TY - JOUR
T1 - Analytical protocol to identify local ancestry-associated molecular features in cancer
AU - Cancer Genome Atlas Analysis Network
AU - Carrot-Zhang, Jian
AU - Han, Seunghun
AU - Zhou, Wanding
AU - Damrauer, Jeffrey S.
AU - Kemal, Anab
AU - Berger, Ashton C.
AU - Meyerson, Matthew
AU - Hoadley, Katherine A.
AU - Felau, Ina
AU - Caesar-Johnson, Samantha
AU - Demchok, John A.
AU - Mensah, Michael K.A.
AU - Tarnuzzer, Roy
AU - Wang, Zhining
AU - Yang, Liming
AU - Zenklusen, Jean C.
AU - Chambwe, Nyasha
AU - Knijnenburg, Theo A.
AU - Robertson, A. Gordon
AU - Yau, Christina
AU - Benz, Christopher
AU - Huang, Kuan lin
AU - Newberg, Justin
AU - Frampton, Garret
AU - Mashl, R. Jay
AU - Ding, Li
AU - Romanel, Alessandro
AU - Demichelis, Francesca
AU - Sayaman, Rosalyn W.
AU - Ziv, Elad
AU - Laird, Peter W.
AU - Shen, Hui
AU - Wong, Christopher K.
AU - Stuart, Joshua M.
AU - Lazar, Alexander J.
AU - Le, Xiuning
AU - Oak, Ninad
AU - Cherniack, Andrew D.
AU - Beroukhim, Rameen
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/12/17
Y1 - 2021/12/17
N2 - 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).
AB - 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).
KW - Bioinformatics
KW - Cancer
KW - Genomics
UR - http://www.scopus.com/inward/record.url?scp=85122781907&partnerID=8YFLogxK
U2 - 10.1016/j.xpro.2021.100766
DO - 10.1016/j.xpro.2021.100766
M3 - Article
C2 - 34585150
AN - SCOPUS:85122781907
SN - 2666-1667
VL - 2
JO - STAR Protocols
JF - STAR Protocols
IS - 4
M1 - 100766
ER -