Acknowledgements: N.K., R.R., K.B.M., T.H. and S.A.T. were supported by Wellcome Sanger core funding (grant no. WT206194) and the Human Induced Pluripotent Stem Cell Initiative. T.H. was supported by the Israel Science Foundation (grant no. 435/20) and by the Chan Zuckerberg Initiative (Single-Cell Analysis of Inflammation, grant no. DAF2020-217532). R.R. was supported by the BBSRC Doctoral Training Programme. F.J.C.-N. and B.G. were supported by Wellcome (grant no. 206328/Z/17/Z) and MRC (grant no. MR/S036113/1). P.A.L. was supported by the Evelyn Trust (grant no. 20/75) and the UKRI/NIHR through the UK Coronavirus Immunology Consortium. K.B.W. acknowledges funding from University College London, Birkbeck MRC Doctoral Training Programme. M.Z.N. acknowledges funding from an MRC Clinician Scientist Fellowship (grant no. MR/W00111X/1) and the Rutherford Fund Fellowship allocated by the MRC and from the UK Regenerative Medicine Platform 2 (grant no. MR/5005579/1). M.Z.N. and K.B.M. have been funded by the Rosetrees Trust (grant no. M944) and from Action Medical Research (grant no. GN2911). We thank M. D. Morgan for careful reading of the manuscript and data sharing.Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution
