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

通过使用多位点全基因组预测因子提高 GxE 检测的能力

基本信息

批准号：
8806011
负责人：
ISAAC S. KOHANE
金额：
$ 15.61万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-01-01 至 2017-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8806011
关键词：
Accounting Bioinformatics Blood Pressure Body mass index Cardiovascular Diseases Cohort Studies Complex Data Data Set Deposition Detection Disease Documentation Environment Environmental Exposure Environmental Health Environmental Risk Factor Epidemiology Etiology Genes Genetic Genetic Risk Genome Genomics Genotype Goals Heritability Human Human Genetics Individual Informatics Inherited International Investigation Lead Linear Models Measurement Methods Non-Insulin-Dependent Diabetes Mellitus Polygenic Traits Population Quantitative Genetics Research Design Research Personnel Risk Factors Role Sample Size Single Nucleotide Polymorphism Testing Variant Work analytical method burden of illness cohort database of Genotypes and Phenotypes disorder risk gene environment interaction genome wide association study genome-wide human disease improved novel public health priorities public health relevance standardize measure trait

项目摘要

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.

描述（由申请人提供）：直观地说，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并不相同。这个概念被称为“基因与环境”相互作用（GxE），并且假设通过识别 GxE 可以更好地解释疾病风险。尽管理解 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 有多种解释。首先，有几种方法可以测量环境的标准化指标（与单核苷酸多态性 [SNP] 不同）。当研究 GxE 时，选择的环境因素没有足够的证据证明其先前与疾病特征的关联。其次，研究 GxE 需要大量样本来识别单个 SNP 与环境因素之间的相互作用。当考虑到数百万个主效应较小的 SNP 的多次测试时，这个问题会更加严重。使用当今的方法和非标准化的环境数据，很难收集相互作用的证据数百万个特定 SNP 和环境因素之间的关系。现在，通过开发定量遗传学的新方法并利用环境暴露生物信息学中的现有方法，可以检测导致重大疾病负担的复杂疾病特征中的 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，研究人员将开发并验证全基因组多基因预测评分，以总结所有常见 SNP 对 BMI 和 BP 的贡献。研究人员将开发并验证现有全基因组关联研究（GWAS）联盟数据中的分数。第二个目标是，研究人员将对基因型和表型数据库 (dbGaP) 中存放的 7 个独立队列研究的环境变量进行标准化，以构建一个 N ~ 30K 的大型队列用于 GxE 测试。第三，研究人员将开发方法来检测和验证多基因性状评分与从 BMI 和 BP 的环境关联研究 (EWAS) 中选择的特定环境因素之间的 GxE 以及组合的 dbGaP 队列。第四，研究人员将估计由于 GxE 相互作用导致的 BMI 和 BP 变化的比例。 R21 中提出的方法通过利用基因组上的所有 SNP，同时在 EWAS 的有力支持下考虑大量环境因素，为 GxE 估计提供了新的范例。这将导致对疾病负担最高的复杂性状中基因和环境的变异性有更全面的了解。如果成功，这些方法将能够快速记录可重复的 GxE，这是人类遗传学和环境健康领域的需求。