Analysis of germline-driven ancestry-associated gene expression in cancers

Nyasha Chambwe; Rosalyn W Sayaman; Donglei Hu; Scott Huntsman; Cancer Genome Analysis Network; Anab Kemal; Samantha Caesar-Johnson; Jean C Zenklusen; Elad Ziv; Rameen Beroukhim; Andrew D Cherniack

doi:10.1016/j.xpro.2022.101586

Analysis of germline-driven ancestry-associated gene expression in cancers

STAR Protoc. 2022 Jul 31;3(3):101586. doi: 10.1016/j.xpro.2022.101586. eCollection 2022 Sep 16.

Affiliations

¹ Institute for Systems Biology, Seattle, WA 98109, USA.
² Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA; Biological Sciences and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. Electronic address: rwsayaman@gmail.com.
³ Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
⁴ National Cancer Institute, Bethesda, MD 20892, USA.
⁵ The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Electronic address: rameen_beroukhim@dfci.harvard.edu.
⁶ The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA. Electronic address: achernia@broadinstitute.org.

Abstract

Differential mRNA expression between ancestry groups can be explained by both genetic and environmental factors. We outline a computational workflow to determine the extent to which germline genetic variation explains cancer-specific molecular differences across ancestry groups. Using multi-omics datasets from The Cancer Genome Atlas (TCGA), we enumerate ancestry-informative markers colocalized with cancer-type-specific expression quantitative trait loci (e-QTLs) at ancestry-associated genes. This approach is generalizable to other settings with paired germline genotyping and mRNA expression data for a multi-ethnic cohort. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020), Robertson et al. (2021), and Sayaman et al. (2021).

Keywords: Bioinformatics; Cancer; Computer sciences; Gene Expression; Genomics; RNAseq; Sequence analysis.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Gene Expression
Germ Cells
Humans
Neoplasms* / genetics
Quantitative Trait Loci* / genetics
RNA, Messenger

Substances

RNA, Messenger

Analysis of germline-driven ancestry-associated gene expression in cancers

Authors

Collaborators

Affiliations

Abstract

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MeSH terms

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