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For the outcomes and phenotypes of complex diseases, multiple types ofmolecular (genetic, genomic, epigenetic, etc.) changes, environmental riskfactors, and their interactions have been found to have importantcontributions. In each of the existing studies, only the interactions betweenone type of molecular changes and environmental risk factors have beenanalyzed. In recent biomedical studies, multidimensional profiling, under whichdata on multiple types of molecular changes is collected on the same subjects,is becoming popular. A myriad of recent studies have shown that collectivelyanalyzing multiple types of molecular changes is not only biologically sensiblebut also leads to improved estimation and prediction. In this study, we conductM-E interaction analysis, with M standing for multidimensional molecularchanges and E standing for environmental risk factors, which can accommodatemultiple types of molecular measurements and sufficiently account for theiroverlapping information (attributable to regulations) as well as independentinformation. The proposed approach is based on the penalization technique, hasa solid statistical ground, and can be effectively realized. Extensivesimulation shows that it outperforms multiple closely relevant alternatives. Inthe analysis of TCGA (The Cancer Genome Atlas) data on lung adenocarcinoma andcutaneous melanoma, sensible findings with superior stability and predictionare made.

Multidimensional molecular changes-environment interaction analysis for disease outcomes