Novel methods for identifying genetic interactions in cancer prognosis

 描述(申请人提供)：癌症预后的项目摘要，除了环境/临床(E)和遗传(G)风险因素的主要影响外，G和E因素之间的相互作用(G*E相互作用)以及G和G因素之间的相互作用(G*G相互作用)也起着关键作用。现有的发现是不充分的，迫切需要确定更多的预后相互作用。现有的大部分工作都集中在数据收集上。相比之下，有效分析方法的开发一直滞后。与数据收集相比，方法开发需要的资源要少得多，但在得出可靠的结果方面同样至关重要。现有的大多数交互分析方法都存在健壮性不足的局限性。在实践中，数据污染和模型错误指定并不少见，并可能导致严重的模型参数估计偏差和错误的标记识别。稳健遗传互作分析方法的发展非常有限。病例对照资料有几种方法，但不适用于预后资料。在预测数据和交互作用分析方面，分位数回归和基于等级的方法有一些最新的进展，但发展有限且不系统。最后但并非最不重要的是，现有的稳健性方法都有一个共同的缺点，即采用了无效的标记选择技术。我们小组一直处于开发稳健交互分析方法的前沿。我们的统计调查和模拟提供了令人信服的证据，表明使用惩罚技术的稳健方法的性能优于其他方法，因为它们具有明显更准确的标记识别和模型参数估计。在数据分析中，现有分析遗漏的重要相互作用已被确定为多种癌症类型。然而，我们也发现，现有研究的范围在方法论发展和数据分析方面都需要显著扩大。这个项目的动机是相互作用在癌症预后中的重要性和现有研究的局限性。我们的目标如下。(目标1)开发新的边际分析方法，该方法对数据污染和模型错误规范具有鲁棒性，以识别重要的交互作用。(目标2)开发新的联合分析方法，这些方法对数据污染和模型错误规范具有鲁棒性，以确定重要的交互作用。(目标3)开发量身定制的推理方法，以对已确定的相互作用得出更明确的结论。(目标4)开发公共关系软件和动态项目网站。确定多种癌症的预后交互作用。对于已确定的相互作用，我们将进行广泛的生物信息学和统计分析、评估和比较。凭借我们独特的专业知识、丰富的经验和充满希望的初步研究，这个项目成功的可能性很高。

 DESCRIPTION (provided by applicant): Project Summary In cancer prognosis, beyond the main effects of environmental/clinical (E) and genetic (G) risk factors, the interactions between G and E factors (G*E interactions) and those between G and G factors (G*G interactions) also play critical roles. The existing findings are insufficient, and there is a strong need for identifing more prognostic interactions. Most of the existing effort has been focused on data collection. In contrast, the development of effective analysis methods has been lagging behind. Compared to data collection, methodological development takes much less resources but is equally critical in making reliable findings. Most of the existing interaction analysis methods share the limitation of lacking robustness properties. In practice, data contamination and model mis-specification are not uncommon and can lead to severely biased model parameter estimation and false marker identification. The development of robust genetic interaction analysis methods is very limited. There are a few methods for case-control data, but they are not applicable to prognosis data. For prognosis data and interaction analysis, there is some very recent progress in quantile regression and rank-based methods, but the development has been limited and unsystematic. Last but not least, the existing robust methods have the common drawback of adopting ineffective marker selection techniques. Our group has been at the frontier of developing robust interaction analysis methods. Our statistical investigations and simulations have provided convincing evidences that the robust methods using the penalization technique outperform alternatives with significantly more accurate marker identification and model parameter estimation. In data analysis, important interactions missed by the existing analyses have been identified for multiple cancer types. However, we have also found that the scope of the existing studies needs to be significantly expanded in terms of both methodological development and data analysis. This project has been motivated by the importance of interactions in cancer prognosis and limitations of the existing studies. Our objectives are as follows. (Aim 1) Develop novel marginal analysis methods that are robust to data contamination and model mis-specification for identifying important interactions. (Aim 2) Develop novel joint analysis methods that are robust to data contamination and model mis-specification for identifying important interactions. (Aim 3) Develop tailored inference approaches to draw more definitive conclusions on the identified interactions. (Aim 4) Develop public R software and a dynamic project website. Identify prognostic interactions for multiple cancers. For the identified interactions, we will conduct extensive bioinformatic and statistical analysis, evaluations, and comparisons. With our unique expertise, extensive experiences, and promising preliminary studies, this project has a high likelihood of success.