The paper introduces Matrix eQTL, a new software tool designed for efficient analysis of expression quantitative trait loci (eQTL) in large datasets. eQTL analysis links gene expression levels to genotypes, but it is computationally intensive, especially for modern datasets with billions of transcript-SNP pairs. Matrix eQTL significantly reduces computation time by leveraging large matrix operations, achieving 2–3 orders of magnitude faster performance compared to existing tools while maintaining the same level of accuracy. The tool supports additive linear and ANOVA models with covariates and can handle heteroskedastic and correlated errors. It also calculates false discovery rates for multiple testing, distinguishing between cis- and trans-eQTLs. Matrix eQTL is available in both Matlab and R, and its performance is validated through extensive testing on large datasets, demonstrating its superior efficiency and versatility in eQTL analysis.The paper introduces Matrix eQTL, a new software tool designed for efficient analysis of expression quantitative trait loci (eQTL) in large datasets. eQTL analysis links gene expression levels to genotypes, but it is computationally intensive, especially for modern datasets with billions of transcript-SNP pairs. Matrix eQTL significantly reduces computation time by leveraging large matrix operations, achieving 2–3 orders of magnitude faster performance compared to existing tools while maintaining the same level of accuracy. The tool supports additive linear and ANOVA models with covariates and can handle heteroskedastic and correlated errors. It also calculates false discovery rates for multiple testing, distinguishing between cis- and trans-eQTLs. Matrix eQTL is available in both Matlab and R, and its performance is validated through extensive testing on large datasets, demonstrating its superior efficiency and versatility in eQTL analysis.