Accounting for Isoform Expression in eQTL Mapping Substantially Increases Power

A core problem in genetics is eQTL mapping, in which genetic variants associated with changes in expression of genes are identified. It is common in eQTL mapping to compute gene expression by aggregating the expression levels of individual isoforms from the same gene and then performing linear regression between SNPs and this aggregated gene expression level. However, SNPs may regulate isoforms from the same gene in different directions due to alternative splicing, or only regulate the expression level of one isoform, causing this approach to lose power. In this study, we provide a systematic evaluation of methods for accounting for individual isoform expression levels based on generative isoform expression heritability models and real data. Over a range of conditions, we show that these approaches substantially increase the power to map eQTLs in both simulations and commonly analyzed large data sets. We identify settings in which different approaches yield an inflated number of false discoveries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate correction. We show that similar trends are observed in real data from the GEUVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.