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.

  • Review Article
  • Published:

Advancing the use of genome-wide association studies for drug repurposing

Abstract

Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.

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: Genome-wide significant variants associated with Crohn’s disease spanning the IL-23 receptor provide drug repurposing opportunities.
Fig. 2: Mendelian randomization approach for causal inference leveraging GWAS data.
Fig. 3: Triangulating causal inference with the PES method to inform drug repurposing.

Similar content being viewed by others

References

  1. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18, 41–58 (2019). This review provides a comprehensive overview of the rationale for drug repurposing.

    Article  CAS  PubMed  Google Scholar 

  3. Pammolli, F., Magazzini, L. & Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nat. Rev. Drug Discov. 10, 428–438 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Nosengo, N. Can you teach old drugs new tricks? Nature 534, 314–316 (2016).

    Article  PubMed  Google Scholar 

  5. Ashburn, T. T. & Thor, K. B. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3, 673–683 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Verbaanderd, C., Rooman, I., Meheus, L. & Huys, I. On-label or off-label? Overcoming regulatory and financial barriers to bring repurposed medicines to cancer patients. Front. Pharmacol. 10, 1664 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. 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 

  9. Park, J.-H. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570–575 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yang, J., Zeng, J., Goddard, M. E., Wray, N. R. & Visscher, P. M. Concepts, estimation and interpretation of SNP-based heritability. Nat. Genet. 49, 1304–1310 (2017).

    Article  CAS  PubMed  Google Scholar 

  13. Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pe’er, I., Yelensky, R., Altshuler, D. & Daly, M. J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    Article  PubMed  Google Scholar 

  15. Fadista, J., Manning, A. K., Florez, J. C. & Groop, L. The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. Eur. J. Hum. Genet. 24, 1202–1205 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Paleari, L. et al. Aromatase inhibitors as adjuvant treatment for ER/PgR positive stage I endometrial carcinoma: a retrospective cohort study. Int. J. Mol. Sci. 21, 2227 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  17. van Weelden, W. J., Massuger, L. F. A. G., ENITEC, Pijnenborg, J. M. A. & Romano, A. Anti-estrogen treatment in endometrial cancer: a systematic review. Front. Oncol. 9, 359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. O’Mara, T. A. et al. Identification of nine new susceptibility loci for endometrial cancer. Nat. Commun. 9, 3166 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. 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 

  20. Duerr, R. H. et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science 314, 1461–1463 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Stritesky, G. L., Yeh, N. & Kaplan, M. H. IL-23 promotes maintenance but not commitment to the TH17 lineage. J. Immunol. 181, 5948–5955 (2008).

    Article  CAS  PubMed  Google Scholar 

  22. Bunte, K. & Beikler, T. TH17 cells and the IL-23/IL-17 axis in the pathogenesis of periodontitis and immune-mediated inflammatory diseases. Int. J. Mol. Sci. 20, 3394 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  23. Sandborn, W. J. et al. A randomized trial of ustekinumab, a human interleukin-12/23 monoclonal antibody, in patients with moderate-to-severe Crohn’s disease. Gastroenterology 135, 1130–1141 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Sandborn, W. J. et al. Ustekinumab induction and maintenance therapy in refractory Crohn’s disease. N. Engl. J. Med. 367, 1519–1528 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. Feagan, B. G. et al. Induction therapy with the selective interleukin-23 inhibitor risankizumab in patients with moderate-to-severe Crohn’s disease: a randomised, double-blind, placebo-controlled phase 2 study. Lancet 389, 1699–1709 (2017).

    Article  CAS  PubMed  Google Scholar 

  26. Feagan, B. G. et al. Ustekinumab as induction and maintenance therapy for Crohn’s disease. N. Engl. J. Med. 375, 1946–1960 (2016).

    Article  CAS  PubMed  Google Scholar 

  27. Savage, L. J., Wittmann, M., McGonagle, D. & Helliwell, P. S. Ustekinumab in the treatment of psoriasis and psoriatic arthritis. Rheumatol. Ther. 2, 1–16 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Banaszczyk, K. Risankizumab in the treatment of psoriasis — literature review. Reumatologia 57, 158–162 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Singh, S. et al. Selective targeting of the IL23 pathway: generation and characterization of a novel high-affinity humanized anti-IL23A antibody. MAbs 7, 778–791 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Feagan, B. G. et al. Risankizumab in patients with moderate to severe Crohn’s disease: an open-label extension study. Lancet Gastroenterol. Hepatol. 3, 671–680 (2018).

    Article  PubMed  Google Scholar 

  31. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015). This paper demonstrates the extent to which currently indicated drugs are supported by findings from GWAS.

    Article  CAS  PubMed  Google Scholar 

  32. Beveridge, L. A. et al. Effect of vitamin D supplementation on blood pressure: a systematic review and meta-analysis incorporating individual patient data. JAMA Intern. Med. 175, 745 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zhang, D. et al. Effect of vitamin D on blood pressure and hypertension in the general population: an update meta-analysis of cohort studies and randomized controlled trials. Prev. Chronic Dis. 17, E03 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Marigorta, U. M. et al. Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn’s disease. Nat. Genet. 49, 1517–1521 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Morris, J. A. et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat. Genet. 51, 258–266 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Chandran, T. & Venkatachalam, I. Efficacy and safety of denosumab compared to bisphosphonates in improving bone strength in postmenopausal osteoporosis: a systematic review. Singap. Med. J. 60, 364–378 (2019).

    Article  Google Scholar 

  38. McGovern, D. & Powrie, F. The IL23 axis plays a key role in the pathogenesis of IBD. Gut 56, 1333–1336 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hue, S. et al. Interleukin-23 drives innate and T cell-mediated intestinal inflammation. J. Exp. Med. 203, 2473–2483 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Morris, J. A. et al. Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/2021.04.07.438882 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Nasser, J. et al. Genome-wide enhancer maps link risk variants to disease genes. Nature 593, 238–243 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491–504 (2018). This review provides a comprehensive description of fine-mapping techniques for GWAS signals.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Conrad, D. F. et al. A worldwide survey of haplotype variation and linkage disequilibrium in the human genome. Nat. Genet. 38, 1251–1260 (2006).

    Article  CAS  PubMed  Google Scholar 

  44. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. GTEx Consortium et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    Article  PubMed Central  CAS  Google Scholar 

  46. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Reay, W. R. et al. Genetic association and causal inference converge on hyperglycaemia as a modifiable factor to improve lung function. eLife 10, e63115 (2021). This study demonstrates how causal inference can be integrated with the PES approach to support specific repurposing opportunities.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Schizophrenia Working Group of the Psychiatric Genomics Consortium et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).

    Article  PubMed Central  CAS  Google Scholar 

  49. Han, S. et al. Integrating brain methylome with GWAS for psychiatric risk gene discovery. Preprint at bioRxiv https://doi.org/10.1101/440206 (2018).

    Article  Google Scholar 

  50. Zhang, J. et al. Large Bi-ethnic study of plasma proteome leads to comprehensive mapping of cis-pQTL and models for proteome-wide association studies. Preprint at bioRxiv https://doi.org/10.1101/2021.03.15.435533 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wingo, A. P. et al. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nat. Genet. 53, 143–146 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Luningham, J. M. et al. Bayesian genome-wide TWAS method to leverage both cis- and trans-eQTL information through summary statistics. Am. J. Hum. Genet. 107, 714–726 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hu, Y. et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat. Genet. 51, 568–576 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Feng, H. et al. Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies. PLoS Genet. 17, e1008973 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. GTEx Consortium et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

    Article  CAS  Google Scholar 

  56. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ratnapriya, R. et al. Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat. Genet. 51, 606–610 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wright, G. E. B. et al. Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease. Hum. Mol. Genet. 29, 2788–2802 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. International League Against Epilepsy Consortium on Complex Epilepsies. Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. Nat. Commun. 9, 5269 (2018).

    Article  CAS  Google Scholar 

  60. Gerring, Z. F., Gamazon, E. R., White, A. & Derks, E. M. An integrative network-based analysis reveals gene networks, biological mechanisms, and novel drug targets in Alzheimer’s disease. Preprint at bioRxiv https://doi.org/10.1101/853580 (2019).

  61. Zhang, W. et al. Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits. Nat. Commun. 10, 3834 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Zhang, C., Wang, Y., Wang, D., Zhang, J. & Zhang, F. NSAID exposure and risk of Alzheimer’s disease: an updated meta-analysis from cohort studies. Front. Aging Neurosci. 10, 83 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Heneka, M. T., Reyes-Irisarri, E., Hüll, M. & Kummer, M. P. Impact and therapeutic potential of PPARs in Alzheimer’s disease. Curr. Neuropharmacol. 9, 643–650 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Musa, A. et al. A review of Connectivity Map and computational approaches in pharmacogenomics. Brief. Bioinform 19, 506–523 (2018).

    CAS  PubMed  Google Scholar 

  65. Wang, Z. et al. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nat. Commun. 7, 12846 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Svoboda, D. L., Saddler, T. & Auerbach, S. S. in Advances in Computational Toxicology Vol. 30 (ed. Hong, H.) 141–157 (Springer, 2019).

  67. Chen, Y.-W. et al. PharmOmics: a species- and tissue-specific drug signature database and online tool for drug repurposing. Preprint at bioRxiv https://doi.org/10.1101/837773 (2019).

  68. Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019). This Perspective comprehensively describes the utility and limitations of the TWAS methodology.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Mancuso, N. et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat. Genet. 51, 675–682 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  72. de Leeuw, C. A., Neale, B. M., Heskes, T. & Posthuma, D. The statistical properties of gene-set analysis. Nat. Rev. Genet. 17, 353–364 (2016). This review summarizes the different approaches and statistical considerations for performing gene-set association.

    Article  PubMed  CAS  Google Scholar 

  73. Liu, J. Z. et al. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87, 139–145 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Li, M.-X., Gui, H.-S., Kwan, J. S. H. & Sham, P. C. GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am. J. Hum. Genet. 88, 283–293 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Lamparter, D., Marbach, D., Rueedi, R., Kutalik, Z. & Bergmann, S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol. 12, e1004714 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Liu, Y. & Xie, J. Cauchy combination test: a powerful test with analytic P-value calculation under arbitrary dependency structures. J. Am. Stat. Assoc. 115, 393–402 (2020).

    Article  CAS  PubMed  Google Scholar 

  78. de Jong, S., Vidler, L. R., Mokrab, Y., Collier, D. A. & Breen, G. Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia. J. Psychopharmacol. 30, 826–830 (2016).

    Article  PubMed  CAS  Google Scholar 

  79. So, H.-C., Chau, C. K.-L., Lau, A., Wong, S.-Y. & Zhao, K. Translating GWAS findings into therapies for depression and anxiety disorders: gene-set analyses reveal enrichment of psychiatric drug classes and implications for drug repositioning. Psychol. Med. 49, 2692–2708 (2019).

    Article  PubMed  Google Scholar 

  80. Gaspar, H. A. & Breen, G. Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach. Sci. Rep. 7, 12460 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  82. Slob, E. A. W. & Burgess, S. A comparison of robust Mendelian randomization methods using summary data. Genet. Epidemiol. 44, 313–329 (2020). This study compares different Mendelian randomization methods and their underlying assumptions.

    Article  PubMed  PubMed Central  Google Scholar 

  83. VanderWeele, T. J., Tchetgen Tchetgen, E. J., Cornelis, M. & Kraft, P. Methodological challenges in mendelian randomization. Epidemiology 25, 427–435 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Burgess, S., Bowden, J., Fall, T., Ingelsson, E. & Thompson, S. G. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology 28, 30–42 (2017).

    Article  PubMed  Google Scholar 

  85. Burgess, S. et al. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur. J. Epidemiol. 30, 543–552 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Burgess, S. et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 4, 186 (2019).

    Article  PubMed  Google Scholar 

  87. McGowan, L. M., Davey Smith, G., Gaunt, T. R. & Richardson, T. G. Integrating Mendelian randomization and multiple-trait colocalization to uncover cell-specific inflammatory drivers of autoimmune and atopic disease. Hum. Mol. Genet. 28, 3293–3300 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Suhre, K., McCarthy, M. I. & Schwenk, J. M. Genetics meets proteomics: perspectives for large population-based studies. Nat. Rev. Genet. https://doi.org/10.1038/s41576-020-0268-2 (2020).

    Article  PubMed  Google Scholar 

  92. Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Zheng, J. et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat. Genet. https://doi.org/10.1038/s41588-020-0682-6 (2020). This study demonstrates how pQTLs could be utilized through Mendelian randomization to inform drug repurposing.

  94. Schmidt, A. F. et al. Genetic drug target validation using Mendelian randomisation. Nat. Commun. 11, 3255 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Folkersen, L. et al. Genomic evaluation of circulating proteins for drug target characterisation and precision medicine. Preprint at bioRxiv https://doi.org/10.1101/2020.04.03.023804 (2020).

    Article  Google Scholar 

  96. Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Mokry, L. E. et al. Vitamin D and risk of multiple sclerosis: a mendelian randomization study. PLoS Med. 12, e1001866 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Lotta, L. A. et al. Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a Mendelian randomisation analysis. PLoS Med. 13, e1002179 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Aikens, R. C. et al. Systolic blood pressure and risk of type 2 diabetes: a Mendelian randomization study. Diabetes 66, 543–550 (2017).

    Article  CAS  PubMed  Google Scholar 

  100. Yin, P. et al. Serum calcium and risk of migraine: a Mendelian randomization study. Hum. Mol. Genet. 26, 820–828 (2016).

    PubMed Central  Google Scholar 

  101. Adams, D. M., Reay, W. R., Geaghan, M. P. & Cairns, M. J. Investigation of glycaemic traits in psychiatric disorders using Mendelian randomisation revealed a causal relationship with anorexia nervosa. Neuropsychopharmacology 46, 1093–1102 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  102. Koellinger, P. D. & de Vlaming, R. Mendelian randomization: the challenge of unobserved environmental confounds. Int. J. Epidemiol. 48, 665–671 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Gkatzionis, A. & Burgess, S. Contextualizing selection bias in Mendelian randomization: how bad is it likely to be? Int. J. Epidemiol. 48, 691–701 (2019).

    Article  PubMed  Google Scholar 

  104. O’Connor, L. J. & Price, A. L. Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nat. Genet. 50, 1728–1734 (2018). This study reveals that genetic correlation can bias Mendelian randomization and provides a novel causal inference method, which explicitly models genetic correlation, to overcome this.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. International Schizophrenia Consortium et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    Article  PubMed Central  CAS  Google Scholar 

  106. Wray, N. R., Goddard, M. E. & Visscher, P. M. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 17, 1520–1528 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Arnedo, J. et al. PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS. Nucleic Acids Res. 41, W142–W149 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Hari Dass, S. A. et al. A biologically-informed polygenic score identifies endophenotypes and clinical conditions associated with the insulin receptor function on specific brain regions. EBioMedicine 42, 188–202 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Reay, W. R., Atkins, J. R., Carr, V. J., Green, M. J. & Cairns, M. J. Pharmacological enrichment of polygenic risk for precision medicine in complex disorders. Sci. Rep. 10, 879 (2020). This study describes the rationale for the PES approach.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Ghoussaini, M. et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311–D1320 (2021).

    Article  PubMed  Google Scholar 

  112. Fang, H. et al. A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nat. Genet. 51, 1082–1091 (2019). This study demonstrates how individual GWAS loci can be integrated with systems biology to repurpose drugs for immunological disorders.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Sakaue, S. & Okada, Y. GREP: Genome for REPositioning drugs. Bioinformatics 35, 3821–3823 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Gaspar, H. A., Hübel, C. & Breen, G. Drug Targetor: a web interface to investigate the human druggome for over 500 phenotypes. Bioinformatics 35, 2515–2517 (2019).

    Article  CAS  PubMed  Google Scholar 

  115. Konuma, T., Ogawa, K. & Okada, Y. Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates. Hum. Mol. Genet. 30, 294–304 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Emon, M. A., Domingo-Fernández, D., Hoyt, C. T. & Hofmann-Apitius, M. PS4DR: a multimodal workflow for identification and prioritization of drugs based on pathway signatures. BMC Bioinforma. 21, 231 (2020).

    Article  Google Scholar 

  117. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).

    Article  CAS  PubMed  Google Scholar 

  118. Freshour, S. et al. Integration of the Drug–Gene Interaction Database (DGIdb) with open crowdsource efforts. Preprint at biorxiv https://doi.org/10.1101/2020.09.18.301721 (2020).

    Article  Google Scholar 

  119. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Cordell, H. J. Detecting gene–gene interactions that underlie human diseases. Nat. Rev. Genet. 10, 392–404 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. McAllister, K. et al. Current challenges and new opportunities for gene–environment interaction studies of complex diseases. Am. J. Epidemiol. 186, 753–761 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682–690 (2008).

    Article  CAS  PubMed  Google Scholar 

  124. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  PubMed Central  CAS  Google Scholar 

  125. Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Estrada, K. et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44, 491–501 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. 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 

  128. Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Nair, R. P. et al. Genome-wide scan reveals association of psoriasis with IL-23 and NF-κB pathways. Nat. Genet. 41, 199–204 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Nielsen, J. B. et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat. Genet. 50, 1234–1239 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  133. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Hartwig, F. P., Davey Smith, G. & Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int. J. Epidemiol. 46, 1985–1998 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Burgess, S., Foley, C. N., Allara, E., Staley, J. R. & Howson, J. M. M. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat. Commun. 11, 376 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Pierce, B. L., Ahsan, H. & VanderWeele, T. J. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int. J. Epidemiol. 40, 740–752 (2011).

    Article  PubMed  Google Scholar 

  137. Bowden, J. et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR–Egger regression: the role of the I2 statistic. Int. J. Epidemiol. 45, 1961–1974 (2016).

    PubMed  PubMed Central  Google Scholar 

  138. Burgess, S., Zuber, V., Gkatzionis, A. & Foley, C. N. Modal-based estimation via heterogeneity-penalized weighting: model averaging for consistent and efficient estimation in Mendelian randomization when a plurality of candidate instruments are valid. Int. J. Epidemiol. 47, 1242–1254 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  139. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  142. Mavaddat, N. et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am. J. Hum. Genet. 104, 21–34 (2019).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

M.J.C. is supported by a National Health and Medical Research Council (NHMRC) Senior Research Fellowship (1121474) and a University of Newcastle Faculty of Health and Medicine Gladys M Brawn Senior Fellowship. W.R.R. is supported by an Australian government research training programme stipend.

Author information

Authors and Affiliations

Authors

Contributions

W.R.R. researched the literature. The authors contributed equally to all other aspects of the article.

Corresponding author

Correspondence to Murray J. Cairns.

Ethics declarations

Competing interests

W.R.R. and M.J.C. have filed a patent related to the use of the pharmagenic enrichment score (PES) framework in complex disorders (WIPO Patent Application WO/2020/237314).

Additional information

Peer review information

Nature Reviews Genetics thanks S. Burgess and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

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

Glossary

Polygenic

A term that denotes the contribution of many genes to the genetic component of a trait.

Genome-wide association studies

(GWAS). Studies using a design that tests the association (relationship) between sequence nucleotide alterations (genetic variants) throughout the genome with a trait of interest, such as a disease phenotype.

Pleiotropy

A term to denote the influence of a gene or genetic variant on multiple different biological traits.

Imputation

Using genetic variants to predict (impute) a particular variable.

Heritability

The proportion of variance in a phenotype in a population that is explained by genetic variation.

Gene-set association

A technique that examines whether a set of genes is associated with a trait by combining the association of individual genetic variants within the set.

Single-nucleotide polymorphism

(SNP). A single-nucleotide alteration in the genomic sequence at any given position (locus).

Quantitative trait loci

Genetic variants or intervals that are linked to or associated with a quantitative trait (measurable continuous phenotype); for example, expression quantitative trait loci (eQTLs) are variants associated with mRNA expression for a given gene.

Linkage disequilibrium

Genetic variants that are inherited together at a higher rate than by chance alone are said to exhibit linkage disequilibrium.

Fine-mapping

Investigating which genetic variant or variants within a region of the genome significantly associated with a trait (genome-wide association study (GWAS) locus) causally influence the trait in question, rather than merely being inherited with the causal variant(s) through linkage disequilibrium.

Transcriptome-wide association study

(TWAS). A technique that tests the association between the predicted expression of a gene based on genetic variants from expression quantitative trait loci (eQTLs) analysis in an independent cohort and a trait of interest.

Mendelian randomization

Randomization using single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) (proxies of a trait, termed the exposure) to test the causal effect of that trait on another (termed the outcome).

Biological pathways

Genes whose products exert biologically related functions or interact together.

Instrumental variables

(IVs). Independent variables that are used to evaluate whether an exposure causes an outcome or is simply correlated with it.

Polygenic score

A sum of the effect sizes of genetic variants throughout the genome for a particular trait.

Pharmagenic enrichment score

(PES). A polygenic score that is constructed from variants specifically within a biological pathway that is targeted by an approved drug, rather than genome-wide like a traditional polygenic score.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reay, W.R., Cairns, M.J. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet 22, 658–671 (2021). https://doi.org/10.1038/s41576-021-00387-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41576-021-00387-z

This article is cited by

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