TY - JOUR
T1 - A kernel machine method for detecting higher order interactions in multimodal datasets
T2 - Application to schizophrenia
AU - Alam, Md Ashad
AU - Lin, Hui Yi
AU - Deng, Hong Wen
AU - Calhoun, Vince D.
AU - Wang, Yu Ping
N1 - Funding Information:
The authors wish to thank the department of energy (DE-FG02-99ER62764), the National Institutes of Health (1RC1MH089257, P20GM103472, R01GM109068, R01MH104680, R01MH107354) and the National Science foundation EPSCoR program (1539067) for the support.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - 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.
AB - 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.
KW - Higher order interaction
KW - Imaging genetics and epigenetics
KW - Kernel machine methods
KW - Multimodal datasets
KW - Schizophrenia
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U2 - 10.1016/j.jneumeth.2018.08.027
DO - 10.1016/j.jneumeth.2018.08.027
M3 - Article
C2 - 30184473
AN - SCOPUS:85053105058
VL - 309
SP - 161
EP - 174
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
ER -