Identifying complex gene-gene interactions : a mixed kernel omnibus testing approach
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissionsoup.com..
Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene-gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene-gene interactions associated with complex traits.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
Briefings in bioinformatics - 22(2021), 6 vom: 05. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Yan [VerfasserIn] |
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| Links: |
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| Themen: |
Gene–gene interaction |
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| Anmerkungen: |
Date Completed 10.03.2022 Date Revised 10.03.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/bib/bbab305 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM329179748 |
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| 520 | |a © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene-gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene-gene interactions associated with complex traits | ||
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| 650 | 4 | |a high-dimensional testing | |
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| 650 | 4 | |a linear interaction | |
| 650 | 4 | |a nonlinear interaction | |
| 700 | 1 | |a Gao, Yuzhao |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Ruiling |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Hongyan |e verfasserin |4 aut | |
| 700 | 1 | |a Sa, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jianrong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hongqi |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Tong |e verfasserin |4 aut | |
| 700 | 1 | |a Cui, Yuehua |e verfasserin |4 aut | |
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