Health status and outcomes are frequently measured on an ordinal scale. For example, in acute lymphoblastic leukemia, minimal residual disease is an initial measure of treatment response that has been strongly predictive of event free survival and risk of relapse, where patients are commonly stratified into one of three ordinal groups: standard, intermediate, or high risk. In acute myeloid leukemia, based on cytogenetic findings and selected muta- tions at diagnosis, the European LeukemiaNet (ELN) classification system assigns patients into one of three risk groups: favorable, intermediate, or adverse. Molecular features monotonically associated with these ordinal re- sponses may be prognostically relevant or potential therapeutic targets, so linking these ordinal responses to data from high-throughput genomic assays is of clinical interest. We previously developed frequentist-based penalized ordinal response models and software to enable modeling an ordinal response when high-dimensional genomic data comprises the predictor space. Although frequentist-based penalized models provide a sparse solution and so perform automatic variable selection, they require some method for selecting the penalty parameter (e.g., AIC, BIC, or cross-validation) to identify a final model. However, once a specific penalty value is selected, all parameter estimates are conditional on that value. Also, the frequentist-based approach does not yield much information about the coefficients other than whether they are non-zero or not. That is, there are no resulting confidence intervals or p-values associated with the coefficient estimates. Therefore this project will fill a critical barrier to progress in this field by developing penalized Bayesian ordinal response models applicable for high-dimensional datasets. Advantages of the Bayesian approach is that there is no need to select a value for the penalty param- eter and it yields credible intervals which provide useful interpretations about the significance of each predictor. The specific aims of this application are to: (1) Develop penalized Bayesian cumulative link, adjacent category, and stereotype logit models for high-dimensional datasets; (2) Develop penalized Bayesian forward continuation ratio (FCR) models with a complementary log-log link that allow for censoring for high-dimensional datasets. For both aims we will characterize the performance of the methods using extensive simulation studies and application to publicly available cancer datasets, develop software, and distribute R packages to CRAN. This research will fill a critical gap as there are currently no Bayesian LASSO ordinal response models for high-dimensional data. Through our proposed variable inclusion indicator methodology, our Bayesian approach and software developed in this application will provide unique research methods for integrating clinical, demographic, high-throughput genomic, and ordinal response data. Moreover, the ordinal response extensions propo...