@article { ISI:000242951200002,
	title = {Dynamic variable selection in SNP genotype autocalling from APEX microarray data},
	journal = {BMC BIOINFORMATICS},
	volume = {7},
	year = {2006},
	month = {NOV 30},
	pages = {521},
	publisher = {BIOMED CENTRAL LTD},
	type = {Article},
	address = {MIDDLESEX HOUSE, 34-42 CLEVELAND ST, LONDON W1T 4LB, ENGLAND},
	abstract = {Background: Single nucleotide polymorphisms ( SNPs) are DNA sequence variations, occurring when a single nucleotide - adenine ( A), thymine ( T), cytosine ( C) or guanine ( G) - is altered. Arguably, SNPs account for more than 90\% of human genetic variation. Our laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension ( APEX). This mini-sequencing method is a powerful combination of a highly parallel microarray with distinctive Sanger- based dideoxy terminator sequencing chemistry. Using this microarray platform, our current genotype calling system ( known as SNP Chart) is capable of calling single SNP genotypes by manual inspection of the APEX data, which is time-consuming and exposed to user subjectivity bias. Results: Using a set of 32 Coriell DNA samples plus three negative PCR controls as a training data set, we have developed a fully- automated genotyping algorithm based on simple linear discriminant analysis ( LDA) using dynamic variable selection. The algorithm combines separate analyses based on the multiple probe sets to give a final posterior probability for each candidate genotype. We have tested our algorithm on a completely independent data set of 270 DNA samples, with validated genotypes, from patients admitted to the intensive care unit (ICU) of St. Paul{\textquoteright}s Hospital ( plus one negative PCR control sample). Our method achieves a concordance rate of 98.9\% with a 99.6\% call rate for a set of 96 SNPs. By adjusting the threshold value for the final posterior probability of the called genotype, the call rate reduces to 94.9\% with a higher concordance rate of 99.6\%. We also reversed the two independent data sets in their training and testing roles, achieving a concordance rate up to 99.8\%. Conclusion: The strength of this APEX chemistry- based platform is its unique redundancy having multiple probes for a single SNP. Our model- based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down- weighting any {\textquoteleft}bad data{\textquoteright} corresponding to image artifacts on the microarray slide or failure of a specific chemistry. In this regard, our method is able to automatically select the probes which work well and reduce the effect of other so- called bad performing probes in a sample- specific manner, for any number of SNPs.},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-7-521},
	author = {Podder, Mohua and Welch, William J. and Zamar, Ruben H. and Tebbutt, Scott J.}
}
