Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk (original) (raw)

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Acknowledgements

This research makes use of resources provided by the Type 1 Diabetes Genetics Consortium (T1DGC), a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the National Institute of Allergy and Infectious Diseases (NIAID), the National Human Genome Research Institute (NHGRI), the National Institute of Child Health and Human Development (NICHHD) and Juvenile Diabetes Research Foundation International (JDRFI) and supported by grant U01DK062418 (NIDDK). This work is supported in part by funding from the US National Institutes of Health (5R01AR062886-02 (P.I.W.d.B.), 1R01AR063759 (S.R.), 5U01GM092691-05 (S.R.), 1UH2AR067677-01 (S.R.) and R01AR065183 (P.I.W.d.B.)), a Doris Duke Clinical Scientist Development Award (S.R.), the Wellcome Trust (J.A.T.) and the UK National Institute for Health Research (NIHR; J.A.T. and J.M.M.H.) and by a Vernieuwingsimpuls VIDI Award (016.126.354) from the Netherlands Organization for Scientific Research (P.I.W.d.B.). T.L.L. was supported by the German Research Foundation (LE 2593/1-1 and LE 2593/2-1).

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Author notes

  1. Xinli Hu and Aaron J Deutsch: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Medicine, Division of Rheumatology, Brigham and Women's Hospital, Immunology and Allergy, Boston, Massachusetts, USA
    Xinli Hu, Aaron J Deutsch & Soumya Raychaudhuri
  2. Department of Medicine, Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
    Xinli Hu, Aaron J Deutsch, Tobias L Lenz, Buhm Han & Soumya Raychaudhuri
  3. Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA
    Xinli Hu, Aaron J Deutsch & Soumya Raychaudhuri
  4. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
    Xinli Hu, Aaron J Deutsch, Buhm Han & Soumya Raychaudhuri
  5. Harvard-MIT Division of Health Sciences and Technology, Boston, Massachusetts, USA
    Xinli Hu & Aaron J Deutsch
  6. Division of Medical Sciences, Harvard Medical School, Boston, Massachusetts, USA
    Xinli Hu
  7. Department of Evolutionary Ecology, Evolutionary Immunogenomics, Max Planck Institute for Evolutionary Biology, Plön, Germany
    Tobias L Lenz
  8. Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
    Suna Onengut-Gumuscu, Wei-Min Chen & Stephen S Rich
  9. Asan Institute for Life Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
    Buhm Han
  10. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
    Joanna M M Howson
  11. Department of Medical Genetics, Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
    John A Todd
  12. Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
    Paul I W de Bakker
  13. Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
    Paul I W de Bakker
  14. Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK
    Soumya Raychaudhuri

Authors

  1. Xinli Hu
  2. Aaron J Deutsch
  3. Tobias L Lenz
  4. Suna Onengut-Gumuscu
  5. Buhm Han
  6. Wei-Min Chen
  7. Joanna M M Howson
  8. John A Todd
  9. Paul I W de Bakker
  10. Stephen S Rich
  11. Soumya Raychaudhuri

Contributions

X.H. and S.R. conceived the study. X.H., A.J.D., T.L.L., S.R., B.H., P.I.W.d.B. and S.S.R. contributed to the study design and analysis strategy. X.H., A.J.D., T.L.L. and S.R. conducted all analyses. X.H. and A.J.D. wrote the initial manuscript. B.H. contributed critical analytical methods. S.O.-G., W.-M.C. and S.S.R. organized and contributed subject samples and provided SNP genotype data. J.M.M.H., J.A.T., P.I.W.d.B., S.S.R. and S.R. contributed critical writing and review of the manuscript. All authors contributed to the final manuscript.

Corresponding author

Correspondence toSoumya Raychaudhuri.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Schematic of the analysis procedure followed in the study.

Supplementary Figure 2 Association quantile-quantile plots indicate that sex and regional code covariates adequately control for population stratification.

Using the UK data set, we tested the association of 807 reading- and/or writing-related SNPs with T1D status, expecting a null distribution. We assessed the performance of five genomic correction schemes by calculating the λ factor (median χ 2 statistic/0.456). “Region” indicates the use of the 13 regional codes provided by T1DGC; “PC” indicates the use of the top ten genotype principal components calculated by EIGENSTRAT. In our final analyses, we included sex and the region codes as covariates.

Supplementary Figure 3 Amino acid position analysis including non-additive terms yielded the same key positions.

We repeated the forward-search analysis after incorporating non-additive terms into the regression model. In this analysis, each variant is coded as 0, 1 or 2 for allelic dosage. An additional heterozygote factor is added, which equals 1 only if the individual is heterozygotic for this allele or haplotype. HLA-DQβ1 position 57 (P = 10–1,240), HLA-DRβ1 position 13 (P = 10–653) and HLA-DRβ1 position 71 (P = 10–55) remained the top independent amino acid associations with either the additive or non-additive model.

Supplementary Figure 4 Haplotype–amino acid sequence permutation analysis ensures that HLA-DQβ1 position 57, HLA-DRβ1 position 13 and HLA-DRβ1 position 71 are the independent risk drivers.

We performed 10,000 rounds of permutated association analysis; during each permutation, the amino acid sequence corresponding to each HLA-DRB1, HLA-DQA1 and HLA-DQB1 classical allele was reassigned before association analysis. (a) Histogram of 10,000 deviance values (improvement upon the null model) while testing for the best combinations of one, two and three amino acid positions. In 3% of the 10,000 trials, the single best position exceeded the deviance achieved by HLA-DQβ1 position 57. No combination of two or three amino acid positions outperformed the fit of HLA-DQβ1 position 57 + HLA-DRβ1 position 13 and HLA-DQβ1 position 57 + HLA-DRβ1 position 13 + HLA-DRβ1 position 71, respectively. The best model achieved by the combination of any three amino acid positions obtained a Δdeviance of 8,244.29 (P = 10–1,774, df = 41); in comparison, the model without permutation including HLA-DQβ1 position 57, HLA-DRβ1 position 13 and HLA-DRβ1 position 71 obtained a Δdeviance of 10,148.53 (P = 10–2,161, df = 31). Red arrows indicate the deviance achieved by the best combination in actual data. (b) Histogram of 10,000 P values while testing for the best combinations of one, two and three amino acid positions. Similarly, 3% of the permuted amino acid positions achieved better P values than HLA-DQβ1 position 57 in actual data. No combination of two or three amino acid positions outperformed the combinations of HLA-DQβ1 position 57 + HLA-DRβ1 position 13 and HLA-DQβ1 position 57 + HLA-DRβ1 position 13 + HLA-DRβ1 position 71, respectively. Red arrows indicate the P value achieved by the best combination in actual data.

Supplementary Figure 5 Concordance between the UK and European data sets.

We repeated the association analyses in the UK case-control set and the European pseudocase-pseudocontrol set separately. (a,b) We confirmed that the two sets yielded highly correlated effect sizes for all binary variants (Pearson r = 0.952, P < 2.2 × 10–16) (a) and for haplotypes formed by HLA-DQβ1 position 57, HLA-DRβ1 position 13 and HLA-DRβ1 position 71 (Pearson r = 0.989, P = 1.02 × 10–14) (b). Error bars represent the 95% confidence interval.

Supplementary Figure 6 HLA-DRβ1 position 13 and HLA-DRβ1 position 71 show discordant effect sizes in RA and T1D.

HLA-DRβ1 position 13 and HLA-DRβ1 position 71, which line the P4 pocket of the HLA-DR peptide-binding groove, are indicated in both rheumatoid arthritis (RA) and T1D. However, the individual amino acid residues at each position confer differential risk or protection for each disease (P < 10–230). Each cross shows an individual residue’s (adjusted) univariate OR (with 95% confidence interval) in RA and T1D. Darker areas indicate the same direction of effect (risk or protection) across the two diseases, whereas the lighter gray areas indicate opposite effects. The slanted dashed line indicates the identity line on which a residue’s effect sizes in both diseases would be equal. At HLA-DRβ1 position 13, serine, tyrosine and arginine confer relative protection for each disease; however, they are located far away from the identity line. Glycine is protective for RA, although it confers strong risk for T1D.

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Hu, X., Deutsch, A., Lenz, T. et al. Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk.Nat Genet 47, 898–905 (2015). https://doi.org/10.1038/ng.3353

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