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.

抽象的现代基因组的关联研究明确证明了复杂的特征极为多基因，每个单独的性状都可能涉及数千至数万个遗传变异。在这个项目，我们将开发一系列新颖的方法来利用大型信号的力量 GWA为疾病的病因和改善风险预测的模型。在（AIM 1）中，我们将开发方法用于对GWAS中的关联信号进行丰富分析与各种人群的关系基因组的功能基因组特征。我们建议建模效应尺寸分布使用柔性正常混合模型与整个标记的整个基因组小组相关联，其中类别标记的成员资格可能是根据各种基因组“协变量”来建模的。推断模型和基础参数将在经验bayes框架中进一步使用，以得出多基因遗传风险预测的风险评分（PR）。在（AIM 2）中，我们将为Mendelian开发新颖的方法随机化分析是一种仪器变量分析的一种形式，用于研究因果关系在风险因素和健康结果之间。我们将利用灵活的模型进行双变量效应大小分布在各对性状上，允许因果关系和非因果关系引起遗传相关性。我们提出了解决拟议框架下因果效应估计的复杂问题的解决方案在某些类型的残留物分布中，使用一种创新的方法来“尖峰检测”。在（AIM 3）中，我们将开发新的方法来使用PRS在案例中增强基因环境相互作用分析的能力对照研究。我们将开发可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，可能是基于潜在人群的其他因素的条件。我们将应用提出的方法对现有的GWAS数据集进行大规模分析，以实现各种特征，并希望使新颖关于遗传易感性机制的科学观察，流行病学的因果基础关联，基因环境相互作用的性质以及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。