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GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression
  1. 1.

    Peery, A. F. et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: update 2018. Gastroenterology 156, 254–272 (2019).

    PubMed  Article  Google Scholar 

  2. 2.

    Williams, J. G. et al. Gastroenterology services in the UK. The burden of disease, and the organisation and delivery of services for gastrointestinal and liver disorders: a review of the evidence. Gut 56, 1 (2007).

    PubMed  Article  Google Scholar 

  3. 3.

    Whitehead, W. E., Palsson, O. & Jones, K. R. Systematic review of the comorbidity of irritable bowel syndrome with other disorders: What are the causes and implications? Gastroenterology 122, 1140–1156 (2002).

    PubMed  Article  Google Scholar 

  4. 4.

    Vakil, N., van Zanten, S. V., Kahrilas, P., Dent, J. & Jones, R. The Montreal definition and classification of gastroesophageal reflux disease: a global evidence-based consensus. Am. J. Gastroenterol. 101, 1900–1920 (2006). quiz 1943.

    PubMed  Article  Google Scholar 

  5. 5.

    Lanas, A. & Chan, F. K. L. Peptic ulcer disease. Lancet 390, 613–624 (2017).

    PubMed  Article  Google Scholar 

  6. 6.

    Charpignon, C. et al. Peptic ulcer disease: one in five is related to neither Helicobacter pylori nor aspirin/NSAID intake. Aliment Pharm. Ther. 38, 946–954 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Böhmer, A. C. & Schumacher, J. Insights into the genetics of gastroesophageal reflux disease (GERD) and GERD-related disorders. Neurogastroenterol. Motil. 29, e13017 (2017).

    Article  Google Scholar 

  8. 8.

    El-Serag, H. B., Sweet, S., Winchester, C. C. & Dent, J. Update on the epidemiology of gastro-oesophageal reflux disease: a systematic review. Gut 63, 871–880 (2014).

    PubMed  Article  Google Scholar 

  9. 9.

    Canavan, C., West, J. & Card, T. The epidemiology of irritable bowel syndrome. Clin. Epidemiol. 6, 71–80 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Camilleri, M. Peripheral mechanisms in irritable bowel syndrome. N. Engl. J. Med. 367, 1626–1635 (2012).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Ananthakrishnan, A. N. Epidemiology and risk factors for IBD. Nat. Rev. Gastroenterol. Hepatol. 12, 205 (2015).

    PubMed  Article  Google Scholar 

  12. 12.

    Ng, S. C. et al. Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet 390, 2769–2778 (2017).

    PubMed  Article  Google Scholar 

  13. 13.

    Malaty, H. M., Graham, D. Y., Isaksson, I., Engstrand, L. & Pedersen, N. L. Are genetic influences on peptic ulcer dependent or independent of genetic influences for helicobacter pylori infection? Arch. Intern. Med. 160, 105–109 (2000).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Mohammed, I., Cherkas, L. F., Riley, S. A., Spector, T. D. & Trudgill, N. J. Genetic influences in gastro-oesophageal reflux disease: a twin study. Gut 52, 1085–1089 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Saito, Y. A. The role of genetics in IBS. Gastroenterol. Clin. N. Am. 40, 45–67 (2011).

    Article  Google Scholar 

  16. 16.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Verstockt, B., Smith, K. G. C. & Lee, J. C. Genome-wide association studies in Crohn’s disease: past, present and future. Clin. Transl. Immunol. 7, e1001 (2018).

    Article  Google Scholar 

  18. 18.

    Tanikawa, C. et al. A genome-wide association study identifies two susceptibility loci for duodenal ulcer in the Japanese population. Nat. Genet. 44, 430 (2012).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Bonfiglio, F. et al. A meta-analysis of reflux genome-wide association studies in 6750 Northern Europeans from the general population. Neurogastroenterol. Motil. 29, e12923 (2017).

  20. 20.

    An, J. et al. Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases. Nat. Commun. 10, 4219 (2019).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  21. 21.

    Ek, W. E. et al. Exploring the genetics of irritable bowel syndrome: a GWA study in the general population and replication in multinational case-control cohorts. Gut 64, 1774–1782 (2015).

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Holliday, E. G. et al. Genome-wide association study identifies two novel genomic regions in irritable bowel syndrome. Am. J. Gastroenterol. 109, 770–772 (2014).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Bonfiglio, F. et al. Female-specific association between variants on chromosome 9 and self-reported diagnosis of irritable bowel syndrome. Gastroenterology 155, 168–179 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Vich Vila, A. et al. Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome. Sci. Transl. Med. 10, eaap8914 (2018).

    PubMed  Article  CAS  Google Scholar 

  25. 25.

    Mayer, E. A. Gut feelings: the emerging biology of gut–brain communication. Nat. Rev. Neurosci. 12, 453 (2011).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Breit, S., Kupferberg, A., Rogler, G. & Hasler, G. Vagus nerve as modulator of the brain–gut axis in psychiatric and inflammatory disorders. Front. Psychiatry 9, 44 (2018).

  27. 27.

    Furness, J. B. The enteric nervous system and neurogastroenterology. Nat. Rev. Gastroenterol. Hepatol. 9, 286 (2012).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Mayer, E. A. The neurobiology of stress and gastrointestinal disease. Gut 47, 861–869 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Hsu, C. C. et al. Depression and the risk of peptic ulcer disease: a Nationwide Population-based study. Medicine 94, e2333 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Yang, X.-J., Jiang, H.-M., Hou, X.-H. & Song, J. Anxiety and depression in patients with gastroesophageal reflux disease and their effect on quality of life. World J. Gastroenterol. 21, 4302–4309 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Fond, G. et al. Anxiety and depression comorbidities in irritable bowel syndrome (IBS): a systematic review and meta-analysis. Eur. Arch. Psychiatry Clin. Neurosci. 264, 651–660 (2014).

    PubMed  Article  Google Scholar 

  32. 32.

    Frolkis, A. D. et al. Depression increases the risk of inflammatory bowel disease, which may be mitigated by the use of antidepressants in the treatment of depression. Gut https://doi.org/10.1136/gutjnl-2018-317182 (2018).

  33. 33.

    Richter, J. E. Effect of Helicobacter pylori eradication on the treatment of gastro-oesophageal reflux disease. Gut 53, 310–311 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Banda, Y. et al. Characterizing race/ethnicity and genetic Ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort. Genetics 200, 1285–1295 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  38. 38.

    Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  39. 39.

    MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Otowa, T. et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol. Psychiatry 21, 1391 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Duncan, L. E. et al. Largest GWAS of PTSD (N = 20,070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol. Psychiatry 23, 666 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

    Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Hammerschlag, A. R. et al. Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Nat. Genet. 49, 1584–1592 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Lane, J. M. et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nat. Genet. 49, 274–281 (2017).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Nikpay, M. et al. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  59. 59.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  61. 61.

    Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    McRae, A. F. et al. Identification of 55,000 replicated DNA methylation QTL. Sci. Rep. 8, 17605 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  66. 66.

    Roadmap Epigenomics, C. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317 (2015).

    Article  CAS  Google Scholar 

  67. 67.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLOS Comput. Biol. 11, e1004219 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. 68.

    Cai, N. et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat. Genet. 52, 437–447 (2020).

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    O’Connor, L. J. & Price, A. L. Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nat. Genet. 50, 1728–1734 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. 70.

    Toyoshima, O. et al. Decrease in PSCA expression caused by Helicobacter pylori infection may promote progression to severe gastritis. Oncotarget 9, 3936–3945 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Edgren, G. et al. Risk of gastric cancer and peptic ulcers in relation to ABO blood type: a cohort study. Am. J. Epidemiol. 172, 1280–1285 (2010).

    PubMed  Article  Google Scholar 

  72. 72.

    Melzer, D. et al. A Genome-Wide Association Study Identifies protein quantitative trait loci (pQTLs). PLOS Genet. 4, e1000072 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  73. 73.

    Ikehara, Y. et al. Polymorphisms of two fucosyltransferase genes (Lewis and Secretor genes) involving type I Lewis antigens are associated with the presence of anti-Helicobacter pylori IgG antibody. Cancer Epidemiol. Biomark. Prev. 10, 971–977 (2001).

    CAS  Google Scholar 

  74. 74.

    Magalhães, A. et al. Muc5ac gastric mucin glycosylation is shaped by FUT2 activity and functionally impacts Helicobacter pylori binding. Sci. Rep. 6, 25575 (2016).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  75. 75.

    Azad, M. B., Wade, K. H. & Timpson, N. J. FUT2 secretor genotype and susceptibility to infections and chronic conditions in the ALSPAC cohort. Wellcome Open Res. 3, 65 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  76. 76.

    McGuckin, M. A. et al. Muc1 mucin limits both Helicobacter pylori colonization of the murine gastric mucosa and associated gastritis. Gastroenterology 133, 1210–1218 (2007).

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Niv, Y. Helicobacter pylori and gastric mucin expression: a systematic review and meta-analysis. World J. Gastroenterol. 21, 9430–9436 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Boltin, D. & Niv, Y. Mucins in gastric cancer—an update. J. Gastrointest. Dig. Syst. 3, 15519 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Asano, N. et al. Cdx2 expression and intestinal metaplasia induced by H. pylori infection of gastric cells is regulated by NOD1-mediated innate immune responses. Cancer Res. 76, 1135 LP–1131145 (2016).

    Article  CAS  Google Scholar 

  80. 80.

    Lenka, A., Arumugham, S. S., Christopher, R. & Pal, P. K. Genetic substrates of psychosis in patients with Parkinson’s disease: a critical review. J. Neurol. Sci. 364, 33–41 (2016).

    PubMed  Article  Google Scholar 

  81. 81.

    Murrough, J. W., Yaqubi, S., Sayed, S. & Charney, D. S. Emerging drugs for the treatment of anxiety. Expert Opin. Emerg. Drugs 20, 393–406 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Levine, D. M. et al. A genome-wide association study identifies new susceptibility loci for esophageal adenocarcinoma and Barrett’s esophagus. Nat. Genet. 45, 1487 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Fröhlich, H. et al. Gastrointestinal dysfunction in autism displayed by altered motility and achalasia in Foxp1+/− mice. Proc. Natl Acad. Sci. 116, 22237 LP–22222245 (2019).

    Article  CAS  Google Scholar 

  84. 84.

    Avetisyan, M., Schill, E. M. & Heuckeroth, R. O. Building a second brain in the bowel. J. Clin. Invest. 125, 899–907 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Fass, R. & Tougas, G. Functional heartburn: the stimulus, the pain, and the brain. Gut 51, 885 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Lagoo, J., Pappas, T. N. & Perez, A. A relic or still relevant: the narrowing role for vagotomy in the treatment of peptic ulcer disease. Am. J. Surg. 207, 120–126 (2014).

    PubMed  Article  Google Scholar 

  87. 87.

    Kim, S. Y. et al. Bidirectional association between gastroesophageal reflux disease and depression: two different nested case-control studies using a national sample cohort. Sci. Rep. 8, 11748 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  88. 88.

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

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  89. 89.

    van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. https://doi.org/10.1038/s41576-019-0137-z (2019).

  90. 90.

    Kamolz, T. & Velanovich, V. Psychological and emotional aspects of gastroesophageal reflux disease. Dis. Esophagus 15, 199–203 (2002).

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    MartÍN-Merino, E., RuigÓMez, A., GarcÍA RodrÍGuez, L. A., Wallander, M. A. & Johansson, S. Depression and treatment with antidepressants are associated with the development of gastro-oesophageal reflux disease. Aliment. Pharmacol. Ther. 31, 1132–1140 (2010).

    PubMed  Google Scholar 

  92. 92.

    Huang, W. S. et al. Use of proton pump inhibitors and risk of major depressive disorder: a nationwide population-based study. Psychother. Psychosom. 87, 62–64 (2018).

    PubMed  Article  Google Scholar 

  93. 93.

    Momen, N. C. et al. Association between mental disorders and subsequent medical conditions. N. Engl. J. Med. 382, 1721–1731 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Nojkov, B. et al. The influence of co-morbid IBS and psychological distress on outcomes and quality of life following PPI therapy in patients with gastro-oesophageal reflux disease. Aliment. Pharmacol. Ther. 27, 473–482 (2008).

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Khandaker, G. M., Dantzer, R. & Jones, P. B. Immunopsychiatry: important facts. Psychol. Med. 47, 2229–2237 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96.

    Munafò, M. R., Tilling, K., Taylor, A. E., Evans, D. M. & Davey Smith, G. Collider scope: when selection bias can substantially influence observed associations. Int. J. Epidemiol. 47, 226–235 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  97. 97.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  98. 98.

    Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in 700,000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  100. 100.

    Mowat, C. et al. Guidelines for the management of inflammatory bowel disease in adults. Gut 60, 571–607 (2011).

    PubMed  Article  Google Scholar 

  101. 101.

    Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Wray, N. R. & Gottesman, I. I. Using summary data from the danish national registers to estimate heritabilities for schizophrenia, bipolar disorder, and major depressive disorder. Front. Genet. 3, 118 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Falconer, D. S. The inheritance of liability to certain diseases, estimated from the incidence among relatives. Ann. Hum. Genet. 29, 51–76 (1965).

    Article  Google Scholar 

  105. 105.

    Reich, T., James, J. W. & Morris, C. A. The use of multiple thresholds in determining the mode of transmission of semi-continuous traits*. Ann. Hum. Genet. 36, 163–184 (1972).

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

    Lloyd-Jones, L. R., Robinson, M. R., Yang, J. & Visscher, P. M. Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio. Genetics 208, 1397–1408 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Jiang, W. & Yu, W. Power estimation and sample size determination for replication studies of genome-wide association studies. BMC Genomics 17, 19–32 (2016).

    Article  CAS  Google Scholar 

  110. 110.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. 111.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  112. 112.

    Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    The, E. P. C. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57 (2012).

    Article  ADS  CAS  Google Scholar 

  115. 115.

    Võsa, U. et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. Preprint at bioRxiv https://doi.org/10.1101/447367 (2018).

  116. 116.

    Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  117. 117.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. 102, 15545–15550 (2005).

    CAS  PubMed  Article  ADS  Google Scholar 

  118. 118.

    Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  119. 119.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple test. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).

    MATH  Google Scholar 

  120. 120.

    Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  121. 121.

    Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  122. 122.

    Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  123. 123.

    Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. 124.

    Davey Smith, G. et al. STROBE-MR: Guidelines for strengthening the reporting of Mendelian randomization studies. PeerJ Prepr. 7, e27857v1 (2019).

    Google Scholar 

  125. 125.

    Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011).

    Article  Google Scholar 

Original Text (This is the original text for your reference.)

  1. 1.

    Peery, A. F. et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: update 2018. Gastroenterology 156, 254–272 (2019).

    PubMed  Article  Google Scholar 

  2. 2.

    Williams, J. G. et al. Gastroenterology services in the UK. The burden of disease, and the organisation and delivery of services for gastrointestinal and liver disorders: a review of the evidence. Gut 56, 1 (2007).

    PubMed  Article  Google Scholar 

  3. 3.

    Whitehead, W. E., Palsson, O. & Jones, K. R. Systematic review of the comorbidity of irritable bowel syndrome with other disorders: What are the causes and implications? Gastroenterology 122, 1140–1156 (2002).

    PubMed  Article  Google Scholar 

  4. 4.

    Vakil, N., van Zanten, S. V., Kahrilas, P., Dent, J. & Jones, R. The Montreal definition and classification of gastroesophageal reflux disease: a global evidence-based consensus. Am. J. Gastroenterol. 101, 1900–1920 (2006). quiz 1943.

    PubMed  Article  Google Scholar 

  5. 5.

    Lanas, A. & Chan, F. K. L. Peptic ulcer disease. Lancet 390, 613–624 (2017).

    PubMed  Article  Google Scholar 

  6. 6.

    Charpignon, C. et al. Peptic ulcer disease: one in five is related to neither Helicobacter pylori nor aspirin/NSAID intake. Aliment Pharm. Ther. 38, 946–954 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Böhmer, A. C. & Schumacher, J. Insights into the genetics of gastroesophageal reflux disease (GERD) and GERD-related disorders. Neurogastroenterol. Motil. 29, e13017 (2017).

    Article  Google Scholar 

  8. 8.

    El-Serag, H. B., Sweet, S., Winchester, C. C. & Dent, J. Update on the epidemiology of gastro-oesophageal reflux disease: a systematic review. Gut 63, 871–880 (2014).

    PubMed  Article  Google Scholar 

  9. 9.

    Canavan, C., West, J. & Card, T. The epidemiology of irritable bowel syndrome. Clin. Epidemiol. 6, 71–80 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Camilleri, M. Peripheral mechanisms in irritable bowel syndrome. N. Engl. J. Med. 367, 1626–1635 (2012).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Ananthakrishnan, A. N. Epidemiology and risk factors for IBD. Nat. Rev. Gastroenterol. Hepatol. 12, 205 (2015).

    PubMed  Article  Google Scholar 

  12. 12.

    Ng, S. C. et al. Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet 390, 2769–2778 (2017).

    PubMed  Article  Google Scholar 

  13. 13.

    Malaty, H. M., Graham, D. Y., Isaksson, I., Engstrand, L. & Pedersen, N. L. Are genetic influences on peptic ulcer dependent or independent of genetic influences for helicobacter pylori infection? Arch. Intern. Med. 160, 105–109 (2000).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Mohammed, I., Cherkas, L. F., Riley, S. A., Spector, T. D. & Trudgill, N. J. Genetic influences in gastro-oesophageal reflux disease: a twin study. Gut 52, 1085–1089 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Saito, Y. A. The role of genetics in IBS. Gastroenterol. Clin. N. Am. 40, 45–67 (2011).

    Article  Google Scholar 

  16. 16.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Verstockt, B., Smith, K. G. C. & Lee, J. C. Genome-wide association studies in Crohn’s disease: past, present and future. Clin. Transl. Immunol. 7, e1001 (2018).

    Article  Google Scholar 

  18. 18.

    Tanikawa, C. et al. A genome-wide association study identifies two susceptibility loci for duodenal ulcer in the Japanese population. Nat. Genet. 44, 430 (2012).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Bonfiglio, F. et al. A meta-analysis of reflux genome-wide association studies in 6750 Northern Europeans from the general population. Neurogastroenterol. Motil. 29, e12923 (2017).

  20. 20.

    An, J. et al. Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases. Nat. Commun. 10, 4219 (2019).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  21. 21.

    Ek, W. E. et al. Exploring the genetics of irritable bowel syndrome: a GWA study in the general population and replication in multinational case-control cohorts. Gut 64, 1774–1782 (2015).

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Holliday, E. G. et al. Genome-wide association study identifies two novel genomic regions in irritable bowel syndrome. Am. J. Gastroenterol. 109, 770–772 (2014).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Bonfiglio, F. et al. Female-specific association between variants on chromosome 9 and self-reported diagnosis of irritable bowel syndrome. Gastroenterology 155, 168–179 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Vich Vila, A. et al. Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome. Sci. Transl. Med. 10, eaap8914 (2018).

    PubMed  Article  CAS  Google Scholar 

  25. 25.

    Mayer, E. A. Gut feelings: the emerging biology of gut–brain communication. Nat. Rev. Neurosci. 12, 453 (2011).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Breit, S., Kupferberg, A., Rogler, G. & Hasler, G. Vagus nerve as modulator of the brain–gut axis in psychiatric and inflammatory disorders. Front. Psychiatry 9, 44 (2018).

  27. 27.

    Furness, J. B. The enteric nervous system and neurogastroenterology. Nat. Rev. Gastroenterol. Hepatol. 9, 286 (2012).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Mayer, E. A. The neurobiology of stress and gastrointestinal disease. Gut 47, 861–869 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Hsu, C. C. et al. Depression and the risk of peptic ulcer disease: a Nationwide Population-based study. Medicine 94, e2333 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Yang, X.-J., Jiang, H.-M., Hou, X.-H. & Song, J. Anxiety and depression in patients with gastroesophageal reflux disease and their effect on quality of life. World J. Gastroenterol. 21, 4302–4309 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Fond, G. et al. Anxiety and depression comorbidities in irritable bowel syndrome (IBS): a systematic review and meta-analysis. Eur. Arch. Psychiatry Clin. Neurosci. 264, 651–660 (2014).

    PubMed  Article  Google Scholar 

  32. 32.

    Frolkis, A. D. et al. Depression increases the risk of inflammatory bowel disease, which may be mitigated by the use of antidepressants in the treatment of depression. Gut https://doi.org/10.1136/gutjnl-2018-317182 (2018).

  33. 33.

    Richter, J. E. Effect of Helicobacter pylori eradication on the treatment of gastro-oesophageal reflux disease. Gut 53, 310–311 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Banda, Y. et al. Characterizing race/ethnicity and genetic Ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort. Genetics 200, 1285–1295 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  38. 38.

    Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  39. 39.

    MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Otowa, T. et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol. Psychiatry 21, 1391 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Duncan, L. E. et al. Largest GWAS of PTSD (N = 20,070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol. Psychiatry 23, 666 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

    Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Hammerschlag, A. R. et al. Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Nat. Genet. 49, 1584–1592 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Lane, J. M. et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nat. Genet. 49, 274–281 (2017).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Nikpay, M. et al. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  59. 59.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  61. 61.

    Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    McRae, A. F. et al. Identification of 55,000 replicated DNA methylation QTL. Sci. Rep. 8, 17605 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  66. 66.

    Roadmap Epigenomics, C. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317 (2015).

    Article  CAS  Google Scholar 

  67. 67.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLOS Comput. Biol. 11, e1004219 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. 68.

    Cai, N. et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat. Genet. 52, 437–447 (2020).

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    O’Connor, L. J. & Price, A. L. Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nat. Genet. 50, 1728–1734 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. 70.

    Toyoshima, O. et al. Decrease in PSCA expression caused by Helicobacter pylori infection may promote progression to severe gastritis. Oncotarget 9, 3936–3945 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Edgren, G. et al. Risk of gastric cancer and peptic ulcers in relation to ABO blood type: a cohort study. Am. J. Epidemiol. 172, 1280–1285 (2010).

    PubMed  Article  Google Scholar 

  72. 72.

    Melzer, D. et al. A Genome-Wide Association Study Identifies protein quantitative trait loci (pQTLs). PLOS Genet. 4, e1000072 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  73. 73.

    Ikehara, Y. et al. Polymorphisms of two fucosyltransferase genes (Lewis and Secretor genes) involving type I Lewis antigens are associated with the presence of anti-Helicobacter pylori IgG antibody. Cancer Epidemiol. Biomark. Prev. 10, 971–977 (2001).

    CAS  Google Scholar 

  74. 74.

    Magalhães, A. et al. Muc5ac gastric mucin glycosylation is shaped by FUT2 activity and functionally impacts Helicobacter pylori binding. Sci. Rep. 6, 25575 (2016).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  75. 75.

    Azad, M. B., Wade, K. H. & Timpson, N. J. FUT2 secretor genotype and susceptibility to infections and chronic conditions in the ALSPAC cohort. Wellcome Open Res. 3, 65 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  76. 76.

    McGuckin, M. A. et al. Muc1 mucin limits both Helicobacter pylori colonization of the murine gastric mucosa and associated gastritis. Gastroenterology 133, 1210–1218 (2007).

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Niv, Y. Helicobacter pylori and gastric mucin expression: a systematic review and meta-analysis. World J. Gastroenterol. 21, 9430–9436 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Boltin, D. & Niv, Y. Mucins in gastric cancer—an update. J. Gastrointest. Dig. Syst. 3, 15519 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Asano, N. et al. Cdx2 expression and intestinal metaplasia induced by H. pylori infection of gastric cells is regulated by NOD1-mediated innate immune responses. Cancer Res. 76, 1135 LP–1131145 (2016).

    Article  CAS  Google Scholar 

  80. 80.

    Lenka, A., Arumugham, S. S., Christopher, R. & Pal, P. K. Genetic substrates of psychosis in patients with Parkinson’s disease: a critical review. J. Neurol. Sci. 364, 33–41 (2016).

    PubMed  Article  Google Scholar 

  81. 81.

    Murrough, J. W., Yaqubi, S., Sayed, S. & Charney, D. S. Emerging drugs for the treatment of anxiety. Expert Opin. Emerg. Drugs 20, 393–406 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Levine, D. M. et al. A genome-wide association study identifies new susceptibility loci for esophageal adenocarcinoma and Barrett’s esophagus. Nat. Genet. 45, 1487 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Fröhlich, H. et al. Gastrointestinal dysfunction in autism displayed by altered motility and achalasia in Foxp1+/− mice. Proc. Natl Acad. Sci. 116, 22237 LP–22222245 (2019).

    Article  CAS  Google Scholar 

  84. 84.

    Avetisyan, M., Schill, E. M. & Heuckeroth, R. O. Building a second brain in the bowel. J. Clin. Invest. 125, 899–907 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Fass, R. & Tougas, G. Functional heartburn: the stimulus, the pain, and the brain. Gut 51, 885 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Lagoo, J., Pappas, T. N. & Perez, A. A relic or still relevant: the narrowing role for vagotomy in the treatment of peptic ulcer disease. Am. J. Surg. 207, 120–126 (2014).

    PubMed  Article  Google Scholar 

  87. 87.

    Kim, S. Y. et al. Bidirectional association between gastroesophageal reflux disease and depression: two different nested case-control studies using a national sample cohort. Sci. Rep. 8, 11748 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  88. 88.

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

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  89. 89.

    van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. https://doi.org/10.1038/s41576-019-0137-z (2019).

  90. 90.

    Kamolz, T. & Velanovich, V. Psychological and emotional aspects of gastroesophageal reflux disease. Dis. Esophagus 15, 199–203 (2002).

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    MartÍN-Merino, E., RuigÓMez, A., GarcÍA RodrÍGuez, L. A., Wallander, M. A. & Johansson, S. Depression and treatment with antidepressants are associated with the development of gastro-oesophageal reflux disease. Aliment. Pharmacol. Ther. 31, 1132–1140 (2010).

    PubMed  Google Scholar 

  92. 92.

    Huang, W. S. et al. Use of proton pump inhibitors and risk of major depressive disorder: a nationwide population-based study. Psychother. Psychosom. 87, 62–64 (2018).

    PubMed  Article  Google Scholar 

  93. 93.

    Momen, N. C. et al. Association between mental disorders and subsequent medical conditions. N. Engl. J. Med. 382, 1721–1731 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Nojkov, B. et al. The influence of co-morbid IBS and psychological distress on outcomes and quality of life following PPI therapy in patients with gastro-oesophageal reflux disease. Aliment. Pharmacol. Ther. 27, 473–482 (2008).

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Khandaker, G. M., Dantzer, R. & Jones, P. B. Immunopsychiatry: important facts. Psychol. Med. 47, 2229–2237 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96.

    Munafò, M. R., Tilling, K., Taylor, A. E., Evans, D. M. & Davey Smith, G. Collider scope: when selection bias can substantially influence observed associations. Int. J. Epidemiol. 47, 226–235 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  97. 97.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  98. 98.

    Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in 700,000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  100. 100.

    Mowat, C. et al. Guidelines for the management of inflammatory bowel disease in adults. Gut 60, 571–607 (2011).

    PubMed  Article  Google Scholar 

  101. 101.

    Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Wray, N. R. & Gottesman, I. I. Using summary data from the danish national registers to estimate heritabilities for schizophrenia, bipolar disorder, and major depressive disorder. Front. Genet. 3, 118 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Falconer, D. S. The inheritance of liability to certain diseases, estimated from the incidence among relatives. Ann. Hum. Genet. 29, 51–76 (1965).

    Article  Google Scholar 

  105. 105.

    Reich, T., James, J. W. & Morris, C. A. The use of multiple thresholds in determining the mode of transmission of semi-continuous traits*. Ann. Hum. Genet. 36, 163–184 (1972).

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

    Lloyd-Jones, L. R., Robinson, M. R., Yang, J. & Visscher, P. M. Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio. Genetics 208, 1397–1408 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Jiang, W. & Yu, W. Power estimation and sample size determination for replication studies of genome-wide association studies. BMC Genomics 17, 19–32 (2016).

    Article  CAS  Google Scholar 

  110. 110.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. 111.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  112. 112.

    Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    The, E. P. C. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57 (2012).

    Article  ADS  CAS  Google Scholar 

  115. 115.

    Võsa, U. et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. Preprint at bioRxiv https://doi.org/10.1101/447367 (2018).

  116. 116.

    Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  117. 117.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. 102, 15545–15550 (2005).

    CAS  PubMed  Article  ADS  Google Scholar 

  118. 118.

    Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  119. 119.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple test. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).

    MATH  Google Scholar 

  120. 120.

    Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  121. 121.

    Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  122. 122.

    Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  123. 123.

    Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. 124.

    Davey Smith, G. et al. STROBE-MR: Guidelines for strengthening the reporting of Mendelian randomization studies. PeerJ Prepr. 7, e27857v1 (2019).

    Google Scholar 

  125. 125.

    Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011).

    Article  Google Scholar 

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