Cindy Boer

Genetics of Osteoarthritis Consortium GWAS Meta-Analyses | 197 4.1 72. Holt, M., et al., Identification of SNAP-47, a novel Qbc-SNARE with ubiquitous expression. J Biol Chem, 2006. 281(25): p. 17076-83. 73. Jones, D.K., et al., Localization and functional consequences of a direct interaction between TRI- OBP-1 and hERG proteins in the heart. J Cell Sci, 2018. 131(6). 74. Park, S., et al., Emerging roles of TRIO and F-actin-binding protein in human diseases, in Cell Com- mun Signal. 2018. 75. Finan, C., et al., The druggable genome and support for target identification and validation in drug development. Sci Transl Med, 2017. 9(383). 76. Anderson, D.M., et al., A homologue of the TNF receptor and its ligand enhance T-cell growth and dendritic-cell function. Nature, 1997. 390(6656): p. 175-9. 77. Roh, Y.S., J. Dequeker, and J.C. Mulier, Cortical bone remodeling and bone mass in primary osteo- arthrosis of the hip. Invest Radiol, 1973. 8(4): p. 351-4. 78. Kerkhof, H.J., et al., Recommendations for standardization and phenotype definitions in genetic studies of osteoarthritis: the TREAT-OA consortium. Osteoarthritis Cartilage, 2011. 19(3): p. 254- 64. 79. Winkler, T.W., et al., Quality control and conduct of genome-wide association meta-analyses. Nat Protoc, 2014. 9(5): p. 1192-212. 80. Willer, C.J., Y. Li, and G.R. Abecasis, METAL: fast and efficient meta-analysis of genomewide associ- ation scans. Bioinformatics, 2010. 26(17): p. 2190-1. 81. Li, M.X., et al., Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Gen- et, 2012. 131(5): p. 747-56. 82. Purcell, S., et al., PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007. 81(3): p. 559-75. 83. Yang, J., et al., GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet, 2011. 88(1): p. 76-82. 84. Yang, J., et al., Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet, 2012. 44(4): p. 369-75, s1-3. 85. Wakefield, J., Bayes factors for genome-wide association studies: comparison with P-values. Genet Epidemiol, 2009. 33(1): p. 79-86. 86. Ramos, Y.F., et al., Genes involved in the osteoarthritis process identified through genome wide expression analysis in articular cartilage; the RAAK study. PLoS One, 2014. 9(7): p. e103056. 87. Coutinho de Almeida, R., et al., RNA sequencing data integration reveals an miRNA interactome of osteoarthritis cartilage. Ann Rheum Dis, 2019. 78(2): p. 270-277. 88. den Hollander, W., et al., Annotating Transcriptional Effects of Genetic Variants in Disease-Rele- vant Tissue: Transcriptome-Wide Allelic Imbalance in Osteoarthritic Cartilage. Arthritis Rheumatol, 2019. 71(4): p. 561-570. 89. Tuerlings, M., et al., RNA sequencing reveals interacting key determinants of osteoarthritis acting in subchondral bone and articular cartilage. 2020: p. 2020.03.13.969386. 90. Tachmazidou, I., et al., Identification of new therapeutic targets for osteoarthritis through ge- nome-wide analyses of UK Biobank data. Nat Genet, 2019. 51(2): p. 230-236. 91. Steinberg, J., et al., Integrative epigenomics, transcriptomics and proteomics of patient chondro- cytes reveal genes and pathways involved in osteoarthritis. Sci Rep, 2017. 7(1): p. 8935. 92. Ritchie, M.E., et al., limma powers differential expression analyses for RNA-sequencing and mi- croarray studies. Nucleic Acids Res, 2015. 43(7): p. e47. 93. Law, C.W., et al., voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol, 2014. 15(2): p. R29. 94. Chou, C.H., et al., Genome-wide expression profiles of subchondral bone in osteoarthritis. Arthritis Res Ther, 2013. 15(6): p. R190.