sc-pcQTL: hurdle-based co-expression modeling for multi-gene QTL mapping in single-cell RNA-seq data

Motivation: Single-cell expression quantitative trait locus (eQTL) studies can resolve cell-type-specific genetic effects, but conventional gene-by-gene analyses do not directly capture coordinated genetic regulation of neighboring genes. Principal-component QTL (pcQTL) mapping can summarize such multi-gene effects, but existing approaches were developed for bulk expression and are not designed for sparse single-cell counts. Results: We developed sc-pcQTL, a framework that applies two-component hurdle modeling and sliding-window clustering to identify local co-expression clusters, summarizes each cluster using principal components, and maps cis-pcQTLs. In simulations, the individual hurdle components controlled type I error, while the component-union screening rule was substantially more powerful than donor-level pseudobulk correlation tests. Applied to 1.24 million peripheral blood mononuclear cells from 982 OneK1K donors across 10 cell types, sc-pcQTL identified 2,485 local co-expression clusters and conducted QTL mapping for 4,353 cluster-PC phenotypes at single-cell resolution, of which 2,040 had at least one significant cis-pcQTL association. Fine-mapping and colocalization with genome-wide association study loci across 1,163 phenotypes in the FinnGen study identified 394 colocalized QTL-GWAS signal groups. Each group comprised fine-mapped QTL and GWAS signals connected through one or more colocalization links within the same cell type and local gene cluster. Of these groups, 46 were pcQTL-specific and contained no colocalized single-gene eQTL from a constituent gene. Locus-level analyses further revealed cell-type-specific multi-gene regulatory effects. Thus, sc-pcQTL complements conventional single-gene eQTL analysis by identifying trait-relevant regulatory signals shared across neighboring genes.