Phenomics: Joint clustering to associate changes in allergy and asthma over time

描述（由研究者提供）：模式分析是寻求一般指导原则的应用程序的核心。聚类分析是模式分析的一种。本应用程序旨在开发和应用新的基于模型的聚类方法，从1989年至1990年在英国怀特岛（IOW）建立的出生队列纵向数据集。提出的方法旨在联合聚类主题和相互依赖的变量，以提高聚类的同质性。“联合”一词是指聚类主体和聚类变量的能力，以及两个聚类过程之间的依赖关系。同时，我们允许存在非聚类的主题/变量。我们将通过在ASDA子集中寻找一致的时间趋势，应用该方法来识别不同过敏原（ASDA）的过敏致敏簇和属于每个ASDA簇的受试者。通过推断的群集概况，我们评估了两种时间模式之间的关联：哮喘/喘息状态和过敏致敏随时间的变化，并考虑了合并症。现有的聚类方法（参数或非参数）无法实现上述目标。这些方法要么不能解释外部变量的贡献，如时间（外部变量）对过敏致敏的影响（感兴趣的变量），要么忽略了不同变量之间的相互依赖性（如对不同过敏原的过敏致敏）。最近的研究结果支持动态过敏模式。然而，在很大程度上尚不清楚：(1)是否存在一组（或几组）过敏原，其致敏性具有相似的时间趋势（自然历史），如高系统反应期或惰性系统反应期；(2)动态过敏模式是否与哮喘/喘息持续、缓解或新发病（现象关联）有关。该应用程序试图填补这些空白，这将有可能使我们更接近了解哮喘的自然史，并为推进哮喘预防议程提供强大的潜力。英国IOW的出生队列包括1456名在出生时、1岁、2岁、4岁、10岁和18岁时进行检查的儿童，保留率为90%。该队列具有不同年龄的广泛表型数据和环境因素（如过敏原和污染物水平）的记录。本研究的主要变量包括纵向过敏致敏措施和哮喘/喘息状态。所提出的方法不仅限于该数据集，而且可以应用于对一定数量的变量进行连续测量的任何数据，例如高通量基因表达数据或甲基化数据。我们的团队与南卡罗莱纳大学的生物统计学（Zhang）和流行病学（Karmaus）知识以及南安普顿大学和David Hide哮喘和过敏研究中心的临床专家（Arshad和Roberts）在IOW方面有着长期成功的合作记录。张博士具有丰富的统计建模经验[1R03HL095429， Zhang (MPI)]。该小组的几个项目得到了NIH的支持，包括1R01AI091905[主要研究者：Karmaus]和1R01HL082925[主要研究者：Arshad]；在这两个项目中，张博士都是主要研究者。

DESCRIPTION (provided by investigator): Pattern analyses are central in applications seeking for general guidelines. Cluster analysis is one type of pattern analysis. This application aims to develop and apply novel model-based clustering methods to a longitudinal data set from a birth cohort established in 1989 to 1990 on the Isle of Wight (IOW), UK. The proposed methods aim to jointly cluster subjects and interdependent variables aiming to improved cluster homogeneity. The word "joint" refers to the ability of clustering subjects and clustering of variables along with the incorporation of dependence between the two clustering processes. At the meantime, we allow the existence of non-clustered subjects/variables. We will apply the methods to identify clusters of allergic sensitizations to different allergens (ASDA) and subjects belonging to each cluster of ASDA by searching for consistent temporal trend in subsets of ASDA. Through the inferred cluster profiles, we evaluate the association between two temporal patterns: asthma/wheeze status and allergic sensitizations over time with co-morbidities considered. Existing clustering methods (parametric or non-parametric) cannot achieve the goal stated above. These methods either cannot explain the contribution from external variables such as time (external variable) effect in allergic sensitizations (variables of interest), or overook the interdependence between different variables (e.g. allergic sensitizations to different allergens). Recent findings support dynamic allergic patterns. However, it is largely unknown (1) whether there exist a group (or groups) of allergens to which sensitizations share a similar temporal trend (natural history) such as periods of high or inert system responsive, and (2) whether dynamic allergic patterns are associated with asthma/wheeze persistence, remission, or new onset (phenomic association). This application attempts to fill these gaps, which will potentially lead us closer to the understanding of natural history of asthma, and provide strong potential to move forward the asthma prevention agenda. The birth cohort on the IOW in U.K. comprises 1,456 children examined at birth, age 1, 2, 4, 10, and 18 years with retention >90%. The cohort has extensive phenotype data at different ages and records of environmental factors such as allergen and pollutant levels. The main variables in our study include longitudinal allergic sensitization measures and asthma/wheeze status. The proposed methods are not limited to this data set, and can be applied to any data with continuous measures on a certain number of variables, e.g. high throughput gene expression data or methylation data. Our team has a long track record of successful collaboration with biostatistical (Zhang) and epidemiological (Karmaus) knowledge at the University of South Carolina, and clinical experts (Arshad and Roberts) at the University of Southampton and David Hide Asthma & Allergy Research Center on IOW. Dr. Zhang has rich experience in statistical modeling [1R03HL095429, Zhang (MPI)]. Several projects by this group are supported by NIH including 1R01AI091905 [Principal Investigator: Karmaus] and 1R01HL082925 [Principal Investigator: Arshad]; on both projects Dr. Zhang is a key investigator.