Combinations of single nucleotide polymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome
Abstract
Objective: This paper asks whether the presence of chronic fatigue syndrome (CFS) can be more accurately predicted from single nucleotide polymorphism (SNP) profiles than would occur by chance. Methods: Specifically, given SNP profiles for 43 CFS patients, together with 58 controls, we used an enumerative search to identify an ensemble of conjunctive rules that predict whether a patient has CFS. Results: The accuracy of the rules reached 76.3%, with the highest accuracy rules yielding 49 true negatives, 15 false negatives, 28 true positives and nine false positives (odds ratio [OR] 8.94, p < 0.0001). Analysis of the SNPs used most frequently in the overall ensemble of rules gave rise to a list of ‘most important SNPs’, which was not identical to the list of ‘most differentiating SNPs’ that one would calculate via studying each SNP independently. The top three genes containing the SNPs accounting for the highest accumulated importances were neuronal tryptophan hydroxylase (TPH2), catechol-O-methyltransferase (COMT) and nuclear receptor subfamily 3, group C, member 1 glucocorticoid receptor (NR3C1). Conclusion: The fact that only 28 out of several million possible SNPs predict whether a person has CFS with 76% accuracy indicates that CFS has a genetic component that may help to explain some aspects of the illness.
Bibliography
- 1 Kwok PW (Ed.): Single Nucleotide Polymorphisms: Methods and Protocols. AACC Press, San Franscisco, CA, USA (2002).
- 2 Mitchell T: Machine Learning. McGraw-Hill, New York, NY, USA (1997).
- 3 Waddell M, Page D, Zhan F, Barlogie B, Shaughnessy J: Predicting Cancer Susceptibility from Single-Nucleotide Polymorphism Data: A Case Study in Multiple Myeloma, Proceedings of BIOKDD '05, Chicago, IL, USA (2005).
- 4 Listgarten J, Damaraju S, Poulin B et al.: Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms. Clin. Cancer Res.10(8), 2725–2737 (2005).
- 5 Reeves WC, Wagner D, Nisenbaum R et al.: Chronic fatigue syndrome – a clinically empirical approach to its definition and study. BMC Med.3, 19 (2005).
- 6 Smigrodzki R, Goertzel B, Pennachin C, Coelho L, Prosdocimi F, Parker DW: Genetic algorithm for analysis of mutations in Parkinson's disease. Artif. Intell. Med.35(3), 227–241 (2005).
- 7 Koza J: Genetic programming. MIT Press, Cambridge, MA, USA (1992).
- 8 Maloney EM, Gurbaxani BM, Jones JF, Coelho LdS, Pennachin C, Goertzel BN: Chronic fatigue syndrome and high allostatic load. Pharmacogenomics7(3), 467–473 (2006).
- 9 Gurbaxani BM, Jones JF, Goertzel BN, Maloney EM: Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome. Pharmacogenomics7(3), 455–461 (2006).
- 10 Levinson DF: The genetics of depression: a review. Biol. Psychiatry [Epub ahead of print] (2005).
- 11 Brown SM, Peet E, Manuck SB et al.: A regulatory variant of the human tryptophan hydroxylase-2 gene biases amygdala reactivity. Mol. Psychiatry10(9), 884–888 (2005).
- 12 Serretti A, Mandelli L, Lorenzi C et al.: Temperament and character in mood disorders: influence of DRD4, SERTPR, TPH and MAO-A polymorphisms. Neuropsychobiology53(1), 9–16 (2005).
- 13 Craig AD: How do you feel? Interoception: the sense of the physiological condition of the body. Nature Rev. Neurosci.3, 655–666 (2002).
- 14 Strous RD, Lapidus R, Viglin D, Kotler M, Lachman HM: Analysis of an association between the COMT polymorphism and clinical symptomatology in schizophrenia. Neurosci. Lett.393(2–3), 170–173 (2005).
Website
- 101 Critical Assessment of Microarray Data Analysis 2006 Conference Contest Datasets. www.camda.duke.edu/camda06/datasets/
