A Comparison of Five Methods for Selecting Tagging Single-Nucleotide Polymorphisms

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Faculty/Staff
Final version published as: 

BMC Genetics 2005, 6(Suppl 1):S71 doi:10.1186/1471-2156-6-S1-S71

Date created: 
2005
Abstract: 

Our goal was to compare methods for tagging single-nucleotide polymorphisms (tagSNPs) withrespect to the power to detect disease association under differing haplotype-disease associationmodels. We were also interested in the effect that SNP selection samples, consisting of eithercases, controls, or a mixture, would have on power. We investigated five previously describedalgorithms for choosing tagSNPS: two that picked SNPs based on haplotype structure (Chapmanhaplotypicand Stram), two that picked SNPs based on pair-wise allelic association (Chapman-allelicand Cousin), and one control method that chose equally spaced SNPs (Zhai). In two diseaseassociatedregions from the Genetic Analysis Workshop 14 simulated data, we tested theassociation between tagSNP genotype and disease over the tagSNP sets chosen by each methodfor each sampling scheme. This was repeated for 100 replicates to estimate power. The two allelicmethods chose essentially all SNPs in the region and had nearly optimal power. The two haplotypicmethods chose about half as many SNPs. The haplotypic methods had poor performance comparedto the allelic methods in both regions. We expected an improvement in power when the selectionsample contained cases; however, there was only moderate variation in power between thesampling approaches for each method. Finally, when compared to the haplotypic methods, thereference method performed as well or worse in the region with ancestral disease haplotypestructure.

Language: 
English
Document type: 
Article
Rights: 
You are free to copy, distribute and transmit this work under the following conditions: You must give attribution to the work (but not in any way that suggests that the author endorses you or your use of the work); You may not use this work for commercial purposes; You may not alter, transform, or build upon this work. Any further uses require the permission of the rights holder (or author if no rights holder is listed). These rights are based on the Creative Commons Attribution-NonCommercial-NoDerivatives License.
Statistics: