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Genotype by sex interactions in ankylosing spondylitis

Nature Genetics volume 55, pages 14–16 (2023)Cite this article

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Matters Arising to this article was published on 09 January 2023

The Original Article was published on 07 September 2021

arising from: Bernabeu et al. Nature Genetics https://doi.org/10.1038/s41588-021-00912-0 (2021)

Ankylosing spondylitis is a highly heritable inflammatory arthritis that occurs more frequently in men than women. In their recent publication examining sex differences in the genetic etiology of common complex traits and diseases, Bernabeu et al.1 observed differences in heritability of ankylosing spondylitis between the sexes, a genome-wide significant genotype by sex interaction in the risk of ankylosing spondylitis at the major histocompatibility (MHC) locus, and presented evidence suggesting that this genotype by sex interaction arises primarily as a result of differential expression of the MICA gene across the sexes in skeletal muscle tissue1,2. Through a series of conditional association analyses in the UK Biobank, reanalysis of the GTEx gene expression resource and RNA sequencing (RNA-seq) experiments on peripheral blood cells from ankylosing spondylitis cases and controls, we show that the genotype by sex interaction in the report by Bernabeu et al. report is unlikely to be a result of variation in MICA but probably reflects a known interaction between the HLA-B gene, sex and risk of ankylosing spondylitis. We demonstrate that the diagnostic accuracy of ankylosing spondylitis in the UK Biobank is low, particularly among women, likely explaining some of the observed differences in heritability across the sexes and the difficulty in precisely locating association signals in the cohort.

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Fig. 1: Locus zoom plots showing −log10 two-sided P values (y axis) across the MICA and HLA-B region of the MHC (x axis).
Fig. 2: Ordinary least squares linear regression of MICA expression.

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.

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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

  1. Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia

    Zhixiu Li, Jonathan J. Ellis & Jessica Whyte

  2. Queensland University of Technology, Faculty of Health, School of Biomedical Sciences, Brisbane, Queensland, Australia

    Zhixiu Li, Jonathan J. Ellis, Jessica Whyte & Tony J. Kenna

  3. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    Allan F. McRae, Geng Wang & David M. Evans

  4. University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia

    Geng Wang & David M. Evans

  5. Centre for Immunology and Infection Control, Queensland University of Technology, Brisbane, Queensland, Australia

    Tony J. Kenna

  6. Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King’s College London, London, UK

    Matthew A. Brown

  7. Genomics England, London, UK

    Matthew A. Brown

  8. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    David M. Evans

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  1. Zhixiu Li
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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.

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Correspondence to David M. Evans.

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The authors declare no competing interests.

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Nature Genetics thanks Seunggeun Lee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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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).

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Supplementary Information

Supplementary Note (Materials and Methods), Tables 1–3 and references.

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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

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