New directions in genetic association studies

概述：这项拟议的研究计划的总体目标是开发在遗传和基因组研究中使用高维数据的方法。这项方法论工作与同事们高度相关，他们越来越多地处理包含大样本和许多测量变量(大特征空间)的数据集，有时包括来自不同组织或测量类型的数据。提议的想法是几个活跃项目的合乎逻辑的延续，包括我目前的NSERC DG，围绕三个主题松散地组织。 主题1：具有一个高维特征集的交互模型中的推理和预测。对于许多人类特征，对数千种遗传变异(通常是单核苷酸多态(SNPs))中的每一种，都准确地估计了小的和近似的加性效应。最近，这些SNPs的线性组合现在被用来创建基因测量，这些测量共同解释了相当大比例的性状变异。在这里，我们关注惩罚和变量选择方法，它们将优化寻找高维特征集(例如SNPs)和单个协变量(例如暴露)之间的交互作用的敏感度。我们将在主题1(Bhatnagar、Yang等人)过去进展的基础上再接再厉。2018年，Bhatnagar，Yang等人。2018年，Jolicoeur-Martineau，Wazana等人。2018年)，提高处理更大数据集的能力，并将外部信息纳入处罚条款。 主题2：估计和利用受限制和受处罚的预测。基于工具变量的因果推理方法建立在一个经常被违反的假设之上：工具变量只通过利益的暴露来影响结果。然而，当使用遗传变异作为工具时，变异往往表现出水平多向效应，从而影响多个序列，从而违反了这一关键假设。我们已经开发了一种变量选择工具，它与约束线性投影相结合，最小化水平多效性(酱，Oualkacha等人。已呈交)。在主题2中，我们建议通过使该估计器更稳健来改进该估计器，并将这一研究继续到更高维度的环境中。 主题3：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年)。我们使用精度矩阵的非对角元的拉普拉斯先验来估计稀疏网络。我们建议继续这一研究，以允许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检查网络如何随协变量变化。 影响：所有三个主题都涉及开发统计方法和软件，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要素。

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.