Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
11 11 2022
11 11 2022
Historique:
received:
06
12
2021
accepted:
25
10
2022
pubmed:
12
11
2022
medline:
16
11
2022
entrez:
11
11
2022
Statut:
epublish
Résumé
Immunoglobulin A (IgA) mediates mucosal responses to food antigens and the intestinal microbiome and is involved in susceptibility to mucosal pathogens, celiac disease, inflammatory bowel disease, and IgA nephropathy. We performed a genome-wide association study of serum IgA levels in 41,263 individuals of diverse ancestries and identified 20 genome-wide significant loci, including 9 known and 11 novel loci. Co-localization analyses with expression QTLs prioritized candidate genes for 14 of 20 significant loci. Most loci encoded genes that produced immune defects and IgA abnormalities when genetically manipulated in mice. We also observed positive genetic correlations of serum IgA levels with IgA nephropathy, type 2 diabetes, and body mass index, and negative correlations with celiac disease, inflammatory bowel disease, and several infections. Mendelian randomization supported elevated serum IgA as a causal factor in IgA nephropathy. African ancestry was consistently associated with higher serum IgA levels and greater frequency of IgA-increasing alleles compared to other ancestries. Our findings provide novel insights into the genetic regulation of IgA levels and its potential role in human disease.
Identifiants
pubmed: 36369178
doi: 10.1038/s41467-022-34456-6
pii: 10.1038/s41467-022-34456-6
pmc: PMC9651905
doi:
Substances chimiques
Immunoglobulin A
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
6859Subventions
Organisme : NHGRI NIH HHS
ID : U01 HG011172
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK105124
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK078244
Pays : United States
Organisme : NIDDK NIH HHS
ID : RC2 DK116690
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK082753
Pays : United States
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s).
Références
Woof, J. M. & Kerr, M. A. The function of immunoglobulin A in immunity. J. Pathol. 208, 270–282 (2006).
doi: 10.1002/path.1877
Fasching, C. E. et al. Impact of the molecular form of immunoglobulin A on functional activity in defense against Streptococcus pneumoniae. Infect. Immun. 75, 1801–1810 (2007).
doi: 10.1128/IAI.01758-06
Woof, J. M. & Russell, M. W. Structure and function relationships in IgA. Mucosal Immunol. 4, 590–597 (2011).
doi: 10.1038/mi.2011.39
Corthesy, B. Role of secretory IgA in infection and maintenance of homeostasis. Autoimmun. Rev. 12, 661–665 (2013).
doi: 10.1016/j.autrev.2012.10.012
Yu, H. Q. et al. Distinct features of SARS-CoV-2-specific IgA response in COVID-19 patients. Eur. Respir. J. 56, 2001526 (2020).
Ma, H. et al. Serum IgA, IgM, and IgG responses in COVID-19. Cell Mol. Immunol. 17, 773–775 (2020).
doi: 10.1038/s41423-020-0474-z
Sterlin, D. et al. IgA dominates the early neutralizing antibody response to SARS-CoV-2. Sci. Transl. Med. 13, eabd2223 (2021).
Maeda, A. et al. Significance of serum IgA levels and serum IgA/C3 ratio in diagnostic analysis of patients with IgA nephropathy. J. Clin. Lab Anal. 17, 73–76 (2003).
doi: 10.1002/jcla.10071
Papista, C., Berthelot, L. & Monteiro, R. C. Dysfunctions of the Iga system: a common link between intestinal and renal diseases. Cell Mol. Immunol. 8, 126–134 (2011).
doi: 10.1038/cmi.2010.69
Rodriguez-Segade, S. et al. High serum IgA concentrations in patients with diabetes mellitus: agewise distribution and relation to chronic complications. Clin. Chem. 42, 1064–1067 (1996).
doi: 10.1093/clinchem/42.7.1064
Gonzalez-Quintela, A. et al. Serum levels of immunoglobulins (IgG, IgA, IgM) in a general adult population and their relationship with alcohol consumption, smoking and common metabolic abnormalities. Clin. Exp. Immunol. 151, 42–50 (2008).
doi: 10.1111/j.1365-2249.2007.03545.x
Di Franco, P. et al. Genetic and environmental influences on serum levels of immunoglobulins and complement components in monozygotic and dizygotic twins. J. Clin. Lab Immunol. 27, 5–10 (1988).
Stoica, G., Macarie, E., Michiu, V. & Stoica, R. C. Biologic variation of human immunoglobulin concentration. I. Sex-age specific effects on serum levels of IgG, IgA, IgM and IgD. Med. Interne 18, 323–332 (1980).
Grundbacher, F. J. & Shreffler, D. C. Changes in human serum immunoglobulin levels with age and sex. Z. Immunitatsforsch Allerg. Klin. Immunol. 141, 20–26 (1970).
Lomax-Browne, H. J. et al. IgA1 Glycosylation Is Heritable in Healthy Twins. J. Am. Soc. Nephrol. 28, 64–68 (2017).
doi: 10.1681/ASN.2016020184
Viktorin, A. et al. IgA measurements in over 12 000 Swedish twins reveal sex differential heritability and regulatory locus near CD30L. Hum. Mol. Genet 23, 4177–4184 (2014).
doi: 10.1093/hmg/ddu135
Hatagima, A., Cabello, P. H. & Krieger, H. Causal analysis of the variability of IgA, IgG, and IgM immunoglobulin levels. Hum. Biol. 71, 219–229 (1999).
Grundbacher, F. J. Heritability estimates and genetic and environmental correlations for the human immunoglobulins G, M, and A. Am. J. Hum. Genet. 26, 1–12 (1974).
Yang, C. et al. Genome-wide association study identifies TNFSF13 as a susceptibility gene for IgA in a South Chinese population in smokers. Immunogenetics 64, 747–753 (2012).
doi: 10.1007/s00251-012-0636-y
Jonsson, S. et al. Identification of sequence variants influencing immunoglobulin levels. Nat. Genet. 49, 1182–1191 (2017).
doi: 10.1038/ng.3897
Shi, J. & Lee, S. A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis. Biometrics 72, 945–954 (2016).
doi: 10.1111/biom.12481
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
doi: 10.1038/ng.3211
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
doi: 10.1038/ng.3406
Field, Y. et al. Detection of human adaptation during the past 2000 years. Science 354, 760–764 (2016).
doi: 10.1126/science.aag0776
Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).
doi: 10.1093/nar/gkp427
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
doi: 10.1038/ncomms6890
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).
doi: 10.1093/nar/gky1120
Ingram, D. G. & Friedman, N. R. Toward adenotonsillectomy in children: a review for the general pediatrician. JAMA Pediatr. 169, 1155–1161 (2015).
doi: 10.1001/jamapediatrics.2015.2016
Shilatifard, A. et al. ELL2, a new member of an ELL family of RNA polymerase II elongation factors. Proc. Natl Acad. Sci. USA 94, 3639–3643 (1997).
doi: 10.1073/pnas.94.8.3639
Vosa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
doi: 10.1038/s41588-021-00913-z
Schmiedel, B. J. et al. Impact of genetic polymorphisms on human immune. Cell Gene Expr. Cell 175, 1701–1715.e16 (2018).
Wahl, A. et al. Genome-wide association study on immunoglobulin G glycosylation patterns. Front Immunol. 9, 277 (2018).
doi: 10.3389/fimmu.2018.00277
Durst, K. L. & Hiebert, S. W. Role of RUNX family members in transcriptional repression and gene silencing. Oncogene 23, 4220–4224 (2004).
doi: 10.1038/sj.onc.1207122
Ito, Y., Bae, S. C. & Chuang, L. S. The RUNX family: developmental regulators in cancer. Nat. Rev. Cancer 15, 81–95 (2015).
doi: 10.1038/nrc3877
Huttlin, E. L. et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome. Cell 184, 3022–3040 e28 (2021).
doi: 10.1016/j.cell.2021.04.011
Watanabe, K. et al. Requirement for Runx proteins in IgA class switching acting downstream of TGF-beta 1 and retinoic acid signaling. J. Immunol. 184, 2785–2792 (2010).
doi: 10.4049/jimmunol.0901823
Bushell, K. N. et al. LITAF mediation of increased TNF-alpha secretion from inflamed colonic lamina propria macrophages. PLoS One 6, e25849 (2011).
doi: 10.1371/journal.pone.0025849
Stucchi, A. et al. A new transcription factor that regulates TNF-alpha gene expression, LITAF, is increased in intestinal tissues from patients with CD and UC. Inflamm. Bowel Dis. 12, 581–587 (2006).
doi: 10.1097/01.MIB.0000225338.14356.d5
Tang, X., Metzger, D., Leeman, S. & Amar, S. LPS-induced TNF-alpha factor (LITAF)-deficient mice express reduced LPS-induced cytokine: Evidence for LITAF-dependent LPS signaling pathways. Proc. Natl Acad. Sci. USA 103, 13777–13782 (2006).
doi: 10.1073/pnas.0605988103
Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune. Cells Cell 167, 1398–1414.e24 (2016).
doi: 10.1016/j.cell.2016.10.026
Dinarello, C. A. Overview of the IL-1 family in innate inflammation and acquired immunity. Immunol. Rev. 281, 8–27 (2018).
doi: 10.1111/imr.12621
Chen, Y. & Wang, X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 48, D127–D131 (2020).
doi: 10.1093/nar/gkz757
Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, e05005 (2015).
Schmitz, N., Kurrer, M. & Kopf, M. The IL-1 receptor 1 is critical for Th2 cell type airway immune responses in a mild but not in a more severe asthma model. Eur. J. Immunol. 33, 991–1000 (2003).
doi: 10.1002/eji.200323801
He, J. Q., Saha, S. K., Kang, J. R., Zarnegar, B. & Cheng, G. Specificity of TRAF3 in its negative regulation of the noncanonical NF-kappa B pathway. J. Biol. Chem. 282, 3688–3694 (2007).
doi: 10.1074/jbc.M610271200
Bista, P. et al. TRAF3 controls activation of the canonical and alternative NFkappaB by the lymphotoxin beta receptor. J. Biol. Chem. 285, 12971–12978 (2010).
doi: 10.1074/jbc.M109.076091
Chen, Z. et al. TRAF3 acts as a checkpoint of B cell receptor signaling to control antibody class switch recombination and anergy. J. Immunol. 205, 830–841 (2020).
doi: 10.4049/jimmunol.2000322
Xie, P., Kraus, Z. J., Stunz, L. L., Liu, Y. & Bishop, G. A. TNF receptor-associated factor 3 is required for T cell-mediated immunity and TCR/CD28 signaling. J. Immunol. 186, 143–155 (2011).
doi: 10.4049/jimmunol.1000290
Arkee, T., Hostager, B. S., Houtman, J. C. D. & Bishop, G. A. TRAF3 in T cells restrains negative regulators of LAT to promote TCR/CD28 Signaling. J. Immunol. 207, 322–332 (2021).
Lauc, G. et al. Loci associated with N-glycosylation of human immunoglobulin G show pleiotropy with autoimmune diseases and haematological cancers. PLoS Genet. 9, e1003225 (2013).
doi: 10.1371/journal.pgen.1003225
Lopez de Lapuente, A. et al. Novel insights into the multiple sclerosis risk gene ANKRD55. J. Immunol. 196, 4553–4565 (2016).
doi: 10.4049/jimmunol.1501205
Kasler, H. G., Lee, I. S., Lim, H. W. & Verdin, E. Histone Deacetylase 7 mediates tissue-specific autoimmunity via control of innate effector function in invariant Natural Killer T Cells. Elife 7, e32109 (2018).
Vallabhapurapu, S. & Karin, M. Regulation and function of NF-kappaB transcription factors in the immune system. Annu. Rev. Immunol. 27, 693–733 (2009).
doi: 10.1146/annurev.immunol.021908.132641
Hodson, D. J. et al. Regulation of normal B-cell differentiation and malignant B-cell survival by OCT2. Proc. Natl Acad. Sci. USA 113, E2039–E2046 (2016).
doi: 10.1073/pnas.1600557113
Bult, C. J. et al. Mouse Genome Database (MGD) 2019. Nucleic Acids Res. 47, D801–D806 (2019).
doi: 10.1093/nar/gky1056
Chou, Y. T. et al. CITED2 functions as a molecular switch of cytokine-induced proliferation and quiescence. Cell Death Differ. 19, 2015–2028 (2012).
doi: 10.1038/cdd.2012.91
Batlle, E. & Massague, J. Transforming growth factor-beta signaling in immunity and cancer. Immunity 50, 924–940 (2019).
doi: 10.1016/j.immuni.2019.03.024
Swaminathan, B. et al. Variants in ELL2 influencing immunoglobulin levels associate with multiple myeloma. Nat. Commun. 6, 7213 (2015).
doi: 10.1038/ncomms8213
Graham, D. B. et al. TMEM258 is a component of the oligosaccharyltransferase complex controlling ER stress and intestinal inflammation. Cell Rep. 17, 2955–2965 (2016).
doi: 10.1016/j.celrep.2016.11.042
Sonar, S. & Lal, G. Role of tumor necrosis factor superfamily in neuroinflammation and autoimmunity. Front. Immunol. 6, 364 (2015).
doi: 10.3389/fimmu.2015.00364
Tian, C. et al. Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections. Nat. Commun. 8, 599 (2017).
doi: 10.1038/s41467-017-00257-5
Trynka, G. et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat. Genet. 43, 1193–201 (2011).
doi: 10.1038/ng.998
Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
doi: 10.1038/nature14177
Cheung, C. K., Rajasekaran, A., Barratt, J. & Rizk, D. V. An update on the current state of management and clinical trials for IgA nephropathy. J. Clin. Med. 10, 2493 (2021).
Bild, D. E. et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am. J. Epidemiol. 156, 871–81 (2002).
doi: 10.1093/aje/kwf113
Zhao, X. et al. Whole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants. Nat. Commun. 11, 5182 (2020).
doi: 10.1038/s41467-020-18334-7
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
doi: 10.1038/ng.3656
Loh, P. R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet 48, 1443–1448 (2016).
doi: 10.1038/ng.3679
Genomes Project, C. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
doi: 10.1038/nature15393
Galinsky, K. J. et al. Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia. Am. J. Hum. Genet 98, 456–472 (2016).
doi: 10.1016/j.ajhg.2015.12.022
Stanaway, I. B. et al. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet Epidemiol. 43, 63–81 (2019).
Khan, A. et al. Medical records-based genetic studies of the complement system. J. Am. Soc. Nephrol. 32, 2031–2047 (2021).
Shang, N. et al. Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies. Npj Digital Medicine 4, 70 (2021).
Kiryluk, K. et al. GWAS for serum galactose-deficient IgA1 implicates critical genes of the O-glycosylation pathway. PLoS Genet. 13, e1006609 (2017).
doi: 10.1371/journal.pgen.1006609
Pasaniuc, B. et al. Fast and accurate imputation of summary statistics enhances evidence of functional enrichment. Bioinformatics 30, 2906–14 (2014).
doi: 10.1093/bioinformatics/btu416
Hoffmann, T. J. et al. Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat. Genet. 49, 54–64 (2017).
doi: 10.1038/ng.3715
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–7 (2016).
doi: 10.1038/ng.3538
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–9 (2006).
doi: 10.1038/ng1847
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–1 (2010).
doi: 10.1093/bioinformatics/btq340
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–75 (2007).
doi: 10.1086/519795
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–75 (2012).
doi: 10.1038/ng.2213
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
doi: 10.1093/nar/gkq603
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
doi: 10.1371/journal.pgen.1004383
Li, T. et al. A scored human protein-protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2017).
doi: 10.1038/nmeth.4083
Kaimal, V., Bardes, E. E., Tabar, S. C., Jegga, A. G. & Aronow, B. J. ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems. Nucleic Acids Res. 38, W96–102 (2010).
doi: 10.1093/nar/gkq418
Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).
doi: 10.1093/bioinformatics/btw613
Kiryluk, K. et al. Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens. Nat. Genet. 46, 1187–96 (2014).
doi: 10.1038/ng.3118
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–504 (2003).
doi: 10.1101/gr.1239303
Vilhjalmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).
doi: 10.1016/j.ajhg.2015.09.001
Abraham, G. & Inouye, M. Fast principal component analysis of large-scale genome-wide data. PLoS ONE 9, e93766 (2014).
doi: 10.1371/journal.pone.0093766
Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
doi: 10.1371/journal.pmed.1001779
Altshuler, D. M. et al. A global reference for human genetic variation. Nature 526, 68 (2015).
doi: 10.1038/nature15393
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955 (2012).
doi: 10.1038/ng.2354
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203 (2018).
doi: 10.1038/s41586-018-0579-z
Denny, J. C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26, 1205–1210 (2010).
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, e34408 (2018).