Document Type
Journal Article
Publication Date
1-1-2014
Journal
PLoS ONE
Volume
9
Issue
10
Inclusive Pages
e109454
Keywords
Models, Genetic; Polymorphism, Single Nucleotide; Schizophrenia--genetics
Abstract
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
APA Citation
Jiang, Z., Du, C., Jablensky, A., Liang, H., Lu, Z., Ma, Y., & Teo, K. L. (2014). Analysis of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model. PLoS ONE, 9(10), e109454.
Peer Reviewed
1
Open Access
1
Comments
Reproduced with permission of PLoS ONE.