Abstract
In recent years, systematic genome-wide association studies of quantitative immune cell traits, represented by circulating levels of cell subtypes established by flow cytometry, have revealed numerous association signals, a large fraction of which overlap perfectly with genetic signals associated with autoimmune diseases. By identifying further overlaps with association signals influencing gene expression and cell surface protein levels, it has also been possible, in several cases, to identify causal genes and infer candidate proteins affecting immune cell traits linked to autoimmune disease risk. Overall, these results provide a more detailed picture of how genetic variation affects the human immune system and autoimmune disease risk. They also highlight druggable proteins in the pathogenesis of autoimmune diseases; predict the efficacy and side effects of existing therapies; provide new indications for use for some of them; and optimize the research and development of new, more effective and safer treatments for autoimmune diseases. Here we review the genetic-driven approach that couples systematic multi-parametric flow cytometry with high-resolution genetics and transcriptomics to identify endophenotypes of autoimmune diseases for the development of new therapies.