Laura Lorés-Motta1, Constantin C Paun1, Jordi Corominas2, Marc Pauper2, Maartje J Geerlings1, Lebriz Altay3, Tina Schick3, Mohamed R Daha4, Sascha Fauser5, Carel B Hoyng1, Anneke I den Hollander2, Eiko K de Jong6. 1. Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands. 2. Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands. 3. Department of Ophthalmology, University Hospital of Cologne, Cologne, Germany. 4. Department of Nephrology, Leiden University Medical Center, Leiden, The Netherlands. 5. Department of Ophthalmology, University Hospital of Cologne, Cologne, Germany; Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd., Basel, Switzerland. 6. Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: Eiko.deJong@radboudumc.nl.
Abstract
PURPOSE: To identify genetic variants associated with complement activation, which may help to select age-related macular degeneration (AMD) patients for complement-inhibiting therapies. DESIGN: Genome-wide association study (GWAS) followed by replication and meta-analysis. PARTICIPANTS: AMD patients and controls (n = 2245). METHODS: A GWAS on serum C3d-to-C3 ratio was performed in 1548 AMD patients and controls. For replication and meta-analysis, 697 additional individuals were genotyped. A model for complement activation including genetic and non-genetic factors was built, and the variance explained was estimated. Haplotype analysis was performed for 8 SNPs across the CFH/CFHR locus. Association with AMD was performed for the variants and haplotypes found to influence complement activation. MAIN OUTCOME MEASURES: Normalized C3d/C3 ratio as a measure of systemic complement activation. RESULTS: Complement activation was associated independently with rs3753396 located in CFH (Pdiscovery = 1.09 × 10-15; Pmeta = 3.66 × 10-21; β = 0.141; standard error [SE] = 0.015) and rs6685931 located in CFHR4 (Pdiscovery = 8.18 × 10-7; Pmeta = 6.32 × 10-8; β = 0.054; SE = 0.010). A model including age, AMD disease status, body mass index, triglycerides, rs3753396, rs6685931, and previously identified SNPs explained 18.7% of the variability in complement activation. Haplotype analysis revealed 3 haplotypes (H1-2 and H6 containing rs6685931 and H3 containing rs3753396) associated with complement activation. Haplotypes H3 and H6 conferred stronger effects on complement activation compared with the single variants (P = 2.53 × 10-14; β = 0.183; SE = 0.024; and P = 4.28 × 10-4; β = 0.144; SE = 0.041; respectively). Association analyses with AMD revealed that SNP rs6685931 and haplotype H1-2 containing rs6685931 were associated with a risk for AMD development, whereas SNP rs3753396 and haplotypes H3 and H6 were not. CONCLUSIONS: The SNP rs3753396 in CFH and SNP rs6685931 in CFHR4 are associated with systemic complement activation levels. The SNP rs6685931 in CFHR4 and its linked haplotype H1-2 also conferred a risk for AMD development, and therefore could be used to identify AMD patients who would benefit most from complement-inhibiting therapies.
PURPOSE: To identify genetic variants associated with complement activation, which may help to select age-related macular degeneration (AMD) patients for complement-inhibiting therapies. DESIGN: Genome-wide association study (GWAS) followed by replication and meta-analysis. PARTICIPANTS: AMDpatients and controls (n = 2245). METHODS: A GWAS on serum C3d-to-C3 ratio was performed in 1548 AMDpatients and controls. For replication and meta-analysis, 697 additional individuals were genotyped. A model for complement activation including genetic and non-genetic factors was built, and the variance explained was estimated. Haplotype analysis was performed for 8 SNPs across the CFH/CFHR locus. Association with AMD was performed for the variants and haplotypes found to influence complement activation. MAIN OUTCOME MEASURES: Normalized C3d/C3 ratio as a measure of systemic complement activation. RESULTS: Complement activation was associated independently with rs3753396 located in CFH (Pdiscovery = 1.09 × 10-15; Pmeta = 3.66 × 10-21; β = 0.141; standard error [SE] = 0.015) and rs6685931 located in CFHR4 (Pdiscovery = 8.18 × 10-7; Pmeta = 6.32 × 10-8; β = 0.054; SE = 0.010). A model including age, AMD disease status, body mass index, triglycerides, rs3753396, rs6685931, and previously identified SNPs explained 18.7% of the variability in complement activation. Haplotype analysis revealed 3 haplotypes (H1-2 and H6 containing rs6685931 and H3 containing rs3753396) associated with complement activation. Haplotypes H3 and H6 conferred stronger effects on complement activation compared with the single variants (P = 2.53 × 10-14; β = 0.183; SE = 0.024; and P = 4.28 × 10-4; β = 0.144; SE = 0.041; respectively). Association analyses with AMD revealed that SNP rs6685931 and haplotype H1-2 containing rs6685931 were associated with a risk for AMD development, whereas SNP rs3753396 and haplotypes H3 and H6 were not. CONCLUSIONS: The SNP rs3753396 in CFH and SNP rs6685931 in CFHR4 are associated with systemic complement activation levels. The SNP rs6685931 in CFHR4 and its linked haplotype H1-2 also conferred a risk for AMD development, and therefore could be used to identify AMDpatients who would benefit most from complement-inhibiting therapies.
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