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Unified Cox model based multifactor dimensionality reduction method for gene-gene interaction analysis of the survival phenotype
DC Field | Value | Language |
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dc.contributor.author | Lee, Seungyeoun | - |
dc.contributor.author | Son, Donghee | - |
dc.contributor.author | Kim, Yongkang | - |
dc.contributor.author | Yu, Wenbao | - |
dc.contributor.author | Park, Taesung | - |
dc.date.accessioned | 2019-03-12T04:37:14Z | - |
dc.date.available | 2019-03-12T13:39:11Z | - |
dc.date.issued | 2018-12-14 | - |
dc.identifier.citation | BioData Mining, 11(1):27 | ko_KR |
dc.identifier.issn | 1756-0381 | - |
dc.identifier.uri | https://hdl.handle.net/10371/146979 | - |
dc.description.abstract | Background
One strategy for addressing missing heritability in genome-wide association study is gene-gene interaction analysis, which, unlike a single gene approach, involves high-dimensionality. The multifactor dimensionality reduction method (MDR) has been widely applied to reduce multi-levels of genotypes into high or low risk groups. The Cox-MDR method has been proposed to detect gene-gene interactions associated with the survival phenotype by using the martingale residuals from a Cox model. However, this method requires a cross-validation procedure to find the best SNP pair among all possible pairs and the permutation procedure should be followed for the significance of gene-gene interactions. Recently, the unified model based multifactor dimensionality reduction method (UM-MDR) has been proposed to unify the significance testing with the MDR algorithm within the regression model framework, in which neither cross-validation nor permutation testing are needed. In this paper, we proposed a simple approach, called Cox UM-MDR, which combines Cox-MDR with the key procedure of UM-MDR to identify gene-gene interactions associated with the survival phenotype. Results The simulation study was performed to compare Cox UM-MDR with Cox-MDR with and without the marginal effects of SNPs. We found that Cox UM-MDR has similar power to Cox-MDR without marginal effects, whereas it outperforms Cox-MDR with marginal effects and more robust to heavy censoring. We also applied Cox UM-MDR to a dataset of leukemia patients and detected gene-gene interactions with regard to the survival time. Conclusion Cox UM-MDR is easily implemented by combining Cox-MDR with UM-MDR to detect the significant gene-gene interactions associated with the survival time without cross-validation and permutation testing. The simulation results are shown to demonstrate the utility of the proposed method, which achieves at least the same power as Cox-MDR in most scenarios, and outperforms Cox-MDR when some SNPs having only marginal effects might mask the detection of the causal epistasis. | ko_KR |
dc.description.sponsorship | This research was supported by the Basic Science Research Program through the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (2016R1D1A1B03934908) and (2017M3A9C4065964), and by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI16C2037). | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | Survival time | ko_KR |
dc.subject | Cox model | ko_KR |
dc.subject | Multifactor dimensionality reduction method | ko_KR |
dc.subject | Gene-gene interaction | ko_KR |
dc.subject | Unified model based method | ko_KR |
dc.title | Unified Cox model based multifactor dimensionality reduction method for gene-gene interaction analysis of the survival phenotype | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 이승연 | - |
dc.contributor.AlternativeAuthor | 손동희 | - |
dc.contributor.AlternativeAuthor | 김영강 | - |
dc.contributor.AlternativeAuthor | 박태성 | - |
dc.identifier.doi | 10.1186/s13040-018-0189-1 | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s). | - |
dc.date.updated | 2018-12-16T04:14:40Z | - |
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