Supervised selection of single nucleotide polymorphisms in chronic fatigue syndrome
Keywords:
genetic polymorphism, chronic fatigue syndrome, computational biology
Abstract
Introduction: The different ways for selecting single nucleotide polymorphisms have been related to paradoxical conclusions about their usefulness in predicting chronic fatigue syndrome even when using the same dataset.
Objective: To evaluate the efficacy in predicting this syndrome by using polymorphisms selected by a supervised approach that is claimed to be a method that helps identifying their optimal profile.
Materials and methods: We eliminated those polymorphisms that did not meet the Hardy-Weinberg equilibrium. Next, the profile of polymorphisms was obtained through the supervised approach and three aspects were evaluated: comparison of prediction accuracy with the accuracy of a profile that was based on linkage disequilibrium, assessment of the efficacy in determining a higher risk stratum, and
estimating the algorithm influence on accuracy.
Results: A valid profile (p<0.01) was obtained with a higher accuracy than the one based on linkage disequilibrium, 72.8 vs. 62.2% (p<0.01). This profile included two known polymorphisms associated with chronic fatigue syndrome, the NR3C1_11159943 major allele and the 5HTT_7911132 minor allele. Muscular pain or sinus nasal symptoms in the stratum with the profile predicted V with a higher accuracy than those symptoms in the entire dataset, 87.1 vs. 70.4% (p<0.01) and 92.5 vs. 71.8% (p<0.01) respectively. The profile led to similar accuracies with different algorithms.
Conclusions: The supervised approach made it possible to discover a reliable profile of polymorphisms associated with this syndrome. Using this profile, accuracy for this dataset was the highest reported and it increased when the profile was combined with clinical data.
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References
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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.
How to Cite
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Cifuentes RA, Barreto E. Supervised selection of single nucleotide polymorphisms in chronic fatigue syndrome. biomedica [Internet]. 2011 Jun. 30 [cited 2024 May 11];31(4):613-21. Available from: https://revistabiomedica.org/index.php/biomedica/article/view/425
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