Causal Gene Prioritization in Systemic Sclerosis: Multi-Omics Analyses from Population Genetics to Single Cell Biology

Project Summary/Abstract This proposal outlines a five-year research career development program aimed at prioritizing genes that are potentially causal to the development of systemic sclerosis (SSc) using multi- omics analyses. The candidate is currently an Instructor in Medicine at the Columbia University Irving Medical Center and a rheumatologist at the New York-Presbyterian Hospital. The proposal leverages the candidate's established research background in genetic epidemiology and computational biology and broadens his expertise to encompass multi-omics analysis across the realms of both population genetics and single-cell sequencing. The proposed research and training will equip the candidate with a distinctive set of cross-disciplinary skills, fostering his transition to an independent physician-scientist in -omics science and precision medicine in SSc. SSc is a systemic autoimmune rheumatic disease with a 10-year survival rate of 71.7%, a statistic that has unfortunately remained stagnant over the past two decades. There is an unmet need to understand the causal biological signals that drive organ-specific disease activity. With the advent of multiple population quantitative trait loci (QTL) datasets, which concurrently measure genomic variation and other -omics data, innovative methodologies have been developed to predict the causal genes which mediate the genomic loci discovered in the genome-wide association studies (GWAS). The understanding derived from population-level post-GWAS analyses can generate pivotal hypotheses that guide the analyses of single-cell sequencing data. The specific aims of this proposal are: 1. Prioritize putative causal genes associated with SSc using population-level post-GWAS analyses, including the transcriptome- wide association study (TWAS), proteome-wide association study (PWAS), and multi-omics Mendelian randomization. 2. Discover key pathological B cell states using simultaneous scRNA- seq with scCITE-seq and scBCR-seq, with data-driven and hypothesis-driven analyses. The proposed research could enhance our understanding of SSc mechanisms and generate hypotheses for the discovery of novel therapeutic targets and biomarkers.