Electrophoresis, Vol.27, No.19, 3856-3863, 2006
Parallel optimization and genotyping of multiple single-nucleotide polymorphism markers by sample pooling approach using cycling-gradient CE with multiple injections
Increasing importance of single-nucleotide polymorphisms (SNPs) in determination of disease susceptibility or in prediction of therapy response brings attention of many molecular diagnostic laboratories to simple and low-cost SNP genotyping methodologies. We have recently introduced a mutation detection technique based on analysis of homo-and heteroduplex PCR fragments resolved in cycling temperature gradient conditions on a conventional multicapillary-array DNA sequencer. The main advantage of this technique is in its simplicity with no requirement for sample cleanup prior to the analysis. In this report we present a practical application of the technology for genotyping of SNP markers in two separate clinical projects resulting in a combined set of 44 markers screened in over 500 patients. Initially, a design of PCR primers and conditions was performed for each SNP marker. Then, optimization of CE running conditions (limited just to the proper selection of temperature cycling) was performed on pools of 20 DNA samples to increase the probability of having each of the two allele types represented in the sample. After selecting the optimum conditions, screening of markers in patients was performed using a multiple-injection approach for further acceleration of the sample throughput. The rate of successful optimization of experimental conditions without any pre-selection based on the SNP sequence or melting characteristics was 80% from the initial SNP marker candidates. By studying the failed markers, we attempt to identify critical factors enabling successful typing. The presented technique is very useful for low to medium sized SNP genotyping projects mostly applied in pharmacogenomic research as well as in clinical diagnostics. The main advantages include low cost, simple setup and validation of SNP markers.