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Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics.
Lu, Haojie; Zhang, Jinhui; Jiang, Zhou; Zhang, Meng; Wang, Ting; Zhao, Huashuo; Zeng, Ping.
Afiliação
  • Lu H; Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Zhang J; Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Jiang Z; Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Zhang M; Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Wang T; Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Zhao H; Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Zeng P; Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
Front Genet ; 12: 656545, 2021.
Article em En | MEDLINE | ID: mdl-33815486
BACKGROUND: Clinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown. METHODS: Our analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE. RESULTS: We found there existed a significant positive genetic correlation (r g = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway. CONCLUSION: This study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article