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MODIMA, a Method for Multivariate Omnibus Distance Mediation Analysis, Allows for Integration of Multivariate Exposure–Mediator–Response Relationships

1
Program for Human Microbiome Research, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, SC 29425, USA
2
Biomedical Informatics Center, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, SC 29425, USA
3
Department of Public Health Science, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, SC 29425, USA
4
Department of Oral Health Sciences, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, SC 29425, USA
5
Department of Healthcare Leadership and Management, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, SC 29425, USA
*
Author to whom correspondence should be addressed.
Genes 2019, 10(7), 524; https://doi.org/10.3390/genes10070524
Received: 10 June 2019 / Revised: 8 July 2019 / Accepted: 8 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Systems Analytics and Integration of Big Omics Data)
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Abstract

Many important exposure–response relationships, such as diet and weight, can be influenced by intermediates, such as the gut microbiome. Understanding the role of these intermediates, the mediators, is important in refining cause–effect theories and discovering additional medical interventions (e.g., probiotics, prebiotics). Mediation analysis has been at the heart of behavioral health research, rapidly gaining popularity with the biomedical sciences in the last decade. A specific analytic challenge is being able to incorporate an entire ’omics assay as a mediator. To address this challenge, we propose a hypothesis testing framework for multivariate omnibus distance mediation analysis (MODIMA). We use the power of energy statistics, such as partial distance correlation, to allow for analysis of multivariate exposure–mediator–response triples. Our simulation results demonstrate the favorable statistical properties of our approach relative to the available alternatives. Finally, we demonstrate the application of the proposed methods in two previously published microbiome datasets. Our framework adds a new tool to the toolbox of approaches to the integration of ‘omics big data. View Full-Text
Keywords: multivariate analysis; multivariate causal mediation; distance correlation; direct effect; indirect effect; causal inference multivariate analysis; multivariate causal mediation; distance correlation; direct effect; indirect effect; causal inference
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Hamidi, B.; Wallace, K.; Alekseyenko, A.V. MODIMA, a Method for Multivariate Omnibus Distance Mediation Analysis, Allows for Integration of Multivariate Exposure–Mediator–Response Relationships. Genes 2019, 10, 524.

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