Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22903, USA.
STAR Protoc. 2022 Apr 4;3(2):101273. doi: 10.1016/j.xpro.2022.101273. eCollection 2022 Jun 17.
Germline Variants (GVs) are effective in predicting cancer risk and may be relevant in predicting patient outcomes. Here we provide a bioinformatic pipeline to identify GVs from the TCGA lower grade glioma cohort in Genomics Data Commons. We integrate paired whole exome sequences from normal and tumor samples and RNA sequences from tumor samples to determine a patient's GV status. We then identify the subset of GVs that are predictive of patient outcomes by Cox regression. For complete details on the use and execution of this protocol, please refer to Chatrath et al. (2019) and Chatrath et al. (2020).
种系变异（GVs）可有效预测癌症风险，并且可能与预测患者预后相关。在此，我们提供了一个生物信息学管道，用于从基因组学数据共享的 TCGA 低级别神经胶质瘤队列中识别 GVs。我们整合了正常和肿瘤样本的配对全外显子序列以及肿瘤样本的 RNA 序列，以确定患者的 GV 状态。然后，我们通过 Cox 回归确定可预测患者预后的 GV 亚组。有关该方案使用和执行的完整详细信息，请参阅 Chatrath 等人（2019 年）和 Chatrath 等人（2020 年）。
