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J Neurosci Methods. 2018 Nov 1;309:161-174. doi: 10.1016/j.jneumeth.2018.08.027. Epub 2018 Sep 2.

A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia.

Author information

1
Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA. Electronic address: malam@tulane.edu.
2
Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA.
3
Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA 70112, USA.
4
Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM 87131, USA.
5
Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA.

Abstract

BACKGROUND:

Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. Understanding complex interactions among multimodal datasets, however, is challenging.

NEW METHOD:

In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel machine for detecting higher order interactions among biologically relevant multimodal data. Using a semiparametric method on a reproducing kernel Hilbert space, we formulated the proposed method as a standard mixed-effects linear model and derived a score-based variance component statistic to test higher order interactions between multimodal datasets.

RESULTS:

The method was evaluated using extensive numerical simulation and real data from the Mind Clinical Imaging Consortium with both schizophrenia patients and healthy controls. Our method identified 13-triplets that included 6 gene-derived SNPs, 10 ROIs, and 6 gene-specific DNA methylations that are correlated with the changes in hippocampal volume, suggesting that these triplets may be important for explaining schizophrenia-related neurodegeneration.

COMPARISON WITH EXISTING METHOD(S):

The performance of the proposed method is compared with the following methods: test based on only first and first few principal components followed by multiple regression, and full principal component analysis regression, and the sequence kernel association test.

CONCLUSIONS:

With strong evidence (p-value ≤0.000001), the triplet (MAGI2, CRBLCrus1.L, FBXO28) is a significant biomarker for schizophrenia patients. This novel method can be applicable to the study of other disease processes, where multimodal data analysis is a common task.

KEYWORDS:

Higher order interaction; Imaging genetics and epigenetics; Kernel machine methods; Multimodal datasets; Schizophrenia

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