Uncovering Pseudotime-Varying Genetic Causal Effects Along Single-Cell Trajectories for Pulmonary Disease Trait

Abstract

With the increasing accessibility of single-cell RNA sequencing (scRNA-seq) data, cell-type-specific gene expression can be linked to complex traits through pseudo-bulk method, which considered aggregated gene expression from multiple cells of the same annotated cell type per individual and clearly shows the limitation of ignoring intra-individual cell-to-cell variability. Concurrently, pseudotime trajectory inference has gained popularity for its ability to capture continuous biological processes such as cell differentiation and lineage development, instead of individual discrete stages. It is natural to consider whether genetic effects for complex traits, such as individual level disease status, show a dynamic pattern along the inferred trajectories. In this study, we introduce a novel framework that models gene expression as a function of pseudotime along the inferred trajectories. We mapped expression quantitative trait loci (eQTL) effects in the cis-region as functional parameters, which we called "dynamic eQTLs", showing regulatory effects exerted by genetic variants change continuously along the cellular trajectory. For eQTLs of constant effects across pseudotime we leveraged external bulk-eQTL information to enhance the power. Furthermore, we employed significant, variable dynamic eQTLs as instrumental variables to infer causal relationships between gene expression and complex traits. To address challenges inherent to scRNA-seq data--such as sparsity and high variability--we incorporate an empirical likelihood-based inference method, which is non-parametric and self-normalized. Besides, genes associated with trajectory branchpoints may bring confounding, and we also proposed a causal mediation analysis framework to determine whether a gene plays a causal role for the disease directly and indirectly through driving cell fates. Applying our method to scRNA-seq data from human lung tissue of 114 samples (66 pulmonary fibrosis cases and 48 controls), along with meta-analyzed GWAS summary statistics for IPF from 3 studies, we identified pseudotime-dependent causal effects for IPF from genes implicated in the trajectory AT2 - translational AT2 - AT1, which is crucial in lung tissue repair and regeneration. We also found that 30 genes have a mediated effect through cell fates.