Predicting the success of primer extension genotyping assays using statistical modeling

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Abstract
Using an empirical panel of more than 20 000 single base primer extension (SNP-IT) assays we have developed a set of statistical scores for evaluating and rank ordering various parameters of the SNP-IT reaction to facilitate high-throughput assay primer design with improved likelihood of success. Each score predicts either signal magnitude from primer extension or signal noise caused by mispriming of primers and structure of the PCR product. All scores have been shown to correlate with the success/ failure rate of the SNP-IT reaction, based on analysis of assay results. A logistic regression analysis was applied to combine all scored parameters into one measure predicting the overall success/failure rate of a given SNP marker. Three training sets for different types of SNP-IT reaction, each containing about 22 000 SNP markers, were used to assign weights to each score and optimize the prediction of the combined measure. c-Statistics of 0.69, 0.77 and 0.72 were achieved for three training sets. This new statistical prediction can be used to improve primer design for the SNP-IT reaction and evaluate the probability of genotyping success for a given SNP based on analysis of the surrounding genomic sequence.