Computational Methods for Predicting Autism Spectrum Disorder from Gene Expression Data

SFARI Genes and where to find them; classification modelling to identify genes associated with Autism Spectrum Disorder from RNA-seq data

10.1101/2021.01.29.428754 ◽

2021 ◽

Author(s):

Magdalena Navarro ◽

T Ian Simpson

Keyword(s):

Gene Expression ◽

Autism Spectrum Disorder ◽

Differential Gene Expression ◽

Gene Expression Data ◽

Gene List ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Expression Data ◽

Link Type ◽

Differential Gene

AbstractMotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a gene’s status in the SFARI-gene list.ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels.AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980)[email protected]

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T253. A Machine Learning Approach to Predict the Changes of Brain Functional Connectivity in Autism Spectrum Disorder From the Gene Expression Data

Biological Psychiatry ◽

10.1016/j.biopsych.2018.02.590 ◽

2018 ◽

Vol 83 (9) ◽

pp. S227-S228 ◽

Cited By ~ 1

Author(s):

Sanjeevani Choudhery ◽

Chuan Huang ◽

Daifeng Wang

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Gene Expression Data ◽

Autism Spectrum ◽

Learning Approach ◽

Expression Data ◽

Machine Learning Approach ◽

Brain Functional Connectivity

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The Long Noncoding RNA RPS10P2-AS1 Is Implicated in Autism Spectrum Disorder Risk and Modulates Gene Expression in Human Neuronal Progenitor Cells

Frontiers in Genetics ◽

10.3389/fgene.2019.00970 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 3

Author(s):

Stephanie M. Bilinovich ◽

Kristy Lewis ◽

Nicole Grepo ◽

Daniel B. Campbell

Keyword(s):

Gene Expression ◽

Autism Spectrum Disorder ◽

Progenitor Cells ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Neuronal Progenitor Cells ◽

Neuronal Progenitor ◽

Autism Spectrum Disorder Risk

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Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning

Clinical Psychopharmacology and Neuroscience ◽

10.9758/cpn.2017.15.1.47 ◽

2017 ◽

Vol 15 (1) ◽

pp. 47-52 ◽

Cited By ~ 9

Author(s):

Dong Hoon Oh ◽

Il Bin Kim ◽

Seok Hyeon Kim ◽

Dong Hyun Ahn

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Autism Spectrum Disorder ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Gene Expression Signatures

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Noncoding de novo mutations contribute to autism spectrum disorder via chromatin interactions

10.1101/2019.12.15.877324 ◽

2019 ◽

Author(s):

Il Bin Kim ◽

Taeyeop Lee ◽

Junehawk Lee ◽

Jonghun Kim ◽

Hyunseong Lee ◽

...

Keyword(s):

Gene Expression ◽

Autism Spectrum Disorder ◽

Stem Cells ◽

Target Genes ◽

De Novo ◽

Regulatory Elements ◽

Autism Spectrum ◽

Spectrum Disorder ◽

De Novo Mutations ◽

Chromatin Interactions

Three-dimensional chromatin structures regulate gene expression across genome. The significance of de novo mutations (DNMs) affecting chromatin interactions in autism spectrum disorder (ASD) remains poorly understood. We generated 931 whole-genome sequences for Korean simplex families to detect DNMs and identified target genes dysregulated by noncoding DNMs via long-range chromatin interactions between regulatory elements. Notably, noncoding DNMs that affect chromatin interactions exhibited transcriptional dysregulation implicated in ASD risks. Correspondingly, target genes were significantly involved in histone modification, prenatal brain development, and pregnancy. Both noncoding and coding DNMs collectively contributed to low IQ in ASD. Indeed, noncoding DNMs resulted in alterations, via chromatin interactions, in target gene expression in primitive neural stem cells derived from human induced pluripotent stem cells from an ASD subject. The emerging neurodevelopmental genes, not previously implicated in ASD, include CTNNA2, GRB10, IKZF1, PDE3B, and BACE1. Our results were reproducible in 517 probands from MSSNG cohort. This work demonstrates that noncoding DNMs contribute to ASD via chromatin interactions.

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