Statistical Methods for detection of genome-wide GxE interactions in longitudinal

描述（由申请人提供）：已经发表了几项关于各种复杂疾病的全基因组关联研究（GWAS），其中收集了大量单核苷酸多态性（SNP）的基因型数据，以研究这些SNP与疾病之间的关联。虽然在这些GWAS中发现了与不同疾病相关的新基因座，但它们通常解释不了这些疾病的遗传风险。剩余的性状变异很可能是由于基因、环境因素及其相互作用的综合影响。然而，大多数进行全基因组关联研究的研究者在寻找新基因时没有考虑基因-环境（GxE）或基因-基因（GxG）的相互作用。此外，这些研究大多是横断面的。复杂的疾病往往是动态的，随着时间的推移而变化或积累的环境和生理因素。基因对这些疾病的影响也可能随着时间的推移，通过与年龄，发育阶段或其他时间依赖性环境因素等因素的相互作用而变化。在生命的不同阶段，遗传变异的影响的变化可以显着改变性状的轨迹。因此，不考虑遗传关联中纵向变异的可能性的研究可能会导致过度简单化的模型， 不同的影响，因此缺乏检测它们的能力。这部分是由于目前缺乏有效的统计方法和相应的软件来检测大量遗传数据和时间依赖性环境因素的相互作用。本提案的目的是通过开发先进的统计方法和高效的计算算法来分析来自基因-环境纵向研究的高通量数据，并使用无关个体和家族的数据来满足这一迫切需求。我们建议开发两种有效的方法来检测纵向研究中的GxE相互作用。它们是：（1）开发使用基于可能性的降维方法在纵向研究设计中稳健有效地估计GxE相互作用的技术;（2）开发用于高维数据的强大随机效应模型，以检测多个SNP和时间依赖性环境因素的联合效应。所提出的方法的动机，并被应用到明尼苏达中心的双胞胎和家庭研究（MCTFR）的数据，纵向全基因组研究的基因和环境及其相互作用与不同的行为特征。我们打算研究物质使用障碍（SUD）的病因学基础来自各种相互作用的生物和社会心理因素，在发展过程中动态地共同作用。将开发和分发开放式用户友好型统计软件。

DESCRIPTION (provided by applicant): Several genome-wide association studies (GWAS) have been published on various complex diseases, where genotype data on a large number of single nucleotide polymorphisms (SNPs) are collected to study the association between these SNPs and a disease. Although new loci are found to be associated with different diseases in these GWAS, they generally explain very little of the genetic risk for these diseases. Much of the remaining trait variation is likely to be due to the combined effect of genes, environmental factors, and their interactions. How- ever, most investigators conducting genome-wide association studies do not consider gene-environment (GxE) or gene-gene (GxG) interactions in their search for new genes. Moreover, most of these studies are cross-sectional. Complex diseases are frequently dynamic, varying over time with changing or accumulating environmental and physiological factors. The influence of genes on these diseases may also vary over time through interaction with factors such as age, developmental stage or other time-dependent environmental factors. Variation in the effects of genetic variants at different stages of life could significantly alter the trajectories of traits. Hence, studies that do not consider te possibility of longitudinal variation in genetic associations may lead to over-simplistic models of variant effects and hence lack power to detect them. This is in part due to a current lack of efficient statistical methods and corresponding software to detect the interplay of high-volume genetic data and time-dependent environmental factors. The purpose of this proposal responds to this urgent need by developing advanced statistical methods and efficient computing algorithms to analyze high-throughput data from gene-environment longitudinal studies with data on unrelated individuals as well as families. We propose to develop two efficient methods to detect GxE interactions in longitudinal studies. They are as follows: (1) to develop techniques for robust and efficient estimation of GxE interactions in longitudinal study designs using a likelihood-based dimension reduction approach; (2) to develop a powerful random-effect model for high-dimensional data to detect joint-effects of multiple SNPs and time-dependent environmental factors. The proposed methods are motivated by and to be applied to the Minnesota Center for Twin and Family Research (MCTFR) data, a longitudinal genome-wide study on genes and environments and their interactions with different behavioral traits. We intend to study the etiological underpinnings of substance use disorders (SUDs) derived from various interacting biological and psychosocial factors that work together dynamically over the course of development. Open access user-friendly statistical software will be developed and distributed.