People of different ancestries vary in cancer risk and outcome, and their molecular differences may indicate sources of these variations. Determining the "local" ancestry composition at each genetic locus across ancestry-admixed populations can suggest causal associations. We present a protocol to identify local ancestry and detect the associated molecular changes, using data from the Cancer Genome Atlas. This workflow can be applied to cancer cohorts with matched tumor and normal data from admixed patients to examine germline contributions to cancer. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020).
Analytical protocol to identify local ancestry-associated molecular features in cancer / Carrot-Zhang, Jian; Han, Seunghun; Zhou, Wanding; Damrauer, Jeffrey S; Kemal, Anab; Berger, Ashton C; Meyerson, Matthew; Hoadley, Katherine A; Felau, Ina; Caesar-Johnson, Samantha; Demchok, John A; Mensah, Michael Ka; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C; Chambwe, Nyasha; Knijnenburg, Theo A; Robertson, A Gordon; Yau, Christina; Benz, Christopher; Huang, Kuan-Lin; Newberg, Justin; Frampton, Garret; Mashl, R Jay; Ding, Li; Romanel, Alessandro; Demichelis, Francesca; Sayaman, Rosalyn W; Ziv, Elad; Laird, Peter W; Shen, Hui; Wong, Christopher K; Stuart, Joshua M; Lazar, Alexander J; Le, Xiuning; Oak, Ninad; Cherniack, Andrew D; Beroukhim, Rameen. - In: STAR PROTOCOLS. - ISSN 2666-1667. - 2:4(2021), pp. 10076601-10076615. [10.1016/j.xpro.2021.100766]
Analytical protocol to identify local ancestry-associated molecular features in cancer
Romanel, Alessandro;Demichelis, Francesca;
2021-01-01
Abstract
People of different ancestries vary in cancer risk and outcome, and their molecular differences may indicate sources of these variations. Determining the "local" ancestry composition at each genetic locus across ancestry-admixed populations can suggest causal associations. We present a protocol to identify local ancestry and detect the associated molecular changes, using data from the Cancer Genome Atlas. This workflow can be applied to cancer cohorts with matched tumor and normal data from admixed patients to examine germline contributions to cancer. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020).| File | Dimensione | Formato | |
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