Mutual information for testing gene-environment interaction.
Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the contex...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2009-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC2642626?pdf=render |
| _version_ | 1811205481615589376 |
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| author | Xuesen Wu Li Jin Momiao Xiong |
| author_facet | Xuesen Wu Li Jin Momiao Xiong |
| author_sort | Xuesen Wu |
| collection | DOAJ |
| description | Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models. |
| first_indexed | 2024-04-12T03:33:31Z |
| format | Article |
| id | doaj.art-a4ef06ec81754fc6b820accc31d421b4 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T03:33:31Z |
| publishDate | 2009-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a4ef06ec81754fc6b820accc31d421b42022-12-22T03:49:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032009-01-0142e457810.1371/journal.pone.0004578Mutual information for testing gene-environment interaction.Xuesen WuLi JinMomiao XiongDespite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.http://europepmc.org/articles/PMC2642626?pdf=render |
| spellingShingle | Xuesen Wu Li Jin Momiao Xiong Mutual information for testing gene-environment interaction. PLoS ONE |
| title | Mutual information for testing gene-environment interaction. |
| title_full | Mutual information for testing gene-environment interaction. |
| title_fullStr | Mutual information for testing gene-environment interaction. |
| title_full_unstemmed | Mutual information for testing gene-environment interaction. |
| title_short | Mutual information for testing gene-environment interaction. |
| title_sort | mutual information for testing gene environment interaction |
| url | http://europepmc.org/articles/PMC2642626?pdf=render |
| work_keys_str_mv | AT xuesenwu mutualinformationfortestinggeneenvironmentinteraction AT lijin mutualinformationfortestinggeneenvironmentinteraction AT momiaoxiong mutualinformationfortestinggeneenvironmentinteraction |