Increasing the power of GxE detection by using multi-locus genome-wide predictors

描述(由申请人提供)：人类疾病的遗传风险取决于环境，或者暴露在疾病中的影响在不同遗传背景的人类群体中并不相同，这是直观的。这一概念被称为“基因-环境”相互作用(GxE)，并假设通过识别GxE可以更好地解释疾病风险。尽管了解GxE在人类疾病中的重要性，但很少有研究记录这一概念。对于记录很少的GxE，有许多解释。首先，有几种方法可以衡量标准化的环境指标(不同于单核苷酸多态[SNPs])。当研究GxE时，选择环境因素时，没有足够的证据表明它们在疾病特征中的先前关联。其次，研究GxE需要大量样本，以确定单个SNPs与环境因素之间的交互作用。当考虑到对数百万个主效较小的SNP进行多次测试时，这个问题就会加剧。使用目前的方法和非标准化的环境数据，很难收集相互作用的证据 在数百万个特定的SNPs和环境因素之间。现在，通过开发数量遗传学的新方法和利用环境暴露生物信息学的现有方法，可以在导致重大疾病负担的复杂疾病特征中检测GxE，例如体重指数(BMI)和血压(BP)。该项目有四个目标来实现这一目标。首先，研究人员将开发和验证全基因组多基因预测分数，以总结所有常见的SNP在BMI和BP中的贡献。研究人员将开发和验证先前存在的全基因组关联研究(GWAS)联盟数据中的分数。在第二个目标中，研究人员将对保存在基因和表型数据库(DBGaP)中的7项独立队列研究的环境变量进行标准化，以建立用于GxE测试的N~30K的大队列。第三，研究人员将开发方法来检测和验证BMI和BP中多基因性状得分和特定环境因素之间的GxE，这些因素是从BMI和BP中选择的特定环境因素，并结合了DBGaP队列。第四，研究人员将估计由于GxE相互作用而导致的BMI和BP的变异比例。R21中提出的方法提供了一种新的GxE估计范式，它利用了基因组上的所有SNPs，同时考虑了更多的环境因素，并得到了Ewas的有力支持。这将导致对疾病负担最高的复杂性状中归因于基因和环境的变异性的更完整的描述。如果成功，这些方法将能够快速记录可复制的GxE，这是人类遗传学和环境健康领域的需要。

DESCRIPTION (provided by applicant): It is intuitive that the genetic risk for human disease depends on the environment, or that the effect of an exposure in disease is not identical across human populations of different genetic backgrounds. This concept is known as "gene-by-environment" interaction (GxE) and it is hypothesized that disease risk can be better explained by identifying GxE. Despite the importance in understanding GxE in human disease, there have been few studies that have documented the concept. There are a number of explanations for few-recorded GxE. First, there a few ways to measure standardized indicators of the environment (unlike single nucleotide polymorphisms [SNPs]). When GxE are investigated, environmental factors are selected without sufficient evidence of their prior association in disease traits. Second, investigating GxE requires large sample sizes to identify interactions between individual SNPs and environmental factors. The problem is exacerbated when accounting for multiple tests of millions of SNPs with small main effects. Using current day methods and unstandardized environmental data, it is difficult to collect evidence for interactions between millions of specific SNPs and environmental factors. It is now possible to detect GxE in complex disease traits that contribute to significant disease burden, such as body mass index (BMI) and blood pressure (BP), by developing new methods in quantitative genetics and leveraging existing methods in environmental exposure bioinformatics. This project has four aims to achieve this goal. First, the investigators will develop and validate genome-wide polygenic prediction scores to summarize the contribution of all common SNPs in BMI and BP. The investigators will develop and validate the scores in preexisting genome-wide association study (GWAS) consortia data. In the second aim, the investigators will standardize environmental variables from 7 independent cohort studies deposited in the Database of Genotypes and Phenotypes (dbGaP) to build a large cohort of N ~ 30K for GxE testing. Third, the investigators will develop methods to detect and validate GxE between polygenic trait scores and specific environmental factors selected from Environment-Wide Association Studies (EWAS) in BMI and BP with the combined dbGaP cohorts. Fourth, the investigators will estimate the proportion of variation in BMI and BP due to GxE interaction. The methods proposed in the R21 provide a new paradigm for GxE estimation by taking advantage of all SNPs on the genome while considering a larger number of environmental factors with robust support from EWAS. This will lead to a more complete picture of variability ascribed to genes and environment in complex traits of highest disease burden. If successful, the methods will enable the rapid documentation of reproducible GxE, a need in the human genetics and environmental health fields.