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Jose Alquicira-Hernandez
Raychaudhuri Lab, Division of Genetics/Rheumatology, Inflammation, and Immunity Brigham and Women's Hospital,
Department of Medicine, Harvard Medical School

Meeting: Scalable single-cell models for robust cell-state-dependent eQTL mapping

Modelling cell state-dependent genetic associations with single-cell gene expression exhibits statistical and computational challenges. First, parametrization of single-cell gene expression profiles is not a straightforward task because individual genes exhibit distinct distributions. Second, current single-cell datasets consist of hundreds of thousands to millions of cells, which constrains the ability to test associations in a scalable manner. In this talk, I will introduce a new generalizable approach to robustly identify cell state-dependent eQTLs in single-cell data. To overcome the challenge of gene expression parametrization, we implemented a non-parametric bootstrap procedure to compute empirically calibrated p-values for variant-gene expression associations. To speed up the computation, we used the Julia programming language and pre-computed covariate-adjusted gene expression profiles with a linear mixed model before testing cell state-dependent eQTL interactions. Finally, I will demonstrate an application of this approach to identify autoimmune disease risk loci with context-specific effects in memory T cells.

Aparna Nathan
Lecturer on Biomedical Informatics, Harvard Medical School

Primer: 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.

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