Atlas Khan1, Ning Shang2, Lynn Petukhova3,4, Jun Zhang1, Yufeng Shen5, Scott J Hebbring6, Halima Moncrieffe7, Leah C Kottyan7, Bahram Namjou-Khales7, Rachel Knevel8, Soumya Raychaudhuri9,10,11,12,13,14, Elizabeth W Karlson15, John B Harley7, Ian B Stanaway16, David Crosslin16, Joshua C Denny17, Mitchell S V Elkind18,4, Ali G Gharavi1, George Hripcsak2, Chunhua Weng2, Krzysztof Kiryluk19. 1. Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York. 2. Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York. 3. Department of Dermatology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York. 4. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York. 5. Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Medical Center, New York, New York. 6. Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin. 7. Department of Pediatrics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio. 8. Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands. 9. Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 10. Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 11. Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 12. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts. 13. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts. 14. Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom. 15. Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. 16. Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington. 17. Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee. 18. Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York. 19. Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York kk473@columbia.edu.
Abstract
BACKGROUND: Genetic variants in complement genes have been associated with a wide range of human disease states, but well-powered genetic association studies of complement activation have not been performed in large multiethnic cohorts. METHODS: We performed medical records-based genome-wide and phenome-wide association studies for plasma C3 and C4 levels among participants of the Electronic Medical Records and Genomics (eMERGE) network. RESULTS: In a GWAS for C3 levels in 3949 individuals, we detected two genome-wide significant loci: chr.1q31.3 (CFH locus; rs3753396-A; β=0.20; 95% CI, 0.14 to 0.25; P=1.52x10-11) and chr.19p13.3 (C3 locus; rs11569470-G; β=0.19; 95% CI, 0.13 to 0.24; P=1.29x10-8). These two loci explained approximately 2% of variance in C3 levels. GWAS for C4 levels involved 3998 individuals and revealed a genome-wide significant locus at chr.6p21.32 (C4 locus; rs3135353-C; β=0.40; 95% CI, 0.34 to 0.45; P=4.58x10-35). This locus explained approximately 13% of variance in C4 levels. The multiallelic copy number variant analysis defined two structural genomic C4 variants with large effect on blood C4 levels: C4-BS (β=-0.36; 95% CI, -0.42 to -0.30; P=2.98x10-22) and C4-AL-BS (β=0.25; 95% CI, 0.21 to 0.29; P=8.11x10-23). Overall, C4 levels were strongly correlated with copy numbers of C4A and C4B genes. In comprehensive phenome-wide association studies involving 102,138 eMERGE participants, we cataloged a full spectrum of autoimmune, cardiometabolic, and kidney diseases genetically related to systemic complement activation. CONCLUSIONS: We discovered genetic determinants of plasma C3 and C4 levels using eMERGE genomic data linked to electronic medical records. Genetic variants regulating C3 and C4 levels have large effects and multiple clinical correlations across the spectrum of complement-related diseases in humans.
BACKGROUND: Genetic variants in complement genes have been associated with a wide range of human disease states, but well-powered genetic association studies of complement activation have not been performed in large multiethnic cohorts. METHODS: We performed medical records-based genome-wide and phenome-wide association studies for plasma C3 and C4 levels among participants of the Electronic Medical Records and Genomics (eMERGE) network. RESULTS: In a GWAS for C3 levels in 3949 individuals, we detected two genome-wide significant loci: chr.1q31.3 (CFH locus; rs3753396-A; β=0.20; 95% CI, 0.14 to 0.25; P=1.52x10-11) and chr.19p13.3 (C3 locus; rs11569470-G; β=0.19; 95% CI, 0.13 to 0.24; P=1.29x10-8). These two loci explained approximately 2% of variance in C3 levels. GWAS for C4 levels involved 3998 individuals and revealed a genome-wide significant locus at chr.6p21.32 (C4 locus; rs3135353-C; β=0.40; 95% CI, 0.34 to 0.45; P=4.58x10-35). This locus explained approximately 13% of variance in C4 levels. The multiallelic copy number variant analysis defined two structural genomic C4 variants with large effect on blood C4 levels: C4-BS (β=-0.36; 95% CI, -0.42 to -0.30; P=2.98x10-22) and C4-AL-BS (β=0.25; 95% CI, 0.21 to 0.29; P=8.11x10-23). Overall, C4 levels were strongly correlated with copy numbers of C4A and C4B genes. In comprehensive phenome-wide association studies involving 102,138 eMERGE participants, we cataloged a full spectrum of autoimmune, cardiometabolic, and kidney diseases genetically related to systemic complement activation. CONCLUSIONS: We discovered genetic determinants of plasma C3 and C4 levels using eMERGE genomic data linked to electronic medical records. Genetic variants regulating C3 and C4 levels have large effects and multiple clinical correlations across the spectrum of complement-related diseases in humans.
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