Statistical Methods for Modeling Polygenic Architecture in Association and Re-sequencing Studies

关联和重测序研究中多基因结构建模的统计方法

• 批准号: 10159307
• 负责人: Xiang Zhou
• 金额: $ 33.97万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-14 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10159307
• 关键词: Address; Algorithms; Architecture; Base Sequence; Categories; Communities; Complex; Computer software; Data; Data Set; Development; Disease; Evaluation; Genetic; Genetic study; Health; Heritability; Human; Individual; Linkage Disequilibrium; Lipids; Logistics; Methods; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Pattern; Phenotype; Polygenic Traits; Research; Source; Statistical Methods; Structure; Testing; Variant; base; causal variant; disorder risk; flexibility; functional genomics; genetic architecture; genetic variant; genome sequencing; genome wide association study; improved; infancy; novel; open source; pleiotropism; population stratification; precision medicine; risk prediction; simulation; software development; statistics; success; tool; trait; user friendly software; user-friendly; whole genome

Project Summary Array- and sequencing-based association studies have identified many loci harboring genetic variants associated with complex traits and common diseases. Altogether, these associated variants only explain a small proportion of heritability, suggesting that most traits and diseases have a polygenic background and are influenced by many variants with small effects. Early attempts to model polygenic complex traits, notably via the linear mixed models (LMMs) and the best linear unbiased predictor (BLUP), have shown promising outcomes for estimating chip heritability, identifying causal variants, and predicting disease risks. However, statistical methods for modeling polygenic architecture remain in their infancy. In particular, existing methods rely on simple effect size assumptions, are not flexible nor adaptive to the underlying genetic architecture of a given trait or disease, and hence cannot take full advantage of the polygenic natural of most traits and diseases. To increase the power of association test and enable more precise phenotype and risk prediction, I propose to develop a suite of novel statistical methods to accurately and flexibly model the polygenic architecture. These new methods will facilitate evaluation and integration of variant functional annotations, multiple phenotype association mapping, and phenotype and risk prediction in association studies. In particular, we will (1) develop methods to evaluate and integrate variant genomic functional annotations to better understand the polygenic architecture of traits and diseases, and enable powerful association mapping; (2) develop strategies for association mapping with multiple correlated phenotypes to identify pleiotropic associations by taking advantage of the shared polygenic background among phenotypes; and (3) develop methods to flexibly model polygenic architecture and use all variants jointly to achieve accurate phenotype and risk prediction. We will develop efficient algorithms to accompany these methods and implement them in free open-source software. We will perform rigorous simulations and comparisons to evaluate our methods. Finally, we will perform in- depth analysis on several large-scale real data sets, including data from the Global Lipids Genetics Consortium, T2D-GENES and METSIM projects, to demonstrate the power of the proposed methods.

项目摘要 基于阵列和测序的关联研究已经确定了许多含有遗传变异的基因座 与复杂的特征和常见疾病有关。总而言之，这些相关的变体只能解释 小比例的遗传力，这表明大多数性状和疾病具有多基因背景，并且 受许多影响很小的变种的影响。早期对多基因复杂性状建模的尝试，特别是通过 线性混合模型(LMM)和最佳线性无偏预测(BLUP)已显示出良好的应用前景 评估芯片遗传力、识别因果变异和预测疾病风险的结果。然而， 多基因结构建模的统计方法仍处于初级阶段。特别是，现有的方法 依赖简单的效应大小假设，既不灵活，也不适应潜在的遗传架构 特定的性状或疾病，因此不能充分利用大多数性状的多基因自然和 疾病。 为了增加关联测试的能力，并能够更准确地进行表型和风险预测，我建议 开发一套新的统计方法来准确和灵活地模拟多基因结构。 这些新方法将有助于评估和集成各种功能注释、多个 关联研究中的表型关联图谱、表型和风险预测。特别是，我们将 (1)开发评估和整合变异基因组功能注释的方法，以更好地理解 性状和疾病的多基因架构，并实现强大的关联图谱；(2)制定策略 用于具有多个相关表型的关联映射，以通过 表型间共有多基因背景的优势；以及(3)开发灵活建模的方法 多基因架构，并联合使用所有变体，以实现准确的表型和风险预测。我们会 开发有效的算法来伴随这些方法，并在免费开源软件中实现它们。 我们将进行严格的模拟和比较，以评估我们的方法。最后，我们将在- 对几个大规模真实数据集的深度分析，包括来自全球脂质遗传学的数据 联盟、T2D-Genees和METSIM项目，以演示所提出的方法的威力。