scDALI: Modelling allelic heterogeneity of DNA accessibility in single-cells reveals context-specific genetic regulation

Abstract

While the functional impact of genetic variation can vary across cell types and states, capturing this diversity remains challenging. Current studies, using bulk sequencing, ignore much of this heterogeneity, reducing discovery and explanatory power. Single-cell approaches combined with F1 genetic designs provide a new opportunity to address this problem, however suitable computational methods to model these complex relationships are lacking. Here, we developed scDALI, an analysis framework that integrates single-cell chromatin accessibility for unbiased cell state identification with allelic quantifications to assay genetic effects. scDALI builds on Gaussian process regression and can differentiate between homogeneous (pervasive) allelic imbalances and cell state-specific regulation. As a proof-of-principle, we applied scDALI to whole Drosophila embryos from F1 crosses, profiling sciATAC-seq at three embryonic stages. Even in these very complex samples, scDALI discovered hundreds of peaks with heterogeneous allelic imbalance, having effects in specific lineages and/or developmental stages. Our study provides a general strategy to identify the cellular context of allelic imbalance, a crucial step in linking genetic traits to cellular phenotypes.