Supervised selection of single nucleotide polymorphisms in chronic fatigue syndrome.
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 polymor...
| Publicado en: | Biomédica: Revista del Instituto Nacional de Salud Vol. 31; no. 4; pp. 613 - 622 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Instituto Nacional de Salud of Colombia
dic2011
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. Introducciôn. Las diferentes formas de seleccionar polimorfismos de nucleôtido unico se han relacionado con conclusiones paradôjicas respecto a su utilidad para predecir el sindrome de fatiga crônica, incluso utilizando los mismos datos. Objetivo. Evaluar la eficacia para predecir este sindrome de los polimorfismos seleccionados mediante un enfoque supervisado, método que permite ayudar a identificar el perfil ôptimo 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 très aspectos: comparaciôn de la exactitud de predicciôn con la del perfil obtenido mediante una selecciôn basada en el desequilibrio de ligamiento, evaluaciôn de la eficacia para determinar un estrato con mayor riesgo y estimaciôn de la influencia del algoritmo de clasificaciôn sobre la exactitud de predicciôn. Resultados. Se obtuvo un perfil vâlido (p<0,01) con mayor exactitud que el basado en el desequilibrio de ligamiento, 72,8 Vs. 62,2 % (p<0,01 ), que incluyô el alelo mayor de NR3C1_11159943 y el menor de 5HTT_7911132, conocidos polimorfismos asociados a este sindrome. El dolor muscular o los sîntomas de los senos paranasales en el estrato con el perfil, predijeron la presencia del sindrome con mayor exactitud que estos sîntomas en toda la poblaciôn, 87,1 % Vs. 70,4 % (p<0,01) y 92,5 % Vs. 71,8 % (p<0,01) respectivamente. El perfil llevô a una exactitud similar con diferentes algoritmos. Conclusiones. El enfoque supervisado permitiô descubrir un perfil vâlido y confiable de polimorfismos asociado al sindrome de fatiga crônica. Se encontrô la mayor exactitud reportada con estos datos que aumento al combinarse con las variables clinicas. |
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