Statistical Methods for Genetic Epidemiology Studies

遗传流行病学研究的统计方法

• 批准号: 9027514
• 负责人: Li Hsu
• 金额: $ 40.26万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-04 至 2019-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9027514
• 关键词: Address; Age; Biological; Cancer Etiology; Case-Control Studies; Characteristics; Chronic Disease; Clinical; Cohort Studies; Colorectal Cancer; Complex; Computer software; Data; Data Sources; Decision Making; Development; Disease; Early Diagnosis; Elements; Encyclopedia of DNA Elements; Endoscopy; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiologic Studies; Excision; Future; Genes; Genetic; Genetic study; Genomics; Heart Diseases; Human Genome Project; Incidence; Individual; Intervention; Lead; Life Style; Malignant Neoplasms; Methods; Modeling; Neoplasms; Nested Case-Control Study; Participant; Patients; Premalignant; Probability; Procedures; Process; Property; Psyche structure; Public Health; Recommendation; Reporting; Research Design; Risk; Risk Estimate; Source; Statistical Methods; Testing; The Cancer Genome Atlas; Theoretical Studies; Translating; Translations; Variant; Work; base; cancer risk; cohort; collaborative environment; cost; disorder prevention; epidemiology study; gene environment interaction; genetic epidemiology; genetic makeup; genome sequencing; genome wide association study; genome-wide; high risk; high throughput technology; improved; insight; novel; open source; personalized decision; personalized intervention; personalized medicine; population based; predictive modeling; public health relevance; rare variant; response; screening; statistics; study population; tool; whole genome

 DESCRIPTION (provided by applicant): Personalized medicine or individualized lifestyle recommendations based on both genetic and environmental factors are being promoted as the future of public health. Recent developments in The Human Genome Project and high throughput technologies have offered many opportunities in improving risk prediction and elucidating the underlying biological mechanism by integrating both genetic and environmental data. The objective of this application is to develop statistical methods for estimating absolute risk using both gene and environment data, assessing gene-environment interaction and translating findings into public health and personalized recommendations for intervention. Accurate age-specific absolute risk prediction is critical in patient management and disease prevention. Key to such translation is development of statistical tools for risk estimation. There are two urgent and unmet needs: (a) lack of statistical tools to develop robust risk prediction models, which take advantage of multiple sources including both cohort and case control studies and population-wide reports on age-specific disease rates and exposure distributions; (b) lack of guidance on how a developed prediction model should be used in the clinical setting to aid decision making with statistical rigor. Aim 1 is to develop statistical methods for estimatig robust age-specific absolute risk under complex study designs and individualized recommended age to start intervention. To better develop individually tailored risk prediction and provide guidance on potential lifestyle and screening intervention, it is important to understand how gene and environment work in synergy, as differences in genetic makeup can cause people to respond differently to the same environmental exposure (GxE). As whole genome sequencing studies are being conducted, much progress has been made for rare variant association, but little has been done toward GxE for rare variants, in part because there is a lack of adequate data to detect and estimate the effect of GxE for individual rare variants. Toward this end the functional information generated from the recent large collaborative initiatives such as ENCODE and TCGA can provide guidance on how to aggregate variants with shared functional characteristics and therefore leveraging data across variants. To our knowledge, there is no method yet to incorporate such information for GxE. Aim 2 is to develop methods for assessing GxE risks for rare variants by integrating the functional information. The proposed work will be applied to the Genetics and Epidemiology of Colorectal Cancer Consortium (PI: Ulrike Peters; Lead Biostatistician: Li Hsu). The growing consortium has currently over 40,000 participants from population-based case-control and cohort studies with detailed data on both environmental risk factors and genome-wide association and whole genome sequencing data. Since the methods are also applicable to other complex diseases, we will develop open source software based in R and make it publicly available.

 描述（由申请人提供）：基于遗传和环境因素的个性化药物或个性化生活方式建议正在被推广为公共卫生的未来。人类基因组计划和高通量技术的最新发展提供了许多机会，通过整合遗传和环境数据来改善风险预测和阐明潜在的生物学机制。该应用程序的目的是开发统计方法，用于使用基因和环境数据估计绝对风险，评估基因-环境相互作用，并将研究结果转化为公共卫生和个性化的干预建议。 准确的年龄特异性绝对风险预测在患者管理和疾病预防中至关重要。这种转化的关键是开发风险估计的统计工具。目前有两个迫切而未得到满足的需求：（a）缺乏统计工具来开发稳健的风险预测模型，这些模型利用多种来源，包括队列和病例对照研究以及关于特定年龄疾病率和暴露分布的全人群报告;（B）缺乏指导，说明应如何在临床环境中使用已开发的预测模型，以辅助具有统计严谨性的决策。目标1是开发统计方法，用于在复杂的研究设计和个性化的建议开始干预年龄下估计稳健的年龄特定绝对风险。 为了更好地开发个性化的风险预测，并为潜在的生活方式和筛查干预提供指导，重要的是要了解基因和环境如何协同工作，因为基因组成的差异会导致人们对相同的环境暴露（GxE）做出不同的反应。随着全基因组测序研究的进行，在罕见变异相关性方面取得了很大进展，但在罕见变异的GxE方面几乎没有做什么，部分原因是 缺乏足够的数据来检测和估计GxE对个别罕见变异体的作用。为此，从最近的大型合作计划（如ENCODE和TCGA）中生成的功能信息可以为如何聚合具有共享功能特征的变体提供指导，从而利用跨变体的数据。据我们所知，目前还没有方法将这些信息纳入GxE。目的2是通过整合功能信息，开发评估罕见变异GxE风险的方法。 拟议的工作将应用于结直肠癌遗传学和流行病学联盟（PI：Ulrike Peters;首席生物统计学家：Li Hsu）。这个不断壮大的联盟目前有超过40，000名来自基于人群的病例对照和队列研究的参与者，他们拥有关于环境风险因素和全基因组关联的详细数据以及全基因组测序数据。由于这些方法也适用于其他复杂的疾病，我们将开发基于R的开源软件，并公开提供。