Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

Paul Geeleher, Aritro Nath, Fan Wang, Zhenyu Zhang, Alvaro N. Barbeira, Jessica Fessler, Robert L. Grossman, Cathal Seoighe, R. Stephanie Huang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we have demonstrated a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response.

Original languageEnglish (US)
Article number130
JournalGenome biology
Issue number1
StatePublished - Sep 11 2018

Bibliographical note

Funding Information:
This work was supported by a research grant from the Avon Foundation for Women and an NIH/NCI grant (1R01CA204856-01A1). R.S.H. also received support from NIH/National Institute of General Medical Sciences (NIGMS) grant K08GM089941, NIH/NCI grant R21 CA139278, NIH/NIGMS grant U01GM61393, and a Circle of Service Foundation Early Career Investigator award. P.G. received support from the Chicago Biomedical Consortium grant PDR-020 and from the NIH/NHGRI K99/R00 Pathway to Independence Award (1K99HG009679-01A1).

Funding Information:
The Genotype-Tissue Expression (GTEx) project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (NIH) and by the National Cancer Institute (NCI), the National Human Genome Research Institute (NHGRI), the National Heart, Lung, and Blood Institute (NHLBI), the National Institute on Drug Abuse (NIDA), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). We acknowledge the Genomics Data Commons (GDC) and the Bionimbus Protected Data Cloud for data acquisition and analysis services and the patients and research groups who generated the TCGA data. This study also makes use of data generated by the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), funding for which was provided by Cancer Research UK and the British Columbia Cancer Agency Branch.


  • Cancer
  • Deconvolution
  • Expression quantitative trait locus (eQTL)
  • Gene regulation
  • Genome-wide association study (GWAS)

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