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.

Validating therapeutic targets through human genetics

Nature Reviews Drug Discovery volume 12, pages 581–594 (2013)Cite this article

Subjects

Key Points

  • Existing preclinical models have a limited ability to test 'therapeutic hypotheses'; that is, whether perturbing a target in a given manner would benefit patients and have minimal toxicity.

  • 'Experiments of nature', including human genetics, provide an estimate of dose–response curves at the time of target validation.

  • There is an increasing number of studies in the literature demonstrating that genes with a series of disease-associated alleles represent promising drug targets.

  • Here, we provide objective criteria to help prioritize research on the most promising targets and ultimately nominate a gene product as the target for a drug development programme.

  • We highlight important limitations of human genetics in target validation, including a commentary on the genetic architecture of common diseases.

  • We also discuss the role of genome-wide association studies (GWASs) and large-scale sequencing projects in drug discovery, emphasizing the importance of precompetitive collaborations that make clinical and genetic data available in a responsible manner.

Abstract

More than 90% of the compounds that enter clinical trials fail to demonstrate sufficient safety and efficacy to gain regulatory approval. Most of this failure is due to the limited predictive value of preclinical models of disease, and our continued ignorance regarding the consequences of perturbing specific targets over long periods of time in humans. 'Experiments of nature' — naturally occurring mutations in humans that affect the activity of a particular protein target or targets — can be used to estimate the probable efficacy and toxicity of a drug targeting such proteins, as well as to establish causal rather than reactive relationships between targets and outcomes. Here, we describe the concept of dose–response curves derived from experiments of nature, with an emphasis on human genetics as a valuable tool to prioritize molecular targets in drug development. We discuss empirical examples of drug–gene pairs that support the role of human genetics in testing therapeutic hypotheses at the stage of target validation, provide objective criteria to prioritize genetic findings for future drug discovery efforts and highlight the limitations of a target validation approach that is anchored in human genetics.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Figure 1: The therapeutic hypothesis.
Figure 2: Dose–response curves derived from experiments of nature.

References

  1. Scannell, J. W., Blanckley, A., Boldon, H. & Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nature Rev. Drug Discov. 11, 191–200 (2012).

    Article  CAS  Google Scholar 

  2. Kola, I. & Landis, J. Can the pharmaceutical industry reduce attrition rates? Nature Rev. Drug Discov. 3, 711–715 (2004).

    Article  CAS  Google Scholar 

  3. Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Rev. Drug Discov. 9, 203–214 (2010). This article provides a good perspective on the challenges facing the pharmaceutical industry, including the need for better preclinical models to validate drug targets.

    Article  CAS  Google Scholar 

  4. Arrowsmith, J. Trial watch: phase II failures: 2008–2010. Nature Rev. Drug Discov. 10, 328–329 (2011).

    Article  CAS  Google Scholar 

  5. DiMasi, J. A. & Faden, L. B. Competitiveness in follow-on drug R&D: a race or imitation? Nature Rev. Drug Discov. 10, 23–27 (2011).

    Article  CAS  Google Scholar 

  6. Wehling, M. Assessing the translatability of drug projects: what needs to be scored to predict success? Nature Rev. Drug Discov. 8, 541–546 (2009).

    Article  CAS  Google Scholar 

  7. Glyn, J. The discovery and early use of cortisone. J. R. Soc. Med. 91, 513–517 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tobert, J. A. Lovastatin and beyond: the history of the HMG-CoA reductase inhibitors. Nature Rev. Drug Discov. 2, 517–526 (2003).

    Article  CAS  Google Scholar 

  9. Brown, M. S. & Goldstein, J. L. Expression of the familial hypercholesterolemia gene in heterozygotes: mechanism for a dominant disorder in man. Science 185, 61–63 (1974).

    Article  CAS  PubMed  Google Scholar 

  10. Rader, D. J., Cohen, J. & Hobbs, H. H. Monogenic hypercholesterolemia: new insights in pathogenesis and treatment. J. Clin. Invest. 111, 1795–1803 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. The Lovastatin Study Group II. Therapeutic response to lovastatin (mevinolin) in nonfamilial hypercholesterolemia. A multicenter study. JAMA 256, 2829–2834 (1986).

  12. Abifadel, M. et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nature Genet. 34, 154–156 (2003). This is the first study to describe a gain-of-function mutation in PCSK9 that causes hypercholesterolaemia.

    Article  CAS  PubMed  Google Scholar 

  13. Cohen, J. et al. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nature Genet. 37, 161–165 (2005).

    Article  CAS  PubMed  Google Scholar 

  14. Kotowski, I. K. et al. A spectrum of PCSK9 alleles contributes to plasma levels of low-density lipoprotein cholesterol. Am. J. Hum. Genet. 78, 410–422 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006). This is a landmark study that relates loss-of-function mutations in PCSK9 to low LDL cholesterol levels and protection from heart disease.

    Article  CAS  PubMed  Google Scholar 

  16. Park, S. W., Moon, Y. A. & Horton, J. D. Post-transcriptional regulation of low density lipoprotein receptor protein by proprotein convertase subtilisin/kexin type 9a in mouse liver. J. Biol. Chem. 279, 50630–50638 (2004).

    Article  CAS  PubMed  Google Scholar 

  17. Maxwell, K. N. & Breslow, J. L. Adenoviral-mediated expression of Pcsk9 in mice results in a low-density lipoprotein receptor knockout phenotype. Proc. Natl Acad. Sci. USA 101, 7100–7105 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Stein, E. A. et al. Effect of a monoclonal antibody to PCSK9, REGN727/SAR236553, to reduce low-density lipoprotein cholesterol in patients with heterozygous familial hypercholesterolaemia on stable statin dose with or without ezetimibe therapy: a phase 2 randomised controlled trial. Lancet 380, 29–36 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Stein, E. A. et al. Effect of a monoclonal antibody to PCSK9 on LDL cholesterol. N. Engl. J. Med. 366, 1108–1118 (2012). This paper describes one of the first clinical trials demonstrating that a drug that mimics the effect of PCSK9 mutations is effective at lowering LDL cholesterol levels in patients.

    Article  CAS  PubMed  Google Scholar 

  20. Mullard, A. Cholesterol-lowering blockbuster candidates speed into Phase III trials. Nature Rev. Drug Discov. 11, 817–819 (2012).

    Article  CAS  Google Scholar 

  21. Kathiresan, S. et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nature Genet. 40, 189–197 (2008).

    Article  CAS  PubMed  Google Scholar 

  22. Kathiresan, S. Will cholesteryl ester transfer protein inhibition succeed primarily by lowering low-density lipoprotein cholesterol?: insights from human genetics and clinical trials. J. Am. Coll. Cardiol. 60, 2049–2052 (2012).

    Article  CAS  PubMed  Google Scholar 

  23. Barter, P. & Rye, K. A. Cholesteryl ester transfer protein inhibition to reduce cardiovascular risk: where are we now? Trends Pharmacol. Sci. 32, 694–699 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Bots, M. L. et al. Torcetrapib and carotid intima-media thickness in mixed dyslipidaemia (RADIANCE 2 study): a randomised, double-blind trial. Lancet 370, 153–160 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Voight, B. F. et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380, 572–580 (2012). This study is an example of how human genetics can be used to deprioritize therapeutic targets, arguing that drugs that raise HDL cholesterol levels will not be effective at lowering the risk of cardiovascular disease.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mickle, J. E. & Cutting, G. R. Clinical implications of cystic fibrosis transmembrane conductance regulator mutations. Clin. Chest Med. 19, 443–458 (1998).

    Article  CAS  PubMed  Google Scholar 

  27. Kerem, B. et al. Identification of the cystic fibrosis gene: genetic analysis. Science 245, 1073–1080 (1989).

    Article  CAS  PubMed  Google Scholar 

  28. Salvatore, D. et al. An overview of international literature from cystic fibrosis registries. Part 3. Disease incidence, genotype/phenotype correlation, microbiology, pregnancy, clinical complications, lung transplantation, and miscellanea. J. Cyst. Fibros. 10, 71–85 (2011).

    Article  PubMed  Google Scholar 

  29. Ramsey, B. W. et al. A CFTR potentiator in patients with cystic fibrosis and the G551D mutation. N. Engl. J. Med. 365, 1663–1672 (2011). This paper describes clinical trial data for ivacaftor, a drug that has been developed to increase CFTR potentiation and treat patients with cystic fibrosis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cox, J. J. et al. An SCN9A channelopathy causes congenital inability to experience pain. Nature 444, 894–898 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Yang, Y. et al. Mutations in SCN9A, encoding a sodium channel α subunit, in patients with primary erythermalgia. J. Med. Genet. 41, 171–174 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Drenth, J. P. et al. SCN9A mutations define primary erythermalgia as a neuropathic disorder of voltage gated sodium channels. J. Invest. Dermatol. 124, 1333–1338 (2005).

    Article  CAS  PubMed  Google Scholar 

  33. Fertleman, C. R. et al. SCN9A mutations in paroxysmal extreme pain disorder: allelic variants underlie distinct channel defects and phenotypes. Neuron 52, 767–774 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Estacion, M. et al. NaV1.7 gain-of-function mutations as a continuum: A1632E displays physiological changes associated with erythromelalgia and paroxysmal extreme pain disorder mutations and produces symptoms of both disorders. J. Neurosci. 28, 11079–11088 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Drenth, J. P. & Waxman, S. G. Mutations in sodium-channel gene SCN9A cause a spectrum of human genetic pain disorders. J. Clin. Invest. 117, 3603–3609 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Schmalhofer, W. A. et al. ProTx-II, a selective inhibitor of NaV1.7 sodium channels, blocks action potential propagation in nociceptors. Mol. Pharmacol. 74, 1476–1484 (2008).

    Article  CAS  PubMed  Google Scholar 

  37. Muroi, Y. et al. Selective silencing of NaV1.7 decreases excitability and conduction in vagal sensory neurons. J. Physiol. 589, 5663–5676 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Notarangelo, L. D. et al. Mutations in severe combined immune deficiency (SCID) due to JAK3 deficiency. Hum. Mutat. 18, 255–263 (2001).

    Article  CAS  PubMed  Google Scholar 

  39. van Vollenhoven, R. F. et al. Tofacitinib or adalimumab versus placebo in rheumatoid arthritis. N. Engl. J. Med. 367, 508–519 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Fleischmann, R. et al. Placebo-controlled trial of tofacitinib monotherapy in rheumatoid arthritis. N. Engl. J. Med. 367, 495–507 (2012).

    Article  CAS  PubMed  Google Scholar 

  41. Neptune, E. R. et al. Dysregulation of TGF-β activation contributes to pathogenesis in Marfan syndrome. Nature Genet. 33, 407–411 (2003).

    Article  CAS  PubMed  Google Scholar 

  42. Stranger, B. E., Stahl, E. A. & Raj, T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 187, 367–383 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Raychaudhuri, S. et al. Common variants at CD40 and other loci confer risk of rheumatoid arthritis. Nature Genet. 40, 1216–1223 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Fairfax, B. P. et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nature Genet. 44, 502–510 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  47. Todd, J. A. Etiology of type 1 diabetes. Immunity 32, 457–467 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Plenge, R. M. et al. Replication of putative candidate-gene associations with rheumatoid arthritis in >4,000 samples from North America and Sweden: association of susceptibility with PTPN22, CTLA4, and PADI4. Am. J. Hum. Genet. 77, 1044–1060 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  50. Lettre, G. et al. DNA polymorphisms at the BCL11A, HBS1L-MYB, and β-globin loci associate with fetal hemoglobin levels and pain crises in sickle cell disease. Proc. Natl Acad. Sci. USA 105, 11869–11874 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Uda, M. et al. Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of β-thalassemia. Proc. Natl Acad. Sci. USA 105, 1620–1625 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Menzel, S. et al. A QTL influencing F cell production maps to a gene encoding a zinc-finger protein on chromosome 2p15. Nature Genet. 39, 1197–1199 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Eriksson, M. et al. Recurrent de novo point mutations in lamin A cause Hutchinson–Gilford progeria syndrome. Nature 423, 293–298 (2003).

    Article  CAS  PubMed  Google Scholar 

  54. Worman, H. J., Fong, L. G., Muchir, A. & Young, S. G. Laminopathies and the long strange trip from basic cell biology to therapy. J. Clin. Invest. 119, 1825–1836 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. De Sandre-Giovannoli, A. et al. Lamin A truncation in Hutchinson-Gilford progeria. Science 300, 2055 (2003).

    Article  CAS  PubMed  Google Scholar 

  56. Usifo, E. et al. Low-density lipoprotein receptor gene familial hypercholesterolemia variant database: update and pathological assessment. Ann. Hum. Genet. 76, 387–401 (2012).

    Article  CAS  PubMed  Google Scholar 

  57. Imperato-McGinley, J., Guerrero, L., Gautier, T. & Peterson, R. E. Steroid 5α-reductase deficiency in man: an inherited form of male pseudohermaphroditism. Science 186, 1213–1215 (1974).

    Article  CAS  PubMed  Google Scholar 

  58. Andersson, S., Berman, D. M., Jenkins, E. P. & Russell, D. W. Deletion of steroid 5 α-reductase 2 gene in male pseudohermaphroditism. Nature 354, 159–161 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Rittmaster, R. S. Finasteride. N. Engl. J. Med. 330, 120–125 (1994).

    Article  CAS  PubMed  Google Scholar 

  60. Sanseau, P. et al. Use of genome-wide association studies for drug repositioning. Nature Biotech. 30, 317–320 (2012). This is a study that integrated GWAS data with drug databases, thereby showing that the genes targeted by many approved therapies have been implicated by human genetics.

    Article  CAS  Google Scholar 

  61. Burkhardt, R. et al. Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13. Arterioscler. Thromb. Vasc. Biol. 28, 2078–2084 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Altshuler, D. et al. The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nature Genet. 26, 76–80 (2000).

    Article  CAS  PubMed  Google Scholar 

  63. Tsoi, L. C. et al. Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nature Genet. 44, 1341–1348 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  65. Brooke, B. S. et al. Angiotensin II blockade and aortic-root dilation in Marfan's syndrome. N. Engl. J. Med. 358, 2787–2795 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005). This study represents one of the first GWASs. Based on findings from this and other studies, drugs targeting the complement pathway are under development for AMD.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Maller, J. et al. Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nature Genet. 38, 1055–1059 (2006).

    Article  CAS  PubMed  Google Scholar 

  68. Maller, J. B. et al. Variation in complement factor 3 is associated with risk of age-related macular degeneration. Nature Genet. 39, 1200–1201 (2007).

    Article  CAS  PubMed  Google Scholar 

  69. Raychaudhuri, S. et al. A rare penetrant mutation in CFH confers high risk of age-related macular degeneration. Nature Genet. 43, 1232–1236 (2011).

    Article  CAS  PubMed  Google Scholar 

  70. Hillmen, P. et al. Effect of eculizumab on hemolysis and transfusion requirements in patients with paroxysmal nocturnal hemoglobinuria. N. Engl. J. Med. 350, 552–559 (2004).

    Article  CAS  PubMed  Google Scholar 

  71. Troutbeck, R., Al-Qureshi, S. & Guymer, R. H. Therapeutic targeting of the complement system in age-related macular degeneration: a review. Clin. Experiment. Ophthalmol. 40, 18–26 (2012).

    Article  PubMed  Google Scholar 

  72. Katschke, K. J. Jr et al. Inhibiting alternative pathway complement activation by targeting the factor D exosite. J. Biol. Chem. 287, 12886–12892 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Hingorani, A. D. & Casas, J. P. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet 379, 1214–1224 (2012).

    Article  PubMed  CAS  Google Scholar 

  74. Park, H., Bourla, A. B., Kastner, D. L., Colbert, R. A. & Siegel, R. M. Lighting the fires within: the cell biology of autoinflammatory diseases. Nature Rev. Immunol. 12, 570–580 (2012).

    Article  CAS  Google Scholar 

  75. Lunn, M. R. & Stockwell, B. R. Chemical genetics and orphan genetic diseases. Chem. Biol. 12, 1063–1073 (2005).

    Article  CAS  PubMed  Google Scholar 

  76. Russman, B. S., Iannaccone, S. T. & Samaha, F. J. A phase 1 trial of riluzole in spinal muscular atrophy. Arch. Neurol. 60, 1601–1603 (2003).

    Article  PubMed  Google Scholar 

  77. Abbara, C. et al. Riluzole pharmacokinetics in young patients with spinal muscular atrophy. Br. J. Clin. Pharmacol. 71, 403–410 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Wadman, R. I. et al. Drug treatment for spinal muscular atrophy type I. Cochrane Database Syst. Rev. 4, CD006281 (2012).

    Google Scholar 

  79. Dietz, H. C. New therapeutic approaches to Mendelian disorders. N. Engl. J. Med. 363, 852–863 (2010). This is a good review on therapeutic approaches based on genetic findings from Mendelian diseases, including the example of Marfan's syndrome.

    Article  CAS  PubMed  Google Scholar 

  80. Lorson, C. L., Rindt, H. & Shababi, M. Spinal muscular atrophy: mechanisms and therapeutic strategies. Hum. Mol. Genet. 19, R111–R118 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Melki, J. et al. De novo and inherited deletions of the 5q13 region in spinal muscular atrophies. Science 264, 1474–1477 (1994).

    Article  CAS  PubMed  Google Scholar 

  82. Lefebvre, S. et al. Identification and characterization of a spinal muscular atrophy-determining gene. Cell 80, 155–165 (1995).

    Article  CAS  PubMed  Google Scholar 

  83. Hirschhorn, J. N. & Daly, M. J. Genome-wide association studies for common diseases and complex traits. Nature Rev. Genet. 6, 95–108 (2005).

    Article  CAS  PubMed  Google Scholar 

  84. McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Rev. Genet. 9, 356–369 (2008).

    Article  CAS  PubMed  Google Scholar 

  85. Cirulli, E. T. & Goldstein, D. B. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nature Rev. Genet. 11, 415–425 (2010).

    Article  CAS  PubMed  Google Scholar 

  86. Lander, E. & Kruglyak, L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nature Genet. 11, 241–247 (1995).

    Article  CAS  PubMed  Google Scholar 

  87. MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nature Methods 7, 248–249 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature Protoc. 4, 1073–1081 (2009).

    Article  CAS  Google Scholar 

  90. Jonsson, T. et al. A mutation in APP protects against Alzheimer's disease and age-related cognitive decline. Nature 488, 96–99 (2012). This study shows that a loss-of-function mutation in the APP gene protects against Alzheimer's disease.

    Article  CAS  PubMed  Google Scholar 

  91. Kero, M. et al. Amyloid precursor protein (APP) A673T mutation in the elderly Finnish population. Neurobiol. Aging 34, 1518.e1–1518.e3 (2013).

    Article  CAS  Google Scholar 

  92. Gashaw, I., Ellinghaus, P., Sommer, A. & Asadullah, K. What makes a good drug target? Drug Discov. Today 16, 1037–1043 (2011).

    Article  CAS  PubMed  Google Scholar 

  93. Katan, M. B. Commentary: Mendelian randomization, 18 years on. Int. J. Epidemiol. 33, 10–11 (2004).

    Article  PubMed  Google Scholar 

  94. Ebrahim, S. & Davey Smith, G. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum. Genet. 123, 15–33 (2008).

    Article  PubMed  Google Scholar 

  95. Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nature Genet. 41, 56–65 (2009).

    Article  CAS  PubMed  Google Scholar 

  96. Swinney, D. C. Phenotypic versus target-based drug discovery for first-in-class medicines. Clin. Pharmacol. Ther. 93, 299–301 (2013).

    Article  CAS  PubMed  Google Scholar 

  97. Rossin, E. J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  99. Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nature Rev. Genet. 12, 56–68 (2011).

    Article  CAS  PubMed  Google Scholar 

  100. Schadt, E. E., Friend, S. H. & Shaywitz, D. A. A network view of disease and compound screening. Nature Rev. Drug Discov. 8, 286–295 (2009).

    Article  CAS  Google Scholar 

  101. Stahl, E. A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nature Genet. 44, 483–489 (2012).

    Article  CAS  PubMed  Google Scholar 

  102. Dietz, H. C. et al. Marfan syndrome caused by a recurrent de novo missense mutation in the fibrillin gene. Nature 352, 337–339 (1991).

    Article  CAS  PubMed  Google Scholar 

  103. International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).

  104. Altshuler, D., Daly, M. J. & Lander, E. S. Genetic mapping in human disease. Science 322, 881–888 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Hu, X. et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 89, 496–506 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J. A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Li, G. et al. Human genetics in rheumatoid arthritis guides a high-throughput drug screen of the CD40 signaling pathway. PLoS Genet. 9, e1003487 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank S. Kathiresan for his assistance in providing critical comments on the manuscript.

Author information

Authors and Affiliations

  1. Division of Rheumatology, Immunology and Allergy, Brigham And Women's Hospital, Boston, 02115, Massachusetts, USA

    Robert M. Plenge

  2. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, Massachusetts, USA

    Robert M. Plenge, Edward M. Scolnick & David Altshuler

  3. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, 02142, Massachusetts, USA

    Edward M. Scolnick

  4. Department of Molecular Biology and Diabetes Unit, Massachusetts General Hospital, Boston, 02114, Massachusetts, USA

    David Altshuler

  5. Department of Genetics and Medicine, Harvard Medical School, Boston, 02115, Massachusetts, USA

    David Altshuler

Authors
  1. Robert M. Plenge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Edward M. Scolnick
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Altshuler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert M. Plenge.

Ethics declarations

Competing interests

D.A. is on the Board of Directors for Vertex Pharmaceuticals. R.M.P. and E.M.S. declare no competing interests.

Related links

PowerPoint slides

Glossary

Preclinical models

Any of a broad range of approaches to support the therapeutic hypothesis before a drug is tested in a clinical trial.

Therapeutic hypothesis

The hypothesis that perturbing a target in a given manner leads to patient benefit (efficacy with minimal toxicity).

Target validation

The process of gathering information about a potential drug target prior to initiating a screen to find biological or chemical modulators of the target of interest.

First-in-class drug

A drug that is the first to target a new biological mechanism of action.

Alleles

DNA sequence variations between two chromosomes (for example, one maternal chromosome and one paternal chromosome).

'Experiments of nature'

Naturally occurring human conditions or states that modulate a biological target with a reproducible effect on human physiology; in the context of drug discovery, these experiments mimic the effect of therapeutic modulation of the target.

Inherited DNA variation

A variation in DNA sequence that is passed from the parent to the offspring according to the rules of Mendelian segregation.

Causal alleles

DNA variants that are responsible for influencing a clinical phenotype.

Complex traits

Diseases that do not segregate within families according to obvious rules; the underlying genetic cause is often highly polygenic and substantially influenced by environmental and stochastic factors.

Genetic architecture

The underlying genetic basis for a phenotypic trait; variables include: the number of causal genes (monogenic, oligogenic or polygenic); the population frequency of causal alleles (common, low-frequency or rare); and the effect size of the causal alleles (small effect reflecting low penetrance, or large effect reflecting high penetrance).

Genetic locus

A location or region of the genome; the boundaries of a locus can be defined by linkage disequilibrium blocks or other factors.

Functional alleles

Alleles to which a biological function can be ascribed; examples include differential gene expression or mRNA splicing, or differences in protein-coding sequence.

Function–phenotype dose–response curves

An assessment of the effect of modulating the function of a target on a biological phenotype in a way that mirrors the traditional dose–response curves of drug efficacy and toxicity from clinical trials.

Causal gene

A gene that, when perturbed by a mutation, leads to a clinical phenotype.

Genome-wide association studies

(GWASs). Comprehensive testing of genetic variants in a collection of individuals to see whether any variant is associated with a trait; contemporary GWASs are limited to testing common variants, although newer technologies allow the testing of low-frequency variants.

Single nucleotide polymorphisms

(SNPs). DNA sequence variations that occur when a single nucleotide — A, T, C or G — differs between paired chromosomes.

Linkage disequilibrium

A non-random correlation of alleles at a locus (or region) of the genome, such that some combinations of alleles in a population are observed more frequently than would be expected by chance; the extent of linkage disequilibrium can be measured by the square of the correlation coefficient (r2); non-random recombination across the genome during the course of human history results in blocks of linkage disequilibrium (often containing multiple genes).

Mendelian diseases

Diseases that segregate faithfully within a family according to Mendel's laws; for a given family, the underlying genetic cause is generally a single mutation that is rare in the general population and highly penetrant in family members who inherit the mutation.

Spectrum of alleles

Somewhat arbitrary thresholds for the frequency of alleles observed in the general population; 'common alleles' are those that are observed in >5% of the general population; 'low-frequency alleles' are those that are observed in 0.1–5% of the general population; and 'rare alleles' are private to families; in practical terms, alleles that are common or low-frequency can be catalogued in a reference population (for example, the International HapMap Project) to facilitate testing in another population (for example, patients), whereas rare alleles must be discovered and tested in the same individuals.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Plenge, R., Scolnick, E. & Altshuler, D. Validating therapeutic targets through human genetics. Nat Rev Drug Discov 12, 581–594 (2013). https://doi.org/10.1038/nrd4051

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd4051

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research