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.
Search related documents:
Co phrase search for related documents- interaction testing and meta analysis: 1, 2
- logistic regression and mega analysis: 1
- logistic regression and mega analysis approach: 1
- logistic regression and meta analysis: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48
- logistic regression and meta analysis perform: 1
- logistic regression and odd ratio: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39
- logistic regression and principal component: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
- logistic regression perform and meta analysis: 1
- logistic regression perform and meta analysis perform: 1
- mega analysis and meta analysis: 1, 2, 3
- mega analysis approach and meta analysis: 1
- meta analysis and odd ratio: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
- meta analysis and principal component: 1
- meta analysis and variance weight: 1
- odd ratio and principal component: 1
- principal component and variance weight: 1
Co phrase search for related documents, hyperlinks ordered by date