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Robust Methods for Polygenic Analysis to Inform Disease Etiology and Enhance Risk Prediction

多基因分析的稳健方法可告知疾病病因并增强风险预测

基本信息

批准号：
10579942
负责人：
Nilanjan Chatterjee
金额：
$ 57.53万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10579942
关键词：
Acceleration Algorithms Architecture Area Biological Body mass index Breast Cancer Risk Factor Case/Control Studies Characteristics Complex Coronary heart disease Data Data Set Dependence Detection Development Disease Environment Environmental Exposure Environmental Risk Factor Epidemiology Etiology Foundations Genes Genetic Genetic Markers Genetic Models Genetic Predisposition to Disease Genetic Risk Genome Genomics Genotype Health Heritability Human Individual Investigation Joints Linear Regressions Mendelian randomization Methods Modeling Modernization Nature Non-Insulin-Dependent Diabetes Mellitus Normalcy Outcome Performance Phenotype Population Population Genetics Reproductive History Residual state Risk Factors Series Signal Transduction Source Variant biobank biomarker panel case control disorder risk epidemiology study flexibility functional genomics gene environment interaction genetic association genetic epidemiology genetic variant genome wide association study genome-wide genomic data improved innovation insight interest knowledge translation lifestyle factors malignant breast neoplasm novel polygenic risk score population stratification risk prediction risk prediction model simulation statistics tool trait whole genome

项目摘要

Abstract Modern genome-wide association studies have unequivocally demonstrated that complex traits are extremely polygenic, with each individual trait potentially involving thousands to tens of thousands of genetic variants. In this project, we will develop a series of novel methods to harness the power of polygenic signals in large GWAS to inform disease etiology and improve models for risk prediction. In (Aim 1), we will develop methods for conducting enrichment analysis of association signals in GWAS in relationship to various population genetic and functional genomic characteristics of the genome. We propose to model effect-size distributions associated with whole genome panel of markers using flexible normal-mixture models, where class memberships of the markers are modelled probabilistically in terms of various genomic “covariates”. Inferred models and underlying parameters will be further utilized in an empirical-Bayes framework to derive polygenic risk-scores (PRS) for genetic risk prediction. In (Aim 2), we will develop novel methods for Mendelian randomization analysis, a form of instrumental variable analysis, for the investigation of causal relationships between risk-factors and health outcomes. We will utilize flexible models for bivariate effect-size distributions across pairs of traits, allowing for genetic correlation to arise from both causal and non-causal relationships. We propose a solution to the complex problem of estimation of causal effects under the proposed framework using an innovative method for “spike detection” in the distribution of certain types of residuals. In (Aim 3), we will develop novel methods to enhance the power of gene-environment interaction analysis using PRS in case- control studies. We will develop retrospective methods that can take advantage of various natural assumptions about the distribution of PRS, including normality and its independence from environmental exposures, possibly conditional on other factors, in the underlying population. We will apply the proposed methods to conduct large scale analysis of existing GWAS datasets for a wide variety of traits and expect to make novel scientific observations regarding mechanisms of genetic susceptibility, causal basis for epidemiologic associations, nature of gene-environment interactions and utility of genetic risk prediction.

摘要现代全基因组关联研究已经明确表明，复杂的性状是极其复杂的。多基因的，每个个体特征可能涉及数千到数万个遗传变异。在在这个项目中，我们将开发一系列新的方法来大规模利用多基因信号的力量。 GWAS告知疾病病因并改进风险预测模型。在（目标1）中，我们将开发方法用于在GWAS中进行与各种群体遗传相关的关联信号的富集分析，和基因组的功能基因组特征。我们建议对效应量分布进行建模使用灵活的正态混合模型与标记的全基因组面板相关联，其中类标记的成员是根据各种基因组“协变量”概率建模的。推断模型和基本参数将进一步利用在一个统计贝叶斯框架，以得出多基因风险评分（PRS）用于遗传风险预测。在（目标2）中，我们将开发新的方法，随机化分析，一种工具变量分析形式，用于调查因果关系风险因素和健康结果之间的关系。我们将利用灵活的模型来分析双变量效应量分布在成对的性状之间，允许遗传相关性从因果关系和非因果关系中产生。我们提出了一个解决方案的复杂问题的因果效应估计的建议框架下在某些类型的残差分布中使用创新的“尖峰检测”方法。在（目标3）中，我们将开发新的方法，以提高使用PRS的基因-环境相互作用分析的能力，对照研究。我们将开发可以利用各种自然假设的回顾性方法关于PRS的分布，包括正态性及其与环境暴露的独立性，可能取决于其他因素，在潜在人群中。我们将把建议的方法应用于对现有的GWAS数据集进行大规模分析，以获得各种各样的性状，并期望在这些性状中产生新的关于遗传易感性机制的科学观察，流行病学的因果关系关联，基因-环境相互作用的性质和遗传风险预测的实用性。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics.

一种混合模型方法，可结合肿瘤特征对与癌症风险的遗传关联进行强有力的测试。

DOI：
10.1093/biostatistics/kxz065
发表时间：
2021
期刊：
Biostatistics (Oxford, England)
影响因子：
0
作者：
Zhang,Haoyu;Zhao,Ni;Ahearn,ThomasU;Wheeler,William;García-Closas,Montserrat;Chatterjee,Nilanjan
通讯作者：
Chatterjee,Nilanjan

Effect of non-normality and low count variants on cross-phenotype association tests in GWAS.

非正态性和低计数变异对 GWAS 交叉表型关联测试的影响。