Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Complement genes contribute sex-biased vulnerability in diverse disorders

Abstract

Many common illnesses, for reasons that have not been identified, differentially affect men and women. For instance, the autoimmune diseases systemic lupus erythematosus (SLE) and Sjögren’s syndrome affect nine times more women than men1, whereas schizophrenia affects men with greater frequency and severity relative to women2. All three illnesses have their strongest common genetic associations in the major histocompatibility complex (MHC) locus, an association that in SLE and Sjögren’s syndrome has long been thought to arise from alleles of the human leukocyte antigen (HLA) genes at that locus3,4,5,6. Here we show that variation of the complement component 4 (C4) genes C4A and C4B, which are also at the MHC locus and have been linked to increased risk for schizophrenia7, generates 7-fold variation in risk for SLE and 16-fold variation in risk for Sjögren’s syndrome among individuals with common C4 genotypes, with C4A protecting more strongly than C4B in both illnesses. The same alleles that increase risk for schizophrenia greatly reduce risk for SLE and Sjögren’s syndrome. In all three illnesses, C4 alleles act more strongly in men than in women: common combinations of C4A and C4B generated 14-fold variation in risk for SLE, 31-fold variation in risk for Sjögren’s syndrome, and 1.7-fold variation in schizophrenia risk among men (versus 6-fold, 15-fold and 1.26-fold variation in risk among women, respectively). At a protein level, both C4 and its effector C3 were present at higher levels in cerebrospinal fluid and plasma8,9 in men than in women among adults aged between 20 and 50 years, corresponding to the ages of differential disease vulnerability. Sex differences in complement protein levels may help to explain the more potent effects of C4 alleles in men, women’s greater risk of SLE and Sjögren’s syndrome and men’s greater vulnerability to schizophrenia. These results implicate the complement system as a source of sexual dimorphism in vulnerability to diverse illnesses.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Association of SLE and Sjögren’s syndrome with C4 alleles.
Fig. 2: C4 and trans-ancestral analysis of the MHC-association signal in SLE.
Fig. 3: Sex differences in the magnitude of C4 genetic effects and complement protein concentrations.

Data availability

Individual genotype data for Sjögren’s syndrome cases and controls and individual plasma concentrations for C4 and C3 are available in dbGaP under accession number phs000672.v1.p1. Individual genotype data for schizophrenia cases and controls are available by application to the Psychiatric Genomics Consortium (PGC). Questions regarding individual genotype data for SLE cases and controls of European and/or African American ancestry can be directed to T.J.V. Data resources are available on the McCarroll lab website at http://mccarrolllab.org/resources/resources-for-c4/. We have deposited the haplotype reference panel we created for C4 imputation in dbGaP under accession number phs001992.v1.p1. Genotype and protein concentration data for CSF samples are available upon request.

Code availability

Software scripts and instructions for imputing C4 alleles into SNP datasets are available on the McCarroll laboratory website at http://mccarrolllab.org/resources/resources-for-c4/.

References

  1. 1.

    Ngo, S. T., Steyn, F. J. & McCombe, P. A. Gender differences in autoimmune disease. Front. Neuroendocrinol. 35, 347–369 (2014).

    CAS  PubMed  Google Scholar 

  2. 2.

    Abel, K. M., Drake, R. & Goldstein, J. M. Sex differences in schizophrenia. Int. Rev. Psychiatry 22, 417–428 (2010).

    PubMed  Google Scholar 

  3. 3.

    Langefeld, C. D. et al. Transancestral mapping and genetic load in systemic lupus erythematosus. Nat. Commun. 8, 16021 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Rioux, J. D. et al. Mapping of multiple susceptibility variants within the MHC region for 7 immune-mediated diseases. Proc. Natl Acad. Sci. USA 106, 18680–18685 (2009).

    ADS  CAS  PubMed  Google Scholar 

  5. 5.

    Hanscombe, K. B. et al. Genetic fine mapping of systemic lupus erythematosus MHC associations in Europeans and African Americans. Hum. Mol. Genet. 27, 3813–3824 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Cruz-Tapias, P., Rojas-Villarraga, A., Maier-Moore, S. & Anaya, J. M. HLA and Sjögren’s syndrome susceptibility. a meta-analysis of worldwide studies. Autoimmun. Rev. 11, 281–287 (2012).

    CAS  PubMed  Google Scholar 

  7. 7.

    Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Gaya da Costa, M. et al. Age and sex-associated changes of complement activity and complement levels in a healthy Caucasian population. Front. Immunol. 9, 2664 (2018).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Ritchie, R. F. et al. Reference distributions for complement proteins C3 and C4: a practical, simple and clinically relevant approach in a large cohort. J. Clin. Lab. Anal. 18, 1–8 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Lawrence, J. S., Martins, C. L. & Drake, G. L. A family survey of lupus erythematosus. 1. Heritability. J. Rheumatol. 14, 913–921 (1987).

    CAS  PubMed  Google Scholar 

  11. 11.

    Lipsky, P. E. Systemic lupus erythematosus: an autoimmune disease of B cell hyperactivity. Nat. Immunol. 2, 764–766 (2001).

    CAS  PubMed  Google Scholar 

  12. 12.

    Ippolito, A. et al. Autoantibodies in systemic lupus erythematosus: comparison of historical and current assessment of seropositivity. Lupus 20, 250–255 (2011).

    CAS  PubMed  Google Scholar 

  13. 13.

    Lee, K. H., Wucherpfennig, K. W. & Wiley, D. C. Structure of a human insulin peptide–HLA–DQ8 complex and susceptibility to type 1 diabetes. Nat. Immunol. 2, 501–507 (2001).

    CAS  PubMed  Google Scholar 

  14. 14.

    Raychaudhuri, S. et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44, 291–296 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Morris, D. L. et al. MHC associations with clinical and autoantibody manifestations in European SLE. Genes Immun. 15, 210–217 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Bánlaki, Z., Doleschall, M., Rajczy, K., Fust, G. & Szilágyi, A. Fine-tuned characterization of RCCX copy number variants and their relationship with extended MHC haplotypes. Genes Immun. 13, 530–535 (2012).

    PubMed  Google Scholar 

  17. 17.

    Isenman, D. E. & Young, J. R. The molecular basis for the difference in immune hemolysis activity of the Chido and Rodgers isotypes of human complement component C4. J. Immunol. 132, 3019–3027 (1984).

    CAS  PubMed  Google Scholar 

  18. 18.

    Law, S. K., Dodds, A. W. & Porter, R. R. A comparison of the properties of two classes, C4A and C4B, of the human complement component C4. EMBO J. 3, 1819–1823 (1984).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Birmingham, D. J. et al. The complex nature of serum C3 and C4 as biomarkers of lupus renal flare. Lupus 19, 1272–1280 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Ross, S. C. & Densen, P. Complement deficiency states and infection: epidemiology, pathogenesis and consequences of neisserial and other infections in an immune deficiency. Medicine 63, 243–273 (1984).

    CAS  PubMed  Google Scholar 

  21. 21.

    Wu, Y. L., Hauptmann, G., Viguier, M. & Yu, C. Y. Molecular basis of complete complement C4 deficiency in two North-African families with systemic lupus erythematosus. Genes Immun. 10, 433–445 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    International Consortium for Systemic Lupus Erythematosus. Genome-wide association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci. Nat. Genet. 40, 204–210 (2008).

    Google Scholar 

  23. 23.

    Yang, Y. et al. Gene copy-number variation and associated polymorphisms of complement component C4 in human systemic lupus erythematosus (SLE): low copy number is a risk factor for and high copy number is a protective factor against SLE susceptibility in European Americans. Am. J. Hum. Genet. 80, 1037–1054 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Jüptner, M. et al. Low copy numbers of complement C4 and homozygous deficiency of C4A may predispose to severe disease and earlier disease onset in patients with systemic lupus erythematosus. Lupus 27, 600–609 (2018).

    PubMed  Google Scholar 

  25. 25.

    Boteva, L. et al. Genetically determined partial complement C4 deficiency states are not independent risk factors for SLE in UK and Spanish populations. Am. J. Hum. Genet. 90, 445–456 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Pato, M. T. et al. The genomic psychiatry cohort: partners in discovery. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 162, 306–312 (2013).

    Google Scholar 

  27. 27.

    Sanders, S. J. et al. Whole genome sequencing in psychiatric disorders: the WGSPD consortium. Nat. Neurosci. 20, 1661–1668 (2017).

    CAS  PubMed  Google Scholar 

  28. 28.

    Kuo, C. F. et al. Familial risk of Sjögren’s syndrome and co-aggregation of autoimmune diseases in affected families: a nationwide population study. Arthritis Rheumatol. 67, 1904–1912 (2015).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Fayyaz, A., Kurien, B. T. & Scofield, R. H. Autoantibodies in Sjögren’s Syndrome. Rheum. Dis. Clin. North Am. 42, 419–434 (2016).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Ramos-Casals, M. et al. Hypocomplementaemia as an immunological marker of morbidity and mortality in patients with primary Sjögren’s syndrome. Rheumatology 44, 89–94 (2005).

    CAS  PubMed  Google Scholar 

  31. 31.

    Chused, T. M., Kassan, S. S., Opelz, G., Moutsopoulos, H. M. & Terasaki, P. I. Sjögren’s syndrome association with HLA-Dw3. N. Engl. J. Med. 296, 895–897 (1977).

    CAS  PubMed  Google Scholar 

  32. 32.

    Taylor, K. E. et al. Genome-wide association analysis reveals genetic heterogeneity of Sjögren’s syndrome according to ancestry. Arthritis Rheumatol. 69, 1294–1305 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Khramtsova, E. A., Davis, L. K. & Stranger, B. E. The role of sex in the genomics of human complex traits. Nat. Rev. Genet. 20, 173–190 (2019).

    CAS  PubMed  Google Scholar 

  34. 34.

    Hughes, T. et al. Analysis of autosomal genes reveals gene–sex interactions and higher total genetic risk in men with systemic lupus erythematosus. Ann. Rheum. Dis. 71, 694–699 (2012).

    CAS  PubMed  Google Scholar 

  35. 35.

    GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed Central  Google Scholar 

  36. 36.

    Brinks, R. et al. Age-specific and sex-specific incidence of systemic lupus erythematosus: an estimate from cross-sectional claims data of 2.3 million people in the German statutory health insurance 2002. Lupus Sci. Med. 3, e000181 (2016).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Kim, H. J. et al. Incidence, mortality, and causes of death in physician-diagnosed primary Sjögren’s syndrome in Korea: A nationwide, population-based study. Semin. Arthritis Rheum. 47, 222–227 (2017).

    PubMed  Google Scholar 

  38. 38.

    Degn, S. E. et al. Clonal evolution of autoreactive germinal centers. Cell 170, 913–926 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Estrada, K. et al. A whole-genome sequence study identifies genetic risk factors for neuromyelitis optica. Nat. Commun. 9, 1929 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Pittock, S. J. et al. Neuromyelitis optica and non organ-specific autoimmunity. Arch. Neurol. 65, 78–83 (2008).

    PubMed  Google Scholar 

  41. 41.

    Erdei, A. et al. Expression and role of CR1 and CR2 on B and T lymphocytes under physiological and autoimmune conditions. Mol. Immunol. 46, 2767–2773 (2009).

    CAS  PubMed  Google Scholar 

  42. 42.

    Unterman, A. et al. Neuropsychiatric syndromes in systemic lupus erythematosus: a meta-analysis. Semin. Arthritis Rheum. 41, 1–11 (2011).

    PubMed  Google Scholar 

  43. 43.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    ADS  PubMed Central  Google Scholar 

  44. 44.

    Handsaker, R. E. et al. Large multiallelic copy number variations in humans. Nat. Genet. 47, 296–303 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Zheng, X. et al. HIBAG—HLA genotype imputation with attribute bagging. Pharmacogenomics J. 14, 192–200 (2014).

    CAS  PubMed  Google Scholar 

  48. 48.

    Zheng, X. Imputation-based HLA typing with SNPs in GWAS studies. Methods Mol. Biol. 1802, 163–176 (2018).

    CAS  PubMed  Google Scholar 

  49. 49.

    Luykx, J. J. et al. A common variant in ERBB4 regulates GABA concentrations in human cerebrospinal fluid. Neuropsychopharmacology 37, 2088–2092 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Albersen, M. et al. Vitamin B-6 vitamers in human plasma and cerebrospinal fluid. Am. J. Clin. Nutr. 100, 587–592 (2014).

    CAS  PubMed  Google Scholar 

  51. 51.

    Malladi, A. S. et al. Primary Sjögren’s syndrome as a systemic disease: a study of participants enrolled in an international Sjögren’s syndrome registry. Arthritis Care Res. (Hoboken) 64, 911–918 (2012).

    Google Scholar 

  52. 52.

    The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    ADS  PubMed Central  Google Scholar 

  53. 53.

    Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Human Genome Research Institute (HG006855), the National Institute of Mental Health (MH112491, MH105641, MH105653), the Stanley Center for Psychiatric Research, and the National Institute for Health Research Biomedical Research Centre (NIHR BRC) at Guy’s and St Thomas’ NHS Foundation and King’s College London. We thank C. Usher and C. Patil for contributions to the figures and manuscript text, and M. Florio for suggestions regarding figure display.

Author information

Affiliations

Authors

Consortia

Contributions

N.K., A.S., T.J.V. and S.A.M. conceived the genetic studies. M.T.P., C.N.P. and M.B. collected and contributed WGS data for the Genomic Psychiatry Cohort. R.E.H. and C.W.W. genotyped C4 structural variation in the Genomic Psychiatry Cohort and optimized variant selection for use as a reference panel in the imputation of C4 variation into lupus and schizophrenia cohorts (Extended Data Fig. 1). T.J.V., R.R.G., L.A.C., C.D.L., R.P.K., J.B.H., K.M.K., D.L.M. and P.T. contributed genotype data and imputation of non-C4 variation for analysis of SLE cohorts. K.E.T. and L.A.C. contributed genotype and phenotype data along with imputation of non-C4 variation for analysis of the Sjögren’s syndrome cohort. Investigators in the Schizophrenia Working Group of the Psychiatric Genomics Consortium collected and phenotyped cohorts and contributed genotype data for analysis of schizophrenia cohorts. N.K. did the imputation and association analysis (Figs. 1, 2, 3a, b, Extended Data Figs. 26). T.J.V., R.R.G. and D.L.M. provided valuable advice on the analysis and interpretation of SLE-association results. R.A.O. and L.M.O.L. collected and provided CSF samples composing the group from Utrecht, Netherlands. C.E.S. collected and provided CSF samples composing the Brigham & Women’s Hospital group. H.d.R and K.T. performed the C4 and C3 immunoassay experiments on CSF samples (Fig. 3c, d, Extended Data Fig. 7a). N.K. did the analysis of plasma C4 and C3 concentrations (Extended Data Fig. 7bf). S.A.M and N.K. wrote the manuscript with contributions from all authors. Management Committee of Wellcome Trust Case–Control Consortium 2: P.D., I.B., J.M.B., E.B., M.A.B., J.P.C., A.C., P.D., A.D., J.J., H.S.M., C.G.M., C.N.A.P., R.P., A.R., S.J.S., R.C.T., A.C.V. and N.W.W; Data and Analysis Group of Wellcome Trust Case–Control Consortium 2: C.C.A.S., G.B., C.B., P.D., C.F., E.G., G.H., R.P., M.P., A.S., Z.S., D.V.; DNA, Genotyping, Data QC, and Informatics of Wellcome Trust Case–Control Consortium 2: C.L., I.B., H.B., S.J.B., P.D., S.D., S.E., M.G., E.G., R.G., N.H., S.E.H., A.J., J.L., O.T.M., S.C.P., R.R., M.R., A.T.-G., M.W., P.W., P.W., S.W.; Publications Committee of Wellcome Trust Case–Control Consortium 2: C.G.M., J.M.B., M.A.B., A.C., M.I..M. and C.C.A.S.

Corresponding authors

Correspondence to Nolan Kamitaki or Timothy J. Vyse or Steven A. McCarroll.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks John Armour and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 A panel of 2,530 reference haplotypes (created from WGS data) containing C4 alleles and SNPs across the MHC genomic region enables imputation of C4 alleles into SNP data.

a, Distributions (across 1,265 individuals) of total C4 gene copy number (C4A + C4B), as measured from read depth of coverage across the C4 locus, in WGS data. b, The relative numbers of reads that overlap sequences specific to C4A or C4B (together with the total C4 gene copy number as in a) are used to infer the underlying copy numbers of the C4A and C4B genes. For example, in an individual with four C4 genes, the presence of equal numbers of reads specific to C4A or C4B suggests the presence of two copies each of C4A and C4B. Precise statistical approaches (including inference of probabilistic dosages) and further approaches for phasing C4 allelic states with nearby SNPs to create reference haplotypes, are described in Methods. c, The SNP haplotypes flanking each C4 allele are shown as rows (SNPs as columns), with white and black representing the major and minor allele of each SNP. Grey lines at the bottom indicate the physical location of each SNP along chromosome 6. The differences among the haplotypes are most pronounced closest to C4 (towards the centre of the plot), as historical recombination events in the flanking megabases will have caused the haplotypes to be less consistently distinct at greater genomic distances from C4. The patterns indicate that many combinations of C4A and C4B gene copy numbers have arisen recurrently on more than one SNP haplotype, a relationship that can be used in association analyses (Fig. 1b).

Extended Data Fig. 2 Aggregation of joint C4A and C4B genotype probabilities per individual across imputed C4 structural alleles for estimation of SLE risk for each combination.

a, An individual’s joint C4A and C4B gene copy number can be calculated by summing the C4A and C4B gene contents for each possible pair of two inherited alleles. Many pairings of possible inherited alleles result in the same joint C4A and C4B gene copy number. b, Each individual’s C4A and C4B gene copy number was imputed from their SNP data, using the reference haplotypes summarized in Extended Data Fig. 1c. For more than 95% of individuals (exemplified by samples 1–6 in the figure), this inference can be made with >90% certainty or confidence (the areas of the circles represent the posterior probability distribution over possible C4A/C4B gene copy numbers). For the remaining individuals (exemplified by samples 7–9 in the figure), greater statistical uncertainty persists about C4 genotype. To account for this uncertainty, in downstream association analysis, all C4 genotype assignments are handled as probabilistic gene dosages—analogous to the genotype dosages that are routinely used in large-scale genetic association studies that use imputation. c, Odds ratios and 95% confidence intervals underlying each of the C4-genotype risk estimates in Fig. 1a presented as a series of panels for each observed copy number of C4B, with increasing copy number of C4A for that C4B dosage (x-axis). Data are from analysis of 6,748 SLE cases and 11,516 controls of European ancestry.

Extended Data Fig. 3 Conditional association analyses for genetic markers across the extended MHC genomic region within the European-ancestry SLE and Sjögren’s syndrome cohorts.

a, Association of SLE with genetic markers (SNPs and imputed HLA alleles) across the extended MHC locus within the European-ancestry SLE cohort (6,748 cases and 11,516 controls). Orange diamond: an initial estimate of C4-related genetic risk, calculated as a weighted sum of the number of C4A and C4B gene copies: (2.3)C4A+C4B, with the weights derived from the relative coefficients estimated from logistic regression of SLE risk versus C4A and C4B gene dosages. This risk score is imputed with an accuracy (r2) of 0.77. Points representing all other genetic variants in the MHC locus are shaded orange according to their level of LD-based correlation to this C4-derived risk score. b, As in a, but for a European-ancestry Sjögren’s syndrome (SjS) cohort (673 cases and 1,153 controls). The orange diamond here also represents (2.3)C4A + C4B, with this weighting derived from the relative coefficients estimated from logistic regression of Sjögren’s syndrome risk versus C4A and C4B gene dosages. c, Association of SLE with genetic markers (SNPs and imputed HLA alleles) across the extended MHC locus within the European-ancestry SLE cohort controlling for C4 composite risk (weighted sum of risk associated with various combinations of C4A and C4B). Variants are shaded in purple by their LD with rs2105898, an independent association identified from trans-ancestral analyses. d, As in c, but in association with a European-ancestry Sjögren’s syndrome cohort. Here a simpler linear model of risk contributed by C4A and C4B was used instead of a weighted sum across all possible combinations.

Extended Data Fig. 4 Using C4 gene variation to understand the appearance of trans-ancestral disparity in MHC association signals, and to fine-map an additional genetic effect.

Association signals (for SLE and Sjögren’s syndrome) for variants in a multi-megabase region of human chromosome 6 containing the MHC region including the HLA and C4 genes. a, Relationship between SLE association (−log10(p), y-axis) and LD to the weighted C4 risk score (x axis) for genetic markers and imputed HLA alleles across the extended MHC locus. In this European-ancestry cohort, it is unclear (from this analysis alone) whether the association with the markers in the predominant ray of points (at an angle of ~45° from the x axis) is driven by variation at C4 or by the long haplotype containing DRB1*03:01 (green), DQA1*05:01 (blue), B*08:01 (red) and many other SNPs (black). In addition, at least one independent association signal (a ray of points at a higher angle in the plot, with strong association signals and only weak linkage disequilibirum-based correlation to C4 and DRB1*0301) with some LD to DRB1*15:01 (maroon) is also present. b, Analysis as in a, but for associations to Sjögren’s syndrome in a cohort of European ancestry. As in SLE, it is initially unclear whether the genetic association signal is driven by variation at C4 or by linked HLA alleles, DRB1*03:01 (green), DQA1*05:01 (blue), and B*08:01 (red). There is also an independent association signal with LD to DRB1*15:01 (maroon). c, Analysis as in a, but of an African American SLE case–control cohort (in which LD in the MHC region is more limited). Many MHC-region SNPs associate with SLE in proportion to their LD with the weighted C4 risk score inferred from the earlier analysis of the European-ancestry cohort; this C4-derived risk score itself associates with SLE at P = 4.3 × 10−19 in a logistic regression on 1,494 SLE cases and 5,908 controls. No similarly strong association is observed for DRB1*03:01, DQA1*05:01 or B*08:01, HLA alleles which are in strong LD with C4 risk on European-ancestry (but not African American) haplotypes. An independent association signal is also present in this cohort, more clearly in LD with the DRB1*15:03 allele (maroon). d, LD in the European-ancestry SLE cohort between the composite C4 risk term (weighted sum of risk associated with various combinations of C4A and C4B from Fig. 2a) and variants in the MHC region as r2 (y-axis). e, As in d, but for the African American SLE cohort. f, LD (to C4 composite risk) for the same variants in European-ancestry individuals (x axis) and African Americans (y axis). Note the abundance of variants that have greater LD with C4 risk among European-ancestry individuals than among African Americans. Also, several groups of variants have equivalent LD (to C4 risk) in European ancestry individuals but exhibit a range of LD to C4 risk among African Americans. g, Associations with SLE (−log10 P values) for the same variants in European ancestry (x axis) and African American (y axis) case–control cohorts. Orange shading represents the extent of LD with C4 risk in European ancestry individuals. Variants with strong European-specific association to SLE are generally in strong LD with C4 risk among European-ancestry individuals. h, Comparison of the inferred effect size from association of genetic markers with SLE (unconditioned log odds ratios) among European-ancestry (x axis) and African American (y axis) research participants. As also seen in g, variants with discordant associations to SLE (across populations) tend also to be in strong LD to C4 risk among European-ancestry individuals. i, As in g, but now controlling for the effect of C4 variation in analysis of the European-ancestry cohort (x axis). Note that controlling for C4 risk in European-ancestry individuals alone greatly aligns (relative to g) the patterns of association between European ancestry and African American cohorts. j, As in i, but now also controlling for the effect of C4 in associations of the African American cohort. Note that due to the lack of strong LD relationships between C4 and variants in the MHC region in African Americans (e), this further adjustment does not change results strongly (relative to i). The independent signal, rs2105898, and HLA alleles, DRB1*15:01 and DRB1*15:03, are also highlighted. LD with rs2105898 in European-ancestry individuals is indicated by purple shading. k, Comparison of the inferred effect sizes from association of genetic markers with SLE (log odds ratios) controlling for C4-derived risk among European-ancestry (x axis) and African American (y axis) research participants. Two SNPs (rs2105898 and rs9271513) that form a short haplotype common to both ancestry groups are among the strongest associations in both cohorts. (Their association to SLE in the European-ancestry cohort was initially much less remarkable than that of other SNPs that are in strong LD with C4.) LD with rs2105898 in European-ancestry individuals is indicated by purple shading. l, As in i, but with variants shaded by whether they exhibit greater LD to rs2105898 in Europeans (blue) or African Americans (red).

Extended Data Fig. 5 Relationship of rs2105898 alleles to a known ZNF143 binding motif in the XL9 region of the MHC class II locus.

a, Location of rs2105898 (yellow line at centre) within the XL9 region, with relevant tracks showing overlapping histone marks and transcription factor binding peaks (from ENCODE52), visualized with the UCSC genome browser53. b, ZNF143 consensus binding motif as a sequence logo, with the letters coloured if the base is present in more than 5% of observed instances. The alleles of rs2105898 are indicated by outlined box surrounding the base.

Extended Data Fig. 6 Relationships between sex bias of disease associations and LD to C4 risk for variants in the MHC region.

a, Relationship between male bias in SLE risk (difference between male and female log–odds ratios) and LD with C4 risk for common (minor allele frequency (MAF) > 0.1) genetic markers across the extended MHC region (6,748 cases and 11,516 controls). For each SNP, the allele for which sex risk bias is plotted is the allele that is positively correlated (via LD) with C4-derived risk score. b, Relationship between male bias in Sjögren’s syndrome risk (log-odds ratios) and LD with C4 risk for common (MAF > 0.1) genetic markers across the extended MHC region (673 cases and 1,153 controls). For each SNP, the allele for which sex risk bias is plotted is the allele that is positively correlated (via LD) with C4-derived risk score. c, Relationship of male bias in schizophrenia risk (log odds ratios) and LD to C4A expression for common (MAF >0.1) genetic markers across the extended MHC region (28,799 cases and 35,986 controls). For each SNP, the allele for which sex risk bias is plotted is the allele that is positively correlated (via LD) with imputed C4A expression, as previously described7.

Extended Data Fig. 7 Correlation of C4 protein measurements in CSF and blood plasma with imputed C4 gene copy number and relationship of plasma complement to sex and Sjögren’s syndrome status.

a, Measurements of C4 protein in CSF obtained by ELISA (n = 507 total) are presented as log10[concentration (ng ml−1)] (y axis) for each observed or imputed copy number of total C4 (x axis, here showing most likely copy number from imputation). Because C4 gene copy number affects C4 protein levels so strongly, we normalized C4 protein measurements to each donor’s C4 gene copy number in subsequent analyses (Fig. 3c). Bars indicate median values for each C4 copy number. b, Measurements of C4 protein in blood plasma obtained by immunoturbidimetric assays are presented as log10[concentration (mg dl−1)] (y axis) for each imputed most-likely copy number of C4 genes (x axis). Because C4 gene copy number affects C4 protein levels so strongly, we normalized C4 protein measurements by C4 gene copy number in subsequent analyses as in c. Due to the number of observations (n = 1,844 total), the plot is downsampled to 500 points; the median bars shown are for all individuals (before downsampling). c, Levels of C4 protein in blood plasma from 182 adult men and 1,662 adult women as a function of age. Concentrations are normalized to the number of C4 gene copies in an individual’s genome (a strong independent source of variance) and shown on a log10 scale as a LOESS curve. Shaded regions represent 95% confidence intervals derived during LOESS. d, Levels of C3 protein in blood plasma as a function of age from the same individuals in panel c. Concentrations are shown on a log10 scale as a LOESS curve. Shaded regions represent 95% confidence intervals derived during LOESS. e, C4 protein in blood plasma was measured in 670 individuals with Sjögren’s syndrome (red) and 1,151 individuals without Sjögren’s syndrome (black) and is shown on a log10 scale (x axis). Vertical stripes represent median levels for cases and controls separately. Comparison of the two sets was done with a non-parametric two-sided Mann–Whitney rank-sum test (P = 4.8 × 10−21). f, As in e, but concentrations are normalized to the number of C4 gene copies in an individual’s genome; this per-copy amount is shown on a log10 scale (x axis). Comparison of the two sets was done with a non-parametric two-sided Mann–Whitney rank-sum test (P = 7.6 × 10−9).

Extended Data Table 1 Imputation accuracy for C4 copy numbers in European ancestry and African American haplotypes
Extended Data Table 2 Frequency of common C4 alleles and their LD-based correlation with HLA alleles in European ancestry and African American cohorts
Extended Data Table 3 Results of association analyses of SLE risk against C4 variation, HLA alleles, and/or rs2105898 in European ancestry and African American cohorts

Supplementary information

Supplementary Information

This file contains Supplementary Note 1, detailing fine mapping of an independent association signal in the MHC class II region, and Supplementary Note 2 discussing sex bias of C3 and C4 gene expression across human tissues.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kamitaki, N., Sekar, A., Handsaker, R.E. et al. Complement genes contribute sex-biased vulnerability in diverse disorders. Nature 582, 577–581 (2020). https://doi.org/10.1038/s41586-020-2277-x

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing