Software for tag single nucleotide polymorphism selection.
This paper reviews the theoretical basis for single nucleotide polymorphism (SNP) tagging and considers the use of current software made freely available for this task. A distinction between haplotype block-based and non-block-based approaches yields two classes of procedures. Analysis of two different sets of SNP genotype data from the HapMap is used to judge the practical aspects of using each of the programs considered, as well as to make some general observations about the performance of the programs in finding optimal sets of tagging SNPs. Pairwise R2 methods, while the simplest of those considered, do tend to pick more tagging SNPs than are strictly needed to predict unmeasured (non-tagging) SNPs, since a combination of two or more tagging SNPs can form a prediction of SNPs that have no direct (pairwise) surrogate. Block-based methods that exploit the linkage disequilibrium structure within haplotype blocks exploit this sort of redundancy, but run a risk of over-fitting if used without some care. A compromise approach which eliminates the need first to analyse block structure, but which still exploits simple relationships between SNPs, appears promising.
Stram
Daniel O
DO
Division of Biostatistics and Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. stram@usc.edu
D002874:Chromosome Mapping; D005787:Gene Frequency; D005826:Genetics, Medical; D023281:Genomics / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012984:Software