Enhanced Gene Identification in Complex Traits Using Kernel Machines

描述(由申请人提供)： 项目概述/摘要全基因组关联研究已经定位了数千种常见的影响性状的变异，但绝大多数性状基因座尚未被发现。这项建议的目标是开发和应用超越标准Gwas范例的统计方法，以绘制人类基因组中影响其他特征的变异。我们提出的大多数工具都基于一种名为核机器回归的灵活的高维框架，我们过去曾成功地将其用于GWAS和下一代测序(NGS)研究中强大的复杂性状基因图谱。我们相信，内核框架固有的灵活性使其成为探索复杂人类特征基因图谱的新范式的理想选择。目的1提出一种新的核方法，用于对单核苷酸变异数据(来自Gwas和/或NGS)和基因组数据(如基因表达和甲基化模式)进行集成分析，我们相信这将为特征定位提供更好的能力。AIM 2提出了新的核方法，用于跨基因组的大规模基因-基因相互作用分析，以及一种计算方法，当应用这种详尽的测试程序时，该方法能够有效地调整多个测试。目的3建立新的核方法用于X染色体上SNV的关联定位。核机器的灵活性质使其成为对该染色体上潜在的性别特异性效应进行建模的理想方法，并且该方法还可以适应随机的X失活。目的4提出了一种新的核方法，用于稳健地分析家系内罕见性状的变异；在目前的稀有变异程序中，这种基于家系的设计通常不被考虑。我们将在我们积极参与的大型数据集上评估这些方法，并将在用户友好的公开分发软件中实施这些方法(目标5)。

DESCRIPTION (provided by applicant): Project Summary/Abstract Genome-wide association studies (GWAS) have mapped thousands of common trait-influencing variants yet the overwhelming majority of trait loci have yet to be discovered. The goal of this proposal is to develop and apply statistical approaches that move beyond the standard GWAS paradigm to map additional trait-influencing variation within the human genome. Most of our proposed tools are based on a flexible high-dimensional framework called kernel machine regression, which we have had past success employing for powerful gene mapping of complex traits in GWAS and next-generation sequencing (NGS) studies. We believe the inherent flexibility of the kernel framework makes it ideal for exploring new paradigms in gene mapping of complex human traits. Aim 1 proposes novel kernel methods for integrated analysis of both single-nucleotide variation data (derived from GWAS and/or NGS) and genomic data (such as gene-expression and methylation patterns) that we believe will provide improved power for trait mapping. Aim 2 proposes novel kernel methods for large scale gene-gene interaction analysis across the genome, as well as a computational approach that enables efficient adjustment for multiple testing when applying such exhaustive testing procedures. Aim 3 establishes novel kernel methods for association mapping of SNVs on the X chromosome. The flexible nature of kernel machines makes it ideal for modeling potential sex-specific effects on this chromosome and the methods further can accommodate random X inactivation. Aim 4 proposes novel kernel approach for robust analysis of rare trait-influencing variation within families; such family-based designs are generally not considered in current rare-variant procedures. We will evaluate these methods on large-scale datasets that we are actively involved in and will implement the methods in user-friendly software for public distribution (Aim 5).