AeQTL: eQTL analysis using region-based aggregation of rare genomic variants

Abstract

Concurrently available genomic and transcriptomic data from large cohorts provide opportunities to discover expression quantitative trait loci (eQTLs)—genetic variants associated with gene expression changes. However, the statistical power of detecting rare variant eQTLs is often limited and most existing eQTL tools are not compatible with sequence variant file formats. We have developed AeQTL (Aggregated eQTL), a software tool that performs eQTL analysis on variants aggregated according to user-specified regions and is designed to accommodate standard genomic files. AeQTL consistently yielded similar or higher powers for identifying rare variant eQTLs than single-variant tests. Using AeQTL, we discovered that aggregated rare germline truncations in cis exomic regions are significantly associated with the expression of BRCA1 and SLC25A39 in breast tumors. In a somatic mutation pan-cancer analysis, aggregated mutations of those predicted to be missense versus truncations were differentially associated with gene expressions of cancer drivers, and somatic truncation eQTLs were further identified as a new multi-omic classifier of oncogenes versus tumor-suppressor genes. AeQTL is easy to use and customize, allowing a broad application for discovering rare variants, including coding and noncoding variants, associated with gene expression. AeQTL is implemented in Python and the source code is freely available at https://github.com/Huang-lab/AeQTL under the MIT license.

Mutation Impact on mRNA Versus Protein Expression across Human Cancers

“…The false discovery rate (FDR) was corrected from the p-values with the Benjamini-Hochberg procedure. Somatic mutations are grouped at a gene level in the multiple regression model, similar to that implemented by our previously developed AeQTL tool 7 . Mutations separated are analyzed by their mechanisms of action, including nonsynonymous mutations as controls that likely do not affect expression, missense mutations, and truncating mutations including frameshift and in-frame indels, nonsense, splice site, and translation start site mutations.…”

“…However, previous studies characterizing genomic mutations affecting mRNA vs. protein levels have focused on germline variants as expression quantitative trait loci (eQTL) [4][5][6] . While other cancer studies have characterized the effect of somatic mutations on mRNA expression levels [7][8][9] , it remains unclear how somatic mutations may affect protein abundance. The gap of knowledge is critical given that mRNA and protein levels are only moderately correlated [10][11][12][13] .…”