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National Institute of Environmental Health Sciences

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Publication Detail

Title: Optimal two-stage genotyping designs for genome-wide association scans.

Authors: Wang, Hansong; Thomas, Duncan C; Pe'er, Itsik; Stram, Daniel O

Published In Genet Epidemiol, (2006 May)

Abstract: The much-anticipated fixed-array, genome-wide SNP genotyping technologies make large-scale genome-wide association scans now possible for large numbers of subjects. In this paper we reconsider the problem (Satagopan and Elston [2003] Genet Epidemiol 25:149-157) of optimizing a two-stage genotyping design to deal with important new issues that are relevant when studies are expanded from candidate gene size to a genome-wide scale. We investigate how the basic two-stage genotyping approach, in which all markers are genotyped in an initial group of subjects (stage I) and only the promising markers are genotyped in additional subjects (stage II), can be used to reduce genotyping cost in a genome-wide case-control association study even after allowing for much higher per genotype costs using specially designed assays in stage II, compared to the fixed array of SNPs used in stage I. In addition, we consider the problem of using measured SNPs to make (imperfect) prediction of unmeasured SNPs for association tests of all SNPs (measured or unmeasured) genome wide and the utility of expanding genotyping densities in stage II in the regions where significant associations were detected in stage I. Under a set of reasonable but conservative assumptions, we derive optimal two-stage design configurations (sample sizes and the thresholds of significance in both stages) with these optimal designs depending both on the total number of markers tested and upon the ratios of cost in stage II versus stage I. In addition we show how existing software for power and sample size calculations can be used for the purpose of designing two-stage studies, for a wide range of assumptions about the number of markers genotyped and the costs of genotyping in each stage of the study.

PubMed ID: 16607626 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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