Combinations of single nucleotide polymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome, 2006, Goertzel et al

Combinations of single nucleotide polymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome

Benjamin N Goertzel , Cassio Pennachin, Lucio de Souza Coelho, Brian Gurbaxani, Elizabeth M Maloney, James F Jones

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.

Link (Pharmacogenomics) [Paywall]

 

The full paper seems to be available on researchgate


Here’s an AI generated audio summary of the paper:

https://u.pcloud.link/publink/show?code=XZvC5V5ZLtBWzHARrObg2BgsAdEL0poMspB7

So as to not derail discussions of the papers please post any feedback on these audio summaries to this thread:

https://www.s4me.info/threads/enhan...h-technology-feedback-and-ideas-wanted.40207/

 

It needs a much larger cohort supporting the numbers before I get excited. If you search a large enough sample of random data, you can find false correlations.