Human Genetics

, Volume 128, Issue 6, pp 597–608

Using public control genotype data to increase power and decrease cost of case–control genetic association studies

Original Investigation

DOI: 10.1007/s00439-010-0880-x

Cite this article as:
Ho, L.A. & Lange, E.M. Hum Genet (2010) 128: 597. doi:10.1007/s00439-010-0880-x


Genome-wide association (GWA) studies are a powerful approach for identifying novel genetic risk factors associated with human disease. A GWA study typically requires the inclusion of thousands of samples to have sufficient statistical power to detect single nucleotide polymorphisms that are associated with only modest increases in risk of disease given the heavy burden of a multiple test correction that is necessary to maintain valid statistical tests. Low statistical power and the high financial cost of performing a GWA study remains prohibitive for many scientific investigators anxious to perform such a study using their own samples. A number of remedies have been suggested to increase statistical power and decrease cost, including the utilization of free publicly available genotype data and multi-stage genotyping designs. Herein, we compare the statistical power and relative costs of alternative association study designs that use cases and screened controls to study designs that are based only on, or additionally include, free public control genotype data. We describe a novel replication-based two-stage study design, which uses free public control genotype data in the first stage and follow-up genotype data on case-matched controls in the second stage that preserves many of the advantages inherent when using only an epidemiologically matched set of controls. Specifically, we show that our proposed two-stage design can substantially increase statistical power and decrease cost of performing a GWA study while controlling the type-I error rate that can be inflated when using public controls due to differences in ancestry and batch genotype effects.

Supplementary material

439_2010_880_MOESM1_ESM.doc (1.1 mb)
Supplementary material 1 (DOC 1,177 kb)

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillUSA

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