Dataset Information

Identifying Genomic Variations in Monozygotic Twins Discordant for Autism Spectrum Disorder Using Whole-Genome Sequencing.

ABSTRACT: Autism spectrum disorder (ASD) presents a set of childhood neurodevelopmental disorders with impairments in social communication and restricted, repetitive, and stereotyped patterns of behavior. Here, based on the whole-genome sequencing (WGS) data of three monozygotic twins discordant for ASD, we explored multiple patient-specific genetic variations and prioritized a list of ASD risk genes. Our results identified DVMT (discordant variation in monozygotic twin) observed in at least two twin pairs, including 14,310 SNPs, 2,425 indels, and 16,735 CNVs, referring to a total of 2,174 genes, and 37 of these were covered by all three types of variations. Gene ontology (GO) enrichment analysis of biological processes for 2,174 genes showed that the majority of these genes were related to neurodevelopmental processes. In addition, functional network analysis showed that there was a strong functional relevance between 37 genes covered by all three types of variations. In conclusion, for the first time, we conducted a comprehensive scan of genomic differences between monozygotic twins discordant for ASD, providing researchers with in-depth directions. It may also provide effective strategies for clinical treatment of individuals affected by ASD.

SUBMITTER: Huang Y

PROVIDER: S-EPMC6325071 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Identifying Genomic Variations in Monozygotic Twins Discordant for Autism Spectrum Disorder Using Whole-Genome Sequencing.

Huang Yan Y Zhao Yue Y Ren Yue Y Yi Ying Y Li Xiaodan X Gao Zhaomin Z Zhan Xiaolei X Yu Jia J Wang Dong D Liang Shuang S Wu Lijie L

Molecular therapy. Nucleic acids 20181201

Autism spectrum disorder (ASD) presents a set of childhood neurodevelopmental disorders with impairments in social communication and restricted, repetitive, and stereotyped patterns of behavior. Here, based on the whole-genome sequencing (WGS) data of three monozygotic twins discordant for ASD, we explored multiple patient-specific genetic variations and prioritized a list of ASD risk genes. Our results identified DVMT (discordant variation in monozygotic twin) observed in at least two twin pair ...[more]

PMID: 30623854

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Project description:Although monozygotic (MZ) twins share the majority of their genetic makeup, they can be phenotypically discordant on several traits and diseases. DNA methylation is an epigenetic mechanism that can be influenced by genetic, environmental and stochastic events and may have an important impact on individual variability. In this study we explored epigenetic differences in peripheral blood samples in three MZ twin studies on major depressive disorder (MDD). Epigenetic data for twin pairs were collected as part of a previous study using 8.1-K-CpG microarrays tagging DNA modification in white blood cells from MZ twins discordant for MDD. Data originated from three geographical regions: UK, Australia and the Netherlands. Ninety-seven MZ pairs (194 individuals) discordant for MDD were included. Different methods to address non independently-and-identically distributed (non-i.i.d.) data were evaluated. Machine-learning methods with feature selection centered on support vector machine and random forest were used to build a classifier to predict cases and controls based on epivariations. The most informative variants were mapped to genes and carried forward for network analysis. A mixture approach using principal component analysis (PCA) and Bayes methods allowed to combine the three studies and to leverage the increased predictive power provided by the larger sample. A machine-learning algorithm with feature reduction classified affected from non-affected twins above chance levels in an independent training-testing design. Network analysis revealed gene networks centered on the PPAR-γ (NR1C3) and C-MYC gene hubs interacting through the AP-1 (c-Jun) transcription factor. PPAR-γ (NR1C3) is a drug target for pioglitazone, which has been shown to reduce depression symptoms in patients with MDD. Using a data-driven approach we were able to overcome challenges of non-i.i.d. data when combining epigenetic studies from MZ twins discordant for MDD. Individually, the studies yielded negative results but when combined classification of the disease state from blood epigenome alone was possible. Network analysis revealed genes and gene networks that support the inflammation hypothesis of MDD.

Dataset Information

Identifying Genomic Variations in Monozygotic Twins Discordant for Autism Spectrum Disorder Using Whole-Genome Sequencing.

Publications

Identifying Genomic Variations in Monozygotic Twins Discordant for Autism Spectrum Disorder Using Whole-Genome Sequencing.

Similar Datasets

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