CFS -Supervised selection of Single Nucleotide Polymorphisms

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PubMed

Biomedica. 2011 Dec;31(4):613-21

Supervised selection of single

nucleotide polymorphisms in

chronic fatigue síndrome.

Cifuentes RA, Barreto E.

Escuela de Medicina y Ciencias de la Salud,

Universidad del rio, Bogotá, D.C, Colombia.

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.

PMID: 22674373 [PubMed - in process]