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000136657 0247_ $$2ISSN$$a1471-2105
000136657 0247_ $$2doi$$a10.1186/1471-2105-13-231
000136657 0247_ $$2pmid$$apmid:22971100
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000136657 037__ $$aDZNE-2020-02979
000136657 041__ $$aEnglish
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000136657 1001_ $$0P:(DE-2719)2658768$$aMeesters, Christian$$b0$$eFirst author
000136657 245__ $$aQuick, 'imputation-free' meta-analysis with proxy-SNPs.
000136657 260__ $$aHeidelberg$$bSpringer$$c2012
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000136657 520__ $$aMeta-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.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.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.
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000136657 650_2 $$2MeSH$$aAlgorithms
000136657 650_2 $$2MeSH$$aAlleles
000136657 650_2 $$2MeSH$$aDiabetes Mellitus, Type 2: genetics
000136657 650_2 $$2MeSH$$aGenome, Human
000136657 650_2 $$2MeSH$$aGenome-Wide Association Study
000136657 650_2 $$2MeSH$$aGenotype
000136657 650_2 $$2MeSH$$aHapMap Project
000136657 650_2 $$2MeSH$$aHumans
000136657 650_2 $$2MeSH$$aLinkage Disequilibrium
000136657 650_2 $$2MeSH$$aMeta-Analysis as Topic
000136657 650_2 $$2MeSH$$aPolymorphism, Single Nucleotide
000136657 650_2 $$2MeSH$$aSoftware
000136657 7001_ $$0P:(DE-HGF)0$$aLeber, Markus$$b1
000136657 7001_ $$0P:(DE-2719)2802016$$aHerold, Christine$$b2
000136657 7001_ $$0P:(DE-HGF)0$$aAngisch, Marina$$b3
000136657 7001_ $$0P:(DE-HGF)0$$aMattheisen, Manuel$$b4
000136657 7001_ $$0P:(DE-2719)2740473$$aDrichel, Dmitriy$$b5
000136657 7001_ $$0P:(DE-2719)2810305$$aLacour, André$$b6
000136657 7001_ $$0P:(DE-2719)2501867$$aBecker, Tim$$b7$$eLast author
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000136657 8567_ $$2Pubmed Central$$uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472171
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000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1002/gepi.20533$$o10.1002/gepi.20533
000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1038/ng2088$$o10.1038/ng2088
000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1371/journal.pgen.1000529$$o10.1371/journal.pgen.1000529
000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1371/journal.pgen.0030114$$o10.1371/journal.pgen.0030114
000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.ajhg.2009.01.005$$o10.1016/j.ajhg.2009.01.005
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000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1038/nature09534$$o10.1038/nature09534
000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.ajhg.2011.04.014$$o10.1016/j.ajhg.2011.04.014
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000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1038/ng.609$$o10.1038/ng.609
000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1136/bmj.327.7414.557$$o10.1136/bmj.327.7414.557
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000136657 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1006/geno.1995.9003$$o10.1006/geno.1995.9003