Anglia Ruskin University

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

Gene-Environment Interactions and Epigenetic Regulation in Autism Etiology through Multi-Omics Integration and Computational Biology Approaches

Autism Spectrum Disorder (ASD) is a multifactorial neurodevelopmental condition characterized by substantial genetic heterogeneity and complex environmental influences. Emerging evidence suggests that gene-environment interactions, mediated through dynamic epigenetic mechanisms, play a critical role in modulating neurodevelopmental trajectories implicated in ASD. This review synthesizes current advances in understanding the etiological interplay between genetic variants, environmental exposures, and epigenetic regulation, with a focus on DNA methylation, histone modifications, and non-coding RNAs. We explore how these layers of molecular control intersect to dysregulate neurodevelopmental gene networks and contribute to ASD pathophysiology. Central to this investigation is the integration of multi-omics platforms— encompassing genomics, transcriptomics, epigenomics, proteomics, and metabolomics—supported by computational biology, machine learning, and systems-level modeling frameworks. These technologies facilitate the identification of molecular subtypes, predictive biomarkers, and regulatory circuits associated with ASD. Furthermore, we examine the translational implications of these findings in the context of precision medicine, including early diagnosis, patient stratification, and individualized therapeutic development. Despite the challenges of data heterogeneity, scalability, and interpretability, the integration of high-dimensional biological data holds transformative potential for elucidating ASD etiology and advancing targeted interventions.