title: Analysis of schizophrenia data using a nonlinear threshold index logistic model.
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.
authors.affiliation: Department of Mathematics and Statistics, Curtin University, Perth, Australia.
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Jiang
authors.affiliation: Department of Statistics, George Washington University, Washington, D.C., United States of America.
authors.forename: Chengan
authors.initials: C
authors.lastname: Du
authors.affiliation: Centre for Clinical Research in Neuropsychiatry, The University of Western Australia, Crawley, Australia.
authors.forename: Assen
authors.initials: A
authors.lastname: Jablensky
authors.affiliation: Department of Statistics, George Washington University, Washington, D.C., United States of America.
authors.forename: Hua
authors.initials: H
authors.lastname: Liang
authors.affiliation: Southampton Statistical Sciences Research Institute & Mathematical Sciences Academic Unit, University of Southampton, Southampton, United Kingdom.
authors.forename: Zudi
authors.initials: Z
authors.lastname: Lu
authors.affiliation: Biostatistics Center, George Washington University, Washington, D.C., United States of America.
authors.forename: Yang
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Department of Mathematics and Statistics, Curtin University, Perth, Australia.
authors.forename: Kok Lay
authors.initials: KL
authors.lastname: Teo
mesh_terms: D016022:Case-Control Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D012559:Schizophrenia / Q000235:genetics*