Dataset Information

Brain Imaging-Guided Analysis Reveals DNA Methylation Profiles Correlated with Insular Surface Area and Alcohol Use Disorder.

ABSTRACT: BACKGROUND:Alcohol use disorder (AUD) is a wide-spread, heritable brain disease, but few studies have linked genetic variants or epigenetic factors to brain structures related to AUD in humans, due to many factors including the high-dimensional nature of imaging and genomic data. METHODS:To provide potential insights into the links among epigenetic regulation, brain structure, and AUD, we have performed an integrative analysis of brain structural imaging and blood DNA methylome data from 52 AUD and 58 healthy control (HC) subjects collected in the Nathan Kline Institute-Rockland Sample. RESULTS:We first found that AUD subjects had significantly larger insular surface area than HC in both left and right hemispheres. We then found that 7,827 DNA methylation probes on the HumanMethylation450K BeadChip had significant correlations with the right insular surface area (false discovery rate [FDR] < 0.05). Furthermore, we showed that 44 of the insular surface area-correlated methylation probes were also strongly correlated with AUD status (FDR < 0.05). These AUD-correlated probes are annotated to 36 protein-coding genes, with 16 genes (44%) having been reported by others to be related to AUD or alcohol response, including TAS2R16 and PER2. The remaining 20 genes, in particular ARHGAP22, might represent novel genes involved in AUD or responsive to alcohol. CONCLUSIONS:We have identified 36 insular surface area- and AUD-correlated protein-coding genes that are either known to be AUD- or alcohol-related or not yet reported by prior studies. Therefore, our study suggests that the brain imaging-guided epigenetic analysis has a potential of identifying possible epigenetic mechanisms involved in AUD.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC6443499 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Brain Imaging-Guided Analysis Reveals DNA Methylation Profiles Correlated with Insular Surface Area and Alcohol Use Disorder.

Zhao Yihong Y Ge Yongchao Y Zheng Zhi-Liang ZL

Alcoholism, clinical and experimental research 20190304 4

<h4>Background</h4>Alcohol use disorder (AUD) is a wide-spread, heritable brain disease, but few studies have linked genetic variants or epigenetic factors to brain structures related to AUD in humans, due to many factors including the high-dimensional nature of imaging and genomic data.<h4>Methods</h4>To provide potential insights into the links among epigenetic regulation, brain structure, and AUD, we have performed an integrative analysis of brain structural imaging and blood DNA methylome da ...[more]

PMID: 30830696

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Project description:Background:Fetal alcohol spectrum disorder (FASD) is a developmental disorder that manifests through a range of cognitive, adaptive, physiological, and neurobiological deficits resulting from prenatal alcohol exposure. Although the North American prevalence is currently estimated at 2-5%, FASD has proven difficult to identify in the absence of the overt physical features characteristic of fetal alcohol syndrome. As interventions may have the greatest impact at an early age, accurate biomarkers are needed to identify children at risk for FASD. Building on our previous work identifying distinct DNA methylation patterns in children and adolescents with FASD, we have attempted to validate these associations in a different clinical cohort and to use our DNA methylation signature to develop a possible epigenetic predictor of FASD. Methods:Genome-wide DNA methylation patterns were analyzed using the Illumina HumanMethylation450 array in the buccal epithelial cells of a cohort of 48 individuals aged 3.5-18 (24 FASD cases, 24 controls). The DNA methylation predictor of FASD was built using a stochastic gradient boosting model on our previously published dataset FASD cases and controls (GSE80261). The predictor was tested on the current dataset and an independent dataset of 48 autism spectrum disorder cases and 48 controls (GSE50759). Results:We validated findings from our previous study that identified a DNA methylation signature of FASD, replicating the altered DNA methylation levels of 161/648 CpGs in this independent cohort, which may represent a robust signature of FASD in the epigenome. We also generated a predictive model of FASD using machine learning in a subset of our previously published cohort of 179 samples (83 FASD cases, 96 controls), which was tested in this novel cohort of 48 samples and resulted in a moderately accurate predictor of FASD status. Upon testing the algorithm in an independent cohort of individuals with autism spectrum disorder, we did not detect any bias towards autism, sex, age, or ethnicity. Conclusion:These findings further support the association of FASD with distinct DNA methylation patterns, while providing a possible entry point towards the development of epigenetic biomarkers of FASD.

Project description:Background: Fetal alcohol spectrum disorder (FASD) is a developmental disorder that manifests through a range of cognitive, adaptive, physiological, and neurobiological deficits resulting from prenatal alcohol exposure. Although the North American prevalence is currently estimated at 2-5%, FASD has proven difficult to identify in the absence of the overt physical features characteristic of fetal alcohol syndrome. As interventions may have the greatest impact at an early age, accurate biomarkers are needed to identify children at risk for FASD. Building on our previous work identifying distinct DNA methylation patterns in children and adolescents with FASD, we have attempted to validate these associations in a different clinical cohort and to use our DNA methylation signature to develop a possible epigenetic predictor of FASD. Methods: Genome-wide DNA methylation patterns were analyzed using the Illumina HumanMethylation450 array in the buccal epithelial cells of a cohort of 48 individuals aged 3.5-18 (24 FASD cases, 24 controls). The DNA methylation predictor of FASD was built using a stochastic gradient boosting model on our previously published dataset FASD cases and controls (GSE80261). The predictor was tested on the current dataset and an independent dataset of 48 autism spectrum disorder cases and 48 controls (GSE50759). Results: We validated findings from our previous study that identified a DNA methylation signature of FASD, replicating the altered DNA methylation levels of 161/648 CpGs in this independent cohort, which may represent a robust signature of FASD in the epigenome. We also generated a predictive model of FASD using machine learning in a subset of our previously published cohort of 179 samples (83 FASD cases, 96 controls), which was tested in this novel cohort of 48 samples and resulted in a moderately accurate predictor of FASD status. Upon testing the algorithm in an independent cohort of individuals with autism spectrum disorder, we did not detect any bias towards autism, sex, age, or ethnicity. Conclusion: These findings further support the association of FASD with distinct DNA methylation patterns, while providing a possible entry point towards the development of epigenetic biomarkers of FASD.

Dataset Information

Brain Imaging-Guided Analysis Reveals DNA Methylation Profiles Correlated with Insular Surface Area and Alcohol Use Disorder.

Publications

Brain Imaging-Guided Analysis Reveals DNA Methylation Profiles Correlated with Insular Surface Area and Alcohol Use Disorder.

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