Open Access Highly Accessed Open Badges Research article

Accounting for multiple comparisons in a genome-wide association study (GWAS)

Randall C Johnson12, George W Nelson1, Jennifer L Troyer1, James A Lautenberger3, Bailey D Kessing1, Cheryl A Winkler1 and Stephen J O'Brien3*

Author Affiliations

1 Basic Research Program, SAIC-Frederick, Inc. NCI-Frederick, Frederick, MD, USA

2 Chaire de Bioinformatique, Conservatoire National des Arts et Metiers, 75003, Paris, France

3 Laboratory of Genomic Diversity, NCI-Frederick, Frederick, MD, USA

For all author emails, please log on.

BMC Genomics 2010, 11:724  doi:10.1186/1471-2164-11-724

Published: 22 December 2010



As we enter an era when testing millions of SNPs in a single gene association study will become the standard, consideration of multiple comparisons is an essential part of determining statistical significance. Bonferroni adjustments can be made but are conservative due to the preponderance of linkage disequilibrium (LD) between genetic markers, and permutation testing is not always a viable option. Three major classes of corrections have been proposed to correct the dependent nature of genetic data in Bonferroni adjustments: permutation testing and related alternatives, principal components analysis (PCA), and analysis of blocks of LD across the genome. We consider seven implementations of these commonly used methods using data from 1514 European American participants genotyped for 700,078 SNPs in a GWAS for AIDS.


A Bonferroni correction using the number of LD blocks found by the three algorithms implemented by Haploview resulted in an insufficiently conservative threshold, corresponding to a genome-wide significance level of α = 0.15 - 0.20. We observed a moderate increase in power when using PRESTO, SLIDE, and simpleℳ when compared with traditional Bonferroni methods for population data genotyped on the Affymetrix 6.0 platform in European Americans (α = 0.05 thresholds between 1 × 10-7 and 7 × 10-8).


Correcting for the number of LD blocks resulted in an anti-conservative Bonferroni adjustment. SLIDE and simpleℳ are particularly useful when using a statistical test not handled in optimized permutation testing packages, and genome-wide corrected p-values using SLIDE, are much easier to interpret for consumers of GWAS studies.