Accounting for isoform expression increases power to identify genetic regulation of gene expression

In this study, we propose and evaluate several approaches to answering this question, demonstrating that “isoform-aware” methods—those that account for the expression levels of individual isoforms—have substantially greater power to answer this question than standard “gene-level” eQTL mapping methods. We identify settings in which different approaches yield an inflated number of false disco veries 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 GE UVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research