Analysis of schizophrenia data using a nonlinear threshold index logistic model.
Genetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4201476?pdf=render |
| _version_ | 1818412172936478720 |
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| author | Zhenyu Jiang Chengan Du Assen Jablensky Hua Liang Zudi Lu Yang Ma Kok Lay Teo |
| author_facet | Zhenyu Jiang Chengan Du Assen Jablensky Hua Liang Zudi Lu Yang Ma Kok Lay Teo |
| author_sort | Zhenyu Jiang |
| collection | DOAJ |
| description | Genetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction. A maximum likelihood methodology is suggested to estimate the unknown parameters in the models. Simulation studies demonstrate that the proposed methodology works viably well for moderate-size samples. The suggested approach is therefore applied to the analysis of the Schizophrenia classification by using a real set of SNP data from Western Australian Family Study of Schizophrenia (WAFSS). Our empirical findings provide evidence that the proposed nonlinear models well outperform the widely used linear and tree based logistic regression models in class prediction of schizophrenia risk with SNP data in terms of both Types I/II error rates and ROC curves. |
| first_indexed | 2024-12-14T10:43:05Z |
| format | Article |
| id | doaj.art-98b92e195bd843ac9c4d762192753916 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T10:43:05Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-98b92e195bd843ac9c4d7621927539162022-12-21T23:05:35ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01910e10945410.1371/journal.pone.0109454Analysis of schizophrenia data using a nonlinear threshold index logistic model.Zhenyu JiangChengan DuAssen JablenskyHua LiangZudi LuYang MaKok Lay TeoGenetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction. A maximum likelihood methodology is suggested to estimate the unknown parameters in the models. Simulation studies demonstrate that the proposed methodology works viably well for moderate-size samples. The suggested approach is therefore applied to the analysis of the Schizophrenia classification by using a real set of SNP data from Western Australian Family Study of Schizophrenia (WAFSS). Our empirical findings provide evidence that the proposed nonlinear models well outperform the widely used linear and tree based logistic regression models in class prediction of schizophrenia risk with SNP data in terms of both Types I/II error rates and ROC curves.http://europepmc.org/articles/PMC4201476?pdf=render |
| spellingShingle | Zhenyu Jiang Chengan Du Assen Jablensky Hua Liang Zudi Lu Yang Ma Kok Lay Teo Analysis of schizophrenia data using a nonlinear threshold index logistic model. PLoS ONE |
| title | Analysis of schizophrenia data using a nonlinear threshold index logistic model. |
| title_full | Analysis of schizophrenia data using a nonlinear threshold index logistic model. |
| title_fullStr | Analysis of schizophrenia data using a nonlinear threshold index logistic model. |
| title_full_unstemmed | Analysis of schizophrenia data using a nonlinear threshold index logistic model. |
| title_short | Analysis of schizophrenia data using a nonlinear threshold index logistic model. |
| title_sort | analysis of schizophrenia data using a nonlinear threshold index logistic model |
| url | http://europepmc.org/articles/PMC4201476?pdf=render |
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