Abstract
The study of genetic effects on gene expression and other molecular traits using bulk sequencing has allowed for the functional annotation of disease variants in diverse human tissues. Advances in single-cell RNA sequencing and multi-omics protocols provide for unprecedented opportunities to increase the resolution of such genetic analyses, allowing to assess gene regulatory effects at the resolution of cell types, cell states and even in individual cells in human tissues. In the first part of this talk, I will present computational strategies for analyzing and integrating population-scale single-cell dataset. A challenge I will discuss is to leverage these data to map genetic effects at the resolution of cell types but also subtle subtypes in a data-driven manner. I will describe applications of these strategies to population-scale single-cell sequencing dataset from genetically diverse human iPSCs across differentiation towards a neuronal fate, identifying dynamic changes of regulatory variants. In the second part I will discuss extensions to use genetic engineering to assay tissue-targeted perturbations using single-cell readouts.