of Autism and Related Disorders also yielded encouraging results.
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Affiliation(s)
- Asuman S Turkmen
- Statistics Department, The Ohio State University, Columbus, Ohio.,Statistics Department, The Ohio State University, Newark, Ohio
| | - Shili Lin
- Statistics Department, The Ohio State University, Columbus, Ohio
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Liu D, Baskett W, Beversdorf D, Shyu CR. Exploratory Data Mining for Subgroup Cohort Discoveries and Prioritization. IEEE J Biomed Health Inform 2019; 24:1456-1468. [PMID: 31494566 PMCID: PMC9341221 DOI: 10.1109/jbhi.2019.2939149] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Finding small homogeneous subgroup cohorts in large heterogeneous populations is a critical process for hypothesis development in biomedical research. Concurrent computational approaches are still lacking in robust answers to the question "what hypotheses are likely to be novel and to produce clinically relevant results with well thought-out study designs?" We have developed a novel subgroup discovery method which employs a deep exploratory mining process to slice and dice thousands of potential subpopulations and prioritize potential cohorts based on their explainable contrast patterns and which may provide interventionable insights. We conducted computational experiments on both synthesized data and a clinical autism data set to assess performance quantitatively for coverage of pre-defined cohorts and qualitatively for novel knowledge discovery, respectively. We also conducted a scaling analysis using a distributed computing environment to suggest computational resource needs for when the subpopulation number increases. This work will provide a robust data-driven framework to automatically tailor potential interventions for precision health.
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Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes. Trends Cogn Sci 2019; 23:584-601. [PMID: 31153774 PMCID: PMC6821457 DOI: 10.1016/j.tics.2019.03.009] [Citation(s) in RCA: 182] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 03/28/2019] [Accepted: 03/29/2019] [Indexed: 12/12/2022]
Abstract
The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.
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Affiliation(s)
- Eric Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University, Portland, OR 97239, USA.
| | - Oscar Miranda-Dominguez
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - Mollie Marr
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alice M Graham
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joel T Nigg
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center Oregon Health & Science University, Portland, OR 97239, USA.
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9
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McCaulley ME. Autism spectrum disorder and mercury toxicity: use of genomic and epigenetic methods to solve the etiologic puzzle. Acta Neurobiol Exp (Wars) 2019. [DOI: 10.21307/ane-2019-010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Matta J, Zhao J, Ercal G, Obafemi-Ajayi T. Applications of node-based resilience graph theoretic framework to clustering autism spectrum disorders phenotypes. APPLIED NETWORK SCIENCE 2018; 3:38. [PMID: 30839816 PMCID: PMC6214326 DOI: 10.1007/s41109-018-0093-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 08/08/2018] [Indexed: 06/09/2023]
Abstract
With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better understanding of ASD phenotypic heterogeneity useful for further ASD studies. We address appropriate graph constructions suited for representing the ASD phenotype data. The sample population is drawn from a very large rigorous dataset: Simons Simplex Collection (SSC). Analysis of the results performed using graph quality measures, internal cluster validation measures, and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets. We also conduct feature extraction analysis to characterize relevant biomarkers that delineate the resulting subgroups. The optimal results obtained favored predominantly a 5-cluster configuration.
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Affiliation(s)
- John Matta
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL USA
| | - Junya Zhao
- Department of Computer Science, Missouri State University, Springfield, MO USA
| | - Gunes Ercal
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL USA
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