Project Summary: Autoimmune diseases, such as rheumatoid arthritis and systemic lupus erythematosus, are debilitating and highly prevalent chronic conditions that result from pathogenic inflammatory responses. The major histocompatibility (MHC) region on chromosome 6, which contains the human leukocyte antigen (HLA) and other immune-related genes, has the strongest genetic association with autoimmune diseases, but the exact molecular mechanisms behind MHC disease risk are yet unsolved. Previous research has primarily explored how coding variants affect HLA protein structure and antigen binding, but recent studies highlight the potential role of noncoding variants in regulating HLA expression. Increased HLA expression could play a causal role in disease through higher levels of antigen presentation to autoreactive T cells. There is a critical need to better understand how a cell’s biological state impacts genetic control of HLA expression. The proposed research will test the hypothesis that genetic variation in the MHC region modulates HLA expression in a cell-state-dependent and disease-relevant manner. The applicant will develop innovative computational methods to integrate both genetic and single-cell transcriptomic data sampled from inflamed tissues and controls across multiple human immune-mediated disease contexts, comprising >1,088,000 cells from 384 individuals. Specifically, the study aims to (1) quantify the effect of genetic variation on HLA expression in key immune and stromal cell states (T, B, fibroblast, and myeloid cells), (2) identify expression programs and transcriptional regulators that modulate the effect of HLA regulatory variants, and (3) link HLA regulatory variation to autoimmune disease risk loci. This work will generate a resource detailing HLA expression across diverse cell states and identify the specific contexts in which genetic variants regulate HLA expression. This will deepen our fundamental understanding of mechanisms underlying autoimmune disease risk and may pave the way for better informed therapeutic strategies. The proposed training plan will enable the applicant to: (A) strengthen an understanding of the genetic and immune basis of human diseases, (B) cultivate strong skills in computational genomics methods development, (C) develop data science skills in statistical genetics and computational immunology, (D) improve understanding of the clinical aspects of autoimmune diseases, and (E) develop professional scientific communication skills. An enriching and supportive training environment and close mentorship by experts in complex trait genetics, immunology, and single-cell methods development will equip the applicant with knowledge and skills to become an effective physician-scientist who can contribute to the field of disease- focused computational immunogenomics.