Genetic Test, Risk Prediction, and Counseling

  • Maggie Haitian Wang
  • Haoyi Weng
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1005)


Advancement in technology has nurtured the new era of genetic tests for personalized medicine. In this chapter, we will introduce the current development, challenges, and the outlook of genetic test, disease risk prediction, and genetic counseling. In the first section, we will present the success cases in the areas of molecular classification of tumors, pharmacogenomics, and Mendelian disorders, and the challenges of genetic tests implementations. In the second section, common methods for genetic risk prediction models and evaluation measures will be introduced, as well as challenges in feature reliability, risk model stability, and clinical utility. In the final section, key components of genetic counseling will be introduced, covering individual communications, psychosocial concerns, risk assessments, and follow-ups. Current evidences have shown a promising future for genetic testing and risk prediction; we expect that the advancement of analytical methods, technology, integration of omics data, and the increasing clinical implementation and regulation will continue to pave the way for precision medicine in future.


Genetic test Disease risk prediction Genetic counseling 


  1. 1.
    McCarthy MI, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9(5):356–69.CrossRefPubMedGoogle Scholar
  2. 2.
    Katsanis SH, Katsanis N. Molecular genetic testing and the future of clinical genomics. Nat Rev Genet. 2013;14(6):415–26.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Scott AR. Technology: read the instructions. Nature. 2016;537(7619):S54–6.CrossRefPubMedGoogle Scholar
  4. 4.
    Marino, MJ, Traboulsi EI, Genetic counseling and testing, in practical management of pediatric ocular disorders and Strabismus. Springer; 2016. pp. 329–36.Google Scholar
  5. 5.
    Kalf RR, et al. Variations in predicted risks in personal genome testing for common complex diseases. Genet Med. 2013;16(1):85–91.CrossRefPubMedGoogle Scholar
  6. 6.
    Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med. 2011;364(6):524–34.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Hunter DJ, Khoury MJ, Drazen JM. Letting the genome out of the bottle—will we get our wish? N Engl J Med. 2008;358(2):105–7.CrossRefPubMedGoogle Scholar
  8. 8.
    Bamshad MJ, et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011;12(11):745–55.CrossRefPubMedGoogle Scholar
  9. 9.
    Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet. 2016;17(6):333–51.CrossRefPubMedGoogle Scholar
  10. 10.
    Ashley EA. Towards precision medicine. Nat Rev Genet. 2016;17(9):507–22.CrossRefPubMedGoogle Scholar
  11. 11.
    Manolio TA, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med. 2013;15(4):258–67.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    National Research Council (U.S.). Committee on A Framework for Developing a New Taxonomy of Disease. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press (US); 2011.Google Scholar
  13. 13.
    Schrodi SJ, et al. Genetic-based prediction of disease traits: prediction is very difficult, especially about the future†. Front Genet. 2014;5:162.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Hayes DF, et al. Personalized medicine: risk prediction, targeted therapies and mobile health technology. BMC Med. 2014;12(1):37.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Drew L. Pharmacogenetics: the right drug for you. Nature. 2016;537(7619):S60–2.CrossRefPubMedGoogle Scholar
  16. 16.
    Auffray C, et al. From genomic medicine to precision medicine: highlights of 2015. Genome Med. 2016;8(1):1.CrossRefGoogle Scholar
  17. 17.
    Hunter DJ. Uncertainty in the era of precision medicine. N Engl J Med. 2016;375(8):711–3.CrossRefPubMedGoogle Scholar
  18. 18.
    Coote JH, Joyner MJ. Is precision medicine the route to a healthy world? Lancet. 2015;385(9978):1617.CrossRefPubMedGoogle Scholar
  19. 19.
    Joyner MJ, Paneth N. Seven questions for personalized medicine. JAMA. 2015;314(10):999–1000.CrossRefPubMedGoogle Scholar
  20. 20.
    Roberts NJ, et al. The predictive capacity of personal genome sequencing. Sci Transl Med. 2012;4(133):133ra58.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Christensen KD, et al. Assessing the costs and cost-effectiveness of genomic sequencing. J Pers Med. 2015;5(4):470–86.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Miller CE. Genetic counseling. In: Molecular pathology in clinical practice. New York: Springer; 2016. p. 55–62.CrossRefGoogle Scholar
  23. 23.
    Sohn E. Diagnosis: a clear answer. Nature. 2016;537(7619):S64–5.CrossRefPubMedGoogle Scholar
  24. 24.
    Schadt EE, Turner S, Kasarskis A. A window into third-generation sequencing. Hum Mol Genet. 2010;19(R2):R227–40.CrossRefPubMedGoogle Scholar
  25. 25.
    Abraham G, Inouye M. Genomic risk prediction of complex human disease and its clinical application. Curr Opin Genet Dev. 2015;33:10–6.CrossRefPubMedGoogle Scholar
  26. 26.
    Krier J, et al. Reclassification of genetic-based risk predictions as GWAS data accumulate. Genome Med. 2016;8(1):1.CrossRefGoogle Scholar
  27. 27.
    Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet. 2016;17:392–406.CrossRefPubMedGoogle Scholar
  28. 28.
    Müller B, et al. Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives. Hum Genet. 2016;135(3):259–72.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Kong SW, et al. Summarizing polygenic risks for complex diseases in a clinical whole-genome report. Genet Med. 2014;17(7):536–44.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Chatterjee N, et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet. 2013;45(4):400–5.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Wu J, Pfeiffer RM, Gail MH. Strategies for developing prediction models from genome-wide association studies. Genet Epidemiol. 2013;37(8):768–77.CrossRefPubMedGoogle Scholar
  32. 32.
    Vilhjálmsson BJ, et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet. 2015;97(4):576–92.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Gauderman WJ, et al. Testing association between disease and multiple SNPs in a candidate gene. Genet Epidemiol. 2007;31(5):383–95.CrossRefPubMedGoogle Scholar
  34. 34.
    Wang MH, et al. A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Res. 2016;44(12):10526.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Shan Y, et al. Genetic risk models: model size and confidence intervals of the risk estimates. In: 63rd Annual Meeting of The American Society of Human Genetics. 2013.Google Scholar
  36. 36.
    Okser S, et al. Regularized machine learning in the genetic prediction of complex traits. PLoS Genet. 2014;10(11):e1004754.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Kruppa J, Ziegler A, König IR. Risk estimation and risk prediction using machine-learning methods. Hum Genet. 2012;131(10):1639–54.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning : data mining, inference, and prediction, Springer series in statistics. 2nd ed. New York: Springer; 2009. xxii, 745 pCrossRefGoogle Scholar
  39. 39.
    Pfeiffer R, Gail M. Two criteria for evaluating risk prediction models. Biometrics. 2011;67(3):1057–65.CrossRefPubMedGoogle Scholar
  40. 40.
    Steyerberg, E.W., et al., Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology (Cambridge, MA), 2010. 21(1): p. 128.Google Scholar
  41. 41.
    Pencina MJ, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72.CrossRefPubMedGoogle Scholar
  42. 42.
    Paulsen JS, et al. Prediction of manifest Huntington’s disease with clinical and imaging measures: a prospective observational study. Lancet Neurol. 2014;13(12):1193–201.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Walker FO. Huntington’s disease. Lancet. 2007;369(9557):218–28.CrossRefPubMedGoogle Scholar
  44. 44.
    Langbehn DR, et al. A new model for prediction of the age of onset and penetrance for Huntington’s disease based on CAG length. Clin Genet. 2004;65(4):267–77.CrossRefPubMedGoogle Scholar
  45. 45.
    Pharoah PD, et al. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. 2008;358(26):2796–803.CrossRefPubMedGoogle Scholar
  46. 46.
    Meads C, Ahmed I, Riley RD. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res Treat. 2012;132(2):365–77.CrossRefPubMedGoogle Scholar
  47. 47.
    Mavaddat N, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst. 2015;107(5):djv036.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Vachon CM, et al. The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst. 2015;107(5):dju397.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Mega JL, et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015;385(9984):2264–71.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Ripatti S, et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010;376(9750):1393–400.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Thanassoulis G, et al. A genetic risk score is associated with incident cardiovascular disease and coronary artery calcium the Framingham heart study. Circ Cardiovasc Genet. 2012;5(1):113–21.CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Ganna A, et al. Multilocus genetic risk scores for coronary heart disease prediction. Arterioscler Thromb Vasc Biol. 2013;33(9):2267–72.CrossRefPubMedGoogle Scholar
  53. 53.
    Beaney KE, et al. Clinical utility of a coronary heart disease risk prediction gene score in UK healthy middle aged men and in the Pakistani population. PLoS One. 2015;10(7):e0130754.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 2013;9(3):e1003348.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Gibson G. Rare and common variants: twenty arguments. Nat Rev Genet. 2012;13(2):135–45.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Wu MC, et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89(1):82–93.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Madsen BE, Browning SR. A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic. PLoS Genet. 2009;5(2):e1000384.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Liu DJJ, Leal SM. A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet. 2010;6(10):e1001156.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Lee S, et al. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet. 2014;95(1):5–23.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Marteau TM, Lerman C. Genetic risk and behavioural change. BMJ. 2001;322(7293):1056–9.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Vassy JL, et al. Impact of literacy and numeracy on motivation for behavior change after diabetes genetic risk testing. Med Decis Mak. 2012;32(4):606–15.CrossRefGoogle Scholar
  62. 62.
    Grant RW, et al. Personalized genetic risk counseling to motivate diabetes prevention a randomized trial. Diabetes Care. 2013;36(1):13–9.CrossRefPubMedGoogle Scholar
  63. 63.
    Evans C. An overview of genetic counselling. In: Genetic counselling: a psychological approach. Cambridge: Cambridge University Press; 2006. p. 1–16.CrossRefGoogle Scholar
  64. 64.
    Klemm SL, Fulbright J. Genetic counseling. In: Health care for people with intellectual and developmental disabilities across the lifespan. Cham: Springer; 2016. p. 731–6.CrossRefGoogle Scholar
  65. 65.
    Ormond KE. From genetic counseling to “genomic counseling”. Mol Genet Genomic Med. 2013;1(4):189–93.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Shelton CA, Whitcomb DC. Evolving roles for physicians and genetic counselors in managing complex genetic disorders. Clin Transl Gastroenterol. 2015;6(11):e124.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Ropers H-H. On the future of genetic risk assessment. J Community Genet. 2012;3(3):229–36.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Abul-Husn NS, et al. Implementation and utilization of genetic testing in personalized medicine. Pharmacogenomics Pers Med. 2014;7:227–40.Google Scholar
  69. 69.
    Harris A, Kelly SE, Wyatt S. Counseling customers: emerging roles for genetic counselors in the direct-to-consumer genetic testing market. J Genet Couns. 2013;22(2):277–88.CrossRefPubMedGoogle Scholar
  70. 70.
    Wang MH, Weng H, Sun R, Lee J, Wu WK, Chong KC, Zee BC. A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests. Bioinformatics. 2017;33(15):2330–2336. doi: 10.1093/bioinformatics/btx130.

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© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Division of BiostatisticsThe Jockey Club School of Public Health and Primary Care, The Chinese University of Hong KongHong Kong SARChina

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