Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genetic mapping across autoimmune diseases reveals shared associations and mechanisms

Abstract

Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents determining whether the same allele is responsible, indicating a shared underlying mechanism. Here, using a collection of 129,058 cases and controls across 6 diseases, we show that ~40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles twofold by combining cases and controls across diseases, allowing us to identify more expression quantitative trait loci driven by the shared alleles. The patterns indicate widespread sharing of pathogenic mechanisms but not a single global autoimmune mechanism. Our approach can be applied to any set of traits and is particularly valuable as sample collections become depleted.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Joint analysis of shared autoimmune disease risk alleles improves fine-mapping twofold.
Fig. 2: A shared effect on chromosome 1 can be fine-mapped to eight variants across celiac disease, IBD and MS.
Fig. 3: The increased resolution of fine-mapping shared associations across diseases allows identification of more disease–eQTL overlaps.
Fig. 4: Jointly analyzing an association shared between MS and CeD improves fine-mapping resolution and identifies a shared eQTL for RGS1.

Similar content being viewed by others

Data availability

This paper analyzes existing, publicly available data. The accession numbers for these datasets are listed in Supplementary Table 2.

Code availability

Code used in this analysis is available on GitHub at https://github.com/cotsapaslab/CrossDiseaseImmunochip (ref. 80) and archived on Zenodo at https://doi.org/10.5281/zenodo.8371032 (ref. 81).

References

  1. Rosenblum, M. D., Remedios, K. A. & Abbas, A. K. Mechanisms of human autoimmunity. J. Clin. Invest. 125, 2228–2233 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  2. International Multiple Sclerosis Genetics Consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).

  3. de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hemminki, K., Li, X., Sundquist, K. & Sundquist, J. Shared familial aggregation of susceptibility to autoimmune diseases. Arthritis Rheum. 60, 2845–2847 (2009).

    Article  PubMed  Google Scholar 

  8. Eaton, W. W., Rose, N. R., Kalaydjian, A., Pedersen, M. G. & Mortensen, P. B. Epidemiology of autoimmune diseases in Denmark. J. Autoimmun. 29, 1–9 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kuo, C.-F. et al. Familial aggregation of systemic lupus erythematosus and coaggregation of autoimmune diseases in affected families. JAMA Intern. Med. 175, 1518–1526 (2015).

    Article  PubMed  Google Scholar 

  10. Cotsapas, C. et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ellinghaus, D. et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat. Genet. 48, 510–518 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sirota, M., Schaub, M. A., Batzoglou, S., Robinson, W. H. & Butte, A. J. Autoimmune disease classification by inverse association with SNP alleles. PLoS Genet. 5, e1000792 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Shirai, Y. et al. Multi-trait and cross-population genome-wide association studies across autoimmune and allergic diseases identify shared and distinct genetic component. Ann. Rheum. Dis. 81, 1301–1312 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Pouget, J. G. et al. Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk. Hum. Mol. Genet. 28, 3498–3513 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fortune, M. D. et al. Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat. Genet. 47, 839–846 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Parkes, M., Cortes, A., van Heel, D. A. & Brown, M. A. Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat. Rev. Genet. 14, 661–673 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Burren, O. S. et al. Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases. Genome Med. 12, 106 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  18. van de Bunt, M. et al. Evaluating the performance of fine-mapping strategies at common variant GWAS loci. PLoS Genet. 11, e1005535 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhernakova, A., Withoff, S. & Wijmenga, C. Clinical implications of shared genetics and pathogenesis in autoimmune diseases. Nat. Rev. Endocrinol. 9, 646–659 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Matzaraki, V., Kumar, V., Wijmenga, C. & Zhernakova, A. The MHC locus and genetic susceptibility to autoimmune and infectious diseases. Genome Biol. 18, 76 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Cortes, A. & Brown, M. A. Promise and pitfalls of the ImmunoChip. Arthritis Res. Ther. 13, 101 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mohanan, V. et al. C1orf106 is a colitis risk gene that regulates stability of epithelial adherens junctions. Science 359, 1161–1166 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rivas, M. A. et al. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat. Genet. 43, 1066–1073 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Chun, S. et al. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat. Genet. 49, 600–605 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pappalardo, J. L. et al. Transcriptomic and clonal characterization of T cells in the human central nervous system. Sci. Immunol. 5, eabb8786 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kumar, S., Ambrosini, G. & Bucher, P. SNP2TFBS—a database of regulatory SNPs affecting predicted transcription factor binding site affinity. Nucleic Acids Res. 45, D139–D144 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. Kundu, K. et al. Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases. Nat. Genet. 54, 251–262 (2022).

    Article  CAS  PubMed  Google Scholar 

  36. Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat. Commun. 12, 764 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hernández, N. et al. The flashfm approach for fine-mapping multiple quantitative traits. Nat. Commun. 12, 6147 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Umans, B. D., Battle, A. & Gilad, Y. Where are the disease-associated eQTLs? Trends Genet. 37, 109–124 (2021).

    Article  CAS  PubMed  Google Scholar 

  39. The Lenercept Multiple Sclerosis Study Group & The University of British Columbia MS/MRI Analysis Group. TNF neutralization in MS: results of a randomized, placebo-controlled multicenter study. Neurology 53, 457–465 (1999).

  40. Gregory, A. P. et al. TNF receptor 1 genetic risk mirrors outcome of anti-TNF therapy in multiple sclerosis. Nature 488, 508–511 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Buniello, A. et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Paternoster, L. et al. Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nat. Genet. 47, 1449–1456 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ferreira, M. A. et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat. Genet. 49, 1752–1757 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Han, Y. et al. Genome-wide analysis highlights contribution of immune system pathways to the genetic architecture of asthma. Nat. Commun. 11, 1776 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dubois, P. C. A. et al. Multiple common variants for celiac disease influencing immune gene expression. Nat. Genet. 42, 295–302 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lyons, P. A. et al. Genome-wide association study of eosinophilic granulomatosis with polyangiitis reveals genomic loci stratified by ANCA status. Nat. Commun. 10, 5120 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bronson, P. G. et al. Common variants at PVT1, ATG13AMBRA1, AHI1 and CLEC16A are associated with selective IgA deficiency. Nat. Genet. 48, 1425–1429 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Cousminer, D. L. et al. First genome-wide association study of latent autoimmune diabetes in adults reveals novel insights linking immune and metabolic diabetes. Diabetes Care 41, 2396–2403 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cordell, H. J. et al. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways. Nat. Commun. 6, 8019 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Ji, S.-G. et al. Genome-wide association study of primary sclerosing cholangitis identifies new risk loci and quantifies the genetic relationship with inflammatory bowel disease. Nat. Genet. 49, 269–273 (2017).

    Article  CAS  PubMed  Google Scholar 

  51. Aterido, A. et al. Genetic variation at the glycosaminoglycan metabolism pathway contributes to the risk of psoriatic arthritis but not psoriasis. Ann. Rheum. Dis. 78, 355–364 (2019).

    Article  CAS  Google Scholar 

  52. Bentham, J. et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 47, 1457–1464 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. López-Isac, E. et al. GWAS for systemic sclerosis identifies multiple risk loci and highlights fibrotic and vasculopathy pathways. Nat. Commun. 10, 4955 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Jin, Y. et al. Genome-wide association studies of autoimmune vitiligo identify 23 new risk loci and highlight key pathways and regulatory variants. Nat. Genet. 48, 1418–1424 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Feng, B.-J. et al. Multiple loci within the major histocompatibility complex confer risk of psoriasis. PLoS Genet. 5, e1000606 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  58. International HapMap 3 Consortium et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

    Article  Google Scholar 

  59. Trynka, G. et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat. Genet. 43, 1193–1201 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. International Multiple Sclerosis Genetics Consortium (IMSGC) et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 45, 1353–1360 (2013).

    Article  Google Scholar 

  62. Eyre, S. et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Langefeld, C. D. et al. Transancestral mapping and genetic load in systemic lupus erythematosus. Nat. Commun. 8, 16021 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zhao, J. et al. A missense variant in NCF1 is associated with susceptibility to multiple autoimmune diseases. Nat. Genet. 49, 433–437 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Rasmussen, A. et al. The lupus family registry and repository. Rheumatology 50, 47–59 (2011).

    Article  PubMed  Google Scholar 

  67. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289–317 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Abraham, G. & Inouye, M. Fast principal component analysis of large-scale genome-wide data. PLoS ONE 9, e93766 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Delaneau, O., Zagury, J.-F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    Article  CAS  PubMed  Google Scholar 

  71. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  PubMed  Google Scholar 

  73. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

    Article  Google Scholar 

  74. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  75. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  76. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  80. cotsapaslab / CrossDiseaseImmunochip. GitHub https://github.com/cotsapaslab/CrossDiseaseImmunochip (2021).

  81. Lincold, M. R. matthewlincoln/xd-release: release. Zenodo https://doi.org/10.5281/zenodo.8371032 (2023).

Download references

Acknowledgements

We thank the EAGLE eczema consortium for providing GWAS summary statistics. This research utilizes resources provided by the T1DGC, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and JDRF and supported by U01 DK062418. We thank the RACI consortium for access to RA data and the International IBD Genetics Consortium for access to IBD data. Deidentified data were provided from a total of 4,617 samples (2,563 SLE cases and 2,054 population controls) in the Lupus Family Registry and Repository collection at the Oklahoma Medical Research Foundation. The SLE Genentech samples were originally genotyped and analyzed as part of a large SLEGEN Consortium ImmunoChip study. The Alliance for Lupus Research (now Lupus Research Alliance) provided funds for the SLE ImmunoChip study. M.R.L. is supported by a Career Transition Fellowship from the Consortium of MS Centers and the National MS Society (TA-2206-39622) and an Early Career Award in MS from the Waugh Family Foundation. C.G. received a research fellowship from the Deutsche Forschungsgemeinschaft (German Research Foundation) for this project. She further received funding from the Hans und Klementia Langmatz-Stifung and the Hertie Network of Excellence in Clinical Neuroscience, not related to this study. C.W. was supported by an ERC advanced grant (FP/2007-2013/ERC grant 2012-322698), the Spinoza prize grant (NWO SPI 92-266), a grant from Stiftelsen K. G. Jebsen and the Netherlands Organ-on-Chip Initiative—an NWO Gravitation project (024.003.001) funded by the Ministry of Education, Culture and Science of the government of the Netherlands. S.W. was supported by the Netherlands Organ-on-Chip Initiative, an NWO Gravitation project (024.003.001) funded by the Ministry of Education, Culture and Science of the government of the Netherlands. I.H.J. is supported by a Rosalind Franklin Fellowship from the University of Groningen and an NWO VIDI grant (no. 016.171.047).

Author information

Authors and Affiliations

Authors

Consortia

Contributions

M.R.L., N.C., P.-P.A., C.G. and M.M. curated and analyzed data. D.v.H., C.W., S.W., I.H.J., L.P., International Multiple Sclerosis Genetics Consortium, S.S.R., R.R.G., P.M.G., C.D.L., T.J.V. and D.A.H. provided data. M.R.L. and C.C. wrote and edited the paper, with input from all co-authors. S.C. and S.R.S. designed and implemented analytical methods. C.C. conceptualized and oversaw the project.

Corresponding author

Correspondence to Chris Cotsapas.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Carl Anderson, Anne Barton and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–18 and Note.

Reporting Summary

Peer Review File

Supplementary Table

Supplementary Tables 1–4.

Supplementary Data 1

Accounting of study participants.

Supplementary Data 2

Accounting of SNPs.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lincoln, M.R., Connally, N., Axisa, PP. et al. Genetic mapping across autoimmune diseases reveals shared associations and mechanisms. Nat Genet 56, 838–845 (2024). https://doi.org/10.1038/s41588-024-01732-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-024-01732-8

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing