Reconstructing donor genotypes from scRNA-seq for downstream eQTL and HLA-TCR analysis
- 1Kennedy Institute of Rheumatology, University of Oxford
Abstract
Single-cell eQTL studies can reveal heterogeneous genetic effects on gene expression that vary with cell type, but their wider application has been constrained by the frequent absence of matched genotype data. Here, we present single-cell Transcriptome Allelic Prediction and Association Studies (scTAPAS), a bioinformatic framework for reconstructing donor genotypes directly from single-cell RNA-sequencing reads and downstream genetic analysis. Applying scTAPAS to 5' single-cell RNA-sequencing data from the COVID-19 Multi-omic Blood Atlas (COMBAT), we show that reference-based imputation of sparse RNA-derived genetic information generates genotype dosages sufficiently accurate for association testing. Despite testing only 18.0% of the variants available from conventional array-based genotyping and imputation, scTAPAS recovers 68.6% of cell type-eGene pairs identified using conventional array-based genotypes, with strong concordance in effect sizes. We further show that scTAPAS-derived variants can support imputation of classical HLA alleles and enable HLA-aware QTL analyses, including associations between HLA variation and TCR repertoire at single-cell resolution. Using matched single-cell TCR-sequencing data, we identify CD4+ T cell-specific associations between HLA class II alleles and T-cell receptor (TCR) Va; gene segment usage, highlighting the potential of analyses that complement TCR sequencing at single-cell resolution with HLA types. These results show that genetic information recovered from single-cell RNA sequencing data can support both regulatory and immunogenetic association analyses, expanding the discovery potential of single-cell cohorts for which conventional genotyping or HLA typing is unavailable.
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BibTeX
@misc{kockelbergh2026reconstructing,
title = {{Reconstructing donor genotypes from scRNA-seq for downstream eQTL and HLA-TCR analysis}},
author = {H. Kockelbergh and J. Astley and K. M. Kichula and Z. Zhang and E. S. Ng and COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium and C. A. Dendrou and A. Y. Chong and R. A. Watson and B. P. Fairfax and J. C. Knight and G. Band and P. J. Norman and M. N. Carrington and S. N. Sansom and A. J. Mentzer and L. Jostins-Dean and Y. Luo},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.30.755703},
url = {https://www.biorxiv.org/content/10.64898/2026.09.30.755703v1},
note = {CC0},
}
RIS
TY - UNPB
TI - Reconstructing donor genotypes from scRNA-seq for downstream eQTL and HLA-TCR analysis
AU - H. Kockelbergh
AU - J. Astley
AU - K. M. Kichula
AU - Z. Zhang
AU - E. S. Ng
AU - COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium
AU - C. A. Dendrou
AU - A. Y. Chong
AU - R. A. Watson
AU - B. P. Fairfax
AU - J. C. Knight
AU - G. Band
AU - P. J. Norman
AU - M. N. Carrington
AU - S. N. Sansom
AU - A. J. Mentzer
AU - L. Jostins-Dean
AU - Y. Luo
PY - 2026
DA - 2026/09/30
PB - bioRxiv
DO - 10.64898/2026.09.30.755703
UR - https://www.biorxiv.org/content/10.64898/2026.09.30.755703v1
AB - Single-cell eQTL studies can reveal heterogeneous genetic effects on gene expression that vary with cell type, but their wider application has been constrained by the frequent absence of matched genotype data. Here, we present single-cell Transcriptome Allelic Prediction and Association Studies (scTAPAS), a bioinformatic framework for reconstructing donor genotypes directly from single-cell RNA-sequencing reads and downstream genetic analysis. Applying scTAPAS to 5' single-cell RNA-sequencing data from the COVID-19 Multi-omic Blood Atlas (COMBAT), we show that reference-based imputation of sparse RNA-derived genetic information generates genotype dosages sufficiently accurate for association testing. Despite testing only 18.0% of the variants available from conventional array-based genotyping and imputation, scTAPAS recovers 68.6% of cell type-eGene pairs identified using conventional array-based genotypes, with strong concordance in effect sizes. We further show that scTAPAS-derived variants can support imputation of classical HLA alleles and enable HLA-aware QTL analyses, including associations between HLA variation and TCR repertoire at single-cell resolution. Using matched single-cell TCR-sequencing data, we identify CD4+ T cell-specific associations between HLA class II alleles and T-cell receptor (TCR) Va; gene segment usage, highlighting the potential of analyses that complement TCR sequencing at single-cell resolution with HLA types. These results show that genetic information recovered from single-cell RNA sequencing data can support both regulatory and immunogenetic association analyses, expanding the discovery potential of single-cell cohorts for which conventional genotyping or HLA typing is unavailable.
ER -