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

描述(由研究人员提供)：模式分析是寻求一般指南的应用程序的中心。聚类分析是模式分析的一种。这项应用旨在开发和应用新的基于模型的聚类方法到从建立的出生队列到1990年在英国鬼魂岛(IOW)的纵向数据集。所提出的方法旨在联合对被试和相互依赖的变量进行聚类，以提高聚类的同质性。“联合”一词指的是对研究对象和变量进行分类的能力，以及两个分类过程之间的相关性。同时，我们允许非聚集的主题/变量的存在。我们将应用这些方法来识别对不同过敏原(Asda)的过敏性敏化簇，并通过在Asda子集中寻找一致的时间趋势来确定属于每个Asda簇的受试者。通过推断的簇分布，我们评估了两种时间模式之间的关联：哮喘/喘息状态和过敏性敏感化随着时间的推移，并考虑了共病。现有的聚类方法(参数或非参数)不能达到上述目的。这些方法要么不能解释时间(外部变量)等外部变量在过敏反应中的作用(感兴趣的变量)，要么不能解释不同变量之间的相互依赖关系(例如对不同过敏原的过敏反应)。最近的发现支持动态过敏模式。然而，在很大程度上尚不清楚(1)是否存在一组(或多组)过敏原具有相似的时间趋势(自然病史)，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、缓解或新发病(表型关联)相关。这项应用试图填补这些空白，这可能会使我们更接近哮喘的自然历史，并为推动哮喘预防议程提供强大的潜力。在英国，IOW的出生队列包括1,456名出生时接受检查的儿童，他们的年龄分别为1岁、2岁、4岁、10岁和18岁，保留率为90%。该队列有不同年龄的广泛的表型数据和环境因素的记录，如过敏原和污染物水平。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。所提出的方法并不局限于这个数据集，可以应用于任何在一定数量的变量上具有连续测量的数据，例如高通量的基因表达数据或甲基化数据。我们的团队与南卡罗来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(University Of Southampton)的临床专家(Arshad和Roberts)以及IOW上的David Hide哮喘和过敏研究中心有着长期的成功合作记录。张博士在统计建模方面有丰富的经验[1R03HL095429，张(MPI)]。该小组的几个项目得到了NIH的支持，包括1R01AI091905[首席研究员：Karmaus]和1R01HL082925[首席研究员：Arshad]；在这两个项目中，张博士都是关键研究员。

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.