University of Washington researchers are part of a team that has developed a powerful tool that uses single-cell studies to improve our understanding of disease genetics. It’s an innovation on transcriptome-wide association studies called TWiST that uses Expression Quantitative Trait Locus (eQTL) data. The method was outlined in a recent Cell Genomics.
"Cells are heterogenous. Even cells that broadly belong to the same type can have many, almost a continuum, of underlying cell states. TWiST models gene-disease relationship for each cell and thereby leverages the full resolution of single-cell eQTL data, making it more powerful than previous methods,” said Guanghao Qi, a UW professor of biostatistics who led the study.
Qi’s team applied TWiST to data from the ONEK1K study, a cohort of single-cell RNA sequencing data collected from 982 donors. They were able to identify hundreds of genes associated with autoimmune diseases, which provided new insights into disease mechanisms for conditions including rheumatoid arthritis, systemic lupus erythematosus, Crohn’s disease, inflammatory bowel disease, multiple sclerosis, type 1 diabetes mellitus, and ankylosing spondylitis.
“Many genes identified by TWiST have ‘dynamic’ effects on disease. Their effects can vary along the differentiation trajectory of immune cells. Understanding these genes can inform the development of therapeutic interventions,” said Qi, who noted that study findings hold promise for other diseases beyond autoimmune conditions.
Other UW Biostatistics faculty on the team include Eardi Lila, Ali Shojaie, and Wei Sun.
