Epidemiologic and genetic studies in chronic obstructive pulmonary disease (COPD) and many complex diseases suggest subgroup disparities (e.g., by sex). We consider this problem from the standpoint of integrative analysis where we combine information from different views (e.g., genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. To address analytical challenges in the problem of our interest, we propose a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to males and females and that contribute to the variation in COPD, measured by airway wall thickness. HIP (Heterogeneity in Integration and Prediction) accounts for subgroup heterogeneity, allows for sparsity in variable selection, is applicable to multi-class and to univariate or multivariate continuous outcomes, and incorporates covariate adjustment. We develop efficient algorithms in PyTorch. Our COPD findings have identified several proteins, genes, and pathways that are common and specific to males and females, some of which have been implicated in COPD, while others could lead to new insights into sex differences in COPD mechanisms.

翻译：慢性阻塞性肺疾病（COPD）及许多复杂疾病的流行病学和遗传学研究表明存在亚组差异（例如，按性别划分）。我们从整合分析的角度考虑这一问题，即合并来自不同视图（如基因组学、蛋白质组学、临床数据）的信息。现有的整合分析方法忽略了亚组异质性，而简单堆叠视图并考虑亚组异质性则未能建模视图间的关联。为应对我们关注问题中的分析挑战，我们提出了一种联合关联与预测的统计方法，该方法利用各视图的优势，识别男女共有及特异性的分子特征，这些特征贡献于由气道壁厚度测量的COPD变异。HIP（异质整合与预测）方法考虑亚组异质性，允许变量选择中的稀疏性，适用于多类别及单变量或多变量连续型结局，并纳入协变量调整。我们在PyTorch中开发了高效算法。我们的COPD研究发现若干蛋白质、基因和通路在男性和女性中既具有共性又存在特异性，其中部分已被证实与COPD相关，而其他发现可能为COPD机制中的性别差异提供新见解。