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.

  • Essay
  • Published:

Evolving health care through personal genomics

Nature Reviews Genetics volume 18, pages 259–267 (2017)Cite this article

Subjects

Abstract

With the rapid evolution of next-generation DNA sequencing technologies, the cost of sequencing a human genome has plummeted, and genomics has started to pervade health care across all stages of life — from preconception to adult medicine. Challenges to fully embracing genomics in a clinical setting remain, but some approaches are starting to overcome these barriers, such as community-driven data sharing to improve the accuracy and efficiency of applying genomics to patient care.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: The use of genomics throughout an individual's lifespan.
Figure 2: Detection rates across a selection of molecular diagnostic tests.
Figure 3: Centralized and federated databases.
Figure 4: Penetrance of genetic disorders.

References

  1. Turakhia, M. P. & Kaiser, D. W. Transforming the care of atrial fibrillation with mobile health. J. Interv. Card. Electrophysiol. 47, 45–50 (2016).

    Article  Google Scholar 

  2. Khan, Y., Ostfeld, A. E., Lochner, C. M., Pierre, A. & Arias, A. C. Monitoring of vital signs with flexible and wearable medical devices. Adv. Mater. 28, 4373–4395 (2016).

    Article  CAS  Google Scholar 

  3. Kolla, B. P., Mansukhani, S. & Mansukhani, M. P. Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Rev. Med. Devices 13, 497–506 (2016).

    Article  CAS  Google Scholar 

  4. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    Article  CAS  Google Scholar 

  5. Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

    Article  CAS  Google Scholar 

  6. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  7. Aronson, S. J. & Rehm, H. L. Building the foundation for genomics in precision medicine. Nature 526, 336–342 (2015).

    Article  CAS  Google Scholar 

  8. Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

    Article  CAS  Google Scholar 

  9. Zhang, B., Dearing, L. & Amos, J. DNA-based carrier screening in the Ashkenazi Jewish population. Expert Rev. Mol. Diagn. 4, 377–392 (2004).

    Article  CAS  Google Scholar 

  10. American College of Obstetricians and Gynecologists Committee on Genetics. ACOG Committee Opinion No. 486: update on carrier screening for cystic fibrosis. Obstet. Gynecol. 117, 1028–1031 (2011).

  11. Wedekind, C., Seebeck, T., Bettens, F. & Paepke, A. J. MHC-dependent mate preferences in humans. Proc. Biol. Sci. 260, 245–249 (1995).

    Article  CAS  Google Scholar 

  12. Fuchs, F. & Riis, P. Antenatal sex determination. Nature 177, 330 (1956).

    Article  CAS  Google Scholar 

  13. Thung, D. T., Beulen, L., Hehir-Kwa, J. & Faas, B. H. Implementation of whole genome massively parallel sequencing for noninvasive prenatal testing in laboratories. Expert Rev. Mol. Diagn. 15, 111–124 (2015).

    Article  CAS  Google Scholar 

  14. Lo, J. O., Cori, D. F., Norton, M. E. & Caughey, A. B. Noninvasive prenatal testing. Obstet. Gynecol. Surv. 69, 89–99 (2014).

    Article  Google Scholar 

  15. Kitzman, J. O. et al. Noninvasive whole-genome sequencing of a human fetus. Sci. Transl. Med. 4, 137ra76 (2012).

    Article  Google Scholar 

  16. Regier, D. S. & Greene, C. I. Phenylalanine hydroxylase deficiency. GeneReviews https://www.ncbi.nlm.nih.gov/books/NBK1504/ (updated 20 Oct 2016).

  17. Kaback, M. M. Population-based genetic screening for reproductive counseling: the Tay–Sachs disease model. Eur. J. Pediatr. 159 (Suppl. 3), S192–S195 (2000).

    Article  CAS  Google Scholar 

  18. MacDorman, M. F., Hoyert, D. L. & Mathews, T. J. Recent declines in infant mortality in the United States, 2005–2011. NCHS Data Brief 20, 1–8 (2013).

    Google Scholar 

  19. American College of Medical Genetics Newborn Screening Expert Group. Newborn screening: toward a uniform screening panel and system — executive summary. Pediatrics 117, S296–S307 (2006).

  20. Moyer, V. A., Calonge, N., Teutsch, S. M., Botkin, J. R. & United States Preventive Services Task Force. Expanding newborn screening: process, policy, and priorities. Hastings Cent. Rep. 38, 32–39 (2008).

    Article  Google Scholar 

  21. Cirino, A. L. & Ho, C. Y. Hypertrophic cardiomyopathy overview. GeneReviews https://www.ncbi.nlm.nih.gov/books/NBK1768/ (updated 16 Jan 2014).

  22. Lohmann, D. R. & Gallie, B. L. Retinoblastoma. GeneReviews https://www.ncbi.nlm.nih.gov/books/NBK1452/ (updated 19 Nov 2015).

  23. Kingsmore, S. F. Newborn testing and screening by whole-genome sequencing. Genet. Med. 18, 214–216 (2016).

    Article  Google Scholar 

  24. Waisbren, S. E. et al. Parents are interested in newborn genomic testing during the early postpartum period. Genet. Med. 17, 501–504 (2015).

    Article  Google Scholar 

  25. Ceyhan-Birsoy, O. et al. A curated gene list for reporting results of newborn genomic sequencing. Genet. Med. http://dx.doi.org/10.1038/gim.2016.193 (2017).

  26. Berg, J. S. et al. Newborn sequencing in genomic medicine and public health (NSIGHT). Pediatrics http://dx.doi.org/10.1542/peds.2016-2252 (2017).

  27. Lazaridis, K. N. et al. Outcome of whole exome sequencing for diagnostic odyssey cases of an individualized medicine clinic: the Mayo Clinic Experience. Mayo Clin. Proc. 91, 297–307 (2016).

    Article  Google Scholar 

  28. Sawyer, S. L. et al. Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clin. Genet. 89, 275–284 (2016).

    Article  CAS  Google Scholar 

  29. Thevenon, J. et al. Diagnostic odyssey in severe neurodevelopmental disorders: toward clinical whole-exome sequencing as a first-line diagnostic test. Clin. Genet. 89, 700–707 (2016).

    Article  CAS  Google Scholar 

  30. Might, M. & Might, C. What happens when N = 1 and you want plus 1? Prenat. Diagn. http://dx.doi.org/10.1002/pd.4975 (2016).

  31. Alatzoglou, K. S., Webb, E. A., Le Tissier, P. & Dattani, M. T. Isolated growth hormone deficiency (GHD) in childhood and adolescence: recent advances. Endocr. Rev. 35, 376–432 (2014).

    Article  CAS  Google Scholar 

  32. Forlenza, G. P. et al. Next generation sequencing in endocrine practice. Mol. Genet. Metab. 115, 61–71 (2015).

    Article  CAS  Google Scholar 

  33. Merlo, L. M., Pepper, J. W., Reid, B. J. & Maley, C. C. Cancer as an evolutionary and ecological process. Nat. Rev. Cancer 6, 924–935 (2006).

    Article  CAS  Google Scholar 

  34. 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).

    Article  CAS  Google Scholar 

  35. Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).

    Article  CAS  Google Scholar 

  36. Rodig, S. J. & Shapiro, G. I. Crizotinib, a small-molecule dual inhibitor of the c-Met and ALK receptor tyrosine kinases. Curr. Opin. Investig. Drugs 11, 1477–1490 (2010).

    CAS  PubMed  Google Scholar 

  37. Piccart-Gebhart, M. J. et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N. Engl. J. Med. 353, 1659–1672 (2005).

    Article  CAS  Google Scholar 

  38. Romond, E. H. et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl. J. Med. 353, 1673–1684 (2005).

    Article  CAS  Google Scholar 

  39. Chapman, P. B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011).

    Article  CAS  Google Scholar 

  40. Houillier, C. et al. IDH1 or IDH2 mutations predict longer survival and response to temozolomide in low-grade gliomas. Neurology 75, 1560–1566 (2010).

    Article  CAS  Google Scholar 

  41. Lupo, P. J. et al. Patients' perceived utility of whole-genome sequencing for their healthcare: findings from the MedSeq project. Per. Med. 13, 13–20 (2016).

    Article  CAS  Google Scholar 

  42. Vassy, J. L. et al. The MedSeq Project: a randomized trial of integrating whole genome sequencing into clinical medicine. Trials 15, 85 (2014).

    Article  Google Scholar 

  43. Meisel, S. F. et al. Explaining, not just predicting, drives interest in personal genomics. Genome Med. 7, 74 (2015).

    Article  Google Scholar 

  44. Alfares, A. A. et al. Results of clinical genetic testing of 2,912 probands with hypertrophic cardiomyopathy: expanded panels offer limited additional sensitivity. Genet. Med. 17, 880–888 (2015).

    Article  Google Scholar 

  45. Boaretto, F. et al. Diagnosis of primary ciliary dyskinesia by a targeted next-generation sequencing panel: molecular and clinical findings in Italian patients. J. Mol. Diagn. 18, 912–922 (2016).

    Article  CAS  Google Scholar 

  46. Cizmarova, M. et al. New mutations associated with rasopathies in a Central European population and genotype–phenotype correlations. Ann. Hum. Genet. 80, 50–62 (2016).

    Article  CAS  Google Scholar 

  47. Lee, H. et al. Clinical exome sequencing for genetic identification of rare Mendelian disorders. JAMA 312, 1880–1887 (2014).

    Article  Google Scholar 

  48. Pugh, T. J. et al. The landscape of genetic variation in dilated cardiomyopathy as surveyed by clinical DNA sequencing. Genet. Med. 16, 601–608 (2014).

    Article  CAS  Google Scholar 

  49. Schenkel, L. C. et al. Clinical next-generation sequencing pipeline outperforms a combined approach using Sanger sequencing and multiplex ligation-dependent probe amplification in targeted gene panel analysis. J. Mol. Diagn. 18, 657–667 (2016).

    Article  CAS  Google Scholar 

  50. Shearer, A. E. & Smith, R. J. Massively parallel sequencing for genetic diagnosis of hearing loss: the new standard of care. Otolaryngol. Head Neck Surg. 153, 175–182 (2015).

    Article  Google Scholar 

  51. Yang, H. et al. Genetic testing of 248 Chinese aortopathy patients using a panel assay. Sci. Rep. 6, 33002 (2016).

    Article  CAS  Google Scholar 

  52. Chong, J. X. et al. The genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities. Am. J. Hum. Genet. 97, 199–215 (2015).

    Article  CAS  Google Scholar 

  53. Cree, I. A. et al. Guidance for laboratories performing molecular pathology for cancer patients. J. Clin. Pathol. 67, 923–931 (2014).

    Article  CAS  Google Scholar 

  54. Lindeman, N. I. et al. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. J. Thorac. Oncol. 8, 823–859 (2013).

    Article  CAS  Google Scholar 

  55. Febbo, P. G. et al. NCCN Task Force report: evaluating the clinical utility of tumor markers in oncology. J. Natl Compr. Canc. Netw. 9 (Suppl. 5), S1–S32 (2011).

    Article  CAS  Google Scholar 

  56. Aradhya, S. et al. Exon-level array CGH in a large clinical cohort demonstrates increased sensitivity of diagnostic testing for Mendelian disorders. Genet. Med. 14, 594–603 (2012).

    Article  CAS  Google Scholar 

  57. Pugh, T. J. et al. VisCap: inference and visualization of germ-line copy-number variants from targeted clinical sequencing data. Genet. Med. 18, 712–719 (2016).

    Article  CAS  Google Scholar 

  58. Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).

    Article  CAS  Google Scholar 

  59. Rehm, H. L. et al. ClinGen — The Clinical Genome Resource. N. Engl. J. Med. 372, 2235–2242 (2015).

    Article  CAS  Google Scholar 

  60. 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, 405–424 (2015).

    Article  Google Scholar 

  61. Amendola, L. M. et al. Performance of ACMG-AMP variant-interpretation guidelines among nine laboratories in the Clinical Sequencing Exploratory Research Consortium. Am. J. Hum. Genet. 99, 247 (2016).

    Article  CAS  Google Scholar 

  62. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  Google Scholar 

  63. Amendola, L. M. et al. Actionable exomic incidental findings in 6503 participants: challenges of variant classification. Genome Res. 25, 305–315 (2015).

    Article  CAS  Google Scholar 

  64. National Center for Biotechnology Information. ClinVar submissions. ClinVar https://www.ncbi.nlm.nih.gov/clinvar/submitters (2016).

  65. Philippakis, A. A. et al. The Matchmaker Exchange: a platform for rare disease gene discovery. Hum. Mutat. 36, 915–921 (2015).

    Article  Google Scholar 

  66. Thorpe, J. H. & Gray, E. A. Big data and public health: navigating privacy laws to maximize potential. Public Health Rep. 130, 171–175 (2015).

    Article  Google Scholar 

  67. Global Alliance for Genomics and Health. A federated ecosystem for sharing genomic, clinical data. Science 352, 1278–1280 (2016).

  68. Baker, D. B., Kaye, J. & Terry, S. F. Governance through privacy, fairness, and respect for individuals. EGEMS (Wash. DC) 4, 1207 (2016).

    Google Scholar 

  69. Knoppers, B. M. Framework for responsible sharing of genomic and health-related data. HUGO J. 8, 3 (2014).

    Article  Google Scholar 

  70. Botkin, J. R. et al. Points to consider: ethical, legal, and psychosocial implications of genetic testing in children and adolescents. Am. J. Hum. Genet. 97, 6–21 (2015).

    Article  CAS  Google Scholar 

  71. Botkin, J. R. & Rothwell, E. Whole genome sequencing and newborn screening. Curr. Genet. Med. Rep. 4, 1–6 (2016).

    Article  Google Scholar 

  72. Waisbren, S. E., Weipert, C. M., Walsh, R. C., Petty, C. R. & Green, R. C. Psychosocial factors influencing parental interest in genomic sequencing of newborns. Pediatrics 137 (Suppl. 1), S30–S35 (2016).

    Article  Google Scholar 

  73. Alders, M. & Christiaans, I. Long QT syndrome. GeneReviews https://www.ncbi.nlm.nih.gov/books/NBK1129/ (updated 18 Jun 2015).

  74. Charron, P. et al. Penetrance of familial hypertrophic cardiomyopathy. Genet. Couns. 8, 107–114 (1997).

    CAS  PubMed  Google Scholar 

  75. Kohlmann, W. & Gruber, S. B. Lynch syndrome. GeneReviews https://www.ncbi.nlm.nih.gov/books/NBK1211/ (updated 22 May 2014).

  76. Petrucelli, N., Daly, M. B. & Feldman, G. L. BRCA1 and BRCA2 hereditary breast and ovarian cancer. GeneReviews https://www.ncbi.nlm.nih.gov/books/NBK1247/ (updated 26 Sep 2013).

  77. Whitlock, E. P., Garlitz, B. A., Harris, E. L., Beil, T. L. & Smith, P. R. Screening for hereditary hemochromatosis: a systematic review for the U.S. Preventive Services Task Force. Ann. Intern. Med. 145, 209–223 (2006).

    Article  Google Scholar 

  78. Metcalfe, K. et al. Elastin: mutational spectrum in supravalvular aortic stenosis. Eur. J. Hum. Genet. 8, 955–9634 (2000).

    Article  CAS  Google Scholar 

  79. Deo, S. V. et al. Late outcomes for surgical repair of supravalvar aortic stenosis. Ann. Thorac. Surg. 94, 854–859 (2012).

    Article  Google Scholar 

  80. Chong, J. X. et al. Gene discovery for Mendelian conditions via social networking: de novo variants in KDM1A cause developmental delay and distinctive facial features. Genet. Med. 18, 788–795 (2016).

    Article  CAS  Google Scholar 

  81. Kirkpatrick, B. E. et al. GenomeConnect: matchmaking between patients, clinical laboratories, and researchers to improve genomic knowledge. Hum. Mutat. 36, 974–978 (2015).

    Article  Google Scholar 

  82. Lambertson, K. F., Damiani, S. A., Might, M., Shelton, R. & Terry, S. F. Participant-driven matchmaking in the genomic era. Hum. Mutat. 36, 965–973 (2015).

    Article  Google Scholar 

  83. Wauters, A. & Van Hoyweghen, I. Global trends on fears and concerns of genetic discrimination: a systematic literature review. J. Hum. Genet. 61, 275–282 (2016).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

H.L.R. is supported by grants from the US Department of Health and Human Services, the US National Institutes of Health (NIH), and the National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) under award numbers U41HG006834, U01HG006500, U19HD077671, U01HG008676 and UM1HG008900. The author would like to thank S. Hemphill for identifying literature to support Figure 4.

Author information

Authors and Affiliations

  1. Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA; at the Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts 02139, USA; and at The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,

    Heidi L. Rehm

  2. at the Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts 02139, USA,

    Heidi L. Rehm

Authors
  1. Heidi L. Rehm
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heidi L. Rehm.

Ethics declarations

Competing interests

H.L.R. is employed by the Brigham and Women's Hospital, Boston, Massachusetts, USA, and the Broad Institute, Cambridge, Massachusetts, directing laboratories that offer fee-based clinical genomic services.

Related links

PowerPoint slides

Glossary

Chromosome microarray

(CMA). A cytogenetic testing platform that uses DNA probes to detect copy number variants of typically 100,000 bp or larger.

Copy number variants

(CNVs). The loss or gain of chromosomal material often resulting from a deletion or duplication event, respectively.

Genotype-first approaches

The use of genetic and genomic testing to enable earlier identification of disease diagnoses compared to first performing detailed clinical tests and evaluations.

Non-invasive prenatal testing

A prenatal screening test to detect chromosome abnormalities in cell-free fetal DNA in maternal blood.

Penetrance

The likelihood that a disease will be expressed in an individual who harbours an at-risk genotype.

Precision Medicine Initiative

A US National Institutes of Health funded programme launched in 2016 to advance biomedical research, including the aim to enrol one million individuals who consent to contribute detailed medical and genetic data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rehm, H. Evolving health care through personal genomics. Nat Rev Genet 18, 259–267 (2017). https://doi.org/10.1038/nrg.2016.162

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg.2016.162

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