Determining the functional impact of coding and non-coding single nucleotide polymorphisms (SNPs) is one of the primary challenges in establishing genotype-phenotype relations. The SNPs constitute more than 90% of the genetic variation and account for most trait differences among individuals and are one of the primary genotype data captured when studying the genetic basis of disease. The advent of efficient high-throughput DNA sequencers and GeneChips™ necessitates robust computational analysis pipelines to handle the genotype data more efficiently and facilitate seamless integration with clinical data. To address this, we have developed a bioinformatics-based comprehensive analysis pipeline which predicts the effect of coding and non-coding SNPs.
Based on the hypothesis that by integrating multiple coding SNP-impact predictions we can analyze and predict the SNP outcome better, we integrated three impact-prediction scores and one population-based score to obtain a SVM-based meta-prediction model. Through cross-validation studies, we demonstrate that our approach improves the SNP-effect prediction. For the first time, we have used the population-based minor allele frequency (MAF) as one of the features for SNP-effect prediction and prove that it significantly improves the performance of the prediction algorithm. We then extended this approach to predict the impact of non-coding promoter SNPs. Our results, through feature combinations and cross-validation, show that integrating multiple sequence-based features improves performance of the SNP-effect predictor. Also for the first time we demonstrate that the loss or gain of guanine in the SNP-overlapping putative transcription binding sites can be used as a measure of likelihood for an alteration in the native binding site, thereby increasing the odds of the SNP being functional.
Through various test cases, we demonstrate the utility of our algorithm. Using a specific test case of p53 binding sites, we also demonstrate a method for the enhancement of prediction based on the inclusion of experimental-based transactivation data for p53 response-elements (REs) that can enhance the ability to predict the impact of SNPs overlapping p53 REs. Taken together this provides a framework for demonstrating how prediction of TFBS functions may be enhanced in a high throughput fashion using assay screening data.