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PreprintCC BYmedRxiv

Scalable context-dependent single-cell eQTL mapping reveals disease-relevant regulatory variation beyond static models

  • Y. C. Liu
  • A. S. E. Cuomo
  • Y. Huang
  • J. Perez-Schindler
  • B. Min
  • S. Datta
  • N. Nambrath
  • L. Hu
  • K. Nam
  • M. Kanai
  • A. Xue
  • R. J. Xavier
6 more authors
  • M. J. Daly
  • D. G. MacArthur
  • J. E. Powell
  • M. Claussnitzer
  • B. M. Neale
  • W. Zhou1
  1. 1Broad Institute of MIT and Harvard
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Abstract

Many disease-associated variants are thought to act through gene regulation, yet conventional eQTL mapping explains only a fraction of GWAS loci, potentially because regulatory effects vary across cellular states and environments. We present CASTIE, a scalable Poisson mixed-model framework that directly models sparse single-cell read counts and enables genome-wide testing of genotype-by-context interactions without pre-screening for static effects. Applying CASTIE to 1.2 million peripheral blood mononuclear cells from 982 OneK1K donors identified 3,155 context-dependent eQTL associations, including 2,022 eGenes without detectable static effects. These associations yielded 374 colocalizations across 94 traits, representing 270 unique loci, of which 197 were not recovered using the corresponding static eQTLs. The colocalizations linked trait associations to specific cellular contexts and genes, including GCHFR, RNASET2 and ATP1A3. In adipose-derived mesenchymal stem cells exposed to metabolic stimulations, CASTIE increased eGene discovery by 36 - 92% across cell populations and identified stimulation-dependent regulatory effects at metabolic trait loci. Thus, modeling cellular context reveals disease-relevant regulatory variation beyond static eQTL mapping.

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Cite

BibTeXRIS
BibTeX
@misc{liu2026scalable,
  title = {{Scalable context-dependent single-cell eQTL mapping reveals disease-relevant regulatory variation beyond static models}},
  author = {Y. C. Liu and A. S. E. Cuomo and Y. Huang and J. Perez-Schindler and B. Min and S. Datta and N. Nambrath and L. Hu and K. Nam and M. Kanai and A. Xue and R. J. Xavier and M. J. Daly and D. G. MacArthur and J. E. Powell and M. Claussnitzer and B. M. Neale and W. Zhou},
  howpublished = {medRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.13.26360300},
  url = {https://www.medrxiv.org/content/10.64898/2026.08.13.26360300v2},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Scalable context-dependent single-cell eQTL mapping reveals disease-relevant regulatory variation beyond static models
AU  - Y. C. Liu
AU  - A. S. E. Cuomo
AU  - Y. Huang
AU  - J. Perez-Schindler
AU  - B. Min
AU  - S. Datta
AU  - N. Nambrath
AU  - L. Hu
AU  - K. Nam
AU  - M. Kanai
AU  - A. Xue
AU  - R. J. Xavier
AU  - M. J. Daly
AU  - D. G. MacArthur
AU  - J. E. Powell
AU  - M. Claussnitzer
AU  - B. M. Neale
AU  - W. Zhou
PY  - 2026
DA  - 2026/08/13
PB  - medRxiv
DO  - 10.64898/2026.08.13.26360300
UR  - https://www.medrxiv.org/content/10.64898/2026.08.13.26360300v2
AB  - Many disease-associated variants are thought to act through gene regulation, yet conventional eQTL mapping explains only a fraction of GWAS loci, potentially because regulatory effects vary across cellular states and environments. We present CASTIE, a scalable Poisson mixed-model framework that directly models sparse single-cell read counts and enables genome-wide testing of genotype-by-context interactions without pre-screening for static effects. Applying CASTIE to 1.2 million peripheral blood mononuclear cells from 982 OneK1K donors identified 3,155 context-dependent eQTL associations, including 2,022 eGenes without detectable static effects. These associations yielded 374 colocalizations across 94 traits, representing 270 unique loci, of which 197 were not recovered using the corresponding static eQTLs. The colocalizations linked trait associations to specific cellular contexts and genes, including GCHFR, RNASET2 and ATP1A3. In adipose-derived mesenchymal stem cells exposed to metabolic stimulations, CASTIE increased eGene discovery by 36 - 92% across cell populations and identified stimulation-dependent regulatory effects at metabolic trait loci. Thus, modeling cellular context reveals disease-relevant regulatory variation beyond static eQTL mapping.
ER  -