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

摘要 现代全基因组关联研究已经明确地证明，复杂的特征是极其 多基因，每个个体特征可能涉及数千到数万个遗传变异。在……里面 在这个项目中，我们将开发一系列新的方法来大规模地利用多基因信号的力量 为疾病病因学提供信息并改进风险预测模型。在(目标1)中，我们将开发方法 用于在GWA中进行与不同群体遗传相关的关联信号的富集化分析 以及基因组的功能基因组特征。我们建议对效果尺寸分布进行建模 使用灵活的正态-混合模型与全基因组标记面板相关联，其中类 标记的成员关系是根据不同的基因组“协变量”以概率方式建模的。推论 模型和基础参数将在经验-贝叶斯框架中进一步利用，以得出多基因 用于遗传风险预测的风险评分(Risk-Score，PR)。在(目标2)中，我们将开发孟德尔式的新方法 随机化分析是工具变量分析的一种形式，用于研究因果关系 风险因素和健康结果之间的关系。我们将使用灵活的模型来进行双变量效应大小分布 跨性状对，允许从因果和非因果关系中产生遗传相关性。 我们提出了在所提出的框架下对因果效应估计这一复杂问题的解决方案 在分配某些类型的残差时使用一种创新的“尖峰检测”方法。在(目标3)中，我们 将开发新的方法，以增强在案例中使用粗糙集进行基因-环境交互分析的能力 对照研究。我们将开发能够利用各种自然假设的追溯方法 关于PR的分布，包括正常性及其与环境暴露的独立性， 可能取决于潜在人口中的其他因素。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，以获得广泛的各种特征，并期望做出新的 关于遗传易感性机制的科学观察，流行病学的因果基础 遗传风险预测的关联性、基因-环境相互作用的性质和实用性。

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.