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Explore scalable single-cell models for robust cell-state-dependent eQTL mapping in this comprehensive talk and primer. Delve into the challenges of parametrizing single-cell gene expression profiles and testing associations in large datasets. Learn about a new generalizable approach using non-parametric bootstrap procedures and the Julia programming language to identify cell state-dependent eQTLs efficiently. Discover how this method can be applied to identify autoimmune disease risk loci with context-specific effects in memory T cells. The primer covers the evolution of statistical models for eQTL mapping, representations of single-cell states for state-dependent analyses, and ongoing computational challenges in the field. Gain insights into how single-cell RNA-seq datasets enable richer analyses of gene expression variation between cells and individuals, and understand the potential of single-cell eQTL models to capture disease-relevant, state-dependent regulatory effects.
