UCLA

Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-Seq limits sample size, sequencing depth, and therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-Seq of whole blood tissue across 1490 individuals at low-coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-Seq study of 570 individuals at moderate- coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-Seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5- fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-Seq is an effective approach to increase discovery power in eQTL studies. We then build a pipeline using existing tools CIBERSORTx and bMIND to computationally deconvolute low-coverage bulk RNA-seq from a total of 1,996 individuals to estimate cell type expression. We show that cell type expression estimates are consistent with those from scRNA-seq and can be used as a powerful approach to finding ct- eQTLs. Next, we use medication history from this cohort to look for SNP x lithium interactions in ct-eQTLs, finding 110 examples of eGenes whose cell type expression is significantly associated with some SNP dependent of lithium usage.