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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\u2013gene interactions associated with complex traits.<\/jats:p>","DOI":"10.1093\/bib\/bbab305","type":"journal-article","created":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T19:10:49Z","timestamp":1626721849000},"source":"Crossref","is-referenced-by-count":2,"title":["Identifying complex gene\u2013gene interactions: a mixed kernel omnibus testing approach"],"prefix":"10.1093","volume":"22","author":[{"given":"Yan","family":"Liu","sequence":"first","affiliation":[{"name":"Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhao","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Statistics, Shanxi University of Finance and Economics, Taiyuan, PR 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USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-7167","authenticated-orcid":false,"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8099-1753","authenticated-orcid":false,"given":"Yuehua","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Statistics and Probability, Michigan State University, East Lansing, MI, 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