Multivariate association analysis with somatic mutation data.
Multivariate association analysis with somatic mutation data.
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作者：
He Q;Liu Y;Peters U;Hsu L
体细胞突变是肿瘤发展的驱动力，癌症基因组测序的最新进展使得在大样本量中评估体细胞突变与癌症相关性状之间的关联成为可能。然而，尽管样本量越来越大，但对体细胞突变进行统计分析仍然具有挑战性，因为绝大多数体细胞突变的发生频率非常低。此外，癌症是一种复杂的疾病，它通常伴随着反映癌症各个方面的多种性状;如何联合收割机结合这些性状的信息来识别重要的体细胞突变带来了额外的挑战。在这篇文章中，我们介绍了一种统计方法，命名为SOMAT，用于检测与多种癌症相关性状相关的体细胞突变。我们的方法提供了一个灵活的框架，用于分析连续的，二进制的，或两种类型的特征的混合物，是统计功能强大，计算效率高。此外，我们提出了一个数据自适应的过程，这是网格搜索自由，有效地结合测试统计，以提高统计能力。我们进行了广泛的研究，并表明，所提出的方法保持正确的I型错误，是更强大的比现有的方法下考虑的情况。我们还将我们的方法应用于肝脏肿瘤的外显子组测序研究以供说明。
Somatic mutations are the driving forces for tumor development, and recent advances in cancer genome sequencing have made it feasible to evaluate the association between somatic mutations and cancer-related traits in large sample sizes. However, despite increasingly large sample sizes, it remains challenging to conduct statistical analysis for somatic mutations, because the vast majority of somatic mutations occur at very low frequencies. Furthermore, cancer is a complex disease and it is often accompanied by multiple traits that reflect various aspects of cancer; how to combine the information of these traits to identify important somatic mutations poses additional challenges. In this article, we introduce a statistical approach, named as SOMAT, for detecting somatic mutations associated with multiple cancer-related traits. Our approach provides a flexible framework for analyzing continuous, binary, or a mixture of both types of traits, and is statistically powerful and computationally efficient. In addition, we propose a data-adaptive procedure, which is grid-search free, for effectively combining test statistics to enhance statistical power. We conduct an extensive study and show that the proposed approach maintains correct type I error and is more powerful than existing approaches under the scenarios considered. We also apply our approach to an exome-sequencing study of liver tumor for illustration.
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DOI：
10.1056/nejmoa1205119
发表时间：
2012-09-06
期刊：
The New England journal of medicine
影响因子：
--
作者：
Zhuang Z;Yang C;Lorenzo F;Merino M;Fojo T;Kebebew E;Popovic V;Stratakis CA;Prchal JT;Pacak K
通讯作者：
Pacak K
影响因子：
2.1
作者：
Yang, Qiong;Wu, Hongsheng;Guo, Chao-Yu;Fox, Caroline S.
通讯作者：
Fox, Caroline S.
预测蛋白质突变的功能影响：在癌症基因组学中的应用。
影响因子：
14.9
作者：
Reva B;Antipin Y;Sander C
通讯作者：
Sander C
DOI：
10.1158/1940-6207.capr-10-0234
发表时间：
2010-11
期刊：
Cancer prevention research (Philadelphia, Pa.)
影响因子：
--
作者：
Michor F;Polyak K
通讯作者：
Polyak K
影响因子：
5.2
作者：
Sun, Jianping;Oualkacha, Karim;Greenwood, Celia M. T.
通讯作者：
Greenwood, Celia M. T.