Associating Single-Cell Latent Factors with Genetic Risk

Abstract

Rheumatoid arthritis (RA) is a highly heritable disease, yet how its genetic risk translates into cell-type-specific mechanisms remains poorly understood. LIVI is a model that decomposes single-cell expression into donor and cell-state latent spaces, allowing for the reconstruction of the original data, but additionally leaving room for analysis of the retained latent information. The model has been shown to recover polygenic risk signals in healthy cohorts, but whether that is transferable to cohorts with active disease has not been tested. In this work, we apply LIVI to a predominantly RA cohort, with osteoarthritis (OA) patients as control, and ask whether the latent factors carry information about polygenic risk. After first confirming that the clinical cohort cell-state space recovers known immune cell populations, we test each of the 700 donor factors against the polygenic risk scores (PRSs) for 21 diseases, under different testing conditions, and find that one significant factor (D462) is recovered between the latent space and RA PRS. This association survives ancestry correction, and changes in cohort. The factor localises to NK and T cells and drives antigen presentation program whose expression seems to be inversely related to RA risk.