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:9185324
- 负责人:
- 金额:$ 13.88万
- 依托单位:
- 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 2018-11-30
- 项目状态:已结题
- 来源:
- 关键词:AccountingBioinformaticsBlood PressureBody mass indexCardiovascular DiseasesCohort StudiesComplexComplex Genetic TraitDataData SetDepositionDetectionDiseaseDocumentationEnvironmentEnvironmental ExposureEnvironmental HealthEnvironmental Risk FactorEtiologyGenesGeneticGenetic RiskGenomeGenotypeGoalsHeritabilityHumanHuman GeneticsIndividualInformaticsInheritedInternationalIntuitionInvestigationLinear ModelsMeasurementMethodsNon-Insulin-Dependent Diabetes MellitusPhenotypePolygenic TraitsPopulationQuantitative GeneticsReproducibilityResearch DesignResearch PersonnelRoleSample SizeSingle Nucleotide PolymorphismStandardizationTestingVariantWorkanalytical methodburden of illnesscardiovascular risk factorcohortdatabase of Genotypes and Phenotypesdisease phenotypedisorder riskgene environment interactiongenetic predictorsgenome wide association studygenome-widegenomic epidemiologyhuman diseaseimprovednovelpublic health prioritiespublic health relevancestandardize measuretraitwhole genome
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):直观上,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”的相互作用(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,但很少有研究记录了这一概念。 对于很少记录的GxE有许多解释。首先,有几种方法可以测量环境的标准化指标(与单核苷酸多态性不同)。当研究GxE时,选择环境因素,而没有足够的证据表明它们在疾病性状中的先前关联。其次,研究GxE需要大样本量来确定单个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,问题更加严重。使用当前的方法和不标准化的环境数据,很难收集相互作用的证据
数百万个特定的SNPs和环境因素之间的联系。 通过开发定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,现在可以在导致重大疾病负担的复杂疾病性状中检测GxE,如体重指数(BMI)和血压(BP)。为实现这一目标,该项目有四个目标。首先,研究人员将开发和验证全基因组多基因预测评分,以总结BMI和BP中所有常见SNP的贡献。研究人员将在现有的全基因组关联研究(GWAS)联盟数据中开发和验证评分。在第二个目标中,研究人员将标准化基因型和表型数据库(dbGaP)中保存的7项独立队列研究的环境变量,以建立一个用于GxE测试的N ~ 30 K大型队列。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特定环境因素之间的GxE,这些因素选自BMI和BP的环境广泛关联研究(EWAS)与组合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,同时考虑大量环境因素并得到EWAS的有力支持,为GxE估计提供了一种新的范例。这将导致一个更完整的图片的变异归因于基因和环境的复杂性状的最高疾病负担。如果成功,该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。
项目成果
期刊论文数量(11)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Emergency Department Adverse Events Detected Using the Emergency Department Trigger Tool.
使用急诊室触发工具检测到急诊室不良事件。
- DOI:10.1016/j.annemergmed.2022.05.037
- 发表时间:2022
- 期刊:
- 影响因子:6.2
- 作者:Griffey,RichardT;Schneider,RyanM;Todorov,AlexandreA
- 通讯作者:Todorov,AlexandreA
Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images.
- DOI:10.1038/s41467-022-29525-9
- 发表时间:2022-04-13
- 期刊:
- 影响因子:16.6
- 作者:
- 通讯作者:
Using Big Data to Determine Reference Values for Laboratory Tests-Reply.
利用大数据确定实验室测试的参考值-回复。
- DOI:10.1001/jama.2018.10956
- 发表时间:2018
- 期刊:
- 影响因子:0
- 作者:Manrai,ArjunK;Patel,ChiragJ;Ioannidis,JohnPA
- 通讯作者:Ioannidis,JohnPA
A data-driven search for semen-related phenotypes in conception delay.
- DOI:10.1111/andr.12288
- 发表时间:2017-01
- 期刊:
- 影响因子:4.5
- 作者:Patel CJ;Sundaram R;Buck Louis GM
- 通讯作者:Buck Louis GM
Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
- DOI:10.1038/s41588-018-0313-7
- 发表时间:2019-02-01
- 期刊:
- 影响因子:30.8
- 作者:Lakhani, Chirag M.;Tierney, Braden T.;Patel, Chirag J.
- 通讯作者:Patel, Chirag J.
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{{ truncateString('CHIRAG J. PATEL', 18)}}的其他基金
Data-driven identification of environmental factors in cardiovascular disease
心血管疾病环境因素的数据驱动识别
- 批准号:
9169975 - 财政年份:2016
- 资助金额:
$ 13.88万 - 项目类别:
Data-driven identification of environmental factors in cardiovascular disease
心血管疾病环境因素的数据驱动识别
- 批准号:
9198769 - 财政年份:2016
- 资助金额:
$ 13.88万 - 项目类别:
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
- 批准号:
8989538 - 财政年份:2015
- 资助金额:
$ 13.88万 - 项目类别:
Data-driven identification of environmental factors in cardiovascular disease
心血管疾病环境因素的数据驱动识别
- 批准号:
8804261 - 财政年份:2014
- 资助金额:
$ 13.88万 - 项目类别:
Data-driven identification of environmental factors in cardiovascular disease
心血管疾病环境因素的数据驱动识别
- 批准号:
8617098 - 财政年份:2014
- 资助金额:
$ 13.88万 - 项目类别:
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