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医疗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为改善风险预测和治疗提供了许多机会。通过整合遗传和环境数据来阐明潜在的生物学机制，该应用的目的是开发统计方法，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干预建议。 。准确的年龄特异性绝对风险预测对于患者管理和疾病预防至关重要，这种转化的关键是开发用于风险估计的统计工具，有两个迫切且未满足的需求：（a）缺乏开发稳健的风险预测模型的统计工具。 ，利用多种来源，包括队列研究和病例对照研究以及关于特定年龄疾病发生率和暴露分布的人群报告；(b) 缺乏关于如何在临床环境中使用已开发的预测模型来提供帮助的指导；具有统计严谨性的决策目标 1 是开发统计方法，在复杂的研究设计和个性化建议的开始干预年龄下估计稳健的年龄特异性绝对风险。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，了解基因和环境如何发挥作用非常重要。协同作用，因为基因组成的差异可能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，罕见变异关联方面已经取得了很大进展，但在 GxE 方面却进展甚少。罕见的变体，部分原因是 缺乏足够的数据来检测和估计 GxE 对单个罕见变异的影响，为此，最近大型合作项目（例如 ENCODE 和 TCGA）生成的功能信息可以为如何聚合具有共享功能特征的变异提供指导。因此，据我们所知，目前尚无方法将此类信息纳入 GxE 目标 2 中，旨在开发通过整合功能信息来评估罕见变体的 GxE 风险的方法。遗传学结直肠癌流行病学联盟（PI：Ulrike Peters；首席生物统计学家：Li Hsu）目前拥有超过 40,000 名基于人群的病例对照和队列研究参与者，提供有关环境风险因素和全基因组关联的详细数据。由于这些方法也适用于其他复杂疾病，因此我们将开发基于 R 的开源软件并将其公开。