Statistical integration of single-cell eQTL studies and GWAS with application to autoimmune diseases

Project Summary / Abstract Single-cell expression quantitative trait locus (eQTL) studies aim to characterize regulatory effects of genetic variants on gene expression in cell types or cell states. These studies present unprecedented opportunities to elucidate the molecular mechanisms underlying variant-disease associations. However, due to lack of statistical methods, integrative analysis of single-cell eQTL and genome-wide association studies (GWASs) have ignored the cell state heterogeneity within cell types. Previous research showed that autoimmune disease-associated variants can be functional in highly specific states of immune cells. The proposed research aims to develop statistical methods for integration of single-cell eQTL and GWAS data at cell state resolution. Specific aims include 1) transcriptome-wide association studies (TWAS) using single-cell eQTL data, 2) quantifying disease heritability mediated by gene expression, and 3) using the methods to analyze a large single-cell eQTL study of peripheral blood mononuclear cells (PBMCs) and GWASs of autoimmune diseases. The study will provide powerful tools to gain biological insights from the rapidly growing single-cell eQTL studies, prioritize causal genes and specific cell states for autoimmune diseases, and guide the design of future studies. Dr. Guanghao Qi is a biostatistician and statistical geneticist who has extensive expertise in GWAS, and experience in allele-specific expression. This career development award will build on his existing expertise and provide in-depth training in single-cell genomics and immunology. Training will be achieved through hands-on research, intensive coursework, and regular meetings with the mentors: primary mentor Dr. Wei Sun (single-cell genomics), co-mentors Dr. Ali Shojaie (high-dimensional statistics and multi-omics) and Dr. Ram Savan (immunology and RNA biology). The mentoring team has deep expertise in complementary areas and a successful track record of mentoring junior scientists. The University of Washington is an excellent research environment with rich resources and numerous collaboration opportunities. The training and mentoring provided by this award, combined with Dr. Qi’s existing expertise and strong institutional support, will propel Dr. Qi to develop into an independent investigator at the interface of genetics, genomics, immunology, and data science.