【佳学基因检测】整合种系和体细胞遗传学以识别与肺癌相关的基因

品牌基因检测中心引言

研究体会到《Genet Epidemiol》在 2020 Apr;44(3):233-247发表了一篇题目为《整合种系和体细胞遗传学以识别与肺癌相关的基因》肿瘤靶向药物治疗基因检测临床研究文章。该研究由Jack Pattee, Xiaowei Zhan, Guanghua Xiao, Wei Pan 等完成。促进了肿瘤的正确治疗与个性化用药的发展，进一步强调了基因信息检测与分析的重要性。

肿瘤靶向药物及正确治疗临床研究内容关键词：

PrediXcan, SSU测试, TWAS, aSPU 测试, eQTL,总和测试。

肿瘤靶向治疗基因检测临床应用结果

全基因组关联研究 (GWAS) 已成功鉴定出许多与复杂性状相关的遗传变异。但是，GWAS 遇到电源问题，导致无法检测到某些相关变体。此外，GWAS 通常无法解析驱动关联的生物学机制。现有的基于基因的关联测试框架，全转录组关联研究 (TWAS)，利用表达数量性状基因座数据来增加关联测试的能力，并阐明遗传变异调节复杂性状的生物学机制。我们扩展了 TWAS 方法以整合来自肿瘤的体细胞信息。通过整合种系和体细胞数据，我们能够利用来自肿瘤细微体细胞景观的信息。因此，我们可以增强 TWAS 类型测试的能力，以检测与癌症表型相关的种系遗传变异。我们使用来自癌症基因组图谱的肺腺癌的体细胞和生殖细胞数据以及荟萃分析的肺癌 GWAS 来识别与肺癌相关的新基因。

肿瘤发生与反复转移国际数据库描述：

Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex traits. However, GWAS experience power issues, resulting in the failure to detect certain associated variants. Additionally, GWAS are often unable to parse the biological mechanisms of driving associations. An existing gene-based association test framework, Transcriptome-Wide Association Studies (TWAS), leverages expression quantitative trait loci data to increase the power of association tests and illuminate the biological mechanisms by which genetic variants modulate complex traits. We extend the TWAS methodology to incorporate somatic information from tumors. By integrating germline and somatic data we are able to leverage information from the nuanced somatic landscape of tumors. Thus we can augment the power of TWAS-type tests to detect germline genetic variants associated with cancer phenotypes. We use somatic and germline data on lung adenocarcinomas from The Cancer Genome Atlas in conjunction with a meta-analyzed lung cancer GWAS to identify novel genes associated with lung cancer.