scholarly journals Common risk variants identified in autism spectrum disorder

2017 ◽  
Author(s):  
Jakob Grove ◽  
Stephan Ripke ◽  
Thomas D. Als ◽  
Manuel Mattheisen ◽  
Raymond Walters ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Complex Disorders ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
A Genome ◽  
Significance Levels ◽  
Near Term

AbstractAutism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes, in contrast to what is typically seen in other complex disorders. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.

scholarly journals Nexus Between Genome-Wide Copy Number Variations and Autism Spectrum Disorder in Northeast Han Chinese Population

2021 ◽  
Author(s):  
Shuang Qiu ◽  
Xianling Cong ◽  
Yan Li ◽  
Jikang Shi ◽  
Yingjia Qiu ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Copy Number ◽  
Chinese Population ◽  
Autism Spectrum ◽  
Han Chinese ◽  
Spectrum Disorder ◽  
Comparative Genomic ◽  
Han Chinese Population ◽  
Genome Wide ◽  
A Genome

Abstract Background: Autism spectrum disorder (ASD) is a common neurodevelopmental condition, with an increasing prevalence worldwide. Copy number variation (CNV), as one of genetic factors, is involved in ASD etiology. However, there exist substantial differences in terms of location and frequency of some CNVs in the general Asian population. Whole-genome studies of CNVs in Northeast Han Chinese samples are still lacking, necessitating our ongoing work to investigate the characteristics of CNVs in a Northeast Han Chinese population with clinically diagnosed ASD.Methods: We performed a genome-wide CNVs screening in Northeast Han Chinese individuals with ASD using array-based comparative genomic hybridization.Results: We found 22 kinds of CNVs (six deletions and 16 duplications) were potential pathogenic. These CNVs were distributed in chromosome 1p36.33, 1p36.31, 1q42.13, 2p23.1-p22.3, 5p15.33, 5p15.33-p15.2, 7p22.3, 7p22.3-p22.2, 7q22.1-q22.2, 10q23.2-q23.31, 10q26.2-q26.3, 11p15.5, 11q25, 12p12.1-p11.23, 14q11.2, 15q13.3, 16p13.3, 16q21, 22q13.31-q13.33, and Xq12-q13.1. Additionally, we found 20 potential pathogenic genes of ASD in our population, including eight protein coding genes (six duplications [DRD4, HRAS, OPHN1, SHANK3, SLC6A3, and TSC2] and two deletions [CHRNA7 and PTEN]) and 12 microRNAs genes (ten duplications [MIR202, MIR210, MIR3178, MIR339, MIR4516, MIR4717, MIR483, MIR675, MIR6821, and MIR940] and two deletions [MIR107 and MIR558]).Limitations: The sample size in our study may confer limited statistical power to discover significant findings. De novo or inherited of the CNVs were not be classified because of the lack of data from parents.Conclusions: We identified CNVs and genes implicated in ASD risks, conferring perception to further reveal ASD etiology.

scholarly journals Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder

Science ◽  
2018 ◽  
Vol 362 (6420) ◽  
pp. eaat6576 ◽  
Author(s):  
Joon-Yong An ◽  
Kevin Lin ◽  
Lingxue Zhu ◽  
Donna M. Werling ◽  
Shan Dong ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Risk Score ◽  
De Novo ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
De Novo Mutations ◽  
Promoter Regions ◽  
Complex Disorders ◽  
Genome Wide ◽  
Evolutionarily Conserved

Whole-genome sequencing (WGS) has facilitated the first genome-wide evaluations of the contribution of de novo noncoding mutations to complex disorders. Using WGS, we identified 255,106 de novo mutations among sample genomes from members of 1902 quartet families in which one child, but not a sibling or their parents, was affected by autism spectrum disorder (ASD). In contrast to coding mutations, no noncoding functional annotation category, analyzed in isolation, was significantly associated with ASD. Casting noncoding variation in the context of a de novo risk score across multiple annotation categories, however, did demonstrate association with mutations localized to promoter regions. We found that the strongest driver of this promoter signal emanates from evolutionarily conserved transcription factor binding sites distal to the transcription start site. These data suggest that de novo mutations in promoter regions, characterized by evolutionary and functional signatures, contribute to ASD.

scholarly journals A genome-wide linkage study of autism spectrum disorder and the broad autism phenotype in extended pedigrees

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Marc Woodbury-Smith ◽  
Andrew D. Paterson ◽  
Irene O’Connor ◽  
Mehdi Zarrei ◽  
Ryan K. C. Yuen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Linkage Study ◽  
Broad Autism Phenotype ◽  
Spectrum Disorder ◽  
Extended Pedigrees ◽  
Genome Wide ◽  
A Genome ◽  
Autism Phenotype

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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