MOTIVATION: Identifying single nucleotide polymorphisms (SNPs) that underlie common and complex human diseases, such as cancer, is of major interest in current molecular epidemiology. Nevertheless, the tremendous number of SNPs on the human genome requires computational methods for prioritizing SNPs according to their potentially deleterious effects to human health, and as such, for expediting genotyping and analysis. As of yet, little has been done to quantitatively assess the possible deleterious effects of SNPs for effective association studies. RESULTS: We propose a new integrative scoring system for prioritizing SNPs based on their possible deleterious effects within a probabilistic framework. We applied our system to 580 disease-susceptibility genes obtained from the OMIM (Online Mendelian Inheritance in Man) database, which is one of the most widely used databases of human genes and genetic disorders. The scoring results clearly show that the distribution of the functional significance (FS) scores for already known disease-related SNPs is significantly different from that of neutral SNPs. In addition, we summarize distinct features of potentially deleterious SNPs based on their FS score, such as functional genomic regions where they occur or bio-molecular functions that they mainly affect. We also demonstrate, through a comparative study, that our system improves upon other function-assessment systems for SNPs, by assigning significantly higher FS scores to already known disease-related SNPs than to neutral SNPs.

Due to the tremendous number of single nucleotide polymorphisms (SNPs), there is a clear need to expedite genotyping by considering only a subset of all SNPs called haplotype tagging SNPs (htSNPs). Recently, the approach that selects htSNPs by maximizing their prediction accuracy has demonstrated very promising results. Here we propose a new prediction system for htSNP selection based on neural network models. We applied our system to three public data sets, and compared its prediction performance to that of two state-of-the-art prediction rules. The results demonstrate that our system consistently outperforms compared methods with robust performance.

MOTIVATION: Inferring the genetic interaction mechanism using Bayesian networks has recently drawn increasing attention due to its well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. RESULTS: We propose a novel method to infer genetic networks by alleviating the shortage of available mRNA expression data with prior knowledge. We call the proposed method 'modularized network learning' (MONET). Firstly, the proposed method divides a whole gene set to overlapped modules considering biological annotations and expression data together. Secondly, it infers a Bayesian network for each module, and integrates the learned subnetworks to a global network. An algorithm that measures a similarity between genes based on hierarchy, specificity and multiplicity of biological annotations is presented. The proposed method draws a global picture of inter-module relationships as well as a detailed look of intra-module interactions. We applied the proposed method to analyze Saccharomyces cerevisiae stress data, and found several hypotheses to suggest putative functions of unclassified genes. We also compared the proposed method with a whole-set-based approach and two expression-based clustering approaches.