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Open Access Methodology article

Quick, “Imputation-free” meta-analysis with proxy-SNPs

Christian Meesters123, Markus Leber2, Christine Herold1, Marina Angisch2, Manuel Mattheisen24, Dmitriy Drichel1, André Lacour1 and Tim Becker12*

Author Affiliations

1 German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

2 Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany

3 AESKU.KIPP-Institute, Wendelsheim, Germany

4 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA

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BMC Bioinformatics 2012, 13:231  doi:10.1186/1471-2105-13-231

Published: 12 September 2012

Abstract

Background

Meta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increase the power to detect strong or weak genotype effects or b) as a result verification method. As a consequence of differing SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia to avoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software), however, enables cross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming.

Results

Here we present a fast method to avoid forfeiting SNPs present in only a subset of studies, without relying on imputation. This is accomplished by using reference linkage disequilibrium data from 1,000 Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least one study. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Our algorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applications can be used as input files for MA, tremendously speeding up MA compared to the conventional imputation approach. We show that our proxy algorithm is well-powered and yields valuable ad hoc results, possibly providing an incentive for follow-up studies. We propose our method as a quick screening step prior to imputation-based MA, as well as an additional main approach for studies without available reference data matching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II Diabetes GWAS and found that the proxy algorithm clearly outperforms naïve MA on the p-value level: for 17 out of 23 we observe an improvement on the p-value level by a factor of more than two, and a maximum improvement by a factor of 2127.

Conclusions

YAMAS is an efficient and fast meta-analysis program which offers various methods, including conventional MA as well as inserting proxy-SNPs for missing markers to avoid unnecessary power loss. MA with YAMAS can be readily conducted as YAMAS provides a generic parser for heterogeneous tabulated file formats within the GWAS field and avoids cumbersome setups. In this way, it supplements the meta-analysis process.