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FLT3 stop mutation increases FLT3 ligand level and risk of autoimmune thyroid disease

Abstract

Autoimmune thyroid disease is the most common autoimmune disease and is highly heritable1. Here, by using a genome-wide association study of 30,234 cases and 725,172 controls from Iceland and the UK Biobank, we find 99 sequence variants at 93 loci, of which 84 variants are previously unreported2,3,4,5,6,7. A low-frequency (1.36%) intronic variant in FLT3 (rs76428106-C) has the largest effect on risk of autoimmune thyroid disease (odds ratio (OR) = 1.46, P = 2.37 × 10−24). rs76428106-C is also associated with systemic lupus erythematosus (OR = 1.90, P = 6.46 × 10−4), rheumatoid factor and/or anti-CCP-positive rheumatoid arthritis (OR = 1.41, P = 4.31 × 10−4) and coeliac disease (OR = 1.62, P = 1.20 × 10−4). FLT3 encodes fms-related tyrosine kinase 3, a receptor that regulates haematopoietic progenitor and dendritic cells. RNA sequencing revealed that rs76428106-C generates a cryptic splice site, which introduces a stop codon in 30% of transcripts that are predicted to encode a truncated protein, which lacks its tyrosine kinase domains. Each copy of rs76428106-C doubles the plasma levels of the FTL3 ligand. Activating somatic mutations in FLT3 are associated with acute myeloid leukaemia8 with a poor prognosis and rs76428106-C also predisposes individuals to acute myeloid leukaemia (OR = 1.90, P = 5.40 × 10−3). Thus, a predicted loss-of-function germline mutation in FLT3 causes a reduction in full-length FLT3, with a compensatory increase in the levels of its ligand and an increased disease risk, similar to that of a gain-of-function mutation.

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Fig. 1: Sequence variants associated with AITD in Iceland and the UK Biobank for which a candidate gene was identified.
Fig. 2: FLT3 intron variant rs76428106-C creates a novel splice site that generates a truncated protein and is associated with higher plasma levels of FLT3 ligand.

Data availability

Sequence variants passing GATK filters that support the findings of this study have been deposited in the European Variation Archive, accession number PRJEB15197 and the GWAS results are deposited at https://www.decode.com/summarydata/. Other data generated or analysed during this study are included in this published article (and its Supplementary Information or source data). Source data are provided with this paper.

Code availability

We used publicly available software (URLs are listed below) in conjunction with the above described algorithms in the sequence-processing pipeline (whole-genome sequencing, association testing and RNA-sequencing mapping and analysis): BWA-MEM version 0.7.10, https://github.com/lh3/bwa; GenomeAnalysisTKLite v.2.3.9, https://github.com/broadgsa/gatk/; Picard tools v.1.117, https://broadinstitute.github.io/picard/; SAMtools v.1.3, http://samtools.github.io/; Bedtools v.2.25.0-76-g5e7c696z, https://github.com/arq5x/bedtools2/; Variant Effect Predictor, https://github.com/Ensembl/ensembl-vep 401; BOLT-LMM, https://data.broadinstitute.org/alkesgroup/BOLT-LMM/downloads/; Ingenuity Pathway Analysis (IPA) software (QIAGEN), https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis; STAR software v.2.5.3, https://github.com/alexdobin/STAR.

References

  1. 1.

    Hwangbo, Y. & Park, Y. J. Genome-wide association studies of autoimmune thyroid diseases, thyroid function, and thyroid cancer. Endocrinol. Metab. 33, 175–184 (2018).

    CAS  Google Scholar 

  2. 2.

    Chu, X. et al. A genome-wide association study identifies two new risk loci for Graves’ disease. Nat. Genet. 43, 897–901 (2011).

    PubMed  CAS  Google Scholar 

  3. 3.

    Cooper, J. D. et al. Seven newly identified loci for autoimmune thyroid disease. Hum. Mol. Genet. 21, 5202–5208 (2012).

    PubMed  PubMed Central  CAS  Google Scholar 

  4. 4.

    Denny, J. C. et al. Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. Am. J. Hum. Genet. 89, 529–542 (2011).

    CAS  Google Scholar 

  5. 5.

    Eriksson, N. et al. Novel associations for hypothyroidism include known autoimmune risk loci. PLoS ONE 7, e34442 (2012).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  6. 6.

    Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    PubMed  PubMed Central  CAS  Google Scholar 

  7. 7.

    Zhao, S. X. et al. Robust evidence for five new Graves’ disease risk loci from a staged genome-wide association analysis. Hum. Mol. Genet. 22, 3347–3362 (2013).

    PubMed  CAS  Google Scholar 

  8. 8.

    Daver, N., Schlenk, R. F., Russell, N. H. & Levis, M. J. Targeting FLT3 mutations in AML: review of current knowledge and evidence. Leukemia 33, 299–312 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  9. 9.

    Antonelli, A., Ferrari, S. M., Corrado, A., Di Domenicantonio, A. & Fallahi, P. Autoimmune thyroid disorders. Autoimmun. Rev. 14, 174–180 (2015).

    CAS  Google Scholar 

  10. 10.

    Fallahi, P. et al. The aggregation between AITD with rheumatologic, or dermatologic, autoimmune diseases. Best Pract. Res. Clin. Endocrinol. Metab. 33, 101372 (2019).

    PubMed  Google Scholar 

  11. 11.

    Cope, A. P. Considerations for optimal trial design for rheumatoid arthritis prevention studies. Clin. Ther. 41, 1299–1311 (2019).

    PubMed  Google Scholar 

  12. 12.

    Burch, H. B. Drug effects on the thyroid. N. Engl. J. Med. 381, 749–761 (2019).

    PubMed  CAS  Google Scholar 

  13. 13.

    Gaitonde, D. Y., Rowley, K. D. & Sweeney, L. B. Hypothyroidism: an update. Am. Fam. Physician 86, 244–251 (2012).

    PubMed  Google Scholar 

  14. 14.

    Wu, Y. et al. Genome-wide association study of medication-use and associated disease in the UK Biobank. Nat. Commun. 10, 1891 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Luo, Y. et al. Exploring the genetic architecture of inflammatory bowel disease by whole-genome sequencing identifies association at ADCY7. Nat. Genet. 49, 186–192 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

  16. 16.

    Musumeci, A., Lutz, K., Winheim, E. & Krug, A. B. What makes a pDC: recent advances in understanding plasmacytoid DC development and heterogeneity. Front. Immunol. 10, 1222 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  17. 17.

    Muskardin, T. L. W. & Niewold, T. B. Type I interferon in rheumatic diseases. Nat. Rev. Rheumatol. 14, 214–228 (2018).

    PubMed  PubMed Central  CAS  Google Scholar 

  18. 18.

    Sandhöfer, N. et al. The new and recurrent FLT3 juxtamembrane deletion mutation shows a dominant negative effect on the wild-type FLT3 receptor. Sci. Rep. 6, 28032 (2016).

    ADS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Jain, D. et al. Genome-wide association of white blood cell counts in Hispanic/Latino Americans: the Hispanic Community Health Study/Study of Latinos. Hum. Mol. Genet. 26, 1193–1204 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

  20. 20.

    Holm, L. E., Blomgren, H. & Löwhagen, T. Cancer risks in patients with chronic lymphocytic thyroiditis. N. Engl. J. Med. 312, 601–604 (1985).

    PubMed  CAS  Google Scholar 

  21. 21.

    Boddu, P. C. & Zeidan, A. M. Myeloid disorders after autoimmune disease. Best Pract. Res. Clin. Haematol. 32, 74–88 (2019).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Tussiwand, R., Onai, N., Mazzucchelli, L. & Manz, M. G. Inhibition of natural type I IFN-producing and dendritic cell development by a small molecule receptor tyrosine kinase inhibitor with Flt3 affinity. J. Immunol. 175, 3674–3680 (2005).

    PubMed  CAS  Google Scholar 

  23. 23.

    Maraskovsky, E. et al. In vivo generation of human dendritic cell subsets by Flt3 ligand. Blood 96, 878–884 (2000).

    PubMed  CAS  Google Scholar 

  24. 24.

    Bigley, V. et al. The human syndrome of dendritic cell, monocyte, B and NK lymphoid deficiency. J. Exp. Med. 208, 227–234 (2011).

    PubMed  PubMed Central  CAS  Google Scholar 

  25. 25.

    Vasu, C., Dogan, R. N., Holterman, M. J. & Prabhakar, B. S. Selective induction of dendritic cells using granulocyte macrophage-colony stimulating factor, but not fms-like tyrosine kinase receptor 3-ligand, activates thyroglobulin-specific CD4+/CD25+ T cells and suppresses experimental autoimmune thyroiditis. J. Immunol. 170, 5511–5522 (2003).

    PubMed  CAS  Google Scholar 

  26. 26.

    Dehlin, M. et al. Intra-articular fms-like tyrosine kinase 3 ligand expression is a driving force in induction and progression of arthritis. PLoS ONE 3, e3633 (2008).

    ADS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Ramos, M. I. et al. FMS-related tyrosine kinase 3 ligand (Flt3L)/CD135 axis in rheumatoid arthritis. Arthritis Res. Ther. 15, R209 (2013).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Chen, Y. L. et al. A type I IFN–Flt3 ligand axis augments plasmacytoid dendritic cell development from common lymphoid progenitors. J. Exp. Med. 210, 2515–2522 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Horwitz, D. A., Fahmy, T. M., Piccirillo, C. A. & La Cava, A. Rebalancing immune homeostasis to treat autoimmune diseases. Trends Immunol. 40, 888–908 (2019).

    PubMed  CAS  Google Scholar 

  30. 30.

    Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

    PubMed  CAS  Google Scholar 

  31. 31.

    Gulcher, J. R., Kristjánsson, K., Gudbjartsson, H. & Stefánsson, K. Protection of privacy by third-party encryption in genetic research in Iceland. Eur. J. Hum. Genet. 8, 739–742 (2000).

    PubMed  CAS  Google Scholar 

  32. 32.

    Welsh, S., Peakman, T., Sheard, S. & Almond, R. Comparison of DNA quantification methodology used in the DNA extraction protocol for the UK Biobank cohort. BMC Genomics 18, 26 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Sigurdardottir, L. G. et al. Data quality at the Icelandic Cancer Registry: comparability, validity, timeliness and completeness. Acta Oncol. 51, 880–889 (2012).

    PubMed  Google Scholar 

  34. 34.

    Benonisdottir, S. et al. Epigenetic and genetic components of height regulation. Nat. Commun. 7, 13490 (2016).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  35. 35.

    Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  36. 36.

    Styrkarsdottir, U. et al. Meta-analysis of Icelandic and UK data sets identifies missense variants in SMO, IL11, COL11A1 and 13 more new loci associated with osteoarthritis. Nat. Genet. 50, 1681–1687 (2018).

    PubMed  CAS  Google Scholar 

  37. 37.

    Gudbjartsson, D. F. et al. Sequence variants from whole genome sequencing a large group of Icelanders. Sci. Data 2, 150011 (2015).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Sveinbjornsson, G. et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nat. Genet. 48, 314–317 (2016).

    PubMed  CAS  Google Scholar 

  39. 39.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  40. 40.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    PubMed  CAS  Google Scholar 

  41. 41.

    Stegle, O., Parts, L., Durbin, R. & Winn, J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLOS Comput. Biol. 6, e1000770 (2010).

    ADS  MathSciNet  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Google Scholar 

  43. 43.

    Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  44. 44.

    Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We thank the individuals who participated in this study and the staff at the Icelandic Patient Recruitment Center, the deCODE genetics core facilities, SRQ-Biobank, EIRA and EIMS study secretary in Sweden, as well as funding from NORDFORSK and the Swedish Research Council; all our colleagues who contributed to the data collection and phenotypic characterization of clinical samples as well as to the genotyping and analysis of the whole-genome association data. This research has been conducted using the UK Biobank Resource (application number 24711) and the study was approved by the National Bioethics Committees in Iceland (IRB approval no. VSN-16-042) and Sweden (IRB approval no. 96-174, 2006/476-31/4, 2007/889-31/2 and 2015.1746-31.4).

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Authors

Contributions

S.S, T.A.O., P.S., D.F.G., P.M., G.T., G.L.N, U.T., I.J. and K.S. designed the study and interpreted the results. S.S., A.J., J.G., G.M.G., Kr.S., L.A., J.A., R.Be., R.Bj., A.J.G., B.G., H.G., H.H., A.B.H., L.K., I.K., H.K., T.J.L., B.R.L., T.O., P.T.O., K.B.O., B.S., V.T., T.R. and I.J. carried out the subject ascertainment and recruitment. S.S., T.A.O., E.V.I., G.H.H., A.S., J.K.S., T.J., S.H.L., E.L.S., L.P., G.M., P.S., D.F.G., P.M., G.T., U.T. and I.J. performed the sequencing, genotyping, expression and proteomics analyses. S.S., T.A.O., K.G., A.O.A., K.B., G.L.N., U.T. and I.J. planned and performed the functional lab work. S.S., T.A.O., E.V.I., J.K.S., T.J., G.H.H., P.S., D.F.G., G.T., U.T. and I.J. performed the statistical and bioinformatics analyses. S.S., T.A.O., P.S., D.F.G., P.M., G.T., G.L.N., U.T., I.J. and K.S. drafted the manuscript. All authors contributed to the final version of the paper.

Corresponding authors

Correspondence to Saedis Saevarsdottir or Kari Stefansson.

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Competing interests

Authors affiliated with deCODE Genetics/Amgen declare competing interests as employees. The remaining authors declare no competing financial interests.

Additional information

Peer review information Nature thanks Marta Alarcón-Riquelme, Mark J. Levis 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 Genome-wide association meta-analysis results for AITD in Iceland and the UK Biobank and network analysis of candidate genes.

a, Manhattan plot illustrating the findings. In total, 30,234 individuals with AITD and 725,172 controls were included in a logistic regression analysis, assuming a multiplicative model adjusted for year of birth, sex and origin (Iceland) or the first 40 principal components (the UK Biobank). Sequence variants (n = 42,907,111) were split into five classes based on their genome annotation, and the significance threshold for each class was adjusted for the number of variants in that class (Methods). In total, 93 loci passed the significant thresholds (Extended Data Table 1), whereas the red line on the plot represents a P value of 10−8. Coding variants are marked in orange and loss of function variants in red. b, Network analysis based on 37 candidate genes (outlined in Fig. 1, here marked in red), where AITD lead or correlated variants (r2 > 0.8) affect protein-coding of genes (Extended Data Table 1) or mRNA expression (top cis-eQTL) in whole blood or adipose tissue (Extended Data Table 2). The network illustrates that the proteins coded by 24 of the 37 candidate genes have experimental evidence for direct interactions (blue lines) or indirect interactions (grey dotted lines, for example, one affecting the level of another), which supports a biological connection. The network analysis was performed with the IPA software (QIAGEN, https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis).

Extended Data Fig. 2 Expression of normally and abnormally spliced FLT3 transcripts.

The findings are shown separately for rs76428106 wild-type individuals (TT, n = 12,769) and heterozygous carriers (TC, n = 358), using violin plots. The white box plots show the distribution (interquartile range and median levels) and whiskers represent ±1.5× the interquartile range. The filled circles correspond to expression values representing outliers that lie beyond the extremes of the whiskers. For display purposes, coverage counts over 15 are truncated. Source data

Extended Data Fig. 3 Effect of sequence variants associated with AITD for its subphenotypes and two other prototypic organ-specific autoimmune diseases.

a, Graves’ disease (GD, n = 2,400). b, Hashimoto’s thyroiditis (HT, n = 397). c, Type 1 diabetes (T1D, n = 5,345). d, Rheumatoid arthritis positive for rheumatoid factor and/or anti-CCP antibodies (RA+, n = 6,618). e, Comparison of the effects of AITD-associated variants between its subphenotypes, Graves’ disease and Hashimoto’s thyroiditis. The x axis and the y axis show the logarithm of the estimated odds ratios for the diseases and AITD, respectively. All effects are shown for the AITD-risk-increasing allele based on meta-analysis of the Icelandic and the UK sample sets (n = 30,234) except for RA+, where the study populations (d) were based on the Icelandic and a Swedish sample set, as the UK samples lacked data on rheumatoid factor and anti-CCP antibodies. Error bars are shown for sequence variants with P < 5.1 × 10−4 and represent 95% confidence intervals. The red line represents results from a simple linear regression through the origin using MAF (1 − MAF) as weights and the grey line indicates the reference line with slope = 1. The weighted correlation coefficients (r) and two-sided P values (t-test) are shown in the graphs. Source data

Extended Data Fig. 4 RNA-sequencing coverage for the FLT3 ligand stratified by FLT3 rs76428106-C genotypes.

a, Median coverage plot of RNA-sequenced reads from whole blood of rs76428106 wild-type individuals (green, n = 12,632) and heterozygous carriers (blue, n = 356). b, Expression distribution of FLT3 ligand isoforms stratified by rs76428106-C genotypes, wild-type individuals (TT, n = 12,816) and heterozygous carriers (CT, n = 358). The left and right edge of the white boxes represent the first and third quantiles, the line inside the box is the median and whiskers represent ±1.5× the interquartile range. The filled circles correspond to expression values representing outliers that lie beyond the extremes of the whiskers. No statistical difference was found in the expression between wild-type individuals and heterozygous carriers of any of the FLT3 ligand isoforms, tested with two-sided t-test of the genotype co-efficient from a linear regression model. Source data

Extended Data Table 1 Sequence variants associated with AITD in GWAS meta-analysis
Extended Data Table 2 AITD lead variants that are top cis-eQTL variants or correlated with top cis-eQTLs
Extended Data Table 3 Study populations and diseases included in the present study
Extended Data Table 4 Correlation of the effect of AITD variants with effect in AITD subsets and other autoimmune diseases
Extended Data Table 5 Association of ADCY7 rs78534766-A with autoimmune diseases
Extended Data Table 6 Plasma proteins associated with FLT3 rs76428106-C in a proteome-wide association study

Supplementary information

Supplementary Information 1

Replication of reported associations with autoimmune thyroid disease (AITD), its subphenotypes or with ATC drug codes for thyroid medications in previously reported genome-wide association studies. The results are aligned with the results from the current genome-wide meta-analysis of AITD in study populations from Iceland and UK (n=30,234), using logistic regression (Methods).

Reporting Summary

Supplementary Information 2

Association of sequence variants identified in the current genome-wide meta-analysis of autoimmune thyroid disease (AITD, n=30,234) with AITD subphenotypes (Graves‘ disease, GD, n=2,400; Hashimoto‘s thyroiditis, HT, n=397) and other autoimmune diseases, based on study populations from Iceland and UK or Sweden (described in Methods and summarized in Extended Data Table 4). Type 1 diabetes (T1D n=5,345), celiac disease (Cel, n=2,067), rheumatoid factor/anti-CCP positive rheumatoid arthritis (RA+, n=6,618), systemic lupus erythematosus (SLE, n=850), multiple sclerosis (MS, n=2,927), ankylosing spondylitis (AS, n=1,045), ulcerative colitis (UC, n=5,039), Crohn‘s disease (CD, n=2,276), psoriasis (Pso, n=8,673), psoriatic arthritis (PsA, n=1,158), rheumatoid factor/anti-CCP negative rheumatoid arthritis (RA-, n=2,406). Odds ratios (OR) and two-sided P-values were calculated using logistic regression analysis, with adjustments as for AITD (Methods).

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Saevarsdottir, S., Olafsdottir, T.A., Ivarsdottir, E.V. et al. FLT3 stop mutation increases FLT3 ligand level and risk of autoimmune thyroid disease. Nature 584, 619–623 (2020). https://doi.org/10.1038/s41586-020-2436-0

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