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- López-Isac, Elena, et al.
(författare)
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Brief Report : IRF4 Newly Identified as a Common Susceptibility Locus for Systemic Sclerosis and Rheumatoid Arthritis in a Cross-Disease Meta-Analysis of Genome-Wide Association Studies
- 2016
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Ingår i: Arthritis & Rheumatology. - : Wiley. - 2326-5191 .- 2326-5205. ; 68:9, s. 2338-2344
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Systemic sclerosis (SSc) and rheumatoid arthritis (RA) are autoimmune diseases that have similar clinical and immunologic characteristics. To date, several shared SSc–RA genetic loci have been identified independently. The aim of the current study was to systematically search for new common SSc–RA loci through an interdisease meta–genome-wide association (meta-GWAS) strategy. Methods: The study was designed as a meta-analysis combining GWAS data sets of patients with SSc and patients with RA, using a strategy that allowed identification of loci with both same-direction and opposite-direction allelic effects. The top single-nucleotide polymorphisms were followed up in independent SSc and RA case–control cohorts. This allowed an increase in the sample size to a total of 8,830 patients with SSc, 16,870 patients with RA, and 43,393 healthy controls. Results: This cross-disease meta-analysis of the GWAS data sets identified several loci with nominal association signals (P < 5 × 10−6) that also showed evidence of association in the disease-specific GWAS scans. These loci included several genomic regions not previously reported as shared loci, as well as several risk factors that were previously found to be associated with both diseases. Follow-up analyses of the putatively new SSc–RA loci identified IRF4 as a shared risk factor for these 2 diseases (Pcombined = 3.29 × 10−12). Analysis of the biologic relevance of the known SSc–RA shared loci identified the type I interferon and interleukin-12 signaling pathways as the main common etiologic factors. Conclusion: This study identified a novel shared locus, IRF4, for the risk of SSc and RA, and highlighted the usefulness of a cross-disease GWAS meta-analysis strategy in the identification of common risk loci.
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| 2. |
- Zaitlen, Noah, et al.
(författare)
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Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies
- 2012
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Ingår i: PLoS Genetics. - : Public Library of Science (PLoS). - 1553-7404. ; 8:11
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Tidskriftsartikel (refereegranskat)abstract
- Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-controlcovariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled falsepositive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1x10(-9)). The improvement varied across diseases with a 16% median increase in chi(2) test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.
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