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Molecular genetic testing and the future of clinical genomics

Subjects

Key Points

  • Clinical molecular genetic testing is transforming personalized medicine and is appropriate for a range of applications, such as rare disease diagnostics and predictive testing for common disorders.

  • Whole-exome and whole-genome sequencing may become a first-line clinical test for some naive diagnostic cases, but classic genetic tests will continue to be used for the high analytical sensitivity of specific defects and for the confirmation of genome findings.

  • There remains no single test to detect the wide array of genetic defects that may be inherited or arise de novo; clinical diagnostics requires multiple approaches to determine a causal genetic defect.

  • Although genome sequencing may transform diagnostic approaches in large academic medical centres, access to expensive and sophisticated tests are not universal. Genetic testing must be available globally through validated simple technologies for molecular diagnostics (such as direct PCR, linkage analysis or multiplex ligation-dependent probe amplification).

  • The greatest challenge to clinical genomics is the reliable interpretation of the multiple and novel variants found through genome sequencing. Pathogenicity of genetic variants can be examined with bioinformatics prediction approaches, protein stability studies, transcriptional activity studies and allele- and/or gene-specific animal models.

  • As broader genomic information becomes available to providers and patients, partnerships will develop to convey patient-centred data, including incidental findings. The regulatory environment must adapt to the coming volume of genomic information to maximize benefit to patients and health-care systems and to match the expectations of the patient population with regard to these technologies.

Abstract

Genomic technologies are reaching the point of being able to detect genetic variation in patients at high accuracy and reduced cost, offering the promise of fundamentally altering medicine. Still, although scientists and policy advisers grapple with how to interpret and how to handle the onslaught and ambiguity of genome-wide data, established and well-validated molecular technologies continue to have an important role, especially in regions of the world that have more limited access to next-generation sequencing capabilities. Here we review the range of methods currently available in a clinical setting as well as emerging approaches in clinical molecular diagnostics. In parallel, we outline implementation challenges that will be necessary to address to ensure the future of genetic medicine.

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References

  1. Pasche, B. & Absher, D. Whole-genome sequencing: a step closer to personalized medicine. JAMA 305, 1596–1597 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Green, E. D. & Guyer, M. S. Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204–213 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Bainbridge, M. N. et al. Whole-genome sequencing for optimized patient management. Sci. Transl. Med. 3, 87re3 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Berg, J. S. et al. Next generation massively parallel sequencing of targeted exomes to identify genetic mutations in primary ciliary dyskinesia: implications for application to clinical testing. Genet. Med. 13, 218–229 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Choi, M. et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc. Natl Acad. Sci. USA 106, 19096–19101 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Lupski, J. R. et al. Whole-genome sequencing in a patient with Charcot–Marie–Tooth neuropathy. N. Engl. J. Med. 362, 1181–1191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Maxmen, A. Exome sequencing deciphers rare diseases. Cell 144, 635–637 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Ng, S. B. et al. Exome sequencing identifies the cause of a Mendelian disorder. Nature Genet. 42, 30–35 (2010).

    CAS  PubMed  Google Scholar 

  9. Rios, J., Stein, E., Shendure, J., Hobbs, H. H. & Cohen, J. C. Identification by whole-genome resequencing of gene defect responsible for severe hypercholesterolemia. Hum. Mol. Genet. 19, 4313–4318 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Roach, J. C. et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328, 636–639 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Worthey, E. A. et al. Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet. Med. 13, 255–262 (2010).

    Article  Google Scholar 

  12. Mestan, K. K., Ilkhanoff, L., Mouli, S. & Lin, S. Genomic sequencing in clinical trials. J. Transl. Med. 9, 222 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Quail, M. A. et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 13, 341 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Liu, L. et al. Comparison of next-generation sequencing systems. J. Biomed. Biotechnol. 2012, 251364 (2012).

    PubMed  PubMed Central  Google Scholar 

  15. de Jong, A., Dondorp, W. J., Frints, S. G., de Die-Smulders, C. E. & de Wert, G. M. Advances in prenatal screening: the ethical dimension. Nature Rev. Genet. 12, 657–663 (2011). This Review covers prenatal screening strategies from ultrasound scans to genome-wide molecular tests and considers the important ethical questions concerning reproductive choice, autonomy rights of future children, equity of access and the proportionality of testing.

    Article  CAS  PubMed  Google Scholar 

  16. Phimister, E. G., Feero, W. G. & Guttmacher, A. E. Realizing genomic medicine. N. Engl. J. Med. 366, 757–759 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Sequeiros, J. et al. The wide variation of definitions of genetic testing in international recommendations, guidelines and reports. J. Commun. Genet. 3, 113–124 (2012).

    Article  Google Scholar 

  18. Kiezun, A. et al. Exome sequencing and the genetic basis of complex traits. Nature Genet. 44, 623–630 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lipman, P. J. et al. On the analysis of sequence data: testing for disease susceptibility loci using patterns of linkage disequilibrium. Genet. Epidemiol. 35, 880–886 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Massaro, J. D. et al. Analysis of five polymorphic DNA markers for indirect genetic diagnosis of haemophilia A in the Brazilian population. Haemophilia 17, e936–943 (2011).

    CAS  PubMed  Google Scholar 

  22. Michaelides, M. et al. ABCA4 mutations and discordant ABCA4 alleles in patients and siblings with bull's-eye maculopathy. Br. J. Ophthalmol. 91, 1650–1655 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pereira Fdos, S. et al. Mutations, clinical findings and survival estimates in South American patients with X-linked adrenoleukodystrophy. PLoS ONE 7, e34195 (2012).

    Article  CAS  PubMed  Google Scholar 

  24. Phylipsen, M. et al. Non-invasive prenatal diagnosis of β-thalassemia and sickle-cell disease using pyrophosphorolysis-activated polymerization and melting curve analysis. Prenat. Diagn. 32, 578–587 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. Kearns, W. G. et al. Preimplantation genetic diagnosis and screening. Semin. Reprod. Med. 23, 336–347 (2005). This paper reviews the scope of PGD to identify genetic abnormalities prior to embryo transfer and the techniques used for single cell detection of genetic variants.

    Article  CAS  PubMed  Google Scholar 

  26. Laurie, A. D. et al. Preimplantation genetic diagnosis for hemophilia A using indirect linkage analysis and direct genotyping approaches. J. Thromb. Haemost. 8, 783–789 (2010).

    Article  CAS  PubMed  Google Scholar 

  27. Wallace, A. J. Detection of unstable trinucleotide repeats. Methods Mol. Med. 5, 37–62 (1996).

    CAS  PubMed  Google Scholar 

  28. Bakker, E. Is the DNA sequence the gold standard in genetic testing? Quality of molecular genetic tests assessed. Clin. Chem. 52, 557–558 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Murphy, K. M., Berg, K. D. & Eshleman, J. R. Sequencing of genomic DNA by combined amplification and cycle sequencing reaction. Clin. Chem. 51, 35–39 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. SenGupta, D. J. & Cookson, B. T. SeqSharp: A general approach for improving cycle-sequencing that facilitates a robust one-step combined amplification and sequencing method. J. Mol. Diagn. 12, 272–277 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Robin, N. H., Falk, M. J. & Haldeman-Englert, C. R. FGFR-related craniosynostosis syndromes. GeneReviews [online], (updated 7 Jun 2011).

    Google Scholar 

  32. Katsanis, S. H. & Jabs, E. W. Treacher Collins syndrome. GeneReviews [online], (updated 30 Aug 2012).

    Google Scholar 

  33. Tartaglia, M., Gelb, B. D. & Zenker, M. Noonan syndrome and clinically related disorders. Best Pract. Res. Clin. Endocrinol. Metab. 25, 161–179 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hageman, G. S. et al. Clinical validation of a genetic model to estimate the risk of developing choroidal neovascular age-related macular degeneration. Hum. Genom. 5, 420–440 (2011).

    Article  CAS  Google Scholar 

  35. Zanke, B., Hawken, S., Carter, R. & Chow, D. A genetic approach to stratification of risk for age-related macular degeneration. Can. J. Ophthalmol. 45, 22–27 (2010).

    Article  PubMed  Google Scholar 

  36. Schaaf, C. P., Wiszniewska, J. & Beaudet, A. L. Copy number and SNP arrays in clinical diagnostics. Annu. Rev. Genom. Hum. Genet. 12, 25–51 (2011).

    Article  CAS  Google Scholar 

  37. Meschia, J. F. et al. Genomic risk profiling of ischemic stroke: results of an international genome-wide association meta-analysis. PLoS ONE 6, e23161 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tiu, R. V. et al. Prognostic impact of SNP array karyotyping in myelodysplastic syndromes and related myeloid malignancies. Blood 117, 4552–4560 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Imai, K., Kricka, L. J. & Fortina, P. Concordance study of 3 direct-to-consumer genetic-testing services. Clin. Chem. 57, 518–521 (2010).

    Article  PubMed  Google Scholar 

  40. Reid, R. J. et al. Association between health-service use and multiplex genetic testing. Genet. Med. 14, 852–859 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Haines, J. L. et al. Complement factor H variant increases the risk of age-related macular degeneration. Science 308, 419–421 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Edwards, A. O. et al. Complement factor H polymorphism and age-related macular degeneration. Science 308, 421–424 (2005).

    Article  CAS  PubMed  Google Scholar 

  44. Hageman, G. S. et al. A common haplotype in the complement regulatory gene factor H (HF1/CFH) predisposes individuals to age-related macular degeneration. Proc. Natl Acad. Sci. USA 102, 7227–7232 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gold, B. et al. Variation in factor B (BF) and complement component 2 (C2) genes is associated with age-related macular degeneration. Nature Genet. 38, 458–462 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Despriet, D. D. et al. Complement factor H polymorphism, complement activators, and risk of age-related macular degeneration. JAMA 296, 301–309 (2006).

    Article  PubMed  Google Scholar 

  47. Tsuchiya, K. D. Fluorescence in situ hybridization. Clin. Lab. Med. 31, 525–542 (2011).

    Article  PubMed  Google Scholar 

  48. Raphael, B. Chapter 6: structural variation and medical genomics. PLoS Comput. Biol. 8, e1002821 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Fruhman, G. & Van den Veyver, I. B. Applications of array comparative genomic hybridization in obstetrics. Obstet. Gynecol. Clin. North Am. 37, 71–85 (2010).

    Article  PubMed  Google Scholar 

  50. Swaminathan, G. J. et al. DECIPHER: Web-based, community resource for clinical interpretation of rare variants in developmental disorders. Hum. Mol. Genet. 21, R37–R44 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wapner, R. J. et al. Chromosomal microarray versus karyotyping for prenatal diagnosis. N. Engl. J. Med. 367, 2175–2184 (2012). This paper compares the accuracy, efficacy and yield of chromosomal microarray analysis to karyotyping as a primary diagnostic tool for the evaluation of developmental delay and structural malformations in children.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kozlowski, P., Jasinska, A. J. & Kwiatkowski, D. J. New applications and developments in the use of multiplex ligation-dependent probe amplification. Electrophoresis 29, 4627–4636 (2008). This paper describes the MLPA technique and explores its utility in copy number variation diagnostics.

    Article  CAS  PubMed  Google Scholar 

  53. Hills, A. et al. MLPA for confirmation of array CGH results and determination of inheritance. Mol. Cytogenet. 3, 19 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Talkowski, M. E. et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell 149, 525–537 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Berg, J. S., Khoury, M. J. & Evans, J. P. Deploying whole genome sequencing in clinical practice and public health: meeting the challenge one bin at a time. Genet. Med. (2011). To address the challenge of interpreting WGS data for personal use, this paper describes a 'binning' approach to reporting genomic variants.

  56. Cooper, G. M. & Shendure, J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nature Rev. Genet. 12, 628–640 (2011). A Review of approaches is presented here for determining pathogenicity of single-nucleotide variants using comparative and in silico functional genomics.

    Article  CAS  PubMed  Google Scholar 

  57. Majewski, J., Schwartzentruber, J., Lalonde, E., Montpetit, A. & Jabado, N. What can exome sequencing do for you? J. Med. Genet. 48, 580–589 (2011).

    Article  CAS  PubMed  Google Scholar 

  58. Worthey, E. A. et al. Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet. Med. 13, 255–262 (2011).

    Article  PubMed  Google Scholar 

  59. Need, A. C. et al. Clinical application of exome sequencing in undiagnosed genetic conditions. J. Med. Genet. 49, 353–361 (2012).

    Article  CAS  PubMed  Google Scholar 

  60. Mayer, A. N. et al. A timely arrival for genomic medicine. Genet. Med. 13, 195–196 (2010).

    Article  Google Scholar 

  61. Drmanac, R. The advent of personal genome sequencing. Genet. Med. 13, 188–190 (2011).

    Article  PubMed  Google Scholar 

  62. Gonzaga-Jauregui, C., Lupski, J. R. & Gibbs, R. A. Human genome sequencing in health and disease. Annu. Rev. Med. 63, 35–61 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Rehm, H. L. Disease-targeted sequencing: a cornerstone in the clinic. Nature Rev. Genet. 14, 295–300 (2013).

    Article  CAS  PubMed  Google Scholar 

  64. Branton, D. et al. The potential and challenges of nanopore sequencing. Nature Biotech. 26, 1146–1153 (2008).

    Article  CAS  Google Scholar 

  65. Skovgaard, O., Bak, M., Lobner-Olesen, A. & Tommerup, N. Genome-wide detection of chromosomal rearrangements, indels, and mutations in circular chromosomes by short read sequencing. Genome Res. 21, 1388–1393 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Smith, H. E. Identifying insertion mutations by whole-genome sequencing. Biotechniques 50, 96–97 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Metzker, M. L. Sequencing technologies — the next generation. Nature Rev. Genet. 11, 31–46 (2010). This is a technical Review of NGS technologies and recent advances in commercially available NGS instruments.

    Article  CAS  PubMed  Google Scholar 

  68. Hedges, D. J. et al. Comparison of three targeted enrichment strategies on the SOLiD sequencing platform. PLoS ONE 6, e18595 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Quail, M. et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 13, 341 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Sulonen, A. M. et al. Comparison of solution-based exome capture methods for next generation sequencing. Genome Biol. 12, R94 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Ashley, E. A. et al. Clinical assessment incorporating a personal genome. Lancet 375, 1525–1535 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Saunders, C. J. et al. Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci. Transl. Med. 4, 154ra135 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012).

    Article  CAS  PubMed  Google Scholar 

  74. de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012).

    Article  CAS  PubMed  Google Scholar 

  75. Roberts, N. J. et al. The predictive capacity of personal genome sequencing. Sci. Transl. Med. 4, 133ra58 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Zuvich, R. L. et al. Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality. Genet. Epidemiol. 35, 887–898 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Pathak, J. et al. Evaluating phenotypic data elements for genetics and epidemiological research: experiences from the eMERGE and PhenX network projects. AMIA Summits Transl. Sci. Proc. 2011, 41–45 (2011).

    PubMed  PubMed Central  Google Scholar 

  79. McCarty, C. A. et al. The eMERGE network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genom. 4, 13 (2011). This paper describes the eMERGE (Electronic Medical Records and Genomics) network and how it is exploring the utility of DNA repositories coupled with EMR systems.

    Article  Google Scholar 

  80. Belmont, J. & McGuire, A. L. The futility of genomic counseling: essential role of electronic health records. Genome Med. 1, 48 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Richards, C. S. et al. ACMG recommendations for standards for interpretation and reporting of sequence variations: revisions 2007. Genet. Med. 10, 294–300 (2008). ACMG developed recommendations for standards for interpretation of sequence variations.

    Article  CAS  PubMed  Google Scholar 

  82. Stenson, P. D. et al. The Human Gene Mutation Database: 2008 update. Genome Med. 1, 13 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Bale, S. et al. MutaDATABASE: a centralized and standardized DNA variation database. Nature Biotech. 29, 117–118 (2011).

    Article  CAS  Google Scholar 

  84. MacArthur, D. G. & Tyler-Smith, C. Loss-of-function variants in the genomes of healthy humans. Hum. Mol. Genet. 19, R125–R130 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012). This paper determined how many genetic variants predicted to cause loss of function of protein-coding genes humans carry.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Ng, S. B., Nickerson, D. A., Bamshad, M. J. & Shendure, J. Massively parallel sequencing and rare disease. Hum. Mol. Genet. 19, R119–R124 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 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 

  88. 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 

  89. Stone, E. A. & Sidow, A. Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Res. 15, 978–986 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Amberger, J., Bocchini, C. & Hamosh, A. A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®). Hum. Mutat. 32, 564–567 (2011).

    Article  PubMed  Google Scholar 

  91. Robinson, P. N. et al. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 83, 610–615 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Blake, J. A., Bult, C. J., Kadin, J. A., Richardson, J. E. & Eppig, J. T. The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics. Nucleic Acids Res. 39, D842–D848 (2011).

    Article  CAS  PubMed  Google Scholar 

  93. Sprague, J. et al. The Zebrafish Information Network: the zebrafish model organism database. Nucleic Acids Res. 34, D581–D585 (2006).

    Article  CAS  PubMed  Google Scholar 

  94. Minoche, A. E., Dohm, J. C. & Himmelbauer, H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome analyzer systems. Genome Biol. 12, R112 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Houdayer, C. et al. Evaluation of in silico splice tools for decision-making in molecular diagnosis. Hum. Mut. 29, 975–982 (2008).

    Article  CAS  PubMed  Google Scholar 

  96. Cartegni, L., Wang, J., Zhu, Z., Zhang, M. Q. & Krainer, A. R. ESEfinder: A web resource to identify exonic splicing enhancers. Nucleic Acids Res. 31, 3568–3571 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Yandell, M. et al. A probabilistic disease-gene finder for personal genomes. Genome Res. 21, 1529–1542 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Pelak, K. et al. The characterization of twenty sequenced human genomes. PLoS Genet. 6, e1001111 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Zaghloul, N. A. et al. Functional analyses of variants reveal a significant role for dominant negative and common alleles in oligogenic Bardet–Biedl syndrome. Proc. Natl Acad. Sci. USA 107, 10602–10607 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Chassaing, N. et al. OTX2 mutations contribute to the otocephaly–dysgnathia complex. J. Med. Genet. 49, 373–379 (2012).

    Article  CAS  PubMed  Google Scholar 

  101. Kato, S. et al. Understanding the function–structure and function–mutation relationships of p53 tumor suppressor protein by high-resolution missense mutation analysis. Proc. Natl Acad. Sci. USA 100, 8424–8429 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Merveille, A. C. et al. CCDC39 is required for assembly of inner dynein arms and the dynein regulatory complex and for normal ciliary motility in humans and dogs. Nature Genet. 43, 72–78 (2011).

    Article  CAS  PubMed  Google Scholar 

  103. Rosenthal, N. & Brown, S. The mouse ascending: perspectives for human-disease models. Nature Cell Biol. 9, 993–999 (2007).

    Article  CAS  PubMed  Google Scholar 

  104. Siddiqui, S. S. et al. C. elegans as a model organism for in vivo screening in cancer: effects of human c-Met in lung cancer affect C. elegans vulva phenotypes. Cancer Biol. Ther. 7, 856–863 (2008).

    Article  CAS  PubMed  Google Scholar 

  105. Bick, D. & Dimmock, D. Whole exome and whole genome sequencing. Curr. Opin. Pediatr. 23, 594–600 (2011).

    Article  PubMed  Google Scholar 

  106. Drmanac, R. Medicine. The ultimate genetic test. Science 336, 1110–1112 (2012).

    Article  CAS  PubMed  Google Scholar 

  107. Brunham, L. R. & Hayden, M. R. Medicine. Whole-genome sequencing: the new standard of care? Science 336, 1112–1113 (2012).

    Article  CAS  PubMed  Google Scholar 

  108. Facio, F. M. et al. Intentions to receive individual results from whole-genome sequencing among participants in the ClinSeq study. Eur. J. Hum. Genet. 21, 261–265 (2013).

    Article  PubMed  Google Scholar 

  109. Murphy, J. et al. Public expectations for return of results from large-cohort genetic research. Am. J. Bioeth. 8, 36–43 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Fullerton, S. M. et al. Return of individual research results from genome-wide association studies: experience of the Electronic Medical Records and Genomics (eMERGE) network. Genet. Med. 14, 424–431 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Jamal, S. M. et al. Practices and policies of clinical exome sequencing providers: analysis and implications. Am. J. Med. Genet. A 5 Apr 2013 (doi:10.1002/j.1552-4833.2013.35942.x). This paper presents ACMG guidelines for clinical exome sequencing.

  112. Tabor, H. K. et al. Informed consent for whole genome sequencing: a qualitative analysis of participant expectations and perceptions of risks, benefits, and harms. Am. J. Med. Genet. A 158, 1310–1319 (2012).

    Article  Google Scholar 

  113. Feero, W. G. & Green, E. D. Genomics education for health care professionals in the 21st century. JAMA 306, 989–990 (2011).

    Article  CAS  PubMed  Google Scholar 

  114. Haga, S. et al. Survey of genetic counselors and clinical geneticists' use and attitudes toward pharmacogenetic testing. Clin. Genet. 82, 115–120 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Dougherty, M. J., Pleasants, C., Solow, L., Wong, A. & Zhang, H. A comprehensive analysis of high school genetics standards: are States keeping pace with modern genetics? CBE Life Sci. Educ. 10, 318–327 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Redfield, R. J. “Why do we have to learn this stuff?”—a new genetics for 21st century students. PLoS Biol. 10, e1001356 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Guttmacher, A. E., Porteous, M. E. & McInerney, J. D. Educating health-care professionals about genetics and genomics. Nature Rev. Genet. 8, 151–157 (2007).

    Article  CAS  PubMed  Google Scholar 

  118. O'Daniel, J. M. The prospect of genome-guided preventive medicine: a need and opportunity for genetic counselors. J. Genet. Couns. 19, 315–327 (2010).

    Article  PubMed  Google Scholar 

  119. Jenkins, J. & Calzone, K. A. Establishing the essential nursing competencies for genetics and genomics. J. Nurs. Scholarsh. 39, 10–16 (2007).

    Article  PubMed  Google Scholar 

  120. Atkinson, N. L., Saperstein, S. L. & Pleis, J. Using the internet for health-related activities: findings from a national probability sample. J. Med. Internet Res. 11, e4 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  121. van Uden-Kraan, C. F. et al. Health-related Internet use by patients with somatic diseases: frequency of use and characteristics of users. Inform Health Soc. Care 34, 18–29 (2009).

    Article  PubMed  Google Scholar 

  122. Bell, R. A., Hu, X., Orrange, S. E. & Kravitz, R. L. Lingering questions and doubts: online information-seeking of support forum members following their medical visits. Patient Educ. Couns. 85, 525–528 (2011).

    Article  PubMed  Google Scholar 

  123. Nambisan, P. Evaluating patient experience in online health communities: implications for health care organizations. Health Care Manage. Rev. 36, 124–133 (2010).

    Article  Google Scholar 

  124. Shute, N. Connecting and sharing on the Web. At 'crowd-sourced' disease sites, patients can swap stories—and data. US News World Rep. 146, 82–83 (2009).

    PubMed  Google Scholar 

  125. Swan, M. Crowdsourced health research studies: an important emerging complement to clinical trials in the public health research ecosystem. J. Med. Internet Res. 14, e46 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Wicks, P. et al. Sharing health data for better outcomes on PatientsLikeMe. J. Med. Internet Res. 12, e19 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Bollinger, J. M., Scott, J., Dvoskin, R. & Kaufman, D. Public preferences regarding the return of individual genetic research results: findings from a qualitative focus group study. Genet. Med. 14, 451–457 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Kaufman, D., Murphy, J., Scott, J. & Hudson, K. Subjects matter: a survey of public opinions about a large genetic cohort study. Genet. Med. 10, 831–839 (2008).

    Article  PubMed  Google Scholar 

  129. Lemke, A. A., Wolf, W. A., Hebert-Beirne, J. & Smith, M. E. Public and biobank participant attitudes toward genetic research participation and data sharing. Publ. Health Genom. 13, 368–377 (2010).

    Article  CAS  Google Scholar 

  130. Murphy, J. et al. Public perspectives on informed consent for biobanking. Am. J. Publ. Health 99, 2128–2134 (2009).

    Article  Google Scholar 

  131. Willison, D. J. et al. Consent for use of personal information for health research: do people with potentially stigmatizing health conditions and the general public differ in their opinions? BMC Med. Eth. 10, 10 (2009).

    Article  Google Scholar 

  132. Leighton, J. W., Valverde, K. & Bernhardt, B. A. The general public's understanding and perception of direct-to-consumer genetic test results. Pub. Health Genom. 15, 11–21 (2011).

    Article  Google Scholar 

  133. Carbone, J. et al. DNA patents and diagnostics: not a pretty picture. Nature Biotech. 28, 784–791 (2010).

    Article  CAS  Google Scholar 

  134. Cook-Deegan, R. et al. Impact of gene patents and licensing practices on access to genetic testing for inherited susceptibility to cancer: comparing breast and ovarian cancers with colon cancers. Genet. Med. 12, S15–S38 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Grosse, S. D., Kalman, L. & Khoury, M. J. Evaluation of the validity and utility of genetic testing for rare diseases. Adv. Exp. Med. Biol. 686, 115–131 (2010).

    Article  PubMed  Google Scholar 

  136. Eckermann, S. & Willan, A. R. Presenting evidence and summary measures to best inform societal decisions when comparing multiple strategies. Pharmacoeconomics 29, 563–577 (2011).

    Article  PubMed  Google Scholar 

  137. Douglas, P. S. & Ginsburg, G. S. Clinical genomic testing: getting it right. J. Cardiovasc. Transl. Res. 1, 17–20 (2008).

    Article  PubMed  Google Scholar 

  138. Dinan, M. A., Simmons, L. A. & Snyderman, R. Commentary: personalized health planning and the patient protection and affordable care act: an opportunity for academic medicine to lead health care reform. Acad. Med. 85, 1665–1668 (2010).

    Article  PubMed  Google Scholar 

  139. Chan, I. S. & Ginsburg, G. S. Personalized medicine: progress and promise. Annu. Rev. Genom. Hum. Genet. 12, 217–244 (2011).

    Article  CAS  Google Scholar 

  140. Strauss, K. A., Puffenberger, E. G. & Morton, D. H. One community's effort to control genetic disease. Am. J. Publ. Health 102, 1300–1306 (2012).

    Article  Google Scholar 

  141. Schadt, E. E., Turner, S. & Kasarskis, A. A window into third-generation sequencing. Hum. Mol. Genet. 19, R227–R240 (2010).

    Article  CAS  PubMed  Google Scholar 

  142. Sanderson, K. Personal genomes: standard and pores. Nature 456, 23–25 (2008).

    Article  CAS  PubMed  Google Scholar 

  143. Badano, J. L. & Katsanis, N. Beyond Mendel: an evolving view of human genetic disease transmission. Nature Rev. Genet. 3, 779–789 (2002). This Review describes muti-locus inheritance as seen in both rare and complex disorders.

    Article  CAS  PubMed  Google Scholar 

  144. Zaghloul, N. A. & Katsanis, N. Functional modules, mutational load and human genetic disease. Trends Genet. 26, 168–176 (2010). This is a review of approaches to model genomic variants in vivo to determine functionality.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Giess, R. et al. Early onset of severe familial amyotrophic lateral sclerosis with a SOD-1 mutation: potential impact of CNTF as a candidate modifier gene. Am. J. Hum. Genet. 70, 1277–1286 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Lyon, G. J. et al. Exome sequencing and unrelated findings in the context of complex disease research: ethical and clinical implications. Discov. Med. 12, 41–55 (2011).

    PubMed  PubMed Central  Google Scholar 

  147. Christenhusz, G. M., Devriendt, K. & Dierickx, K. To tell or not to tell? A systematic review of ethical reflections on incidental findings arising in genetics contexts. Eur. J. Hum. Genet. 21, 248–255 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Lunshof, J. E., Chadwick, R., Vorhaus, D. B. & Church, G. M. From genetic privacy to open consent. Nature Rev. Genet. 9, 406–411 (2008). This paper describes the challenges of handling genome-wide data in a research setting.

    Article  CAS  PubMed  Google Scholar 

  149. Ball, M. P. et al. A public resource facilitating clinical use of genomes. Proc. Natl Acad. Sci. USA 109, 11920–11927 (2012). The Genome–Environment–Trait Evidence (GET-Evidence) tool is introduced here; it is used for processing personal whole-genome data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Marchant, G. E., Milligan, R. J. & Wilhelmi, B. Legal pressures and incentives for personalized medicine. Per. Med. 3, 391–397 (2006).

    Article  PubMed  Google Scholar 

  151. Bradbury, A. R. et al. Parent opinions regarding the genetic testing of minors for BRCA1/2. J. Clin. Oncol. 28, 3498–3505 (2010).

    Article  PubMed  Google Scholar 

  152. Clarke, A. The genetic testing of children. Working Party of the Clinical Genetics Society (UK). J. Med. Genet. 31, 785–797 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Duncan, R. E., Savulescu, J., Gillam, L., Williamson, R. & Delatycki, M. B. An international survey of predictive genetic testing in children for adult onset conditions. Genet. Med. 7, 390–396 (2005).

    Article  PubMed  Google Scholar 

  154. Hawkins, A. K., Ho, A. & Hayden, M. R. Lessons from predictive testing for Huntington disease: 25 years on. J. Med. Genet. 48, 649–650 (2011).

    Article  PubMed  Google Scholar 

  155. Cohen, C. B. Wrestling with the future: should we test children for adult-onset genetic conditions? Kennedy Inst. Eth. J. 8, 111–130 (1998).

    Article  Google Scholar 

  156. Robertson, S. & Savulescu, J. Is there a case in favour of predictive genetic testing in young children? Bioethics 15, 26–49 (2001).

    Article  CAS  PubMed  Google Scholar 

  157. Van Hoyweghen, I. & Horstman, K. European practices of genetic information and insurance: lessons for the Genetic Information Nondiscrimination Act. JAMA 300, 326–327 (2008).

    Article  CAS  PubMed  Google Scholar 

  158. Hudson, K. L. Genomics, health care, and society. N. Engl. J. Med. 365, 1033–1041 (2011).

    Article  CAS  PubMed  Google Scholar 

  159. Department of Health and Human Services. HIPAA administrative simplification: standards for privacy of individually identifiable health information. Federal Register [online], (2009).

  160. Allain, D. C., Friedman, S. & Senter, L. Consumer awareness and attitudes about insurance discrimination post enactment of the Genetic Information Nondiscrimination Act. Fam. Cancer 11, 637–644 (2012).

    Article  PubMed  Google Scholar 

  161. Haga, S. B. et al. Genomic risk profiling: attitudes and use in personal and clinical care of primary care physicians who offer risk profiling. J. Gen. Intern. Med. 26, 834–840 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  162. Hudson, K. L. et al. Oversight of US genetic testing laboratories. Nature Biotech. 24, 1083–1090 (2006).

    Article  CAS  Google Scholar 

  163. Horn, E. J. & Terry, S. F. Regulating genetic tests: issues that guide policy decisions. Genet. Test. Mol. Biomarkers 16, 1–2 (2012).

    Article  PubMed  Google Scholar 

  164. Borry, P., Nys, H. & Dierickx, K. Carrier testing in minors: conflicting views. Nature Rev. Genet. 8, 828 (2007).

    Article  CAS  PubMed  Google Scholar 

  165. Borry, P., Stultiens, L., Nys, H. & Dierickx, K. Attitudes towards predictive genetic testing in minors for familial breast cancer: a systematic review. Crit. Rev. Oncol. Hematol. 64, 173–181 (2007).

    Article  PubMed  Google Scholar 

  166. Moran, C., Thornburg, C. D. & Barfield, R. C. Ethical considerations for pharmacogenomic testing in pediatric clinical care and research. Pharmacogenomics 12, 889–895 (2011).

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors thank E. Davis and M. Angrist for their thoughtful suggestions for this manuscript.

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Glossary

Large-insert clone

A large haplotype fragment that is inserted into, for example, a bacterial artificial chromosome.

Oligonucleotide arrays

Hybridization of a nucleic acid sample to a very large set of oligonucleotide probes, which are attached to a solid support, to determine sequence, to detect variations or to carry out gene expression or mapping.

Exome

The collection of protein-coding regions (exons) in the genome. As exons comprise only 1% of the genome and contain the most easily understood and functionally relevant information, sequencing of only the exome is an efficient method of identifying many variants that are likely to affect a trait.

Next-generation sequencing

(NGS). NGS platforms sequence as many as billions of DNA strands in parallel, yielding substantially more throughput than Sanger sequencing and minimizing the need for the fragment-cloning methods that are often used in Sanger sequencing of genomes.

Direct genetic testing

Testing that looks at the presence or absence of known genetic variants that contribute to pathogenicity.

Indirect genetic testing

Testing that compares the genetic regions of multiple affected persons to unaffected persons. Indirect genetic tests may evaluate patterns of inheritance in multiple family members with a known trait and look at the segregation of the trait with genetic markers.

Linkage analysis

A statistical method for identifying a region of the genome that is implicated in a trait by observing which region is inherited from the parental strain carrying the trait in offspring that carry the trait.

Single-nucleotide polymorphisms

(SNPs). Differences in the nucleotide composition at single positions in the DNA sequence.

Short tandem repeats

(STRs). DNA sequences containing a variable number of highly polymorphic, tandemly repeated short (2–6 bp) sequences.

Non-invasive prenatal testing

(NIPT). A method of obtaining a prenatal diagnosis by detecting fetal cells circulating in maternal blood.

Pre-implantation genetic diagnosis

(PGD). An in vitro method of identifying genetic defects in in vitro fertilization embryos before maternal transfer and implant.

Sanger sequencing

A method used to determine the nucleotides present in a fragment of DNA. It is based on the chain terminator method developed by Frederick Sanger but currently uses labelling of the chain terminator dideoxynucleotides, allowing sequencing in a single reaction.

Array comparative genomic hybridization

(Array CGH). A microarray-based method of identifying differences in DNA copy number by comparing a sampled genome to a reference genome.

Penetrance

The proportion of individuals with a given genotype who display a particular phenotype.

Fluorescent in situ hybridization

(FISH). A molecular and cytogenetic method using a fluorescently labelled DNA probe to detect a particular chromosome or gene using fluorescence microscopy.

Uniparental disomy

(UPD). An occurrence of an individual inheriting both copies of her chromosome from one parent.

Restriction fragment length polymorphisms

(RFLP). Variations between individuals in the lengths of DNA regions that are cut by a particular endonuclease.

Multiplex ligation-dependent probe amplification

(MLPA). A molecular technique involving the ligation of two adjacent annealing oligonucleotides followed by quantitative PCR amplification of the ligated products, allowing the characterization of chromosomal aberrations in copy number or sequence and single-nucleotide polymorphism or mutation detection.

Copy number variants

(CNVs). Structural genomic variants that result in copy number changes in specific chromosomal regions. Usually, there are two copies of each locus, but if, for example, duplications or triplications occur, then the number of copies will increase.

Variants of unknown significance

(VUSs). Alterations in the sequence of a gene, the significance of which are unclear.

Genetic determinism

The idea that genes and genetic variants are the primary factor determining and shaping human traits.

Epigenomics

Describes a heritable effect on chromosome or gene function that is not accompanied by a change in DNA sequence but rather by modifications of chromatin or DNA.

Epialleles

An epigenetic variant of an allele. The activity of an epiallele is dependent on epigenetic modifications such as histone deacetylation or cytosine methylation.

Genetic exceptionalism

The view that genetic information, traits and properties are qualitatively different and deserving of exceptional consideration.

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Katsanis, S., Katsanis, N. Molecular genetic testing and the future of clinical genomics. Nat Rev Genet 14, 415–426 (2013). https://doi.org/10.1038/nrg3493

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