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PMID: 19075295 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

Optimal multistage designs--a general framework for efficient genome-wide association studies.

Biostatistics (Oxford, England) ·Vol. 10 ·No. 2 ·2009-04-00 ·Pages 297-309

Pahl R, Schäfer H, Müller HH

Abstract

Genome-wide association studies (GWAS) have become increasingly affordable but they are still costly. Therefore, cost saving 2-stage designs were proposed in the literature. The restriction to 2 stages, however, seems artificial and does not exploit the full potential of the underlying methods. We extend the 2-stage approach to the general framework of any number of stages. Based on the theory of group sequential methods, we derive optimal multistage designs. With current genotyping cost structures, our results suggest that up to 4 stages are sufficient in order to get feasible and efficient designs. Furthermore, we consider the problem of choosing the optimal number of stages depending on the costs of the statistical interim analysis at each stage and provide guidelines for planning the number of stages in practice. In particular, we found that in the majority of cases both 3-stage designs and 4-stage designs are more efficient than 2-stage designs. Although prices for marker panels are showing a continuing downward trend, we still recommend implementing and using optimal multistage designs in practice. In addition to the immediate benefit, it will be necessary to acquire know-how regarding the application of multistage designs in order to be able to adapt the general framework of multistage designs to upcoming technologies in the area of GWAS.

MeSH Terms
Biomarkers Genetic Predisposition to Disease Genome-Wide Association Study/economics,methods Genotype Humans Models, Statistical Polymorphism, Single Nucleotide Research Design
Chemicals
Biomarkers
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Pahl Roman
Institute of Medical Biometry and Epidemiology, Philipps University, Marburg, Germany.
Schäfer Helmut
Müller Hans-Helge
Article Info
Journal
Biostatistics (Oxford, England)
Abbr.
Biostatistics
ISSN
1468-4357
Published
2009-04-00
Epub
2008-00-15
Pages
297-309
Language
English
Region
England
NLM ID
100897327
Subset
IM
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