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，或者疾病暴露的影响在不同遗传背景的人群中是不相同的，这是直观的。这个概念被称为“基因-环境”相互作用（GxE），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，但很少有研究记录了这一概念。对于很少记录的GxE，有很多解释。首先，有几种方法可以测量环境的标准化指标（不像单核苷酸多态性[SNPs]）。在研究GxE时，环境因素的选择没有充分的证据表明它们与疾病性状的先验关联。其次，研究GxE需要大量的样本来确定单个snp与环境因素之间的相互作用。当考虑到对数百万个主效应较小的snp进行多次测试时，问题就更加严重了。使用目前的方法和未标准化的环境数据，很难收集相互作用的证据

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.