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: | Journal Article |
| Published: |
PLOS
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/4219 |
| _version_ | 1848744454390808576 |
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| author | Jiang, Z. Du, C. Jablensky, A. Liang, H. Lu, Z. Ma, Y. Teo, Kok Lay |
| author_facet | Jiang, Z. Du, C. Jablensky, A. Liang, H. Lu, Z. Ma, Y. Teo, Kok Lay |
| author_sort | Jiang, Z. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| 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 | 2025-11-14T06:01:43Z |
| format | Journal Article |
| id | curtin-20.500.11937-4219 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:01:43Z |
| publishDate | 2014 |
| publisher | PLOS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-42192017-09-13T14:44:36Z Analysis of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model Jiang, Z. Du, C. Jablensky, A. Liang, H. Lu, Z. Ma, Y. Teo, Kok Lay 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. 2014 Journal Article http://hdl.handle.net/20.500.11937/4219 10.1371/journal.pone.0109454 PLOS fulltext |
| spellingShingle | Jiang, Z. Du, C. Jablensky, A. Liang, H. Lu, Z. Ma, Y. Teo, Kok Lay Analysis of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model |
| 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://hdl.handle.net/20.500.11937/4219 |