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南的应用程序的核心。聚类分析是模式分析的一种。这个应用程序的目的是开发和应用新的基于模型的聚类方法的纵向数据集，从出生队列成立于1989年至1990年的怀特岛（IOW），英国。所提出的方法的目的是联合集群的主题和相互依赖的变量，旨在提高聚类的均匀性。“联合”一词指的是聚类主体和聚类变量沿着的能力，以及两个聚类过程之间的相关性。同时，我们允许存在非聚类的主题/变量。我们将应用这些方法来识别对不同过敏原（ASDA）的过敏性致敏的聚类，并通过搜索ASDA子集中一致的时间趋势来识别属于ASDA每个聚类的受试者。通过推断的群集配置文件，我们评估了两个时间模式之间的关联：哮喘/喘息状态和过敏性致敏随着时间的推移与合并症的考虑。 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）在过敏性致敏（感兴趣的变量）中的作用，要么忽略了不同变量之间的相互依赖性（例如，对不同过敏原的过敏性）。最近的研究结果支持动态过敏模式。然而，很大程度上还不清楚（1）是否存在一组（或多组）过敏原，其致敏作用具有相似的时间趋势（自然史），如高或惰性系统反应期，以及（2）动态过敏模式是否与哮喘/喘息持续、缓解或新发相关（表型相关）。该应用试图填补这些空白，这将可能使我们更接近哮喘自然史的理解，并为推进哮喘预防议程提供强大的潜力。 英国IOW的出生队列研究包括1 456名出生时、1岁、2岁、4岁、10岁和18岁时接受检查的儿童，保留率> 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。我们研究的主要变量包括纵向过敏性致敏措施和哮喘/喘息状态。所提出的方法不限于该数据集，并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来纳州大学的生物统计学（Zhang）和流行病学（Karmaus）知识，以及南安普顿大学的临床专家（Arshad和Roberts）和大卫海德哮喘和过敏研究中心在IOW上的成功合作有着长期的记录。Zhang博士在统计建模方面具有丰富的经验[1 R 03 HL 095429，Zhang（MPI）]。该小组的几个项目得到了NIH的支持，包括1 R 01 AI 091905 [主要研究者：Karmaus]和1 R 01 HL 082925 [主要研究者：Arshad];张博士是这两个项目的关键研究者。

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.