Gene regulatory networks (GRN) must be robust to maintain cellular identity across individuals yet flexible to accommodate population genetic variants. Therefore, comparing expression quantitative trait loci (eQTL) in health and disease can reveal hidden players in GRN and post-transcriptional regulation. Here, we developed a computational pipeline to investigate the gain and loss of eQTLs in the 3’ untranslated region of genes using single-cell RNA sequencing. We repurposed datasets from human islets of donors with and without type 2 diabetes (T2D), and found that beta cells lose identity by gaining alpha cell-specific eQTLs in T2D. By integrating the eQTL landscape with islet miRNA expression, we inferred miRNA-mediated gene regulation in islets. Further validation of miR-127-5p targeting PTEN mRNA revealed its association with EGFR signaling and insulin secretion. Hence, our pipeline provides a flexible framework to unravel post-transcriptional regulatory mechanisms in health and disease with cell type resolution.