UC San Diego

Type 1 Diabetes (T1D) is a heterogenous polygenic disease characterized by beta cell loss and insulin deficiency. Genome wide association studies (GWAS) have been conducted to identify genetic risk associated with T1D, but the totality of genetic risk which can lead to a spectrum of phenotypes has not been fully examined. In this thesis, I present three studies that identified novel T1D associated signals and generated genetic risk scores for specific applications. In the first study, I performed a fine-mapped genetic association study in the largest European cohort to date, identifying 19 novel signals across the genome. I leveraged 199 associated T1D variants into a machine learning model to generate the most robust T1D genetic risk score to date. Additionally, cluster analysis revealed distinct genetic patterns associated with phenotypic features such as age of onset and renal complications. In the second study, I performed association analyses and heterogeneity tests to examine differences between individuals with and without high-risk HLA-DR3/DR4 haplotypes in T1D. The non-DR3/DR4 individuals exhibited stronger risk in an HLA class I factor, HLA-B*39:06, and increased polygenic risk across the genome including a novel risk signal, OSTN. I used these new effect sizes to generate a genetic risk score that better identifies those with T1D in the non-DR3/DR4 population and outperforms existing models. In the third study, I created an additive genetic risk score to better target individuals of differing ancestry by using European and African American associated variants. I examined the effect of genetic risk in a multi-ancestry single cell dataset and identified changes in high-risk individuals. I highlight differential changes in pancreatic cell types associated with higher T1D risk including GLIS3-AS1 in beta cells. This thesis demonstrates how identifying and characterizing genetic risk in T1D can create novel risk scores that more accurately capture the diverse risk profiles of the disease and enhance understanding of its underlying biological mechanisms.