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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.