Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research

 描述(申请人提供)：随着技术的进步，癌症研究企业正迅速走向数据密集型和数据驱动型。一个例子是生物技术的爆炸性增长和大量遗传和基因组数据的产生，例如全基因组测序数据。另一个例子是健康信息学，它允许快速访问大型管理医疗保健数据库，如电子病历和联邦医疗保险索赔数据。癌症数据科学在癌症研究中变得越来越重要。事实上，海量数据为癌症的新发现提供了前所未有的机会。该项目旨在开发和应用统计和计算方法，以分析大量和复杂的遗传和基因组数据，以及流行病学和临床数据，在人口和癌症医学研究中。我们的最终目标是利用丰富的数据源了解癌症的病因、风险和预后，并发现癌症预防、干预和治疗的新的有效策略。越来越明显的是，适用于分析海量数据的有限方法已经成为有效地将丰富的信息转化为有意义的知识的瓶颈。迫切需要为海量的癌症数据开发统计和计算方法，以弥合技术和信息传输的差距，并加快癌症预防和治疗的创新。该项目旨在缩小这一差距。具体地说，为了推进遗传和基因组癌症流行病学，我们将开发统计和计算方法，用于(A)全基因组测序关联研究的分析；(B)遗传、基因组和环境数据的综合分析；(C)基因-环境相互作用的研究；(D)使用全基因组遗传和基因组数据和环境数据进行风险预测。为了推进癌症基因组医学，我们将开发统计和计算方法，对遗传、基因组和临床数据进行综合分析，以了解癌症预后，并使用(A)遗传流行病学队列研究数据；(B)将遗传流行病学队列研究数据与行政数据库(如电子医疗记录和联邦医疗保险索赔数据)相结合。我们已经组建了一支由生物统计学家、计算生物学家、健康信息学家、遗传流行病学家和临床科学家组成的强大的跨学科协作研究团队。我们将把TE建议的方法应用于肺癌、乳腺癌和鼻咽癌的遗传流行病学和临床研究。我们将开发开放获取、用户友好的软件，分发给研究社区，并开放在线教育模块，培训癌症研究人员使用本项目开发的方法。

 DESCRIPTION (provided by applicant): With the advances of technologies, cancer research enterprise is rapidly becoming data-intensive and data- driven. One example is the explosion of biotechnologies and the generation of massive genetic and genomic data, such as whole genome sequencing data. Another example is health informatics, which allows rapid avail- ability of large administrative health care databases, such as electronic medical records and Medicare claim data. Cancer data science has emerged to be increasingly important in cancer research. Indeed, massive data provide unprecedented opportunities for new discovery in cancer. This project aims at development and application of statistical and computational methods for analysis of massive and complex genetic and genomic data, together with epidemiological and clinical data, in population and medical science of cancer research. Our ultimate goal is to use rich data sources to understand cancer etiology, risk, and prognosis, and discover new effective strategies for cancer prevention, intervention and treatment. It has become increasingly evident that limited methods suitable for analyzing massive data have emerged as a bottleneck to effectively translate rich information into meaningful knowledge. There is a pressing need to develop statistical and computational methods for massive cancer data to bridge the technology and information transfer gap, and accelerate innovations in cancer prevention and treatment. This Project aims at narrowing this gap. Specifically, to advance genetic and genomic cancer epidemiology, we will develop statistical and computational methods for (a) analysis of whole genome sequencing association studies; (b) integrative analysis of genetic, genomic, and environment data; (c) study of gene-environment interactions; (d) risk prediction using whole genome genetic and genomic data and environmental data. To advance cancer genomic medicine, we will develop statistical and computational methods for integrative analysis of genetic, genomic and clinical data to understand cancer prognosis and advance precision medicine using (a) data from genetic epidemiological cohort studies; (b) combining data from genetic epidemiological cohort studies with administrative databases such as electronic medical records and Medicare claim data. We have assembled a strong collaborative interdisciplinary team of researchers involving biostatisticians, computational biologists, health informaticians, genetic epidemiologists and clinical scientists. We will apply te proposed methods to lung, breast and nasopharynx cancer genetic epidemiological and clinical studies. We will develop open access user friendly software to be distributed to the research community, and open online educational modules for training cancer researchers in using the methods developed in this Project.