Luigi Donato, Concetta Scimone, Simona Alibrandi, Rosalia D’Angelo, Antonina Sidoti
Abstract
The most of chronic and common pathologies represent the result of intricate and heterogeneous causes, from heritable components to environmental elements. This complex picture represents a strong challenge towards the acknowledgement of diseases etiology, which could be fight by discovery and use of disease predisposing alleles. This purpose could be realized using many genetic tests, which could facilitate early treatment, preemptive selection of efficacious drugs, and more accurate estimation of risk, because severity and response to cure reflects the underlying individual allelic picture. But, if the effective advantages of such model are relevant for monogenic disorders, more complex results the situation for polygenic ones, as Retinitis pigmentosa and Cerebral Cavernous Malformations. Moreover, elements like lifestyle and environment, risk of false positive or negative, and accessibility to analysis data make the results and risks determined by predictive medicine more difficult to quantify. Finally, prediction could represent the future of translational research.
References
1. Liley, J., J.A. Todd, and C. Wallace, A method for identifying genetic heterogeneity within phenotypically defined disease subgroups. Nat Genet, 2016.
2. Bin, L. and D.Y. Leung, Genetic and epigenetic studies of atopic dermatitis. Allergy Asthma Clin Immunol, 2016. 12: p. 52.
3. Dalle Molle, R., H. Fatemi, A. Dagher, R.D. Levitan, P.P. Silveira, and L. Dube, Gene and environment interaction: is the differential susceptibility hypothesis relevant for obesity? Neurosci Biobehav Rev, 2016.
4. Gatica, L.V. and A.L. Rosa, A complex interplay of genetic and epigenetic events leads to abnormal expression of the DUX4 gene in facioscapulohumeral muscular dystrophy. Neuromuscul Disord, 2016. 26(12): p. 844-852.
5. Moheimani, F., A.C. Hsu, A.T. Reid, T. Williams, A. Kicic, S.M. Stick, P.M. Hansbro, P.A. Wark, and D.A. Knight, The genetic and epigenetic landscapes of the epithelium in asthma. Respir Res, 2016. 17(1): p. 119.
6. Park, J.H., M.H. Gail, C.R. Weinberg, R.J. Carroll, C.C. Chung, Z. Wang, S.J. Chanock, J.F. Fraumeni, Jr., and N. Chatterjee, Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants. Proc Natl Acad Sci U S A, 2011. 108(44): p. 18026-31.
7. Janssens, A.C. and C.M. van Duijn, Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet, 2008. 17(R2): p. R166-73.
8. Mroziewicz, M. and R.F. Tyndale, Pharmacogenetics: a tool for identifying genetic factors in drug dependence and response to treatment. Addict Sci Clin Pract, 2010. 5(2): p. 17-29.
9. Chen, L., D.W. Au, C. Hu, D.R. Peterson, B. Zhou, and P.Y. Qian, Identification of Molecular Targets for 4,5-Dichloro-2-n-octyl-4-isothiazolin-3-one (DCOIT) in Teleosts: New Insight into Mechanism of Toxicity. Environ Sci Technol, 2016.
10. Naj, A.C., G.D. Schellenberg, and C. Alzheimer’s Disease Genetics, Genomic variants, genes, and pathways of Alzheimer’s disease: An overview. Am J Med Genet B Neuropsychiatr Genet, 2017. 174(1): p. 5-26.
11. Bek, S., J.V. Nielsen, A.B. Bojesen, A. Franke, S. Bank, U. Vogel, and V. Andersen, Systematic review: genetic biomarkers associated with anti-TNF treatment response in inflammatory bowel diseases. Aliment Pharmacol Ther, 2016. 44(6): p. 554-67.
12. Bayraktar, S. and B. Arun, BRCA mutation genetic testing implications in the United States. Breast, 2016. 31: p. 224-232.
13. Chiang, S.W., D.Y. Wang, W.M. Chan, P.O. Tam, K.K. Chong, D.S. Lam, and C.P. Pang, A novel missense RP1 mutation in retinitis pigmentosa. Eye (Lond), 2006. 20(5): p. 602-5.
14. Corbo, C., A. Cevenini, and F. Salvatore, Biomarker discovery by proteomics-based approaches for early detection and personalized medicine in colorectal cancer. Proteomics Clin Appl, 2016.
15. Wang, L., K. Hara, J.M. Van Baaren, J.C. Price, G.W. Beecham, P.J. Gallins, P.L. Whitehead, G. Wang, C. Lu, M.A. Slifer, S. Zuchner, E.R. Martin, D. Mash, J.L. Haines, M.A. Pericak-Vance, and J.R. Gilbert, Vitamin D receptor and Alzheimer’s disease: a genetic and functional study. Neurobiol Aging, 2012. 33(8): p. 1844 e1-9.
16. Li, H., I. Achour, L. Bastarache, J. Berghout, V. Gardeux, J. Li, Y. Lee, L. Pesce, X. Yang, K.S. Ramos, I. Foster, J.C. Denny, J.H. Moore, and Y.A. Lussier, Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions. NPJ Genom Med, 2016. 1.
17. Abul-Husn, N.S., K. Manickam, L.K. Jones, E.A. Wright, D.N. Hartzel, C. Gonzaga-Jauregui, C. O’Dushlaine, J.B. Leader, H. Lester Kirchner, D.M. Lindbuchler, M.L. Barr, M.A. Giovanni, M.D. Ritchie, J.D. Overton, J.G. Reid, R.P. Metpally, A.H. Wardeh, I.B. Borecki, G.D. Yancopoulos, A. Baras, A.R. Shuldiner, O. Gottesman, D.H. Ledbetter, D.J. Carey, F.E. Dewey, and M.F. Murray, Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science, 2016. 354(6319).
18. Lussier, Y.A., H. Li, N. Pouladi, and Q. Li, Accelerating precision biology and medicine with computational biology and bioinformatics. Genome Biol, 2014. 15(9): p. 450.
19. Wang, W., F.Y. Hu, X.T. Wu, D.M. An, B. Yan, and D. Zhou, Genetic susceptibility to the cross-reactivity of aromatic antiepileptic drugs-induced cutaneous adverse reactions. Epilepsy Res, 2014. 108(6): p. 1041-5.
20. Taherian-Fard, A., S. Srihari, and M.A. Ragan, Breast cancer classification: linking molecular mechanisms to disease prognosis. Brief Bioinform, 2015. 16(3): p. 461-74.
21. Koopman, R.J. and A.G. Mainous, 3rd, Evaluating multivariate risk scores for clinical decision making. Fam Med, 2008. 40(6): p. 412-6.
22. Kamatani, Y., [Genome Wide Association Study:its theory and methodological review]. Clin Calcium, 2016. 26(4): p. 525-35.
23. Khoury, M.J., A.C. Janssens, and D.F. Ransohoff, How can polygenic inheritance be used in population screening for common diseases? Genet Med, 2013. 15(6): p. 437-43.
24. Mackey, D.A. and A.W. Hewitt, Genome-wide association study success in ophthalmology. Curr Opin Ophthalmol, 2014. 25(5): p. 386-93.
25. Berry, S.A., Newborn screening. Clin Perinatol, 2015. 42(2): p. 441-53, x.
26. Mand, C., L. Gillam, M.B. Delatycki, and R.E. Duncan, Predictive genetic testing in minors for late-onset conditions: a chronological and analytical review of the ethical arguments. J Med Ethics, 2012. 38(9): p. 519-24.
27. van Ravesteijn, H., I. van Dijk, D. Darmon, F. van de Laar, P. Lucassen, T.O. Hartman, C. van Weel, and A. Speckens, The reassuring value of diagnostic tests: a systematic review. Patient Educ Couns, 2012. 86(1): p. 3-8.
28. van Kampen, A.H. and P.D. Moerland, Taking Bioinformatics to Systems Medicine. Methods Mol Biol, 2016. 1386: p. 17-41.
29. Latendresse, G. and A. Deneris, An update on current prenatal testing options: first trimester and noninvasive prenatal testing. J Midwifery Womens Health, 2015. 60(1): p. 24-36; quiz 111.
30. Vears, D.F. and S.A. Metcalfe, Carrier testing in children and adolescents. Eur J Med Genet, 2015. 58(12): p. 659-67.
31. Nypaver, C., M. Arbour, and E. Niederegger, Preconception Care: Improving the Health of Women and Families. J Midwifery Womens Health, 2016. 61(3): p. 356-64.
32. Tanriover, G., B. Sozen, A. Seker, T. Kilic, M. Gunel, and N. Demir, Ultrastructural analysis of vascular features in cerebral cavernous malformations. Clin Neurol Neurosurg, 2013. 115(4): p. 438-44.
33. Tu, J., M.A. Stoodley, M.K. Morgan, and K.P. Storer, Ultrastructural characteristics of hemorrhagic, nonhemorrhagic, and recurrent cavernous malformations. J Neurosurg, 2005. 103(5): p. 903-9.
34. Choquet, H., L. Pawlikowska, M.T. Lawton, and H. Kim, Genetics of cerebral cavernous malformations: current status and future prospects. J Neurosurg Sci, 2015. 59(3): p. 211-20.
35. McDonald, D.A., C. Shi, R. Shenkar, C.J. Gallione, A.L. Akers, S. Li, N. De Castro, M.J. Berg, D.L. Corcoran, I.A. Awad, and D.A. Marchuk, Lesions from patients with sporadic cerebral cavernous malformations harbor somatic mutations in the CCM genes: evidence for a common biochemical pathway for CCM pathogenesis. Hum Mol Genet, 2014. 23(16): p. 4357-70.
36. Fisher, O.S. and T.J. Boggon, Signaling pathways and the cerebral cavernous malformations proteins: lessons from structural biology. Cell Mol Life Sci, 2014. 71(10): p. 1881-92.
37. Rinaldi, C., P. Bramanti, A. Fama, C. Scimone, L. Donato, C. Antognelli, C. Alafaci, F. Tomasello, R. D’Angelo, and A. Sidoti, Glyoxalase I A111e, Paraoxonase 1 Q192r and L55m Polymorphisms in Italian Patients with Sporadic Cerebral Cavernous Malformations: A Pilot Study. J Biol Regul Homeost Agents, 2015. 29(2): p. 493-500.
38. Sakamoto, K., A. Mori, T. Nakahara, and K. Ishii, [Cause of retinitis pigmentosa and new therapeutics under development]. Nihon Yakurigaku Zasshi, 2011. 137(1): p. 22-6.
39. Zobor, D. and E. Zrenner, [Retinitis pigmentosa – a review. Pathogenesis, guidelines for diagnostics and perspectives]. Ophthalmologe, 2012. 109(5): p. 501-14;quiz 515.
40. Veltel, S. and A. Wittinghofer, RPGR and RP2: targets for the treatment of X-linked retinitis pigmentosa? Expert Opin Ther Targets, 2009. 13(10): p. 1239-51.
41. Daiger, S.P., L.S. Sullivan, and S.J. Bowne, Genes and mutations causing retinitis pigmentosa. Clin Genet, 2013. 84(2): p. 132-41.
42. L. Sebastio, L.V., B. Festa, R. Valliani, V. Ventruto, F. Simonelli, G.Restagno, M. Ferrone, A.O .Carbonara, Retinite pigmentosa e ritardo mentale in due fratelli: Sindrome X-linked da probabile microdelezione, in Simp Int Retinite Pigmentosa. 1992: Napoli.
43. G. Restagno, A.N., P. Danese, A. Fea, F.M. Grignolo, A. Carbonara, Eterogeneita’ genetica e clinica nelle forme autosomiche dominanti di retinite pigmentosa, in X Cong Naz FISME. 1995: Spoleto.
44. G. Restagno, P.D., M. Ferrone, S. Garnerone, V. Gualandri, G. Molteni, A. Porta, S. Samudly, A. Fea, F.M. Grignolo, A. Carbonara, Caratterizzazione di mutazioni in pazienti affetti da retinite pigmentosa autosomica dominante, in VIII Cong Naz FISME. 1993: Sorrento.
45. Anasagasti, A., C. Irigoyen, O. Barandika, A. Lopez de Munain, and J. Ruiz-Ederra, Current mutation discovery approaches in Retinitis Pigmentosa. Vision Res, 2012. 75: p. 117-29.
46. Chiang, J.P., T. Lamey, T. McLaren, J.A. Thompson, H. Montgomery, and J. De Roach, Progress and prospects of next-generation sequencing testing for inherited retinal dystrophy. Expert Rev Mol Diagn, 2015. 15(10): p. 1269-75.
47. Chothia, C. and A.M. Lesk, The relation between the divergence of sequence and structure in proteins. EMBO J, 1986. 5(4): p. 823-6.
48. Dick, D.M., A. Agrawal, M.C. Keller, A. Adkins, F. Aliev, S. Monroe, J.K. Hewitt, K.S. Kendler, and K.J. Sher, Candidate gene-environment interaction research: reflections and recommendations. Perspect Psychol Sci, 2015. 10(1): p. 37-59.
49. Jackson, B.R., The dangers of false-positive and false-negative test results: false-positive results as a function of pretest probability. Clin Lab Med, 2008. 28(2): p. 305-19, vii.
50. Moser, K.W., J.H. O’Keefe, Jr., T.M. Bateman, and I.A. McGhie, Coronary calcium screening in asymptomatic patients as a guide to risk factor modification and stress myocardial perfusion imaging. J Nucl Cardiol, 2003. 10(6): p. 590-8.
51. Mensaert, K., S. Denil, G. Trooskens, W. Van Criekinge, O. Thas, and T. De Meyer, Next-generation technologies and data analytical approaches for epigenomics. Environ Mol Mutagen, 2014. 55(3): p. 155-70.
52. Brunotto, M. and A.M. Zarate, [Predictive models for complex diseases]. Rev Fac Cien Med Univ Nac Cordoba, 2012. 69(1): p. 33-41.
53. Ballester, L.Y., R. Luthra, R. Kanagal-Shamanna, and R.R. Singh, Advances in clinical next-generation sequencing: target enrichment and sequencing technologies. Expert Rev Mol Diagn, 2016. 16(3): p. 357-72.
54. Dale, J.M., L. Popescu, and P.D. Karp, Machine learning methods for metabolic pathway prediction. BMC Bioinformatics, 2010. 11: p. 15.
55. Berger, J.O., X. Wang, and L. Shen, A Bayesian approach to subgroup identification. J Biopharm Stat, 2014. 24(1): p. 110-29.
