Genetic Architecture of Complex Psychiatric Disorders -- Discoveries and Methods
Impacting individual’s social and physical well-being, psychiatric disorders have been a substantial burden on public health. As such disorders are frequently observed aggregating in families, we can expect a large involvement of heritable components underlying their etiologies. Therefore, studying the genetic architecture and basis is one of the most important aims toward developing effective treatments for psychiatric disorders. The overall objective of this dissertation is to contribute to understanding the genetics of psychiatric disorders. Analyzing summary statistics from genomewide association studies (GWAS) of psychiatric disorders, we mainly present results of two projects. In the first one, we evaluated commonalities and distinctions in genetic risk of four highly comorbid childhood onset neuropsychiatric disorders: attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and Tourette’s syndrome (TS). Through systematic analysis of genetic architecture and correlation, we confirmed exitance of genetic components shared across ADHD, ASD and TS, as well as OCD and TS. Subsequently, we identified those components at variant, gene, and tissue specificity levels through meta-analyses. Our results pointed toward possible involvement of hypothalamus-pituitary-adrenal (HPA) axis, a human stress response system, in the etiology of these childhood onset disorders. The second project includes the proposition of a novel framework for general GWAS summary statistics-based analyses. Instead of regular odds ratio and standard errors archived in the summary statistics, we proposed a recounstruction approach to rewrite the results in terms of single nucleotide polymorphisms (SNP) allelic and genotypic frequencies. We also put forward three applications built-upon the proposed framework, and evaluated the performance on both synthetic data and real GWAS results of psychiatric disorders for each of them. Through these three applications, we demonstrated that this framework can broaden the scope of GWAS summary statistics-based analyses and unify various of analyses pipelines. We hope our work can serve as a stepping-stone for future researchers aiming at understanding and utilizing GWAS results of complex psychiatric disorders.