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American Journal of Epidemiology Advance Access originally published online on October 6, 2009
American Journal of Epidemiology 2009 170(10):1197-1206; doi:10.1093/aje/kwp262
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American Journal of Epidemiology © The Author 2009. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Discovery Properties of Genome-wide Association Signals From Cumulatively Combined Data Sets

Tiago V. Pereira, Nikolaos A. Patsopoulos, Georgia Salanti and John P. A. Ioannidis*

* Correspondence to Dr. John P. A. Ioannidis, Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece (e-mail: jioannid{at}cc.uoi.gr).

Received for publication February 5, 2009. Accepted for publication August 4, 2009.

Genetic effects for common variants affecting complex disease risk are subtle. Single genome-wide association (GWA) studies are typically underpowered to detect these effects, and combination of several GWA data sets is needed to enhance discovery. The authors investigated the properties of the discovery process in simulated cumulative meta-analyses of GWA study-derived signals allowing for potential genetic model misspecification and between-study heterogeneity. Variants with null effects on average (but also between-data set heterogeneity) could yield false-positive associations with seemingly homogeneous effects. Random effects had higher than appropriate false-positive rates when there were few data sets. The log-additive model had the lowest false-positive rate. Under heterogeneity, random-effects meta-analyses of 2–10 data sets averaging 1,000 cases/1,000 controls each did not increase power, or the meta-analysis was even less powerful than a single study (power desert). Upward bias in effect estimates and underestimation of between-study heterogeneity were common. Fixed-effects calculations avoided power deserts and maximized discovery of association signals at the expense of much higher false-positive rates. Therefore, random- and fixed-effects models are preferable for different purposes (fixed effects for initial screenings, random effects for generalizability applications). These results may have broader implications for the design and interpretation of large-scale multiteam collaborative studies discovering common gene variants.

epidemiology; genetics; genome-wide association study; Human Genome Project; meta-analysis; models, genetic; polymorphism, single nucleotide

Abbreviations: GWA, genome-wide association; OR, odds ratio

Editor's note: This article also appears on the website of the Human Genome Epidemiology Network (http://www.cdc.gov/genomics/hugenet/default.htm).


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