136657 20240424120353.0 1471-2105 ISSN 10.1186/1471-2105-13-231 doi pmid:22971100 pmid pmc:PMC3472171 pmc DZNE-2020-02979 English 610 Meesters, Christian P:(DE-2719)2658768 0 First author Quick, 'imputation-free' meta-analysis with proxy-SNPs. Heidelberg 2012 Springer print Crossref Springer Science and Business Media LLC 2012-01-01 article DRIVER Output Types/Journal article DataCite Journal Article journal journal PUB:(DE-HGF)16 1713882797_10932 PUB:(DE-HGF) ARTICLE BibTeX JOURNAL_ARTICLE ORCID Journal Article 0 EndNote 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.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. 345 - Population Studies and Genetics (POF3-345) G:(DE-HGF)POF3-345 POF3-345 POF III 0 Dataset connected to CrossRef, PubMed, Algorithms MeSH Alleles MeSH Diabetes Mellitus, Type 2: genetics MeSH Genome, Human MeSH Genome-Wide Association Study MeSH Genotype MeSH HapMap Project MeSH Humans MeSH Linkage Disequilibrium MeSH Meta-Analysis as Topic MeSH Polymorphism, Single Nucleotide MeSH Software MeSH Leber, Markus P:(DE-HGF)0 1 Herold, Christine P:(DE-2719)2802016 2 Angisch, Marina P:(DE-HGF)0 3 Mattheisen, Manuel P:(DE-HGF)0 4 Drichel, Dmitriy P:(DE-2719)2740473 5 Lacour, André P:(DE-2719)2810305 6 Becker, Tim P:(DE-2719)2501867 7 Last author 10.1186/1471-2105-13-231 : Springer Science and Business Media LLC, 2012-01-01 1 231 journal-article Crossref BMC Bioinformatics 13 2012 1471-2105 10.1186/1471-2105-13-231 Vol. 13, no. 1, p. 231 - PERI:(DE-600)2041484-5 1 13:1<231 - 231 BMC bioinformatics 13 2012 1471-2105 OpenAccess http://pub.dzne.de/record/136657/files/DZNE-2020-02979.pdf OpenAccess pdfa http://pub.dzne.de/record/136657/files/DZNE-2020-02979.pdf?subformat=pdfa Pubmed Central http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472171 oai:pub.dzne.de:136657 openaire open_access VDB driver dnbdelivery Deutsches Zentrum für Neurodegenerative Erkrankungen I:(DE-588)1065079516 DZNE 0 P:(DE-2719)2658768 Deutsches Zentrum für Neurodegenerative Erkrankungen I:(DE-588)1065079516 DZNE 2 P:(DE-2719)2802016 Deutsches Zentrum für Neurodegenerative Erkrankungen I:(DE-588)1065079516 DZNE 5 P:(DE-2719)2740473 Deutsches Zentrum für Neurodegenerative Erkrankungen I:(DE-588)1065079516 DZNE 6 P:(DE-2719)2810305 Deutsches Zentrum für Neurodegenerative Erkrankungen I:(DE-588)1065079516 DZNE 7 P:(DE-2719)2501867 DE-HGF Gesundheit Erkrankungen des Nervensystems G:(DE-HGF)POF3-340 G:(DE-HGF)POF3-345 G:(DE-HGF)POF3 G:(DE-HGF)POF3-300 G:(DE-HGF)POF Population Studies and Genetics 0 2012 Creative Commons Attribution CC BY 2.0 LIC:(DE-HGF)CCBY2 HGFVOC DBCoverage StatID:(DE-HGF)0200 StatID SCOPUS DBCoverage StatID:(DE-HGF)1050 StatID BIOSIS Previews DBCoverage StatID:(DE-HGF)0600 StatID Ebsco Academic Search JCR StatID:(DE-HGF)0100 StatID BMC BIOINFORMATICS : 2017 DBCoverage StatID:(DE-HGF)0501 StatID DOAJ Seal DBCoverage StatID:(DE-HGF)0500 StatID DOAJ WoS StatID:(DE-HGF)0111 StatID Science Citation Index Expanded DBCoverage StatID:(DE-HGF)0150 StatID Web of Science Core Collection IF < 5 StatID:(DE-HGF)9900 StatID OpenAccess StatID:(DE-HGF)0510 StatID Peer Review StatID:(DE-HGF)0030 StatID ASC DBCoverage StatID:(DE-HGF)0310 StatID NCBI Molecular Biology Database DBCoverage StatID:(DE-HGF)0300 StatID Medline DBCoverage StatID:(DE-HGF)0320 StatID PubMed Central DBCoverage StatID:(DE-HGF)0199 StatID Clarivate Analytics Master Journal List I:(DE-2719)1013007 GenomMathematik Genomische Mathematik in der Neuroepidemiologie 0 I:(DE-2719)1610003 AG Roes Implementation Science & Person-Centered Dementia Care 1 journal VDB UNRESTRICTED I:(DE-2719)1013007 I:(DE-2719)1610003 FullTexts -- missing cx lookup -- 10.1038/nature08494 Crossref 10.1038/nature08494 -- missing cx lookup -- 10.1534/genetics.109.102798 Crossref 10.1534/genetics.109.102798 Crossref -- missing cx lookup -- 10.1371/journal.pone.0000196 Crossref 10.1371/journal.pone.0000196 -- missing cx lookup -- 10.1093/hmg/ddn288 Crossref 10.1093/hmg/ddn288 -- missing cx lookup -- 10.1093/bioinformatics/btq340 Crossref 10.1093/bioinformatics/btq340 -- missing cx lookup -- 10.1086/519795 Crossref 10.1086/519795 -- missing cx lookup -- 10.1002/gepi.20533 Crossref 10.1002/gepi.20533 -- missing cx lookup -- 10.1038/ng2088 Crossref 10.1038/ng2088 -- missing cx lookup -- 10.1371/journal.pgen.1000529 Crossref 10.1371/journal.pgen.1000529 -- missing cx lookup -- 10.1371/journal.pgen.0030114 Crossref 10.1371/journal.pgen.0030114 -- missing cx lookup -- 10.1016/j.ajhg.2009.01.005 Crossref 10.1016/j.ajhg.2009.01.005 -- missing cx lookup -- 10.1002/gepi.20507 Crossref 10.1002/gepi.20507 -- missing cx lookup -- 10.1038/nature06258 Crossref 10.1038/nature06258 -- missing cx lookup -- 10.1038/nature09534 Crossref 10.1038/nature09534 -- missing cx lookup -- 10.1016/j.ajhg.2011.04.014 Crossref 10.1016/j.ajhg.2011.04.014 -- missing cx lookup -- 10.1038/ng.871 Crossref 10.1038/ng.871 -- missing cx lookup -- 10.1038/ng.120 Crossref 10.1038/ng.120 -- missing cx lookup -- 10.1038/ng.609 Crossref 10.1038/ng.609 -- missing cx lookup -- 10.1136/bmj.327.7414.557 Crossref 10.1136/bmj.327.7414.557 -- missing cx lookup -- 10.1016/S0304-3959(99)00302-4 Crossref 10.1016/S0304-3959(99)00302-4 -- missing cx lookup -- 10.1093/bioinformatics/btp596 Crossref 10.1093/bioinformatics/btp596 -- missing cx lookup -- 10.1006/geno.1995.9003 Crossref 10.1006/geno.1995.9003