Integrative modeling identifies genetic ancestry-associated molecular correlates in human cancer.
Document Type
Article
Publication Date
6-18-2021
Publication Title
STAR Protoc
Keywords
washington; seattle; isb
Abstract
Cellular and molecular aberrations contribute to the disparity of human cancer incidence and etiology between ancestry groups. Multiomics profiling in The Cancer Genome Atlas (TCGA) allows for querying of the molecular underpinnings of ancestry-specific discrepancies in human cancer. Here, we provide a protocol for integrative associative analysis of ancestry with molecular correlates, including somatic mutations, DNA methylation, mRNA transcription, miRNA transcription, and pathway activity, using TCGA data. This protocol can be generalized to analyze other cancer cohorts and human diseases. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020).
Clinical Institute
Cancer
Department
Oncology
Department
Institute for Systems Biology
Recommended Citation
Robertson, A Gordon; Yau, Christina; Carrot-Zhang, Jian; Damrauer, Jeffrey S; Knijnenburg, Theo A; Chambwe, Nyasha; Hoadley, Katherine A; Kemal, Anab; Zenklusen, Jean C; Cherniack, Andrew D; Beroukhim, Rameen; and Zhou, Wanding, "Integrative modeling identifies genetic ancestry-associated molecular correlates in human cancer." (2021). Articles, Abstracts, and Reports. 4992.
https://digitalcommons.providence.org/publications/4992
