ArticleNature genetics2025
Modeling heterogeneity in single-cell perturbation states enhances detection of response eQTLs.
Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Scalable context-dependent single-cell eQTL mapping reveals disease-relevant regulatory variation beyond static models.medRxiv : the preprint server for health sciences · 2026Article
- Transcriptional mechanisms of sex-biased gene expression and their connections to disease-associated variation.Human molecular genetics · 2026Article
- Psoriasis risk allele function in activated Th1/17 cells with "memory" to antigen exposure.PloS one · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Identifying response expression quantitative trait loci (reQTLs) can help to elucidate mechanisms of disease associations. Typically, such studies model the effect of perturbation as discrete conditions. However, perturbation experiments usually affect perturbed cells heterogeneously. Here we show that modeling of per-cell perturbation state enhances detection of reQTLs. We use single-cell data to study the effect of perturbations with influenza A virus, Candida albicans, Pseudomonas aeruginosa and Mycobacterium tuberculosis on gene regulation. We found on average 36.9% more reQTLs by accounting for single-cell heterogeneity compared to the standard discrete reQTL model. For example, we detected a decrease in the expression quantitative trait loci effect for PXK with influenza A virus. Furthermore, we found that, on average, 25% of reQTLs have cell-type-specific effects. For example, the reQTL effect for RPS26 was stronger in B cells. Our work provides a general model for more accurate reQTL identification and underscores the value of modeling cell-level variation.
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