A Pseudotime-Dependent TWAS Framework Identifies Disease Genes along Cell Developmental Paths

Abstract Transcriptome-wide association studies (TWAS) link genes to disease risk by integrating gene expression with genome-wide association study (GWAS) data, where the use of bulk-tissue expression data typically provides gene-disease association interpretations at tissue levels. Recently, the increasing availability of single-cell gene expression profiles provides an opportunity to to dissect these associations at finer cellular granularity, allowing identification of cell-level effects that are not discernible from bulk-level analyses. While existing methods leverage single-cell data and map associations into discrete cell types, they may miss the continuous nature of cellular processes and misidentify causal cell stages in which genes exert their effects. To capture these continuous dynamic changes in gene expression, we developed the pseudotime-dependent Transcriptome-wide association study (pt-TWAS), a novel TWAS framework that captures gene effects along cell developmental paths and reveals their associations at a finer cell-stage resolution. By modeling gene expression as a continuous function of pseudotime, pt-TWAS gains statistical advantages over methods analyzing discrete cell types or stages. Specifically, it boosts statistical power by borrowing expression quantitative trait loci (eQTL) information across cell stages and jointly testing the gene-disease associations. Furthermore, pt-TWAS constructs and visualizes simultaneous confidence bands for the gene effect curve to identify the causal cell stage for the disease. As a demonstration of our method, we applied pt-TWAS to a GWAS of B-cell acute lymphoblastic leukemia (ALL) leveraging single-cell data from OneK1K, where we successfully replicated known risk genes from previous analyses and pinpointed their relevant cell stages. An R package implementing pt-TWAS is available at https://github.com/RuiCao34/ptTWAS/. Competing Interest Statement The authors have declared no competing interest. Funding Statement R. Cao gratefully acknowledges support from the Doctoral Dissertation Fellowship by the University of Minnesota. T. Yang is supported by the St.∼Baldrick Career Award (1463960) and NIH grant R01AG074858. C. Li is supported in part by NSF grant DMS-2515789. 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: Ethics committee/IRB of University of Minnesota gave ethnical approval for this work (STUDY00002782). 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 Data Availability All data produced in the present work are contained in the manuscript