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Getting more from digital SNP data
Author(s) -
Karoui Noureddine El,
Zhou Wei,
Whittemore Alice S.
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2379
Subject(s) - snp , loss of heterozygosity , sequential probability ratio test , computer science , single nucleotide polymorphism , computational biology , biology , allele , genetics , algorithm , genotype , gene
The digital SNP method has been proposed for identifying loss of heterozygosity (LOH) in tumour tissue and correlating it with patients' clinical characteristics. The method evaluates a tumour's allelic count at a single nucleotide polymorphism (SNP) for which the patient's normal tissue is heterozygous. The count is used to classify the tumour as positive or negative for LOH, using the sequential probability ratio test (SPRT). However, the SPRT was not developed for analysing digital SNP experiments. When applied to digital SNP data, the SPRT has several anomalies that can result in both loss of data and tumour misclassification. The anomalies are caused by discrepancies between the design of digital SNP experiments and the setting for which SPRT was developed. We propose an alternative classification scheme based on the false discovery rate, and show that it outperforms the SPRT when applied to Digital SNP data. Copyright © 2006 John Wiley & Sons, Ltd.