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@INPROCEEDINGS{Vaitsiakhovich:145229,
      author       = {Vaitsiakhovich, Tatsiana and Becker, Tim},
      title        = {{M}eta-{A}nalysis of {M}ultiple {R}egression {M}odels in
                      {G}enome-{W}ide {A}ssociation {S}tudies},
      reportid     = {DZNE-2020-00587},
      year         = {2015},
      abstract     = {Meta-analysis refers to the statistical synthesis of the
                      results from a series of related studies. Meta-analysis of
                      summary statistics from Genome-wide association studies
                      (GWAS) may lead to dis-covery of new susceptibility loci
                      without the need to exchange gen-otype data.Well-known
                      p-value combination methods can be applied to an arbitrary
                      association test. However, these methods do not pro-vide
                      summary effects and are known to be underpowered for
                      high-dimensional models. The broadly used methods for
                      meta-analysis of individual GWAS results address mostly one
                      parameter models. Results of multiple regression analysis
                      applied, for instance, to ge-nome-wide interaction analysis
                      have rarely been used due to the complexities underlying the
                      process of their synthesis. For high-dimensional models the
                      gain of power can be achieved when meta-analysis methods
                      incorporate information on study-specific properties of
                      parameter estimates, their effect directions, standard
                      errors and covariance structure. In this context, a method
                      for the synthesis of regression slopes (MSRS) represents a
                      perspec-tive approach of meta-analysis for advanced GWAS.
                      MSRS pro-vides meta-analysis p-values and common parameter
                      estimates of multiple regression models for an arbitrary
                      number of parameters, and can be used to test the
                      homogeneity of studies results. We introduce an efficient,
                      powerful and freely available soft-ware tool METAINTER,
                      which implements MSRS and three fur-ther meta-analysis
                      methods: Fisher’s method, Stouffer’s method with weights
                      and Stouffer’s method with weights and effect direc-tions.
                      METAINTER enables meta-analysis of tests for and under
                      gene-gene interaction, single-SNP association tests with
                      several degrees of freedom, global haplotype tests etc.
                      Simulation study shows that MSRS has correct type I error
                      and its power comes close to that of the joint sample
                      analysis. We have conducted a real data analysis of six GWAS
                      of type 2 Diabetes. For each study, a genome-wide
                      interaction analysis of all SNP pairs has been performed by
                      logistic regression tests. The results have then been
                      meta-analysed with METAINTER.},
      month         = {Apr},
      date          = {2015-04-16},
      organization  = {43rd European Mathematical Genetics
                       Meeting, Brest (France), 16 Apr 2015 -
                       17 Apr 2015},
      cin          = {AG Becker},
      cid          = {I:(DE-2719)1013007},
      pnm          = {345 - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://pub.dzne.de/record/145229},
}