Data availability
This research was conducted using the UK Biobank resource (refs. 53641 and 21024). We thank the participants of the UK Biobank for making this work possible. The UK Biobank genotype and phenotype data are available on application from https://www.ukbiobank.ac.uk/. GTEx data are available on application through the database of Genotypes and Phenotypes as described at https://gtexportal.org/home/datasets.
Code availability
All code used in this study is publicly available as described in Methods and Reporting Summary.
References
Bernabeu, E. et al. Sex differences in genetic architecture in the UK Biobank. Nat. Genet. 53, 1283–1289 (2021).
Bernabeu, A. et al. Reply to: Genotype by sex interactions in ankylosing spondylitis. Nat. Genet. (2022).
Schlosstein, L., Terasaki, P. I., Bluestone, R. & Pearson, C. M. High association of an HL-A antigen, W27, with ankylosing spondylitis. N. Engl. J. Med. 288, 704–706 (1973).
Zhou, X. et al. MICA, a gene contributing strong susceptibility to ankylosing spondylitis. Ann. Rheum. Dis. 73, 1552–1557 (2014).
Hammer, R. E., Maika, S. D., Richardson, J. A., Tang, J. P. & Taurog, J. D. Spontaneous inflammatory disease in transgenic rats expressing HLA-B27 and human á2m: an animal model of HLA-B27-associated human disorders. Cell 63, 1099–1112 (1990).
Cortes, A. et al. Imputation-based analysis of MICA alleles in the susceptibility to ankylosing spondylitis. Ann. Rheum. Dis. 77, 1691–1692 (2018).
van der Horst-Bruinsma, I. E., Zack, D. J., Szumski, A. & Koenig, A. S. Female patients with ankylosing spondylitis: analysis of the impact of gender across treatment studies. Ann. Rheum. Dis. 72, 1221–1224 (2013).
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).
Hanson, A. L. et al. Genetic variants in ERAP1 and ERAP2 associated with immune-mediated diseases influence protein expression and the isoform profile. Arthritis Rheumatol. 70, 255–265 (2018).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Roberts, A. & Pachter, L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat. Methods 10, 71–73 (2013).
Videm, V., Thomas, R., Brown, M. A. & Hoff, M. Self-reported diagnosis of rheumatoid arthritis or ankylosing spondylitis has low accuracy: data from the Nord-Trøndelag Health Study. J. Rheumatol. 44, 1134–1141 (2017).
Caffrey, M. F. & James, D. C. Human lymphocyte antigen association in ankylosing spondylitis. Nature 242, 121 (1973).
Reveille, J. D. et al. HLA class I and II alleles in susceptibility to ankylosing spondylitis. Ann. Rheum. Dis. 78, 66–73 (2019).
Li, Z. et al. Polygenic risk scores have high diagnostic capacity in ankylosing spondylitis. Ann. Rheum. Dis. 80, 1168–1174 (2021).
Acknowledgements
D.M.E. is funded by an Australian National Health and Medical Research Council Senior Research Fellowship (APP1137714). A.F.M is funded by an Australian Research Council Future Fellowship (FT200100837). G.W. is supported by the University of Queensland Graduate School Scholarship (UQGSS). Z.L. is funded by Queensland University of Technology Vice-chancellor Research Fellowship.
Author information
Authors and Affiliations
Contributions
Z.L., A.F.M., G.W. and J.J.E. performed the data analysis. J.W. and T.J.K. performed the FACS of blood samples. J.W. and T.J.K. assisted with sample collection. J.W. assisted with RNA-seq of ankylosing spondylitis samples. Sample collection and RNA-seq of ankylosing spondylitis samples was led by M.A.B. M.A.B. and D.M.E. wrote the manuscript. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Genetics thanks Seunggeun Lee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Boxplots showing the distribution of normalised RNA-seq counts between ankylosing spondylitis cases and healthy controls for the PBMC data set and FACS sorted single cell type datasets.
Normalised counts were obtained from DESeq2. Boxes represent the median, lower and upper quartiles of count data. Whiskers extend 1.5 times the interquartile range in both directions. (PBMC = Peripheral blood mononuclear cells; CD4 = CD4 + T cells, CD8 = CD8 + T cells, Mon = Monocytes, GDT = gamma-delta T cells; NKC = natural killer cells).
Supplementary information
Supplementary Information
Supplementary Note (Materials and Methods), Tables 1–3 and references.
Rights and permissions
About this article
Cite this article
Li, Z., McRae, A.F., Wang, G. et al. Genotype by sex interactions in ankylosing spondylitis. Nat Genet 55, 14–16 (2023). https://doi.org/10.1038/s41588-022-01250-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-022-01250-5
- Springer Nature America, Inc.
This article is cited by
-
Reply to: Genotype by sex interactions in ankylosing spondylitis
Nature Genetics (2023)
-
Frontiers of ankylosing spondylitis research: an analysis from the top 100 most influential articles in the field
Clinical and Experimental Medicine (2023)