DISSECTING COMPLEX DISEASE PLEIOTROPY THROUGH MULTI-TRAIT ASSOCIATION STUDIES

The success of biobanks in collecting phenotype and genotype data from millions of people has dramatically changed the scale of genetic associations studies. The collection of these data has made it possible to study the genetics of many more human traits in larger cohorts, and has shown that pleiotropy, the phenomenon where a single genetic locus has an effect on multiple traits, is ubiquitous in the human genome. Pleiotropy is particularly common between cardiometabolic traits and complex diseases, such as circulating lipid levels and coronary artery disease. We can study pleiotropy to better understand the relationships between these traits and detect novel therapeutic opportunities for these diseases. Using a number of different methods, I worked to first detect pleiotropic loci that involved cardiometabolic traits and then understand the genetic mechanisms behind these pleiotropic loci. I did this by using multi-trait genetic association methods to detect loci associated with multiple traits, both in common variants via multi-trait genome-wide association studies (GWAS) and in rare variants using multi-trait gene burden analyses. A vital tool for identifying candidate causal genes at pleiotropic loci identified from multi-trait GWAS was genetic colocalization analysis between GWAS signals and expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL). These analyses allowed us to identify which eQTL and sQTL signals for genes have evidence of sharing the same causal variants as the GWAS signals. Part of the work presented here is the development of a framework for performing these QTL-GWAS colocalization analyses at scale. Throughout these analyses we detected several loci with evidence of pleiotropy and identify many candidate causal genes supported by statistical genetics work as well as functional work. Some of these genes, such as DOCK4 and PCSK6, may be good candidates for therapeutic targets to treat multiple diseases. These experiments show how we can use large-scale genetic and phenotypic data from biobanks to better understand the relationships between human diseases and leverage this to identify potential therapeutic targets. Supplemental files for this document include: Supplementary Methods, Supplementary Tables 1-15, and Supplementary Figures S1-S6 for Chapter 3, Supplementary Methods, Supplementary Tables 1-8, and Supplementary Figures S1-S36 for Chapter 4, and Supplementary Tables 1-3 for Chapter 5.