medRxivGenetic & Genomic MedicinePreprint
Single-cell genetics identifies cell-type-specific effector genes across complex traits and diseases
Albert HenryiD, Anne SenabouthiD, Mubarika TyeballyiD, Blake Bowen, Peter C. AlleniD, Eleanor SpenceleyiD, Eszter Sagi-ZsigmondiD, Rachael A McCloy, Anna S. E. CuomoiD, Jianan FaniD, Hao Lawrence HuangiD, Hope A. TanudisastroiD, Angli XueiD, Oscar Dong, Bradley T HarrisiD, Tobi AlegbeiD, Tim RaineiD, Carl A Anderson, Gibran HemaniiD, Katrina M. de Lange, Gemma Alexandra FigtreeiD, Alex William HewittiD, Daniel G. MacArthuriD, Joseph E. PowelliD
- Citations
- 2
Abstract
Genome-wide association studies (GWAS) have been instrumental in uncovering the genetic basis of complex traits. When integrated with expression quantitative trait loci (eQTL) mapping, they can help identify effector genes within GWAS loci through gene regulatory mechanisms. Recent single-cell eQTL (sc-eQTL) studies suggest that genetic effects on gene expression are often cell type- and subtype-specific, offering opportunities to uncover context-specific roles of effector genes that traditional GWAS and eQTL studies in bulk tissues may obscure. Here, we leverage sc-eQTL mapping results from the TenK10K phase 1 project to prioritise effector genes across 69 diseases and 31 biomarker traits, from 12,266 eGenes identified in 28 peripheral immune cell types. Using Mendelian randomisation (MR) and colocalisation, we present a catalogue of predicted effector genes and their estimated directional effects on the trait at cell type resolution, spanning 54,245 associations across 5,270 genes and 28 cell types for disease traits, and 348,103 associations across 9,581 genes and 28 cell types for biomarker traits. By quantifying polygenic enrichment at both the single-cell and cell type levels, we identify distinct immune cell contributions to immune-related and systemic conditions. We demonstrate differential polygenic enrichment of Crohns disease and COVID-19 amongst dendritic cell subtypes. Integrating clinical drug development data shows that therapeutic compounds targeting the gene-trait associations identified in this study are twice as likely to secure regulatory approval. Using Crohns disease as a motivating example, we demonstrate how population-based sc-eQTL data can pinpoint risk loci, effector genes, and cell types, complementing findings from disease-focused tissue samples. Our findings provide a foundational resource for understanding the cell-type-specific genetic architecture of disease and for guiding therapeutic discovery.
- organism: human
- assay: scRNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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