Statistical Methods for Genetic Epidemiology Studies

 描述(由申请者提供)：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宣传为公共卫生的未来。人类基因组计划和高通量技术的最新发展为通过整合遗传和环境数据来改进风险预测和阐明潜在的生物学机制提供了许多机会。这一应用程序的目标是开发统计方法，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，并将结果转化为公共健康和个性化的干预建议。准确的年龄相关绝对风险预测在患者管理和疾病预防中至关重要。这种转换的关键是开发用于风险估计的统计工具。有两项迫切的、尚未得到满足的需求：(A)缺乏统计工具来开发强有力的风险预测模型，这种模型利用了多个来源，包括队列和病例对照研究以及关于特定年龄段发病率和接触情况分布的全人口报告；(B)缺乏关于应如何在临床环境中使用已开发的预测模型的指导，以帮助进行具有统计严谨性的决策。目的1是开发统计方法，在复杂的研究设计和个性化的建议干预年龄下，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，了解基因和环境如何协同工作非常重要，因为基因构成的差异可能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，稀有变异关联的研究取得了很大进展，但对稀有变异的GxE研究进展甚微，部分原因是 目前还缺乏足够的数据来检测和评估GxE对个别罕见变异的影响。为此，最近的大型协作计划(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体提供指导，从而利用各种变体的数据。据我们所知，目前还没有将这些信息纳入GxE的方法。目标2是开发通过整合功能信息来评估罕见变异的GxE风险的方法。拟议的工作将应用于结直肠癌联合会的遗传学和流行病学(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名参与者，他们来自基于人群的病例对照和队列研究，这些研究包含环境风险因素以及全基因组关联和全基因组测序数据的详细数据。由于这些方法也适用于其他复杂疾病，我们将开发基于R的开源软件并将其公开提供。

 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.