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A strategy for high‐dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological, and environmental factors

机译：高维多变量分析的策略将儿童哮喘表型分类了遗传，免疫学和环境因素的影响

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Abstract Background Associations between childhood asthma phenotypes and genetic, immunological, and environmental factors have been previously established. Yet, strategies to integrate high‐dimensional risk factors from multiple distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a preponderance of missing data and lack of methods to accommodate them. Methods We assembled questionnaire, diagnostic, genotype, microarray, RT ‐ qPCR , flow cytometry, and cytokine data (referred to as data modalities) to use as input factors for a classifier that could distinguish healthy children, mild‐to‐moderate allergic asthmatics, and nonallergic asthmatics. Based on data from 260 German children aged 4‐14 from our university outpatient clinic, we built a novel multilevel prediction approach for asthma outcome which could deal with a present complex missing data structure. Results The optimal learning method was boosting based on all data sets, achieving an area underneath the receiver operating characteristic curve ( AUC ) for three classes of phenotypes of 0.81 (95%‐confidence interval ( CI ): 0.65‐0.94) using leave‐one‐out cross‐validation. Besides improving the AUC , our integrative multilevel learning approach led to tighter CI s than using smaller complete predictor data sets ( AUC ?=?0.82 [0.66‐0.94] for boosting). The most important variables for classifying childhood asthma phenotypes comprised novel identified genes, namely PKN 2 (protein kinase N2), PTK 2 (protein tyrosine kinase 2), and ALPP (alkaline phosphatase, placental). Conclusion Our combination of several data modalities using a novel strategy improved classification of childhood asthma phenotypes but requires validation in external populations. The generic approach is applicable to other multilevel data‐based risk prediction settings, which typically suffer from incomplete data.
机译：摘要以前建立了儿童哮喘表型和遗传，免疫学和环境因素的背景。然而，从多个不同的数据集中集成高维风险因素的策略，从而增加分析的统计力量，通过缺失数据的优势和缺乏适应它们的方法阻碍了它们。方法我们组装调查问卷，诊断，基因型，微阵列，RT - QPCR，流式细胞术和细胞因子数据（称为数据方式），用作可以区分健康儿童，温和至中等过敏性哮喘的分类器的输入因素，和非极剂哮喘。根据来自大学门诊诊所的260名德国儿童的数据，我们为哮喘结果建立了一种新的多级预测方法，可以处理当前复杂的数据结构。结果基于所有数据集进行最佳学习方法，在接收器下面的接收器下方的区域，用于三类表型为0.81的表型（95％-CI）：0.65-0.94）使用休假-out交叉验证。除了改进AUC之外，我们的一体化多级学习方法也会导致CI S更紧密，而不是使用较小的完整预测器数据集（AUC？= 0.82 [0.82 [0.66-0.94]进行升级）。分类儿童哮喘表型的最重要变量包括新型鉴定基因，即PKN 2（蛋白激酶N 2），PTK 2（蛋白质酪氨酸激酶2）和ALPP（碱性磷酸酶，胎盘）。结论我们使用新型策略的几种数据方式的组合改善了儿童哮喘表型的分类，但需要在外部种群中验证。通用方法适用于其他基于多级数据的风险预测设置，其通常遭受不完整的数据。

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