Neurodevelopmental disorders such as autism spectrum disorders (ASD), cerebral palsy (CP) and epilepsy are some of the most prevalent neurological disorders caused by significant damages to the growth and development of the brain. Epigenetic modification, such as DNA methylation, has been implicated both as a mediator and potential biomarker for neurodevelopmental diseases. Distinguishing the extent of effect of genetics and environment however, is confounded by a large number of variables. The study of monozygotic (MZ) twins, in which genetics, age, sex, parental factors and shared environment are controlled for, has led to significant advances in our knowledge of disease mechanisms. Molecular studies that evaluate the differences in DNA methylation between disease-discordant MZ co-twins open up the possibility of singling out environmental effects that contribute to disease aetiology and may facilitate in biomarker development.
We studied DNA methylation within three MZ twin cohorts discordant for a neurodevelopmental disorder. Genome-wide DNA methylation was measured using Illumina’s Infinium HumanMethylation450 and EPIC arrays. Statistical and bioinformatics pipelines were used to analyse methylation data. Analysis of CP-discordant twin pairs performed on DNA from dried blood spots taken at birth, identified various differentially methylated probes and gene regions associated with cell adhesion and inflammation, indicating its significance in CP pathophysiology. Distinct patterns of DNA methylation between epilepsy subtypes were identified highlighting different underlying mechanisms that may account for phenotypic variation. A within pair analysis of focal and generalised epilepsies opened up the possibility of subtype-specific epigenetic marks, which could facilitate biomarker development to assist diagnosis and impact treatment choices in epilepsy.
This project revealed informative results that have implications for research and treatment options for patients suffering from a broad spectrum of neurodevelopmental disorders. Analysing epigenetic data from twins discordant for neurodevelopmental disorders has the power to further refine models of disease mechanisms and biomarkers.