Understanding the Genetic Basis of Complex Traits Through Multi-Omic Studies
Abstract
Complex traits are influenced by genetic and environmental factors and their interactions. Most common human disorders such as cardiovascular, metabolic, autoimmune, and neurological diseases are complex. Understanding their genetic architecture and etiology is an important step to prevent, diagnose and treat these conditions. Genome Wide Association Studies (GWAS) have emerged as a powerful and widely used tool that can be used to explore and identify the genetic variants associated with complex traits. In this dissertation, we present some of the downstream applications of GWAS studies to analyze and understand the genetic risk and etiology of complex traits and provide important insights into the genetic architecture and background of several complex phenotypes. First, we examined whether prevalence of complex disorders around the world correlates to Polygenic Risk Scores (PRS). To do so, we determined the average PRS of 14 such complex disorders across 24 world populations using results of GWAS studies. We found variation in risk across populations and significant correlation was obtained between average disease risk and prevalence for seven of the studied disorders. Further exploring the power of PRS-based calculations, we performed a PRS-based phenome wide association study (PheWAS) for Tourette Syndrome (TS) and identified 57 phenotypic outcomes significantly associated with TS PRS. The strongest associations were found between TS PRS and mental health factors. Cross-disorder comparisons of phenotypic associations with genetic risk for other childhood-onset disorders (e.g.: attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and obsessive-compulsive disorder [OCD]) indicated an overlap in associations between TS and these disorders. Furthermore, we performed a sex specific PheWAS that highlighted differences in associations of complex disorders with TS PRS in males and females. Finally, we used large-scale GWAS results to identify causal associations between different biological markers (proteins, metabolites, and microbes) and subcortical brain structure volumes using Mendelian Randomization (MR) analysis. We identified eleven proteins and six metabolites to be significantly associated with subcortical brain volume structures. Enrichment analysis indicated that the associated proteins were enriched for proteolytic functions and regulation of apoptotic pathways. Overall, our work demonstrates the power of GWAS studies to help disentangle the genetic basis of complex diseases and also provides important insights into the etiology of the studied complex traits.
Degree
Ph.D.
Advisors
Drineas, Purdue University.
Subject Area
Systematic biology|Genetics|Bioinformatics|Biostatistics
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