Methods for Gene-Enviroment Interactions Involving Gene Expression

描述（由申请人提供）：尽管鉴定与疾病性状相关的遗传变异的努力已经导致了相当数量的新发现的疾病SNP，但在该领域存在许多公开的挑战。这些GWAS SNP倾向于解释性状中低比例的变异，并且具有功能上模糊的作用。一些出版物提供的证据表明，同时考虑遗传变异和基因表达在相关组织类型导致更全面的表征复杂的人类特征的分子基础。特别是，基因表达变异捕获来自遗传和环境的影响，这两者最终都有助于复杂的性状。因此，一个非常感兴趣的问题是能够定量表征基因表达变异的遗传，环境及其相互作用的贡献。我们建议建立一个定量的框架，发现和解剖基因与环境（G x E）的相互作用，解释高维性状的变化，如基因表达。我们还建议将该方法应用于几个前沿数据集，并提供软件，以便在未来的研究中广泛使用的方法。我们将应对的关键挑战之一是解决相互作用的统计定义与生物定义不一致的问题。我们表明，统计定义是不不变的规模上的特征被放置的变化。相反，我们开发了一个统计定义，其中存在的相互作用，而不管规模的变化，也同意生物学的定义。这对于基因表达数据尤其重要，因为分析数据的规模是由技术决定的，而不是复杂性状表现过程的直接物理测量。

DESCRIPTION (provided by applicant): Although efforts to identify genetic variations associated with disease traits have lead to a respectable number of newly discovered disease SNPs, there are many open challenges in this area. These GWAS SNPs have tended to explain a low proportion of variation in the traits and have functionally ambiguous roles. A number of publications have provided evidence that simultaneously considering genetic variation and gene expression in relevant tissue types leads to a more comprehensive characterization of the molecular basis of complex human traits. In particular, gene expression variation captures influences from both genetics and environment, which both ultimately contribute to complex traits. Therefore, a problem of much interest is to be able to quantitatively characterize the genetic, environmental, and their interactive contributions to gene expression variation. We propose to build a quantitative framework for discovering and dissecting gene-by-environment (G x E) interactions explaining variation of high-dimensional traits, such as gene expression. We further propose to apply the methodology to several cutting edge data sets and also make software available so that the methods may be widely utilized in future studies. One of the key challenges that we will tackle is to resolve the inconsistency of the statistical definition of interaction with the biological definition. We show that the statistical definition is not invariant to changes in the scale on which the trait is placed. Instead we develop a statistical definition where the interaction exists irrespective of changes to the scale and also agrees with the biological definition. This is particularly important for gene expression data, where the scale on which the data are analyzed is determined by technology and not a direct physical measure of the process by which the complex trait is manifested.