Lynch, T. J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004).
Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).
Morel, C. F. & Clarke, J. T. The use of agalsidase alfa enzyme replacement therapy in the treatment of Fabry disease. Expert Opin. Biol. Ther. 9, 631–639 (2009).
Relling, M. V. et al. Clinical Pharmacogenetics Implementation Consortium guidelines for thiopurine methyltransferase genotype and thiopurine dosing: 2013 update. Clin. Pharmacol. Ther. 93, 324–325 (2013).
Martin, M. A. et al. Clinical pharmacogenetics implementation consortium guidelines for HLA-B genotype and abacavir dosing: 2014 update. Clin. Pharmacol. Ther. 95, 499–500 (2014).
Cutting, G. R. Cystic fibrosis genetics: from molecular understanding to clinical application. Nature Rev. Genet. 16, 45–56 (2015).
Spurdle, A. B. et al. ENIGMA—evidence-based network for the interpretation of germline mutant alleles: an international initiative to evaluate risk and clinical significance associated with sequence variation in BRCA1 and BRCA2 genes. Hum. Mutat. 33, 2–7 (2012).
Domchek, S. M. et al. Association of risk-reducing surgery in BRCA1 or BRCA2 mutation carriers with cancer risk and mortality. J. Am. Med. Assoc. 304, 967–975 (2010).
Audeh, M. W. et al. Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and recurrent ovarian cancer: a proof-of-concept trial. Lancet 376, 245–251 (2010).
Tutt, A. et al. Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and advanced breast cancer: a proof-of-concept trial. Lancet 376, 235–244 (2010).
Rehm, H. L. et al. ClinGen — The Clinical Genome Resource. N. Engl. J. Med. 372, 2235–2242 (2015).
This article describes ClinGen, an NIH-supported programme to build an authoritative central resource that defines the clinical relevance of genomic variants for use in precision medicine and research, employing systematic sharing of clinical knowledge and expert curation.
US Department of Veterans Affairs Office of Research & Development. Informed Consent for Human Subjects Research: a Primer http://www.research.va.gov/resources/pubs/docs/consent_primer_final.pdf (VA Boston Health Care System, 2002).
Jameson, E., Jones, S. & Wraith, J. E. Enzyme replacement therapy with laronidase (Aldurazyme®) for treating mucopolysaccharidosis type I. Cochrane Database Syst. Rev. 11, CD009354 (2013).
Hacein-Bey Abina, S. et al. Outcomes following gene therapy in patients with severe Wiskott–Aldrich syndrome. J. Am. Med. Assoc. 313, 1550–1563 (2015).
Murphy, S. N. et al. High throughput tools to access images from clinical archives for research. J. Digit. Imaging 28, 194–204 (2015).
McCarty, C. A. et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genomics 4, 13 (2011).
Allen, N. L. et al. Biobank participants' preferences for disclosure of genetic research results: perspectives from the OurGenes, OurHealth, OurCommunity project. Mayo Clin. Proc. 89, 738–746 (2014).
Toledo, J. B. et al. A platform for discovery: The University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Alzheimers Dement. 10, 477–484 (2014).
Milani, L., Leitsalu, L. & Metspalu, A. An epidemiological perspective of personalized medicine: the Estonian experience. J. Intern. Med. 277, 188–200 (2015).
Knoppers, B. M. Framework for responsible sharing of genomic and health-related data. HUGO J. 8, 3 (2014).
Korf, B. R. & Rehm, H. L. New approaches to molecular diagnosis. J. Am. Med. Assoc. 309, 1511–1521 (2013).
Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–423 (2015).
These guidelines provide a standardized approach to the interpretation of genetic variants for monogenic disease.
Hoffman, M. A. & Williams, M. S. Electronic medical records and personalized medicine. Hum. Genet. 130, 33–39 (2011).
Del Fiol, G. et al. Integrating genetic information resources with an EHR. AMIA Annu. Symp. Proc. 2006, 904 (2006).
Aronson, S. J. et al. Communicating new knowledge on previously reported genetic variants. Genet. Med. 14, 713–719 (2012).
Starren, J., Williams, M. S. & Bottinger, E. P. Crossing the omic chasm: a time for omic ancillary systems. J. Am. Med. Assoc. 309, 1237–1238 (2013).
Kho, A. N. et al. Practical challenges in integrating genomic data into the electronic health record. Genet. Med. 15, 772–778 (2013).
This review summarizes challenges that the eMERGE consortium has encountered when integrating genetics into the EHR and suggests approaches for addressing these challenges.
Gottesman, O. et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet. Med. 15, 761–771 (2013).
Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).
Béroud, C., Collod-Béroud, G., Boileau, C., Soussi, T. & Junien, C. UMD (Universal Mutation Database): a generic software to build and analyze locus-specific databases. Hum. Mutat. 15, 86–94 (2000).
Sosnay, P. R. et al. Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene. Nature Genet. 45, 1160–1167 (2013).
Firth, H. V. et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am. J. Hum. Genet. 84, 524–533 (2009).
Miller, D. T. et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am. J. Hum. Genet. 86, 749–764 (2010).
Thompson, B. A. et al. Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database. Nature Genet. 46, 107–115 (2014).
Aronson, S. J. et al. The GeneInsight Suite: a platform to support laboratory and provider use of DNA-based genetic testing. Hum. Mutat. 32, 532–536 (2011).
Lerner-Ellis, J., Wang, M., White, S. & Lebo, M. S. & Canadian Open Genetics Repository Group. Canadian Open Genetics Repository (COGR): a unified clinical genomics database as a community resource for standardising and sharing genetic interpretations. J. Med. Genet. 52, 438–445 (2015).
Riggs, E. R., Jackson, L., Miller, D. T. & Van Vooren, S. Phenotypic information in genomic variant databases enhances clinical care and research: the International Standards for Cytogenomic Arrays Consortium experience. Hum. Mutat. 33, 787–796 (2012).
Tryka, K. A. et al. NCBI's Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Res. 42, D975–D979 (2014).
Zhang, J. et al. International Cancer Genome Consortium Data Portal—a one-stop shop for cancer genomics data. Database (Oxford) 2011, bar026 (2011).
The Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genet. 45, 1113–1120 (2013).
Schilsky, R. L., Michels, D. L., Kearbey, A. H., Yu, P. P. & Hudis, C. A. Building a rapid learning health care system for oncology: the regulatory framework of CancerLinQ. J. Clin. Oncol. 32, 2373–2379 (2014).
This article provides an overview of the challenges of applying precision medicine techniques to cancer and then describes the CancerLinQ system and the regulatory framework under which it operates.
Philippakis, A. A. et al. The matchmaker exchange: a platform for rare disease gene discovery. Hum. Mutat. http://dx.doi.org/10.1002/humu.22858 (2015).
This paper describes an international system for sharing genomic cases to aid in gene discovery.
Buske, O. J. et al. The matchmaker exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles. Hum. Mutat. http://dx.doi.org/10.1002/humu.22850 (2015).
Almalki, M., Gray, K. & Sanchez, F. M. The use of self-quantification systems for personal health information: big data management activities and prospects. Health Inf. Sci. Syst. 3 (suppl.), S1 (2015).
Thusberg, J., Olatubosun, A. & Vihinen, M. Performance of mutation pathogenicity prediction methods on missense variants. Hum. Mutat. 32, 358–368 (2011).
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genet. 46, 310–315 (2014).
Jian, X., Boerwinkle, E. & Liu, X. In silico tools for splicing defect prediction: a survey from the viewpoint of end users. Genet. Med. 16, 497–503 (2014).
The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010); erratum 473, 544 (2011).
Stenson, P. D. et al. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet. 133, 1–9 (2014).
Gargis, A. S. et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nature Biotechnol. 30, 1033–1036 (2012).
Jarchum, I. & Jones, S. DREAMing of benchmarks. Nature Biotechnol. 33, 49–50 (2015).
Abdallah, K., Hugh-Jones, C., Norman, T., Friend, S. & Stolovitzky, G. The Prostate Cancer DREAM Challenge: A community-wide effort to use open clinical trial data for the quantitative prediction of outcomes in metastatic prostate cancer. Oncologist 20, 459–460 (2015).
O'Driscoll, A., Daugelaite, J. & Sleator, R. D. 'Big data', Hadoop and cloud computing in genomics. J. Biomed. Inform. 46, 774–781 (2013).
This review discusses cloud computing and big data concepts and their application to the field of genomics.
Joyner, M. J. & Paneth, N. Seven questions for personalized medicine. J. Am. Med. Assoc. http://dx.doi.org/10.1001/jama.2015.7725 (2015).