A unified framework for cell-type-specific eQTLs prioritization by integrating bulk and scRNA-seq data

Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex traits, yet the biological interpretation remains challenging, especially for variants in non-coding regions. Expression quantitative trait loci (eQTLs) studies have linked these variations to gene expression, aiding in identifying genes involved in disease mechanisms. Traditional eQTL analyses using bulk RNA sequencing (bulk RNA-seq) provide tissue-level insights but suffer from signal loss and distortion due to unaddressed cellular heterogeneity. Recently, single-cell RNA sequencing (scRNA-seq) has provided higher resolution enabling cell-type-specific eQTL (ct-eQTL) analyses. However, these studies are limited by their smaller sample sizes and technical constraints. In this paper, we present a novel statistical framework, IBSEP, which integrates bulk RNA-seq and scRNA-seq data for enhanced cteQTLs prioritization. Our method employs a Bayesian hierarchical model to combine summary statistics from both data types, overcoming the limitations while leveraging the advantages associated with each technique. Through extensive simulations and real-data analyses, including peripheral blood mononuclear cells and brain cortex datasets, IBSEP demonstrated superior performance in identifying ct-eQTLs compared to existing methods. Our approach unveils new transcriptional regulatory mechanisms specific to cell types, offering deeper insights into the genetic basis of complex diseases at a cellular resolution. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 All data produced in the present work are contained in the manuscript