Selected article for: "additive model and logistic additive"
Author: A.J.W. Haasnoot; M.W. Schilham; S.S.M. Kamphuis; P.C.E. Hissink Muller; A. Heiligenhaus; D. Foell; R.A. Ophoff; T.R.D.J. Radstake; A.I. Den Hollander; T.H.C.M. Reinards; S. Hiddingh; N. Schalij-Delfos; E.P.A.H. Hoppenreijs; M.A.J. van Rossum; C. Wouters; R.K. Saurenmann; N. Wulffraat; R. ten Cate; J.H. de Boer; S.L. Pulit; J.J.W. Kuiper
Title: An amino acid motif in HLA-DRß1 distinguishes patients with uveitis in juvenile idiopathic arthritis
  • Document date: 2017_5_22
    • ID: 4it5c9n2_11
    Snippet: To identify the amino acids or HLA types driving the genome-wide association signal, we performed a mega-analysis across the imputed MHC data. We first merged imputation dosages from Phase 1 and Phase 2, and then used PLINK 1.9 31 to perform logistic regression, assuming an additive model and correcting for the top 5 principal components, sex, and analysis phase (i.e., Phase 1 or Phase 2). This 'mega-analysis' approach is theoretically and empiri.....
    Document: To identify the amino acids or HLA types driving the genome-wide association signal, we performed a mega-analysis across the imputed MHC data. We first merged imputation dosages from Phase 1 and Phase 2, and then used PLINK 1.9 31 to perform logistic regression, assuming an additive model and correcting for the top 5 principal components, sex, and analysis phase (i.e., Phase 1 or Phase 2). This 'mega-analysis' approach is theoretically and empirically highly similar to inverse variance-weighted meta-analysis. 33, 34 We also performed a meta-analysis across Phase 1 and Phase 2 and found that, indeed, the odds ratios derived from mega-analysis and meta-analysis in the MHC were highly concordant (Pearson's r = 0.95, Supplementary Figure 4) . The mega-analysis allows for the additional advantage of allowing for interaction testing and conditional analysis on any associated variants across the full dataset.

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