Medicine and Health Sciences Commons^™

Analysis Of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model., Zhenyu Jiang, Chengan Du, Assen Jablensky, Hua Liang, Zudi Lu, Yang Ma, Kok Lay Teo

GW Biostatistics Center

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 …

Pd_Ngsatlas: A Reference Database Combining Next-Generation Sequencing Epigenomic And Transcriptomic Data For Psychiatric Disorders, Zheng Zhao, Yongsheng Li, Hong Chen, Jianping Lu, Peter M. Thompson, Juan Cheng, Zishan Wang, Juan Xu, Chun Xu, Xia Li

Health & Biomedical Sciences Faculty Publications and Presentations

Background: Psychiatric disorders such as schizophrenia (SZ) and bipolar disorder (BP) are projected to lead the global disease burden within the next decade. Several lines of evidence suggest that epigenetic- or genetic-mediated dysfunction is frequently present in these disorders. To date, the inheritance patterns have been complicated by the problem of integrating epigenomic and transcriptomic factors that have yet to be elucidated. Therefore, there is a need to build a comprehensive database for storing epigenomic and transcriptomic data relating to psychiatric disorders.

Description: We have developed the PD_NGSAtlas, which focuses on the efficient storage of epigenomic and transcriptomic data based …