Using information from allele-specific gene expression (ASE) can sub-stantially improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to high computational cost and the requirement to access raw data that can take a large amount of storage space. To address these computational challenges, we have developed a computational framework that uses a statistical method named TReCASE as its computational engine, and it is computationally feasible for large scale analysis. We applied it to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. Compared with a popular linear regression method that does not use ASE data, TReCASE can double the number of eGenes (i.e., genes with at least one significant eQTL) when sample size is relatively small, e.g., n 200.