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Power of Single‐ vs. Multi‐Marker Tests of Association
Author(s) -
Wang Xuefeng,
Morris Nathan J.,
Schaid Daniel J.,
Elston Robert C.
Publication year - 2012
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21642
Subject(s) - single nucleotide polymorphism , snp , ancestry informative marker , correlation , genetic association , genetic marker , association (psychology) , computational biology , statistical power , biology , genetics , computer science , statistics , genotype , mathematics , psychology , gene , geometry , psychotherapist
Current genome‐wide association studies still heavily rely on a single‐marker strategy, in which each single nucleotide polymorphism (SNP) is tested individually for association with a phenotype. Although methods and software packages that consider multimarker models have become available, they have been slow to become widely adopted and their efficacy in real data analysis is often questioned. Based on conducting extensive simulations, here we endeavor to provide more insights into the performance of simple multimarker association tests as compared to single‐marker tests. The results reveal the power advantage as well as disadvantage of the two‐ vs. the single‐marker test. Power differentials depend on the correlation structure among tag SNPs, as well as that between tag SNP s and causal variants. A two‐marker test has relatively better performance than single‐marker tests when the correlation of the two adjacent markers is high. However, using H ap M ap data, two‐marker tests tended to have a greater chance of being less powerful than single‐marker tests, due to constraints on the number of actual possible haplotypes in the H ap M ap data. Yet, the average power difference was small whenever the one‐marker test is more powerful, while there were many situations where the two‐marker test can be much more powerful. These findings can be useful to guide analyses of future studies. Genet. Epidemiol. 36:480‐487, 2012. © 2012 Wiley Periodicals, Inc.