Airqtl dissects cell state-specific causal gene regulatory networks with efficient single-cell eQTL mapping.

ABSTRACT: Single-cell expression quantitative trait loci (sceQTL) mapping offers a powerful approach for understanding gene regulation and its heterogeneity across cell types and states. It has profound applications in genetics and genomics, particularly causal gene regulatory network (cGRN) inference to unravel the molecular circuits governing cell identity and function. However, computational scalability remains a critical bottleneck for sceQTL mapping, prohibiting thorough benchmarking and optimization of statistical accuracy. We present airqtl, an efficient method to overcome these challenges through algorithmic advances and efficient implementations of linear mixed models. Airqtl achieves superior time complexity and over 10⁸ times of acceleration, enabling objective method benchmark

SUBMITTER: Funk MW 

PROVIDER: S-EPMC12739144 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature