Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease

Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in dopaminergic neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was supported by the following National Institutes of Health (NIH) grants to E.R.G.: NHGRI R35HG010718, NHGRI R01HG011138, NIA AG068026, NIGMS R01GM140287, and NIMH R01MH126459. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The cell type and cell state adjusted prediction models are available on Zenodo at (to be provided up on publication). Single cell transcriptome data across dopaminergic neuron differentiation can be found on Zenodo (https://zenodo.org/record/4333872). The individual level genotype data are downloadable from the Human Induced Pluripotent Stem Cells Initiative (HipSci) website (https://www.hipsci.org/data). The summary statistics for the PGC data are available on the online data repository (https://pgc.unc.edu/for-researchers/download-results/). Summary statistics for the UKBB GWAS are available at the Neale Lab online data repository (http://www.nealelab.is/uk-biobank). The tissue-specific PrediXcan gene expression models leveraged here are available for download from the JTI repository (https://doi.org/10.5281/zenodo.3842289). Phased individual level genotype data from the 1000 Genomes project can be downloaded from (https://www.internationalgenome.org). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes