Our JHS-QTL is a queryable database for eQTLs and splicing QTLs (sQTLs) identified from 1,012 African Americans from the Jackson Heart Study , leveraging their PBMC RNA-sequencing data (~50 million reads per sample) and whole genome sequencing (WGS) data from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program  (PMID: 33568819).
Our eQTL database contains a total of 4,524,238 variant-gene pairs significant at FDR 10% including 16,670 unique genes. Our sQTL database contains 7,419,368variant-gene-cluster pairs at FDR 10%, involving 9,872 unique genes.
We followed the LeafCutter workflow  to identify sQTLs. Specifically, we quantified intron usage using LeafCutter, and then associated excision proportions of each intron cluster with genotypes of each variant in the +/-1Mb neighborhood to identify sQTLs using the APEX method . Similar to eQTL analysis, we adjusted for age, sex, top 10 genotype PCs, and top 10 PCs from splicing quantification.
* Figure is taken from Figure 1 in Li et al 2018 Nat. Genet.
 Wilson, J. G., et al. (2005). "Study design for genetic analysis in the Jackson Heart Study." Ethn Dis 15(4 Suppl 6): S6-30-37.
.Taliun, Daniel, et al. "Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program." Nature 590.7845 (2021): 290-299.
 Quick, Corbin, et al. "A versatile toolkit for molecular QTL mapping and meta-analysis at scale." bioRxiv (2020).
 Li, Y.I., Knowles, D.A., Humphrey, J. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat Genet 50, 151-158 (2018). https://doi.org/10.1038/s41588-017-0004-9