A strategy for detection of known and unknown SNP using a minimum number of oligonucleotides applicable in the clinical settings

A strategy for detection of known and unknown SNP using a minimum number of oligonucleotides applicable in the clinical settings

Journal of Translational Medicine, August 2003

14527341

Ena Wang, Sharon Adams, Yingdong Zhao, Monica Panelli, Richard Simon, Harvey Klein, Francesco M Marincola

Detection of unknown single nucleotide polymorphism (SNP) relies on large scale sequencing expeditions of genomic fragments or complex high-throughput chip technology. We describe a simplified strategy for fluorimetric detection of known and unknown SNP by proportional hybridization to oligonucleotide arrays based on optimization of the established principle of signal loss or gain that requires a drastically reduced number of matched or mismatched probes. The array consists of two sets of 18-mer oligonucleotide probes. One set includes overlapping oligos with 4-nucleotide tiling representing an arbitrarily selected "consensus" sequence (consensus-oligos), the other includes oligos specific for known SNP within the same genomic region (variant-oligos). Fluorescence-labeled DNA amplified from a homozygous source identical to the consensus represents the reference target and is co-hybridized with a differentially-labeled test sample. Lack of hybridization of the test sample to consensus- with simultaneous hybridization to variant-oligos designates a known allele. Lack of hybridization to consensus- and variant-oligos indicates a new allele. Detection of unknown variants in heterozygous samples depends upon fluorimetric analysis of signal intensity based on the principle that homozygous samples generate twice the amount of signal. This method can identify unknown SNP in heterozygous conditions with a sensitivity of 82% and specificity of 90%. This strategy should dramatically increase the efficiency of SNP detection throughout the human genome and will decrease the cost and complexity of applying genomic wide analysis in the context of clinical trials.