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

纵向检测全基因组 GxE 相互作用的统计方法

• 批准号: 8652967
• 负责人: Saonli Basu
• 金额: $ 27.75万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8652967
• 关键词: Affect; Age; Algorithms; Architecture; Behavioral; Behavioral Genetics; Biological; Biological Assay; Biological Factors; Biological Process; Cohort Studies; Communities; Complex; Computer software; Cross-Sectional Studies; Data; Data Set; Detection; Development; Developmental Process; Dimensions; Disease; Environment; Environmental Exposure; Environmental Risk Factor; Etiology; Family; Family Research; Family Study; Genes; Genetic; Genetic Risk; Genomics; Genotype; Goals; Health; Human; Human Development; Individual; Intake; Intervention; Investigation; Joints; Lead; Libraries; Life; Link; Longitudinal Studies; Maps; Measures; Methods; Minnesota; Modeling; Nature; Parents; Pathway interactions; Phenotype; Physiological; Probability; Process; Programming Languages; Prospective Studies; Psychosocial Factor; Publishing; Research; Research Design; Research Personnel; Risk; Sample Size; Series; Single Nucleotide Polymorphism; Staging; Statistical Methods; Substance Use Disorder; Techniques; Testing; Time; Twin Multiple Birth; Variant; Work; adolescent offspring; base; disorder risk; environmental intervention; flexibility; gene environment interaction; gene interaction; genetic association; genetic variant; genome wide association study; genome-wide; insight; interest; population based; public health relevance; simulation; trait; user-friendly

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.

描述（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。尽管在这些 GWAS 中发现新的基因座与不同的疾病相关，但它们通常很少解释这些疾病的遗传风险。其余的性状变异大部分可能是由于基因、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人员在寻找新基因时并未考虑基因-环境（GxE）或基因-基因（GxG）相互作用。此外，大多数这些研究都是横断面的。复杂的疾病通常是动态的，随着时间的推移，随着环境和生理因素的变化或积累而变化。基因对这些疾病的影响也可能通过与年龄、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随时间而变化。生命不同阶段的遗传变异影响的变化可能会显着改变性状的轨迹。因此，不考虑遗传关联纵向变异可能性的研究可能会导致过于简单化的模型 变异效应，因此缺乏检测它们的能力。部分原因是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。该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响应这一迫切需求，以分析来自基因-环境纵向研究的高通量数据以及无关个人和家庭的数据。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。它们如下：（1）开发使用基于可能性的降维方法稳健且高效地估计纵向研究设计中的 GxE 相互作用的技术； (2) 为高维数据开发强大的随机效应模型，以检测多个 SNP 和时间依赖性环境因素的联合效应。所提出的方法受到明尼苏达双胞胎和家庭研究中心（MCTFR）数据的启发并应用于该数据，这是一项关于基因和环境及其与不同行为特征相互作用的纵向全基因组研究。我们打算研究物质使用障碍（SUD）的病因学基础，这些障碍源自各种相互作用的生物和心理社会因素，这些因素在发展过程中动态地协同作用。将开发和分发开放获取、用户友好的统计软件。