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Hum Hered. 2012;74(2):61-70. doi: 10.1159/000345604. Epub 2012 Dec 21.

Cumulative meta-analysis for genetic association: when is a new study worthwhile?

Author information

  • 1School of Kinesiology and Health Science, York University, and Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada. mrotondi@yorku.ca

Abstract

OBJECTIVES:

In this paper, we address the questions: how large a sample size would be required to show genome-wide significance between a single nucleotide polymorphism (SNP) and a genetic trait in a meta-analysis of a newly planned study together with the existing ones? Or alternatively: will a planned study of size n be able to provide evidence of a genetic association when this study is combined with a current meta-analysis?

METHODS:

We examine the potential impact of a newly planned genetic study on an existing meta-analysis through the use of a simulation-based algorithm. The proposed approach provides an empirical estimate of the power of the updated meta-analysis to detect genome-wide significance (p<5.0×10(-8)) of a complex trait and each of a set of specific SNPs of interest or the expected p value of the updated meta-analysis including the current and proposed studies.

RESULTS:

This technique is illustrated in the context of an updated meta-analysis of case-control studies in Paget's disease. A second example illustrates the impact of adding a newly planned study to a large meta-analysis of SNP associations with human height.

CONCLUSIONS:

The proposed algorithm is particularly useful for the design of studies to assess a selected set of high-priority SNP associations that are 'nearly' significant in meta-analysis of existing studies. The results may help investigators decide whether an updated meta-analysis is likely to achieve genome-wide significance.

Copyright © 2012 S. Karger AG, Basel.

PMID:
23258221
[PubMed - indexed for MEDLINE]
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