Mapping Tumor-Specific Expression QTLs in Impure Tumor Samples

Abstract

The study of gene expression quantitative trait loci (eQTL) is an effective approach to illuminate the functional roles of genetic variants. Computational methods have been developed for eQTL mapping using gene expression data from microarray or RNA-seq technology. Application of these methods for eQTL mapping in tumor tissues is problematic because tumor tissues are composed of both tumor and infiltrating normal cells (e.g., immune cells) and eQTL effects may vary between tumor and infiltrating normal cells. To address this challenge, we have developed a new method for eQTL mapping using RNA-seq data from tumor samples. Our method separately estimates the eQTL effects in tumor and infiltrating normal cells using both total expression and allele-specific expression (ASE). We demonstrate that our method controls Type I error rate and has higher power than some alternative approaches. We applied our method to study RNA-seq data from The Cancer Genome Atlas and illustrated the similarities and differences of eQTL effects in tumor and normal cells. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Keywords:

• Allele specific expression
• eQTL
• RNA-seq
• Tumor purity

Supplementary Material

Supplement to “Mapping Tumor-Specific eQTLs in Impure Tumor

Samples”: Supplementary document containing RNA-seq and genotype array processing information, mathematical details for the optimization of the pTReC and pTReCASE models, and the derivation of the Cis-Trans score test. (PDF)pTReCASE: Open source R-package pTReCASE containing code to perform the pTReCASE analysis presented in the simulation studies and TCGA Data examination. (GNU zipped tar file). This R package will also be posted at GitHub: https://github.com/Sun-lab/.

Acknowledgment

The authors wish to thank the Associate Editor and two reviewers for their constructive comments and suggestions.

Additional information

Funding

This work was partially supported by NIH grants R01 GM105785, R01 GM07335, and Cancer Genomics Training Grant.