Selección supervisada de polimorfismos de nucleótido único en el síndrome de fatiga crónica
Palabras clave:
polimorfismo genético, síndrome de fatiga crónica, biología computacional
Resumen
Introducción. Las diferentes formas de seleccionar polimorfismos de nucleótido único se han relacionado con conclusiones paradójicas respecto a su utilidad para predecir el síndrome de fatiga
crónica, incluso utilizando los mismos datos.
Objetivo. Evaluar la eficacia para predecir este síndrome de los polimorfismos seleccionados mediante un enfoque supervisado, método que permite ayudar a identificar el perfil óptimo de los
polimorfismos.
Materiales y métodos. Se eliminaron los polimorfismos que no estaban en equilibrio de Hardy-Weinberg. Luego obtuvimos el perfil de polimorfismos mediante el enfoque supervisado y evaluamos tres aspectos: comparación de la exactitud de predicción con la del perfil obtenido mediante una selección basada en el desequilibrio de ligamiento, evaluación de la eficacia para determinar un estrato
con mayor riesgo y estimación de la influencia del algoritmo de clasificación sobre la exactitud de predicción.
Resultados. Se obtuvo un perfil válido (p<0,01) con mayor exactitud que el basado en el desequilibrio de ligamiento, 72,8 Vs. 62,2 % (p<0,01), que incluyó el alelo mayor de NR3C1_11159943 y el menor de 5HTT_7911132, conocidos polimorfismos asociados a este síndrome. El dolor muscular o los síntomas de los senos paranasales en el estrato con el perfil, predijeron la presencia del síndrome con mayor exactitud que estos síntomas en toda la población, 87,1 % Vs. 70,4 % (p<0,01) y 92,5 % Vs. 71,8 % (p<0,01) respectivamente. El perfil llevó a una exactitud similar con diferentes algoritmos.
Conclusiones. El enfoque supervisado permitió descubrir un perfil válido y confiable de polimorfismos asociado al síndrome de fatiga crónica. Se encontró la mayor exactitud reportada con estos datos que aumentó al combinarse con las variables clínicas.
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2. Whistler T, Unger ER, Nisenbaum R, Vernon SD. Integration of gene expression, clinical, and epidemiologic data to characterize Chronic Fatigue Syndrome. J Transl Med. 2003;1:10.
3. He J, Zelikovsky A. Informative SNP selection methods based on SNP prediction. IEEE Trans Nanobioscience. 2007;6:60-7.
4. Sun Y, Todorovic S, Goodison S. Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans Pattern Anal Mach Intell. 2010;32:1610-26.
5. Alpaydin E. Introduction to Machine Learning. Cambridge, Massachusetts: Massachussetts Institute of Technology Press; 2004.
6. Lalouel J, White R. Emery and Rimoin's Principles and Practice of Medical Genetics. New York, NY: Churchill and Livingston; 1996.
7. VanLiere JM, Rosenberg NA. Mathematical properties of the r2 measure of linkage disequilibrium. Theor Popul Biol. 2008;74:130-7.
8. Stram DO, Haiman CA, Hirschhorn JN, Altshuler D, Kolonel LN, Henderson BE, et al. Choosing haplotype-tagging SNPS based on unphased genotype data using a preliminary sample of unrelated subjects with an example from the Multiethnic Cohort Study. Hum Hered. 2003;55:27-36.
9. Halperin E, Kimmel G, Shamir R. Tag SNP selection in genotype data for maximizing SNP prediction accuracy. Bioinformatics. 2005;21:i195-203.
10. Liu Q, Yang J, Chen Z, Yang MQ, Sung AH, Huang X. Supervised learning-based tagSNP selection for genomewide disease classifications. BMC Genomics. 2008;9:S6.
11. Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform. 2008;77:81-97.
12. Goertzel BN, Pennachin C, de Souza Coelho L, Gurbaxani B, Maloney EM, Jones JF. Combinations of single nucleotide polymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome. Pharmacogenomics. 2006;7:475-83.
13. Frank E, Hall M, Trigg L, Holmes G, Witten IH. Data mining in bioinformatics using Weka. Bioinformatics.2004;20:2479-81.
14. Hall M, Smith LA. Feature Subset Selection: A Correlation Based Filter Approach. New Zealand: University of Waikato;1998.
15. Witten IH, Frank E. Data Mining Practical Machine Learning Tools and Techniques. Second ed. San Francisco, CA: Elsevier; 2005.
16. Shortliffe E, Perreault L, Wiederhold G, Fagan L. Medical Informatics Computer Applications in Health Care and Biomedicine. New York, NY: Springer-Verlag; 2001.
17. Norman GR, Streiner DL. Biostadística. Madrid, España: Mosby/Doyma; 1996.
18. Lee E, Cho S, Kim K, Park T. An integrated approach to infer causal associations among gene expression, genotype variation, and disease. Genomics. 2009;94:269-77.
19. van den Brandt J, Luhder F, McPherson KG, de Graaf KL, Tischner D, Wiehr S, et al. Enhanced glucocorticoid receptor signaling in T cells impacts thymocyte apoptosis and adaptive immune responses. Am J Pathol. 2007;170:1041-53.
20. Rajeevan MS, Smith AK, Dimulescu I, Unger ER, Vernon SD, Heim C, et al. Glucocorticoid receptor polymorphisms and haplotypes associated with chronic fatigue syndrome. Genes Brain Behav. 2007;6:167-76.
21. Narita M, Narita N. Genetic background of chronic fatigue syndrome. Nippon Rinsho. 2007;65:997-1002.
22. Klimas NG, Koneru AO. Chronic fatigue syndrome: inflammation, immune function, and neuroendocrine interactions. Curr Rheumatol Rep. 2007;9:482-7.
23. van Den Eede F, Moorkens G, Van Houdenhove B, Cosyns P, Claes SJ. Hypothalamic-pituitary-adrenal axis function in chronic fatigue syndrome. Neuropsychobiology. 2007;55:112-20.
24. Miwa S, Takikawa O. Chronic fatigue syndrome and neurotransmitters. Nippon Rinsho. 2007;65:1005-10.
25. Parker AJ, Wessely S, Cleare AJ. The neuroendocrinology of chronic fatigue syndrome and fibromyalgia. Psychol Med. 2001;31:1331-45.
26. Barros Filho MC, Katayama ML, Brentani H, Abreu AP, Barbosa EM, Oliveira CT, et al. Gene trio signatures as molecular markers to predict response to doxorubicin cyclophosphamide neoadjuvant chemotherapy in breast cancer patients. Braz J Med Biol Res. 2010;43:1225-31.
27. Xu H, Lemischka IR, Ma'ayan A. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells. BMC Syst Biol. 2010;4:173.
28. Subramanian J, Simon R. An evaluation of resampling methods for assessment of survival risk prediction in highdimensional settings. Stat Med. 2010 Dec 1.
29. Nijs J, Meeus M, De Meirleir K. Chronic musculoskeletal pain in chronic fatigue syndrome: recent developments and therapeutic implications. Man Ther. 2006;11:187-91.
30. Jammes Y, Steinberg JG, Mambrini O, Bregeon F, Delliaux S. Chronic fatigue syndrome: assessment of increased oxidative stress and altered muscle excitability in response to incremental exercise. J Intern Med. 2005;257:299-310.
31. Naranch K, Park YJ, Repka-Ramirez MS, Velarde A, Clauw D, Baraniuk JN. A tender sinus does not always mean rhinosinusitis. Otolaryngol Head Neck Surg. 2002;127:387-97.
32. Baraniuk JN, Clauw DJ, Gaumond E. Rhinitis symptoms in chronic fatigue syndrome. Ann Allergy Asthma Immunol. 1998;81:359-65.
33. Bhattacharjee M, Botting CH, Sillanpaa MJ. Bayesian biomarker identification based on marker-expression proteomics data. Genomics. 2008;92:384-92.
34. Melville P, Mooney R. Constructing diverse classifier ensembles using artificial training examples. Proceedings of the IJCA. 2003:505-10.
35. Camillo F, Liberati C. The kernel approach in the future of data mining: many subjective choices in a complex landscape. Bologna: Universita di Bologna; 2008.
36. Cawley G, Talbot N. Efficient Model Selection for Kernel Logistic Regression. Norwich, United Kingdom: School of Computing Sciences, University of East Anglia; 2008.
37. Fukuda K, Straus SE, Hickie I, Sharpe MC, Dobbins JG, Komaroff A. The chronic fatigue syndrome: a comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group. Ann Intern Med. 1994;121:953-9.
38. Reeves WC, Wagner D, Nisenbaum R, Jones JF, Gurbaxani B, Solomon L, et al. Chronic fatigue syndrome--a clinically empirical approach to its definition and study. BMC Med. 2005;3:19.
39. Presson AP, Sobel EM, Papp JC, Suarez CJ, Whistler T, Rajeevan MS, et al. Integrated weighted gene coexpression network analysis with an application to chronic fatigue syndrome. BMC Syst Biol. 2008;2:95.
Cómo citar
1.
Cifuentes RA, Barreto E. Selección supervisada de polimorfismos de nucleótido único en el síndrome de fatiga crónica. Biomed. [Internet]. 30 de junio de 2011 [citado 4 de abril de 2025];31(4):613-21. Disponible en: https://revistabiomedicaorg.biteca.online/index.php/biomedica/article/view/425
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