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