Set based tests for genetic association and gene-environment interaction in longitudinal studies

大多数人类疾病具有多因素病因，表现为多基因和环境因素复杂的相互作用。这些遗传和环境因素对疾病风险的影响可能会在不同的生命阶段动态变化。对血压、体重指数等常见和慢性疾病的风险因素进行纵向研究，为探索基因变异如何随着时间的推移影响这些特征提供了宝贵的机会。如果我们联合利用整个纵向结果集，检测疾病易感基因的能力可以得到改善。此外，由于疾病风险因素和表型可能受到基因或基因组区域中多个变异的共同影响，考虑到变异之间的连锁不平衡和潜在相互作用，对这些变异的联合分析可能有助于解释额外的遗传性。将环境暴露数据的重复测量整合到这些遗传关联模型中，将有助于识别可能更容易受到环境暴露影响的特定个体亚群。基因-环境相互作用的识别可能会对有针对性的干预和预防产生影响。在这个项目中，研究人员将尝试利用纵向遗传关联研究中可用的随时间变化的结果暴露曲线，以增强遗传关联和相互作用的统计测试的能力。使用来自多民族队列的数据，该项目团队将探索依赖时间的遗传关联和基因-环境相互作用，以基因和通路为分析单位，而不是在给定基因座上的单一标记。这种方法在生物学上更有意义，因为基因是功能单位，而不是单核苷酸多态，通过联合分析一个区域中罕见和常见的遗传变异，可能更接近捕捉功能变异。在计划中的分析中，将考虑衡量个人饮食、体育活动、心理社会行为和对周围环境的看法的几个环境因素。该项目将解决几个技术挑战。研究团队的主要目标将是开发在纵向研究中涉及多种表型、基因和环境因素的随机场模型下得出的简单通用分数测试。该方法通过将涉及多个预测器的关联测试转化为参数数量减少的关联测试和自由度减少的结果测试来降低推理问题的维度。开发的方法将使用和扩展经典的空间随机场理论和多标记测试的最新结果来表征复杂的依赖于时间的关联和相互作用。将进行几项必要的方法学改进，以处理纵向数据，以增强对主题相关结构误指定的稳健性，并提高计算效率。然后，这些方法将被扩展到使用纵向数据的基因-环境集合关联检验。提出了处理相关环境暴露数据的几种重要降维技术。项目团队还考虑了在这一基于集合的框架下对时变暴露和时变交互影响的处理。目前，文献中还没有使用丰富的纵向结果和暴露数据的基于多标记物的测试，目前的项目有望填补这一空白。综上所述，该项目引入了一种新的遗传随机场框架来描述这类涉及疾病结果、基因、环境和时间的多变量关联问题，以导致强大的统计推断。

Most human diseases have a multifactorial etiology, characterizing complex interplay of multiple genes and environmental factors. The effects of these genetic and environmental factors on disease risk are likely to change dynamically over different life stages. Longitudinal studies of risk factors for common and chronic diseases like blood pressure, body mass index, provide a valuable opportunity to explore how genetic variants affect these traits over time. The ability to detect disease susceptibility genes can be improved if we jointly utilize the entire set of longitudinal outcomes. Moreover, since disease risk factors and phenotypes are likely influenced by the joint effect of multiple variants in a gene or in a genomic region, a joint analysis of these variants considering linkage disequilibrium and potential interactions among the variants may help to explain additional heritability. Integrating repeated measures of environmental exposure data into these genetic association models will help to identify specific sub-groups of individuals who may be more susceptible to environmental exposures. Identification of gene-environment interactions may have implications for targeted intervention and prevention. In this project, the investigators will try to utilize the temporally varying outcome-exposure profile available in a longitudinal genetic association study to enhance the power of statistical tests for genetic association and interaction. Using data from a muti-ethnic cohort, the project team will explore time-dependent genetic associations and gene-environment interactions with genes and pathways as the unit of analysis instead of a single marker at a given locus. This approach is biologically more meaningful as genes are the functional units, not the single nucleotide polymorphisms and by joint analysis of rare and common genetic variants in a region one may be closer to capturing functional variation. Several environmental factors measuring an individual's diet, physical activity, psychosocial behavior and perception of the neighborhood they live in will be considered in the planned analysis.There are several technical challenges that will be addressed in the project. A primary goal of the study team will be to develop simple generalized score tests derived under a random field model involving multiple phenotypes, genes and environmental factors in a longitudinal study. The approach reduces the dimensionality of the inference problem by translating the association testing involving many predictors in terms of a reduced number of parameters and resultant tests with reduced degrees of freedom. The developed methods will use and extend classical spatial random field theory and recent results on multi-marker tests to characterize complex time-dependent associations and interactions. Several essential methodological improvements necessary for handling longitudinal data will be carried out to enhance the robustness to misspecification of within subject correlation structure and to improve computational efficiency. The methods will then be extended to a gene-environment set association test using longitudinal data. Several important dimension reduction techniques to handle correlated environmental exposure data are proposed. The project team also considers treatment of time varying exposure and time varying interaction effects under this set-based framework. There are no multi-marker based tests presently available in the literature that use the richness of longitudinal outcome and exposure data and the current project is expected to fill that gap. To summarize, the project introduces a novel genetic random field framework to formulate this class of multivariable association problems involving disease outcomes, gene, environment, and time to lead to powerful statistical inference.