Exploring the genetic architecture of inflammatory bowel disease by whole-genome sequencing identifies association at ADCY7 (original) (raw)

References

  1. 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
  2. Parkes, M. et al. Sequence variants in the autophagy gene IRGM and multiple other replicating loci contribute to Crohn's disease susceptibility. Nat. Genet. 39, 830–832 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  3. Yamazaki, K. et al. A genome-wide association study identifies 2 susceptibility loci for Crohn's disease in a Japanese population. Gastroenterology 144, 781–788 (2013).
    Article CAS PubMed Google Scholar
  4. Anderson, C.A. et al. Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47. Nat. Genet. 43, 246–252 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  5. Kenny, E.E. et al. A genome-wide scan of Ashkenazi Jewish Crohn's disease suggests novel susceptibility loci. PLoS Genet. 8, e1002559 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  6. Julià, A. et al. A genome-wide association study identifies a novel locus at 6q22.1 associated with ulcerative colitis. Hum. Mol. Genet. 23, 6927–6934 (2014).
    Article PubMed CAS Google Scholar
  7. Yang, S.-K. et al. Genome-wide association study of Crohn's disease in Koreans revealed three new susceptibility loci and common attributes of genetic susceptibility across ethnic populations. Gut 63, 80–87 (2014).
    Article CAS PubMed Google Scholar
  8. 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
  9. Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  10. Zuk, O. et al. Searching for missing heritability: designing rare variant association studies. Proc. Natl. Acad. Sci. USA 111, E455–E464 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  11. 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
  12. Beaudoin, M. et al. Deep resequencing of GWAS loci identifies rare variants in CARD9, IL23R and RNF186 that are associated with ulcerative colitis. PLoS Genet. 9, e1003723 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  13. Hunt, K.A. et al. Negligible impact of rare autoimmune-locus coding-region variants on missing heritability. Nature 498, 232–235 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  14. Prescott, N.J. et al. Pooled sequencing of 531 genes in inflammatory bowel disease identifies an associated rare variant in BTNL2 and implicates other immune related genes. PLoS Genet. 11, e1004955 (2015).
    Article PubMed PubMed Central CAS Google Scholar
  15. Do, R. et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102–106 (2015).
    Article CAS PubMed Google Scholar
  16. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  17. Singh, T. et al. Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat. Neurosci. 19, 571–577 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  18. Huang, H. et al. Association mapping of inflammatory bowel disease loci to single variant resolution. Preprint at bioRxiv http://dx.doi.org/10.1101/028688 (2015).
  19. 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
  20. Li, Y., Sidore, C., Kang, H.M., Boehnke, M. & Abecasis, G.R. Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. 21, 940–951 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  21. CONVERGE Consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).
  22. Danjou, F. et al. Genome-wide association analyses based on whole-genome sequencing in Sardinia provide insights into regulation of hemoglobin levels. Nat. Genet. 47, 1264–1271 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  23. UK10K Consortium. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015).
  24. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  25. Browning, B.L. & Browning, S.R. Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194, 459–471 (2013).
    Article PubMed PubMed Central Google Scholar
  26. Handsaker, R.E. et al. Large multiallelic copy number variations in humans. Nat. Genet. 47, 296–303 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  27. Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  28. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  29. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).
  30. Barrett, J.C. et al. Genome-wide association study of ulcerative colitis identifies three new susceptibility loci, including the HNF4A region. Nat. Genet. 41, 1330–1334 (2009).
    Article CAS PubMed Google Scholar
  31. Durbin, R. Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT). Bioinformatics 30, 1266–1272 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  32. de Lange, K.M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. http://dx.doi.org/10.1038/ng.3760 (2017).
  33. Li, Y.R. et al. Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases. Nat. Med. 21, 1018–1027 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  34. Dahle, M.K., Myhre, A.E., Aasen, A.O. & Wang, J.E. Effects of forskolin on Kupffer cell production of interleukin-10 and tumor necrosis factor α differ from those of endogenous adenylyl cyclase activators: possible role for adenylyl cyclase 9. Infect. Immun. 73, 7290–7296 (2005).
    Article CAS PubMed PubMed Central Google Scholar
  35. Duan, B. et al. Distinct roles of adenylyl cyclase VII in regulating the immune responses in mice. J. Immunol. 185, 335–344 (2010).
    Article CAS PubMed Google Scholar
  36. Jiang, L.I., Sternweis, P.C. & Wang, J.E. Zymosan activates protein kinase A via adenylyl cyclase VII to modulate innate immune responses during inflammation. Mol. Immunol. 54, 14–22 (2013).
    Article CAS PubMed Google Scholar
  37. Risøe, P.K. et al. Higher TNFα responses in young males compared to females are associated with attenuation of monocyte adenylyl cyclase expression. Hum. Immunol. 76, 427–430 (2015).
    Article PubMed CAS Google Scholar
  38. Pierre, S., Eschenhagen, T., Geisslinger, G. & Scholich, K. Capturing adenylyl cyclases as potential drug targets. Nat. Rev. Drug Discov. 8, 321–335 (2009).
    Article CAS PubMed Google Scholar
  39. Bhatia, G. et al. Subtle stratification confounds estimates of heritability from rare variants. Preprint at bioRxiv http://dx.doi.org/10.1101/048181 (2016).
  40. 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
  41. Chen, G.-B. et al. Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and Immunochip data. Hum. Mol. Genet. 23, 4710–4720 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  42. Purcell, S.M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  43. Derkach, A. et al. Association analysis using next-generation sequence data from publicly available control groups: the robust variance score statistic. Bioinformatics 30, 2179–2188 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  44. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  45. Zimmerman, N.P., Kumar, S.N., Turner, J.R. & Dwinell, M.B. Cyclic AMP dysregulates intestinal epithelial cell restitution through PKA and RhoA. Inflamm. Bowel Dis. 18, 1081–1091 (2012).
    Article PubMed Google Scholar
  46. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  47. Genovese, G. et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  48. Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  49. Hinrichs, A.S. et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–D598 (2006).
    Article CAS PubMed Google Scholar
  50. Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  51. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
    Article CAS PubMed Google Scholar
  52. Speed, D. & Balding, D.J. MultiBLUP: improved SNP-based prediction for complex traits. Genome Res. 24, 1550–1557 (2014).
    Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

We thank all individuals who contributed samples to the study. This work was co-funded by the Wellcome Trust (098051) and the Medical Research Council, UK (MR/J00314X/1). Case collections were supported by Crohn's and Colitis UK. K.M.d.L., L.M., Y.L., C.A.L., C.A.A. and J.C.B. are supported by the Wellcome Trust (098051; 093885/Z/10/Z). K.M.d.L. is supported by a Woolf Fisher Trust scholarship. C.A.L. is a clinical lecturer funded by the NIHR. H.U. is supported by the Crohn's and Colitis Foundation of America (CCFA) and the Leona M. and Harry B. Helmsley Charitable Trust. We acknowledge support from the UK Department of Health via NIHR comprehensive Biomedical Research Centre awards to Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London and to Addenbrooke's Hospital, Cambridge, in partnership with the University of Cambridge, and the BRC to the Oxford IBD cohort study, University of Oxford. This research was also supported by the NIHR Newcastle Biomedical Research Centre. The UK Household Longitudinal Study is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. The survey was conducted by NatCen, and the genome-wide scan data were analyzed and deposited by the Wellcome Trust Sanger Institute. Information on how to access the data can be found on the Understanding Society website. We are grateful for genotyping data from the British Society for Surgery of the Hand Genetics of Dupuytren's Disease consortium and L. Southam for assistance with genotype intensities. This research has been conducted using the UK Biobank Resource.

Author information

Author notes

  1. Yang Luo and Katrina M de Lange: These authors contributed equally to this work.
  2. Jeffrey C Barrett and Carl A Anderson: These authors jointly supervised this work.

Authors and Affiliations

  1. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
    Yang Luo, Katrina M de Lange, Loukas Moutsianas, Joshua Randall, Shane McCarthy, Eva Goncalves Serra, Sam Nichols, Martin Pollard, Jeffrey C Barrett & Carl A Anderson
  2. Division of Genetics and Rheumatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
    Yang Luo
  3. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    Yang Luo
  4. Wellcome Trust Centre for Human Genetics, University of Oxford, Headington, UK
    Luke Jostins
  5. Christ Church, University of Oxford, St Aldates, UK
    Luke Jostins
  6. Precision Medicine Exeter, University of Exeter, Exeter, UK
    Nicholas A Kennedy & Tariq Ahmad
  7. IBD Pharmacogenetics, Royal Devon and Exeter Foundation Trust, Exeter, UK
    Nicholas A Kennedy & Tariq Ahmad
  8. Institute of Cellular Medicine, Newcastle University, Newcastle-upon-Tyne, UK
    Christopher A Lamb
  9. Department of Gastroenterology, Torbay Hospital, Torbay, UK
    Cathryn Edwards
  10. Department of Medicine, St Mark's Hospital, Harrow, UK
    Ailsa Hart
  11. Nottingham Digestive Diseases Centre, Queens Medical Centre, Nottingham, UK
    Chris Hawkey
  12. Institute of Human Genetics, Newcastle University, Newcastle-upon-Tyne, UK
    John C Mansfield
  13. Department of Medicine, Ninewells Hospital and Medical School, Dundee, UK
    Craig Mowat
  14. Genetic Medicine, Manchester Academic Health Science Centre, Manchester, UK
    William G Newman
  15. Manchester Centre for Genomic Medicine, University of Manchester, Manchester, UK
    William G Newman
  16. Gastrointestinal Unit, Western General Hospital, University of Edinburgh, Edinburgh, UK
    Jack Satsangi & Charlie W Lees
  17. Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
    Alison Simmons & Holm Uhlig
  18. Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
    Alison Simmons
  19. Gastroenterology and General Medicine, Norfolk and Norwich University Hospital, Norwich, UK
    Mark Tremelling
  20. Department of Paediatrics, University of Oxford, Oxford, UK
    Holm Uhlig
  21. Paediatric Gastroenterology and Nutrition, Royal Hospital for Sick Children, Edinburgh, UK
    David C Wilson
  22. Child Life and Health, University of Edinburgh, Edinburgh, UK
    David C Wilson
  23. Inflammatory Bowel Disease Research Group, Addenbrooke's Hospital, Cambridge, UK
    James C Lee & Miles Parkes
  24. Department of Medical and Molecular Genetics, Faculty of Life Science and Medicine, King's College London, Guy's Hospital, London, UK
    Natalie J Prescott & Christopher G Mathew
  25. Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
    Christopher G Mathew

Authors

  1. Yang Luo
  2. Katrina M de Lange
  3. Luke Jostins
  4. Loukas Moutsianas
  5. Joshua Randall
  6. Nicholas A Kennedy
  7. Christopher A Lamb
  8. Shane McCarthy
  9. Tariq Ahmad
  10. Cathryn Edwards
  11. Eva Goncalves Serra
  12. Ailsa Hart
  13. Chris Hawkey
  14. John C Mansfield
  15. Craig Mowat
  16. William G Newman
  17. Sam Nichols
  18. Martin Pollard
  19. Jack Satsangi
  20. Alison Simmons
  21. Mark Tremelling
  22. Holm Uhlig
  23. David C Wilson
  24. James C Lee
  25. Natalie J Prescott
  26. Charlie W Lees
  27. Christopher G Mathew
  28. Miles Parkes
  29. Jeffrey C Barrett
  30. Carl A Anderson

Contributions

Y.L., K.M.d.L., L.J., L.M., J.C.B. and C.A.A. performed statistical analysis. Y.L., K.M.d.L., L.J., L.M., J.C.L., C.A.L., E.G.S., J.R., M. Pollard, S.N. and S.M. processed the data. T.A., C.E., N.A.K., A.H., C.H., J.C.M., J.C.L., C.M., W.G.N., J.S., A.S., M.T., H.U., D.C.W., N.J.P., C.W.L., M. Parkes and C.G.M. contributed samples and/or materials. Y.L., K.M.d.L., L.M., J.C.L., M. Parkes, C.A.L., N.A.K., J.C.B. and C.A.A. wrote the manuscript. All authors read and approved the final version of the manuscript. J.C.M., M. Parkes, C.W.L., T.A., N.J.P., J.C.B. and C.A.A. conceived and designed experiments.

Corresponding authors

Correspondence toJeffrey C Barrett or Carl A Anderson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Production pipeline of SNVs and indels.

Supplementary Figure 2 Genotypic accuracy of sequencing data.

Dosage _r_2 plots for determining sequencing quality when compared against other genotyping data. The x axis is minor allele frequency calculated based on sequencing samples, and the y axis is correlation between dosages for sequencing and genotype data sets. Numbers in parentheses are the number of individuals with both types of data.

Supplementary Figure 3 Biallelic SNV discovery rate compared to the 1000 Genomes Project Phase 3 European panel.

Percentage of biallelic SNVs in all autosomal regions that are shared by the IBD sequencing set and 1000 Genomes Project (1000GP) Phase 3 European panel (503 individuals). Left, percentage of IBD sequencing SNVs that are also found in 1000GP; right, variants identified in the 1000GP set that are also in the IBD sequencing cohort. MAFs on the left were calculated based on SNVs discovered in the IBD sequencing project, and MAFs on the right were calculated based on the 1000GP set. Different lines represent SNVs in different quality control stages of the analysis.

Supplementary Figure 4 Number of copy number variants called per cohort.

Average number of calls per individual per site, across different copy number variant (CNV) lengths. UK10K controls (6×) are shown in yellow, Crohn’s disease cases (4×) are shown in red, and ulcerative colitis cases (2×) are shown in blue.

Supplementary Figure 5 Quantile–quantile plots of genome-wide association studies for variants with MAF ≥ 0.1% in the sequencing data set.

_λ_1,000 values are reported for ulcerative colitis, Crohn’s disease and inflammatory bowel disease analyses. Gray shapes show the 95% confidence interval.

Supplementary Figure 6 Cluster plots for rs78534766.

(a–c) Cluster plots are shown for rs78534766 for the GWAS3 (a), replication (b) and UK Biobank (c) samples that passed quality control. SNP genotypes have been assigned based on cluster formation in scatterplots of normalized allele intensities X and Y. Each circle represents one individual’s genotype. Blue and red clouds correspond to homozygote genotypes for the SNP (CC/AA), green clouds correspond to the heterozygote genotype (CA) and gray clouds correspond to undetermined genotype.

Supplementary Figure 7 Workflow for heritability estimation.

Supplementary Figure 8 Distribution of INFO scores by cohort, across a range of minor allele frequencies.

(a) INFO scores calculated using genotype probabilities generated directly from the SAMtools Genotype Quality (GQ) field. (b) INFO scores calculated using genotype probabilities after imputation improvement using BEAGLE.

Supplementary Figure 9 Manhattan and quantile–quantile plots showing the results of gene-based burden tests using rare, functional coding variation.

Supplementary Figure 11 Effect of read depth on sensitivity and specificity across the allele frequency spectrum (UC (2×) CD (4×), controls (6×)).

(a–c) Top, full distribution of variant counts per individual at singletons (observed once in the data set) (a), doubletons (observed twice) (b) and variants with a MAF of 5% (c). (d) Plot showing the median of each distribution across a range of MAF values.

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Luo, Y., de Lange, K., Jostins, L. et al. Exploring the genetic architecture of inflammatory bowel disease by whole-genome sequencing identifies association at ADCY7.Nat Genet 49, 186–192 (2017). https://doi.org/10.1038/ng.3761

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