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

ABSTRACT: BACKGROUND: Meta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increasethe power to detect strong or weak genotype effects or b) as a result verification method. As a consequence ofdiffering SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia toavoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software), however, enablescross-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 onimputation. This is accomplished by using reference linkage disequilibrium data from 1,000Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least onestudy. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Ouralgorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applicationscan be used as input files for MA, tremendously speeding up MA compared to the conventional imputationapproach. We show that our proxy algorithm is well-powered and yields valuable ad hoc results, possiblyproviding an incentive for follow-up studies. We propose our method as a quick screening step prior toimputation-based MA, as well as an additional main approach for studies without available reference datamatching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II DiabetesGWAS and found that the proxy algorithm clearly outperforms naive MA on the P-value level: for 17 out of23 we observe an improvement on the p-value level by a factor of more than two, and a maximumimprovement by a factor of 2127. CONCLUSIONS: YAMAS is an efficient and fast meta-analysis program which offers various methods, including conventionalMA as well as inserting proxy-SNPs for missing markers to avoid unnecessary power loss. MA with YAMAScan be readily conducted as YAMAS provides a generic parser for heterogeneous tabulated file formats withinthe GWAS field and avoids cumbersome setups. In this way, it supplements the meta-analysis process.