New directions in genetic association studies

遗传关联研究的新方向

• 批准号: RGPIN-2019-04482
• 负责人: Greenwood, Celia
• 金额: $ 2.19万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714971
• 关键词: New; directions; genetic; association; studies

Overview: The general goal of this proposed research program is development of methods for working with high dimensional data in genetic and genomic studies. This methodological work is highly relevant to colleagues who are increasingly working with datasets containing large sample sizes and many measured variables (large feature space), sometimes including data from different tissues or measurement types. The proposed ideas are a logical continuation of several active projects including my current NSERC DG, loosely organized around three Themes. Theme 1: Inference and prediction in interaction models with one high dimensional feature set. For numerous human traits, small and approximately additive effects have been precisely estimated for each of thousands of genetic variants (usually single nucleotide polymorphisms (SNPs)). Recently, linear combinations of these SNPs are now being used to create genetic measures that collectively explain quite substantial proportions of trait variance. Here we focus on penalization and variable selection approaches that will optimize sensitivity for finding interactions between a high dimensional feature set (e.g. SNPs) and a single covariate such as an exposure. We will build on past progress in Theme 1 (Bhatnagar, Yang et al. 2018, Bhatnagar, Yang et al. 2018, Jolicoeur-Martineau, Wazana et al. 2018) by improving capabilities to cope with larger datasets and incorporating external information into penalty terms. Theme 2: Estimating and exploiting constrained and penalized projections. Causal inference methods based on instrumental variables rest on an often violated assumption: that the instrumental variable influences the outcome only through the exposure of interest. However, when using genetic variants as instruments, the variants often demonstrate horizontal pleiotropyinfluencing multiple traitsthereby violating this key assumption. We have developed a variable selection tool that, combined with a constrained linear projection, minimizes horizontal pleiotropy(Jiang, Oualkacha et al. submitted). In Theme 2, we propose to improve this estimator by making it more robust, and to continue this line of research into higher dimensional settings. Theme 3: Network models for genomic data: We have recently built a model for analysis of network associations in microbiome data where precision matrices vary by group membership (McGregor, Labbe et al. 2018). We are using Laplace priors on the off-diagonal elements of precision matrices to estimate sparse networks. We propose to continue this line of research to allow external annotations to influence the network structure, and furthermore to use concepts in time-varying networks to examine how networks change with covariates. Impact: All three themes involve development of statistical methods and software that will not only be useful to researchers in genetics, but will also generate statistical theory elements applicable to multiple domains.

概述：这项研究计划的总体目标是开发在遗传和基因组研究中处理高维数据的方法。 这种方法学工作与越来越多地使用包含大样本量和许多测量变量（大特征空间）的数据集的同事高度相关，有时包括来自不同组织或测量类型的数据。所提出的想法是几个活跃项目的逻辑延续，包括我目前的NSERC DG，围绕三个主题松散地组织起来。 主题1：具有一个高维特征集的交互模型中的推理和预测。对于许多人类性状，已经精确地估计了数千种遗传变异（通常是单核苷酸多态性（SNP））中每一种的小的和近似加性的影响。最近，这些SNPs的线性组合现在被用来创建遗传措施，共同解释相当大比例的性状变异。在这里，我们专注于惩罚和变量选择方法，这些方法将优化寻找高维特征集（例如SNP）和单个协变量（如暴露）之间相互作用的灵敏度。 我们将在主题1（Bhatnagar，Yang et al. 2018，Bhatnagar，Yang et al. 2018，Jolicoeur-Martineau，Wazana et al. 2018）的基础上，通过提高科普更大数据集的能力，并将外部信息纳入惩罚条款。 主题2：估计和利用约束和惩罚预测。基于工具变量的因果推理方法基于一个经常被违反的假设：工具变量只通过兴趣的暴露来影响结果。 然而，当使用遗传变异作为工具时，这些变异往往表现出影响多个性状的水平多效性，从而违反了这一关键假设。我们已经开发了一个变量选择工具，结合约束线性投影，最大限度地减少水平多效性（Jiang，Oualkacha等人提交）。在主题2中，我们建议通过使其更鲁棒来改进此估计量，并将这一研究路线继续到更高维的设置。 主题三：基因组数据的网络模型：我们最近建立了一个模型，用于分析微生物组数据中的网络关联，其中精度矩阵因组成员而异（McGregor，Labbe等人2018）。我们使用精确矩阵非对角元素的拉普拉斯先验来估计稀疏网络。我们建议继续这条研究路线，让外部注释影响网络结构，并进一步使用时变网络中的概念来研究网络如何随协变量而变化。 影响：所有这三个主题都涉及统计方法和软件的开发，这些方法和软件不仅对遗传学研究人员有用，而且还将产生适用于多个领域的统计理论元素。