Multiple types of molecular (genetic, genomic, epigenetic, etc.) measurements, environmental risk factors, and their interactions have been found to contribute to the outcomes and phenotypes of complex diseases. In each of the previous studies, only the interactions between one type of molecular measurement and environmental risk factors have been analyzed. In recent biomedical studies, multidimensional profiling, in which data from multiple types of molecular measurements are collected from the same subjects, is becoming popular. A myriad of recent studies have shown that collectively analyzing multiple types of molecular measurements is not only biologically sensible but also leads to improved estimation and prediction. In this study, we conduct an M–E interaction analysis, with M standing for multidimensional molecular measurements and E standing for environmental risk factors. This can accommodate multiple types of molecular measurements and sufficiently account for their overlapping as well as independent information. Extensive simulation shows that it outperforms several closely related alternatives. In the analysis of TCGA (The Cancer Genome Atlas) data on lung adenocarcinoma and cutaneous melanoma, we make some stable biological findings and achieve stable prediction.

中文翻译：

---

多维分子测量-疾病结果的环境相互作用分析

多种类型的分子（遗传、基因组、表观遗传等）测量、环境风险因素及其相互作用已被发现有助于复杂疾病的结果和表型。在之前的每项研究中，仅分析了一种分子测量与环境风险因素之间的相互作用。在最近的生物医学研究中，多维分析越来越流行，其中从同一受试者收集多种类型的分子测量数据。最近的大量研究表明，共同分析多种类型的分子测量不仅在生物学上是明智的，而且还可以改进估计和预测。在这项研究中，我们进行了 M-E 交互分析，M代表多维分子测量，E代表环境风险因素。这可以容纳多种类型的分子测量，并充分说明它们的重叠和独立信息。广泛的模拟表明它优于几个密切相关的替代方案。在肺腺癌和皮肤黑色素瘤的TCGA（癌症基因组图谱）数据分析中，我们做出了一些稳定的生物学发现并实现了稳定的预测。