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

癌症研究中大量遗传和基因组数据分析的统计方法

• 批准号: 9980301
• 负责人: XIHONG LIN
• 金额: $ 93.31万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-05 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980301
• 关键词: Advanced Malignant Neoplasm; Biotechnology; Cancer Burden; Cancer Etiology; Cancer Prevention Intervention; Cancer Prognosis; Cancer Science; Clinical; Clinical Data; Clinical Research; Cohort Studies; Communities; Complex; Computerized Medical Record; Computing Methodologies; Data; Data Science; Data Sources; Databases; Development; Environment; Epidemiologist; Epidemiology; Explosion; Generations; Genetic; Genomic medicine; Genomics; Goals; Health; Healthcare; Knowledge; Learning Module; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of nasopharynx; Medical; Medicare claim; Methods; Patient Care; Play; Population; Public Health Informatics; Research; Research Personnel; Risk; Role; Scientist; Statistical Methods; Technology; Time; Training Activity; Translating; administrative database; analytical method; anticancer research; cancer epidemiology; cancer genomics; cancer prevention; cancer therapy; complex data; diverse data; epidemiologic data; gene environment interaction; genetic analysis; genetic epidemiology; genome sequencing; genomic data; improved; innovation; insight; malignant breast neoplasm; outcome forecast; precision medicine; public health relevance; user friendly software; whole genome

 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.

 描述（由申请人提供）：随着技术的进步，癌症研究企业正在迅速变得数据密集型和数据驱动型，其中一个例子是生物技术的爆炸性增长和海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，它可以快速提供大型行政医疗保健数据库，例如电子病历和医疗保险索赔数据，这在癌症研究中变得越来越重要。事实上，海量数据提供了前所未有的机会。的新发现该项目旨在开发和应用统计和计算方法，在癌症研究的人群和医学科学中分析大量复杂的遗传和基因组数据以及流行病学和临床数据。了解癌症的病因、风险和预后，并发现癌症预防、干预和治疗的新有效策略越来越明显，适合分析海量数据的有限方法已成为有效将丰富信息转化为有意义知识的瓶颈。迫切需要发展统计和计算该项目旨在缩小这一差距，具体而言，为了推进遗传和基因组癌症流行病学，我们将开发以下方面的统计和计算方法。 a) 全基因组测序关联研究分析；(b) 遗传、基因组和环境数据的综合分析；(c) 基因与环境相互作用的研究；(d) 使用全基因组遗传和基因组数据以及环境数据进行风险预测。为了推进癌症基因组医学，我们将开发统计数据综合分析遗传、基因组和临床数据的计算方法，以了解癌症预后并推进精准医学，使用（a）遗传流行病学队列研究的数据；（b）将遗传流行病学队列研究的数据与电子病历等管理数据库相结合；我们组建了一支强大的跨学科研究团队，其中包括生物统计学家、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。我们将开发开放获取的用户友好软件，分发给研究界，并开放在线教育模块，以培训癌症研究人员使用本项目开发的方法。