학술논문
Analysis of germline-driven ancestry-associated gene expression in cancers
Document Type
article
Author
Chambwe, Nyasha; Sayaman, Rosalyn W; Hu, Donglei; Huntsman, Scott; Network, The Cancer Genome Analysis; Carrot-Zhang, Jian; Berger, Ashton C; Han, Seunghun; Meyerson, Matthew; Damrauer, Jeffrey S; Hoadley, Katherine A; Felau, Ina; Demchok, John A; Mensah, Michael KA; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Knijnenburg, Theo A; Robertson, A Gordon; Yau, Christina; Benz, Christopher; Huang, Kuan-lin; Newberg, Justin Y; Frampton, Garrett M; Mashl, R Jay; Ding, Li; Romanel, Alessandro; Demichelis, Francesca; Zhou, Wanding; Laird, Peter W; Shen, Hui; Wong, Christopher K; Stuart, Joshua M; Lazar, Alexander J; Le, Xiuning; Oak, Ninad; Kemal, Anab; Caesar-Johnson, Samantha; Zenklusen, Jean C; Ziv, Elad; Beroukhim, Rameen; Cherniack, Andrew D
Source
STAR Protocols. 3(3)
Subject
Language
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).