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，但在这一领域仍有许多开放的挑战。这些GWAS snp倾向于解释性状变异的低比例，并且具有功能模糊的作用。许多出版物提供的证据表明，同时考虑相关组织类型中的遗传变异和基因表达，可以更全面地表征复杂人类特征的分子基础。特别是，基因表达变异捕获了遗传和环境的影响，这两者最终都有助于形成复杂的性状。因此，一个非常有趣的问题是能够定量表征遗传、环境及其相互作用对基因表达变异的贡献。我们建议建立一个定量框架，用于发现和剖析基因-环境（gxe）相互作用，解释高维性状（如基因表达）的变化。我们进一步建议将该方法应用于几个前沿数据集，并提供软件，以便该方法可以在未来的研究中广泛使用。我们将解决的关键挑战之一是解决相互作用的统计定义与生物学定义的不一致。我们表明，统计定义并不是不变的变化的尺度上的性状放置。相反，我们开发了一个统计定义，其中相互作用存在，而不考虑尺度的变化，并且与生物学定义一致。这对于基因表达数据尤其重要，因为数据分析的尺度是由技术决定的，而不是对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. PUBLIC HEALTH RELEVANCE: It is well known that both genetic and environmental factors contribute to human disease. This proposal will provide a better understanding of the interaction between these two factors, especially as it is manifested through gene expression levels. The successful completion of this research will provide a better understanding of the biological basis of complex human diseases.