Power of Single- vs. Multi-Marker Tests of Association

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...

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Bibliographic Details
Published in:Genetic epidemiology 2012-07, Vol.36 (5), p.480-487
Main Authors: Wang, Xuefeng, Morris, Nathan J., Schaid, Daniel J., Elston, Robert C.
Format: Article
Language:English
Subjects:
Alleles
Asymptotic power
Chromosome Mapping - methods
Gene Frequency
Genetic Markers - genetics
genome-wide association
Genome-Wide Association Study
Genotype
Haplotypes
HapMap Project
Humans
Linkage Disequilibrium
Models, Genetic
Models, Statistical
Polymorphism, Single Nucleotide
Reproducibility of Results
single-marker test
two-marker test
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Summary: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 SNPs 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 HapMap 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 HapMap 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.
ISSN:0741-0395
1098-2272
1098-2272
DOI:10.1002/gepi.21642