2021 · Nature Genetics · 1,622 citations
Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies. Genome-wide association analyses of 41,917 bipolar disorder cases and 371,549 controls of European ancestry provide new insights into the etiology of this disorder and identify novel therapeutic leads and potential opportunities for drug repurposing.
2022 · International Journal of Scientific Research and Modern Technology. · 24 citations
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.