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@ARTICLE{Meesters:136657,
      author       = {Meesters, Christian and Leber, Markus and Herold, Christine
                      and Angisch, Marina and Mattheisen, Manuel and Drichel,
                      Dmitriy and Lacour, André and Becker, Tim},
      title        = {{Q}uick, 'imputation-free' meta-analysis with
                      proxy-{SNP}s.},
      journal      = {BMC bioinformatics},
      volume       = {13},
      number       = {1},
      issn         = {1471-2105},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DZNE-2020-02979},
      pages        = {231},
      year         = {2012},
      abstract     = {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.},
      keywords     = {Algorithms / Alleles / Diabetes Mellitus, Type 2: genetics
                      / Genome, Human / Genome-Wide Association Study / Genotype /
                      HapMap Project / Humans / Linkage Disequilibrium /
                      Meta-Analysis as Topic / Polymorphism, Single Nucleotide /
                      Software},
      cin          = {GenomMathematik / AG Roes},
      ddc          = {610},
      cid          = {I:(DE-2719)1013007 / I:(DE-2719)1610003},
      pnm          = {345 - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:22971100},
      pmc          = {pmc:PMC3472171},
      doi          = {10.1186/1471-2105-13-231},
      url          = {https://pub.dzne.de/record/136657},
}