Guanlan Dong1, Michael C. Wendl2, Bin Zhang3, Li Ding2, Kuan-lin Huang3,*
1Department of Biomedical Informatics, Harvard Medical School
2Department of Medicine, McDonnel Genome Institute, Washington University in St. Louis
3Department of Genetics and Genomic Sciences, Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai
*Corresponding author
Email: kuan-lin.huang@mssm.edu
Pacific Symposium on Biocomputing 26:172-183(2021)
© 2021 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
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.