Integration of genetics with transcriptomics has improved our ability to identify genes associated with phenotypes. CD4+ T cells play a central role in modulating allergic disease and asthma. However, this tissue is not well-represented in public repositories such as GTEx, particularly for populations of African ancestry that are disproportionately affected by allergic disease and may have distinct genetic risk factors. From 260 subjects of Afro-Caribbean ancestry participating in the Barbados Asthma Genetics Study (BAGS), we quantified gene expression using RNA-seq from isolated, unstimulated CD4+ T cells. DNA was extracted for genotyping on Illumina’s Multi Ethnic Global Array and imputed to the TOPMed Freeze5 reference panel. Reproducible workflows/tools implementing the Predixcan family of tools were created on the NHLBI BioData Catalyst Ecosystem powered by Seven Bridges and used to build prediction models for gene expression from genotyping data using nested cross-validated elastic net linear models. We limited potential predictors to cis-acting SNPs within 1Mb up or downstream of the gene location for each gene quantified. Models were fit on gene expression residuals after adjustment for sex, asthma status, genetic PC1, and 45 PEER factors. We achieved significant prediction for 4,072 of 16,692 genes tested, defined as R2>0.01 for predicted versus observed gene expression during nested cross-validation and p<0.05 for that statistic. A comparison to publicly available transcription prediction databases on whole blood and monocytes from GTEx and MESA-African Americans, respectively, showed that expression for 1,492 genes were uniquely predicted well in the BAGS CD4+ T cells. This included the asthma candidate genes FLG, RAB5B, IRF1, and KIF3A. Prediction modeling for nasal airway epithelial gene expression, another target tissue of relevance for allergic disease, and the application of these models to impute gene expression in an additional 900 BAGS subjects with TOPMed whole genome sequencing data and to perform transcriptome-wide association tests with asthma are underway. These novel transcriptome prediction databases in asthma target tissues and representing diverse populations may aid in the discovery of novel genetic associations for asthma and other allergic diseases.