Project description:Genome wide DNA methylation profiling of FASD patients versus healthy unexposed controls. The Illumina Infinium 450k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 485,000 CpGs in peripheral blood samples. Samples included 28 healthy controls and7 FASD patients.
Project description:Genome wide DNA methylation profiling of FASD patients versus healthy unexposed controls. The Illumina Infinium 450k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 485,000 CpGs in peripheral blood samples. Samples included 64 healthy controls and 39 FASD patients.
Project description:Prenatal alcohol exposure is the leading preventable cause of behavioural and cognitive deficits, which may affect between 2-5% of children in North America. While the underlying mechanisms of alcohol’s effects on development remain relatively unknown, emerging evidence implicates epigenetic mechanisms in mediating the range of symptoms observed in children with Fetal Alcohol Spectrum Disorder (FASD). Thus, we investigated the effects of prenatal alcohol exposure on genome-wide DNA methylation in the NeuroDevNet FASD cohort, the largest cohort of human FASD samples to date. Genome-wide DNA methylation patterns of buccal epithelial cells were analyzed using the Illumina HumanMethylation450 array on a Canadian cohort of 206 children (110 FASD and 96 controls). Genotyping was performed in parallel using the Infinium HumanOmni2.5-Quad v1.0 BeadChip. After correcting for the effects of genetic background, 658 significantly differentially methylated sites between FASD cases and controls remained, with 41 displaying differences in beta greater than 5%. Furthermore, 203 differentially methylated regions containing 2 or more CpGs were also identified, overlapping with 167 different genes. The majority of differentially methylated genes were highly expressed in samples from the Allen Brain Atlas, which showed high correlations with buccal cell DNA methylation patterns. Furthermore, over-representation analysis of the up-methylated genes displayed a significant enrichment for neurodevelopmental processes and diseases, such as anxiety, epilepsy, and autism spectrum disorders. These findings suggest that prenatal alcohol exposure is associated with distinct DNA methylation patterns in children and adolescents, raising the possibility of an epigenetic biomarker 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.
