Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping

Identifying cell-type-specific eQTL is important to understand the genetic regulation of gene expressions at the cell-type level and its relevance to complex traits. However, existing eQTL fine-mapping methods are limited in power and accuracy when cell types are analyzed separately. To improve eQTL mapping, we present CASE, a Bayesian framework to perform cell-type-specific and shared eQTL fine-mapping that simultaneously analyzes multiple cell types. CASE can effectively capture effect-sharing patterns across cell types while disentangling the confounding effects of linkage disequilibrium. We demonstrate that CASE outperforms the existing single-trait (SuSiE) and multi-trait (mvSuSiE) eQTL methods through comprehensive simulations. When applied to the OneK1K data, CASE identified more genetic regulations of gene expressions, better capturing cell type specificity and functionally enriched and disease-associated eQTL. The CASE framework for single-cell eQTL fine-mapping can be broadly applied to multi-tissue and multi-trait genetic studies.