Single-cell models for state-dependent eQTL analysis

As single-cell RNA-seq datasets grow larger and more complex, they enable richer analyses of how gene expression varies between cells and people. However, methods designed for bulk data fail to account for the unique structure of single-cell gene expression. Researchers are now developing statistical models tailored to single-cell-resolution data for a variety of applications. In this primer, I will focus on single-cell models for the task of mapping expression quantitative trait loci (eQTLs) to find genetic variants associated with a gene's expression. Single-cell eQTL models have the potential to capture disease-relevant, state-dependent regulatory effects with the right statistical models and representations of cell state. This primer will discuss the evolution of statistical models for eQTL mapping from pseudobulk to single-cell resolution, representations of single-cell states for state-dependent analyses, and outstanding computational challenges.