Powerful eQTL mapping through low coverage RNA sequencing

Mapping genetic variants that regulate gene expression (eQTLs) 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 this work, UCLA researchers 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. The researchers 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 an RNA-Seq study of 570 individuals at high-coverage (13.9 million reads/sample). Next, they leverage synthetic datasets derived from real RNA-Seq data 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. This study suggests that lowering coverage while increasing the number of individuals is an effective approach to increase discovery power in RNA-Seq studies.

Concordance of eQTL discovery when using lower-coverage RNA-Seq
vs higher542 coverage RNA-Seq

(1A): Restricting to the 20735 genes with sufficient expression levels to be included in eQTL analysis in both the 5.9M read/sample and 13.9M read/sample dataset, comparison of the median expression (log TPM) across samples, of every gene. R2 544 = 0.91. (1B): In real data, scatterplot of effect sizes of most significant eQTL hits for the 2151 protein coding genes with the same eQTL hit in both eQTL analyses performed (low-coverage and high-coverage). On the x-axis, we show the effect sizes for these genes using low-coverage RNA-Seq, on the y-axis we show the effect sizes for these genes using high-coverage RNA549 Seq. (1C): Real data p-value comparison scatterplot: In real data, scatterplot of -log p-values of most significant eQTL hit for 13950 genes included in both eQTL analyses performed (low551 coverage and high-coverage). On the x-axis, we show the -log p-values for these genes using low-coverage RNA-Seq, on the y-axis we show the -log p-values for these genes using high553 coverage RNA-Seq. The dotted line shows y = x, while the solid line shows the line of best fit for the 3985 protein-coding eGenes with a significant eQTL hit in both datasets. (1D): In real data, scatterplot of effect sizes of the most significant eQTL hit for the 140 eGenes with the same leading SNP identified in both eQTL analyses performed (lower-coverage RNA-Seq with 5.9M reads/sample and GTEX). On the x-axis, we show the effect size for these eGenes from eQTL analysis conducted using the 1490 individuals of EUR ancestry and imputed genotypes, and on the y-axis we show the effect sizes for these eGenes from eQTL analysis published by the GTEX Consortium.