Statistical Methods for Analysis of Next Generation Sequencing Data in Gen

这个提议是为了发展先进的统计方法来分析大的下一代测序 遗传癌症流行病学研究的数据。基因组时代提供了一个前所未有的承诺， 了解多因素疾病，如癌症，并确定可用于 开发针对患者的治疗方法。尽管在过去的几年里， 多年来已经确定了与许多复杂疾病相关的一千多种常见遗传变异， 这些变异只能解释疾病遗传性的一小部分。下一代的最新进展 测序技术为发现基因和生物标志物提供了令人兴奋的新机会 与疾病或性状相关，研究基因-环境相互作用，预测疾病风险， 推进个性化医疗。然而，大量的测序数据，特别是罕见的变异， 数据分析和结果解释中的基本统计和计算挑战。短缺 用于分析下一代测序数据的适当和强大的统计方法已经成为一种 有效利用这些丰富资源快速开发新型分子癌症预防的瓶颈 和治疗策略。本提案的目的就是为了满足这一需要。所提出的方法是 受哈佛肺癌和乳腺癌外显子组和靶向测序的启发并应用于 协会的研究，其中调查人员发挥了主要的领导作用。具体目标是：（1） 开发一个统一的，强大的和强大的统计框架，以测试罕见变异之间的关联， 疾病和性状的测序关联研究;（2）开发惩罚的可能性为基础的方法， 在基于人群的测序研究中进行风险预测;（3）使用因果推理框架， 中介分析，以估计和测试遗传罕见变异的直接影响及其间接影响 通过环境风险因素介导的测序研究中的疾病风险;并解释 曝光测量误差。(4)开发高效、方便用户的开放获取统计软件。 该项目与项目1和项目2紧密结合，具有分析大型复杂 观察性研究数据，并利用项目1和2的专业知识进行因果推理， 中介分析和模拟环境暴露在研究基因的相互作用， 环境它还严重依赖于统计计算核心和组织基础设施， 通过行政核心提供团队建设策略、研讨会和访客计划。 相关性（参见说明）： 该项目旨在通过以下方式开发统计方法，以推进癌症预防和干预策略： 使用下一代测序数据来识别与癌症相关的遗传变异， 癌症风险的风险预测模型;并研究遗传变异对癌症的直接和间接影响。 基因和环境在癌症风险和进展中相互作用。

This proposal is to develop advanced statistical methods for analyzing large next generation sequencing data in genetic cancer epidemiological studies. The genomic era provides an unprecedented promise of understanding multifactorial diseases, such as cancer, and of identifying specific targets that can be used to develop patient-tailored therapies. Although hundreds of genome-wide association studies in the last few years have identified over a thousand common genetic variants associated with many complex diseases, these variants only explain a small fraction of heritability of diseases. The recent advance of next generation sequencing technologies provides an exciting new opportunity for discovering genes and biomarkers associated with diseases or traits, studying gene-environment interactions, predicting disease risk, and advancing personalized medicine. However, large sequencing data, especially rare variants, present fundamental statistical and computational challenges in data analysis and result interpretation. A shortage of appropriate and powerful statistical methods for analysis of next generation sequencing data has become a bottleneck for effectively using these rich resources to rapidly develop novel molecular cancer prevention and treatment strategies. The purpose ofthis proposal is to respond to this need. The proposed methods are motivated by and applied to the Harvard Lung Cancer and Breast Cancer exome and targeted sequencing association studies, in which the investigators play a major leadership role. The specific aims are: (1) To develop a unified, powerful and robust statistical framework to test the association between rare variants and diseases and traits in sequencing association studies; (2) To develop penalized likelihood-based methods for risk prediction in population based sequencing studies; (3) To use the causal inference framework for mediation analysis to estimate and test for the direct effects of genetic rare variants and their indirect effects mediated through environmental risk factors on disease risk in sequencing studies; and account for measurement error in exposures. (4) To develop efficient user-friendly open access statistical software. This project integrates closely with Projects 1 and 2 with a common theme of analysis of large and complex observational study data, and takes advantage ofthe expertise of Projects 1 and 2 in causal inference on mediation analysis and modeling environmental exposures in studying the interplay of genes and environment. It also relies heavily on the Statistical Computing Core, and the organizational infrastructure, team'building strategies, workshops and visitor program provided through the Administrative Core. RELEVANCE (See instructions): This project aims to develop statistical methods to advance cancer prevention and intervention strategies by using next generation sequencing data to identify genetic variants associated with cancer, to build genetic risk prediction models for cancer risk; and to study the direct and indirect effects of genetic variants in the interplay of genes and environment in cancer risk and progression.