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

Guanlan Dong, Michael C. Wendl, Bin Zhang, Li Ding, Kuan Lin Huang

Research output: Contribution to journalArticlepeer-review

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/Huan-glab/AeQTL under the MIT license.

Original languageEnglish
Pages (from-to)172-183
Number of pages12
JournalPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Volume26
StatePublished - 2021

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