학술논문
Analysis of germline-driven ancestry-associated gene expression in cancers
Document Type
article
Author
Nyasha Chambwe; Rosalyn W. Sayaman; Donglei Hu; Scott Huntsman; Anab Kemal; Samantha Caesar-Johnson; Jean C. Zenklusen; Elad Ziv; Rameen Beroukhim; Andrew D. Cherniack; Jian Carrot-Zhang; Ashton C. Berger; Seunghun Han; Matthew Meyerson; Jeffrey S. Damrauer; Katherine A. Hoadley; Ina Felau; John A. Demchok; Michael K.A. Mensah; Roy Tarnuzzer; Zhining Wang; Liming Yang; Theo A. Knijnenburg; A. Gordon Robertson; Christina Yau; Christopher Benz; Kuan-lin Huang; Justin Y. Newberg; Garrett M. Frampton; R. Jay Mashl; Li Ding; Alessandro Romanel; Francesca Demichelis; Wanding Zhou; Peter W. Laird; Hui Shen; Christopher K. Wong; Joshua M. Stuart; Alexander J. Lazar; Xiuning Le; Ninad Oak
Source
STAR Protocols, Vol 3, Iss 3, Pp 101586- (2022)
Subject
Language
English
ISSN
2666-1667
Abstract
Summary: 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). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.