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Objective Approximately 40% of patients receiving conditioning chemotherapy prior to autologous hematopoietic stem cell transplants (a HSCT) develop severe oral mucositis ( SOM). Aside from disabling pain, ulcerative lesions associated with SOM predispose to poor health and economic outcomes. Our objective was to develop a probabilistic graphical model in which a cluster of single-nucleotide polymorphisms ( SNPs) derived from salivary DNA could be used as a tool to predict SOM risk. Methods Salivary DNA was extracted from 153 HSCT patients and applied to Illumina Bead Chips. Using sequential data analysis, we filtered extraneous SNPs, selected loci, and identified a predictive SNP network for OM risk. We then tested the predictive validity of the network using SNP array outputs from an independent HSCT cohort. Results We identified an 82- SNP Bayesian network ( BN) that was related to SOM risk with a 10-fold cross-validation accuracy of 99.3% and an area under the ROC curve of 99.7%. Using samples from a small independent patient cohort ( n = 16), we demonstrated the network's predictive validity with an accuracy of 81.2% in the absence of any false positives. Conclusions Our results suggest that SNP-based BN developed from saliva-sourced DNA can predict SOM risk in patients prior to a HSCT. [ABSTRACT FROM AUTHOR]

GVHD remains the most significant complication of hematopoietic SCT, despite advances in HLA matching and the identification of risk various factors. To account for the variation in the incidence and severity of this disease, many genetic association studies have been performed in order to explore the role of immunoregulatory gene polymorphisms. These genes include those that encode cytokines, chemokines, and costimulatory molecules. Polymorphisms in other classes of genes such as those involved in drug metabolism, protein folding, and DNA replication have also been studied. In this review, we address the current knowledge of the role of genetic polymorphisms in GVHD. We also discuss the potential pitfalls inherent in genetic association testing and alternative strategies to address these problems. [ABSTRACT FROM AUTHOR]