Motulsky, A. G. Drug reactions, enzymes, and biochemical genetics. JAMA 165, 835–837 (1957).
This is a seminal publication establishing the field of pharmacogenetics, with studies of glucose-6-phosphate dehydrogenase (G6PD) deficiency and anaemia for oxidizing treatments (for example, antimalarials), and pseudocholinesterase deficiency on prolonged apnoea during anaesthesia.
Ehmann, F. et al. Pharmacogenomic information in drug labels: European Medicines Agency perspective. Pharmacogenom. J. 15, 201–210 (2015).
PharmGKB. Dosing Guidelines. (online), (2015).
Watson, J. D. & Zinder, N. Genome Project Maps Paths of Diseases and Drugs. New York Times [online], (1990).
Ioannidis, J. P. To replicate or not to replicate: the case of pharmacogenetic studies: have pharmacogenomics failed, or do they just need larger-scale evidence and more replication? Circ. Cardiovasc. Genet. 6, 413–418 (2013).
In this paper, Ioannidis turns his attention to the field of pharmacogenetics and considers the challenge of replication.
Ioannidis, J. P., Ntzani, E. E., Trikalinos, T. A. & Contopoulos-Ioannidis, D. G. Replication validity of genetic association studies. Nat. Genet. 29, 306–309 (2001).
This is an early demonstration of the stark lack of replicability among candidate gene association studies, identifying a serious problem for the field that led to substantial reforms in analysis and reporting.
Collins, S. L., Carr, D. F. & Pirmohamed, M. Advances in the pharmacogenomics of adverse drug reactions. Drug Safety 39, 15–27 (2016).
This is a recent review of the substantial progress made in the study of the genetics of adverse drug reactions.
Ioannidis, J. P. Why most published research findings are false. PLoS Med. 2, e124 (2005).
McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9, 356–369 (2008).
Colquhoun, D. An investigation of the false discovery rate and the misinterpretation of p-values. R. Soc. Open Sci. 1, 140216 (2014).
This is a useful summary of why we are faced with high false discovery rates despite strict adherence to traditional P value thresholds for statistical significance.
Pearson, E. R. et al. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet 362, 1275–1281 (2003).
Fedier, A. et al. The effect of loss of Brca1 on the sensitivity to anticancer agents in p53-deficient cells. Int. J. Oncol. 22, 1169–1173 (2003).
Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).
Bolton, K. L. et al. Association between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer. JAMA 307, 382–390 (2012).
Alsop, K. et al. BRCA mutation frequency and patterns of treatment response in BRCA mutation-positive women with ovarian cancer: a report from the Australian Ovarian Cancer Study Group. J. Clin. Oncol. 30, 2654–2663 (2012).
Ng, K. P. et al. A common BIM deletion polymorphism mediates intrinsic resistance and inferior responses to tyrosine kinase inhibitors in cancer. Nat. Med. 18, 521–528 (2012).
Pearson, E. R. et al. Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. N. Engl. J. Med. 355, 467–477 (2006).
Taylor, F. et al. Statins for the primary prevention of cardiovascular disease. Cochrane Database Syst. Rev. 1 (2013).
Deshmukh, H. A. et al. Genome-wide association study of genetic determinants of LDL-C response to atorvastatin therapy: importance of Lp(a). J. Lipid Res. 53, 1000–1011 (2012).
Donnelly, L. A. et al. Robust association of the LPA locus with low-density lipoprotein cholesterol lowering response to statin treatment in a meta-analysis of 30 467 individuals from both randomized control trials and observational studies and association with coronary artery disease outcome during statin treatment. Pharmacogenet. Genom. 23, 518–525 (2013).
Murphy, R., Ellard, S. & Hattersley, A. T. Clinical implications of a molecular genetic classification of monogenic β-cell diabetes. Nat. Clin. Pract. Endocrinol. Metab. 4, 200–213 (2008).
Pirmohamed, M. et al. A randomized trial of genotype-guided dosing of warfarin. N. Engl. J. Med. 369, 2294–2303 (2013).
Anderson, J. L. et al. A randomized and clinical effectiveness trial comparing two pharmacogenetic algorithms and standard care for individualizing warfarin dosing (CoumaGen-II). Circulation 125, 1997–2005 (2012).
References 22 and 23 represent two of the few examples of randomized controlled trials for genotype-guided effects on drug efficacy and safety.
Roberts, J. D. et al. Point-of-care genetic testing for personalisation of antiplatelet treatment (RAPID GENE): a prospective, randomised, proof-of-concept trial. Lancet 379, 1705–1711 (2012).
Chhibber, A. et al. Genomic architecture of pharmacological efficacy and adverse events. Pharmacogenomics 15, 2025–2048 (2014).
This is a recent review of the genetics of drug safety and efficacy, with particular attention to available estimates of heritability in humans and cell lines.
Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).
Ge, D. et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 461, 399–401 (2009).
Chasman, D. I. et al. Genetic determinants of statin-induced low-density lipoprotein cholesterol reduction: the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial. Circ. Cardiovasc. Genet. 5, 257–264 (2012).
Ledermann, J. et al. Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer: a preplanned retrospective analysis of outcomes by BRCA status in a randomised phase 2 trial. Lancet Oncol. 15, 852–861 (2014).
Franc, M. A. et al. Current practices for DNA sample collection and storage in the pharmaceutical industry, and potential areas for harmonization: perspective of the I-PWG. Clin. Pharmacol. Ther. 89, 546–553 (2011).
Fleming, T. R. & Powers, J. H. Biomarkers and surrogate endpoints in clinical trials. Stat. Med. 31, 2973–2984 (2012).
Zarogianni, E., Moorhead, T. W. & Lawrie, S. M. Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. Neuroimage Clin. 3, 279–289 (2013).
Greshock, J. et al. Molecular target class is predictive of in vitro response profile. Cancer Res. 70, 3677–3686 (2010).
Geeleher, P., Cox, N. J. & Huang, R. S. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 15, R47 (2014).
Frantzi, M., Bhat, A. & Latosinska, A. Clinical proteomic biomarkers: relevant issues on study design and technical considerations in biomarker development. Clin. Transl Med. 3, 7 (2014).
Although the primary focus is on proteomics, this paper provides a careful discussion of the challenges of discovering and applying clinical biomarkers.
Soininen, P., Kangas, A. J., Wurtz, P., Suna, T. & Ala-Korpela, M. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ. Cardiovasc. Genet. 8, 192–206 (2015).
Kirchmair, J. et al. Predicting drug metabolism: experiment and/or computation? Nat. Rev. Drug Discov. 14, 387–404 (2015).
Carter, T. A. et al. Inhibition of drug-resistant mutants of ABL, KIT, and EGF receptor kinases. Proc. Natl Acad. Sci. USA 102, 11011–11016 (2005).
Davis, M. I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051 (2011).
Rannala, B. & Reeve, J. P. High-resolution multipoint linkage-disequilibrium mapping in the context of a human genome sequence. Am. J. Hum. Genet. 69, 159–178 (2001).
Gagliano, S. A., Barnes, M. R., Weale, M. E. & Knight, J. A. Bayesian method to incorporate hundreds of functional characteristics with association evidence to improve variant prioritization. PLoS ONE 9, e98122 (2014).
Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 10, e1004722 (2014).
Stephens, M. & Balding, D. J. Bayesian statistical methods for genetic association studies. Nat. Rev. Genet. 10, 681–690 (2009).
Witte, J. S., Elston, R. C. & Cardon, L. R. On the relative sample size required for multiple comparisons. Stat. Med. 19, 369–372 (2000).
Cardon, L. R., Idury, R. M., Harris, T. J., Witte, J. S. & Elston, R. C. Testing drug response in the presence of genetic information: sampling issues for clinical trials. Pharmacogenetics 10, 503–510 (2000).
This is an early, although still relevant, summary of the statistical modelling of drug efficacy effects.
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. (2015).
Postmus, I. et al. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. Nat. Commun. 5, 5068 (2014).
Klein, T. E. et al. Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med. 360, 753–764 (2009).
Garcia-Donas, J. et al. Single nucleotide polymorphism associations with response and toxic effects in patients with advanced renal-cell carcinoma treated with first-line sunitinib: a multicentre, observational, prospective study. Lancet Oncol. 12, 1143–1150 (2011).
McCarty, C. A., Chapman-Stone, D., Derfus, T., Giampietro, P. F. & Fost, N. Community consultation and communication for a population-based DNA biobank: the Marshfield clinic personalized medicine research project. Am. J. Med. Genet. A 146A, 3026–3033 (2008).
Roden, D. M. et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin. Pharmacol. Ther. 84, 362–369 (2008).
Wilke, R. A. et al. The emerging role of electronic medical records in pharmacogenomics. Clin. Pharmacol. Ther. 89, 379–386 (2011).
Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).
Zhou, K. et al. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat. Genet. 43, 117–120 (2011).
Delaney, J. T. et al. Predicting clopidogrel response using DNA samples linked to an electronic health record. Clin. Pharmacol. Ther. 91, 257–263 (2012).
Wei, W. Q. et al. Characterization of statin dose response in electronic medical records. Clin. Pharmacol. Ther. 95, 331–338 (2014).
Province, M. A. et al. CYP2D6 genotype and adjuvant tamoxifen: meta-analysis of heterogeneous study populations. Clin. Pharmacol. Ther. 95, 216–227 (2014).
Eddershaw, P. J., Beresford, A. P. & Bayliss, M. K. ADME/PK as part of a rational approach to drug discovery. Drug Discov. Today 5, 409–414 (2000).
Ma, M. K., Woo, M. H. & McLeod, H. L. Genetic basis of drug metabolism. Am. J. Health Syst. Pharm. 59, 2061–2069 (2002).
Aminkeng, F. et al. Higher frequency of genetic variants conferring increased risk for ADRs for commonly used drugs treating cancer, AIDS and tuberculosis in persons of African descent. Pharmacogenom. J. 14, 160–170 (2014).
Altman, R. B., Flockhart, D. A. & Goldstein, J. L. (eds) Principles of Pharmacogenetics and Pharmacogenomics (Cambridge Univ. Press, 2012).
Gelissen, I. C. & McLachlan, A. J. The pharmacogenomics of statins. Pharmacol. Res. 88, 99–106 (2014).
Nieminen, T., Kahonen, M., Viiri, L. E., Gronroos, P. & Lehtimaki, T. Pharmacogenetics of apolipoprotein E gene during lipid-lowering therapy: lipid levels and prevention of coronary heart disease. Pharmacogenomics 9, 1475–1486 (2008).
Faber, A. C. et al. BIM expression in treatment-naive cancers predicts responsiveness to kinase inhibitors. Cancer Discov. 1, 352–365 (2011).
Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).
Kjolby, M. et al. Sort1, encoded by the cardiovascular risk locus 1p13.3, is a regulator of hepatic lipoprotein export. Cell. Metab. 12, 213–223 (2010).
Zhou, K. et al. The role of ATM in response to metformin treatment and activation of AMPK. Nat. Genet. 44, 361–362 (2012).
Bossuyt, P. M., Reitsma, J. B., Linnet, K. & Moons, K. G. Beyond diagnostic accuracy: the clinical utility of diagnostic tests. Clin. Chem. 58, 1636–1643 (2012).
Mallal, S. et al. HLA-B*5701 screening for hypersensitivity to abacavir. N. Engl. J. Med. 358, 568–579 (2008).
Zineh, I., Pacanowski, M. & Woodcock, J. Pharmacogenetics and coumarin dosing—recalibrating expectations. N. Engl. J. Med. 369, 2273–2275 (2013).
The National Institute for Health and Care Excellence. Hypertension in adults: diagnosis and management. Clinical guideline CG127. NICE [online], (2011).
Lango, A. H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).
Sanna, S. et al. Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS. Genet. 7, e1002198 (2011).
Spraggs, C. F. et al. Different effects of the BIM deletion polymorphism on treatment of solid tumors by the tyrosine kinase inhibitors (TKI) pazopanib, sunitinib, and lapatinib. Ann. Oncol. 26, 1515–1517 (2015).
Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013).
This is a clear explanation, with compelling examples, of the value of human genetic data to support decisions around selecting targets for drug discovery and development.
Xu, C. F. et al. IL8 polymorphisms and overall survival in pazopanib- or sunitinib-treated patients with renal cell carcinoma. Br. J. Cancer 112, 1190–1198 (2015).
Wu, S. et al. Targeted blockade of interleukin-8 abrogates its promotion of cervical cancer growth and metastasis. Mol. Cell Biochem. 375, 69–79 (2013).
Wadelius, M. et al. Common VKORC1 and GGCX polymorphisms associated with warfarin dose. Pharmacogenom. J. 5, 262–270 (2005).
Takeuchi, F. et al. A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS. Genet. 5, e1000433 (2009).
Aithal, G. P., Day, C. P., Kesteven, P. J. & Daly, A. K. Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications. Lancet 353, 717–719 (1999).
Perera, M. A. et al. Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study. Lancet 382, 790–796 (2013).
Brandt, J. T. et al. Common polymorphisms of CYP2C19 and CYP2C9 affect the pharmacokinetic and pharmacodynamic response to clopidogrel but not prasugrel. J. Thromb. Haemost. 5, 2429–2436 (2007).
Shuldiner, A. R. et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA 302, 849–857 (2009).
Eckhardt, K. et al. Same incidence of adverse drug events after codeine administration irrespective of the genetically determined differences in morphine formation. Pain 76, 27–33 (1998).
Nishimura, J. et al. Genetic variants in C5 and poor response to eculizumab. N. Engl. J. Med. 370, 632–639 (2014).
Ong, S. T., Chuah, C. T., Ko, T. K., Hillmer, A. M. & Lim, W. T. Reply: the BIM deletion polymorphism cannot account for intrinsic TKI resistance of Chinese individuals with chronic myeloid leukemia. Nat. Med. 20, 1090–1091 (2014).
Gorodnova, T. V. et al. High response rates to neoadjuvant platinum-based therapy in ovarian cancer patients carrying germ-line BRCA mutation. Cancer Lett. 369, 363–367 (2015).
Rafiq, M. et al. Effective treatment with oral sulfonylureas in patients with diabetes due to sulfonylurea receptor 1 (SUR1) mutations. Diabetes Care 31, 204–209 (2008).