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Analysis of germline-driven ancestry-associated gene expression in cancers
- 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
- et al.
Published Web Location
https://doi.org/10.1016/j.xpro.2022.101586Abstract
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).
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