Huang en kollega's hebben onderzoek gedaan naar genetische variaties (polymorfismen: klik hier en hier voor een toelichting) bij CVS-patiënten en "chronische vermoeide mensen".

Het merkwaardige aan deze studie, die voortborduren op een CDC-studies uit 2006

(klik hier en hier voor de studies en klik hier, hier, hier  en hier voor kritiek), is

dat de onderzoekers zich uitsluitend richten op 42 genetische variaties

die betrekking hebben op 10 genen gerelateerd aan de HPA-as/stressresponse!

Blijkbaar hebben de psychosociale vakbroeders (klik hier), door de feiten gedwongen,

hun "dysfunctionele gedachten-leiden-tot-dekonditionerings-theorie" definitief ingeruild

voor de "verstoorde-stressresponse-leidt-tot-een-omtregeld-afweersysteem-theorie".

Dat aktivering van het afweersysteem vrijwel leidt tot onderdrukking van de HPA-as, onder meer verminderde gevoeligheid bijnieren voor ACTH (klik hier), wil er blijkbaar niet in.

Dus onderzoek je gewoon alleen 10 genen wél en tienduizenden andere genen niet.

Als je iets wil "aantonen", lukt je dat altijd...

Uit het studierapport

Background

...

Among hypotheses on aetiological aspects of CFS,

one possible cause of CFS is genetic predisposition.

It has been reported that

subjects with CFS

were distinguished by

SNP markers in candidate genes

that were involved in

hypothalamic-pituitary-adrenal (HPA) axis function and

neurotransmitter systems,

including

• catechol-O-methyltransferase (COMT),

• 5-hydroxytryptamine receptor 2A (HTR2A),

• monoamine oxidase A (MAOA),

• monoamine oxidase B (MAOB),

• nuclear receptor subfamily 3; group C, member 1

• glucocorticoid receptor (NR3C1),

• proopiomelanocortin (POMC) and

• tryptophan hydroxylase 2 (TPH2) genes.

Subjects

The dataset, including SNPs, age, gender, and race,

was original to the previous study

by the CDC Chronic Fatigue Syndrome Research Group [18].

More information is available on the website [18].

In the entire data set,

there were

109 subjects,

including 55 subjects

having had experienced

chronic fatigue syndrome (CFS) and

54 non-fatigued controls.

Table 1 demonstrates the demographic characteristics of study subjects.

Candidate genes

In the present study, we only focused on the 42 SNPs ....

As shown in Table 2, there were ten candidate genes including

• COMT,

• corticotropin releasing hormone receptor 1 (CRHR1),

• corticotropin releasing hormone receptor 2 (CRHR2),

• MAOA,

• MAOB,

• NR3C1,

• POMC,

• solute carrier family 6 member 4 (SLC6A4),

• tyrosine hydroxylase (TH), and

• TPH2 genes.

Six of the genes

(COMT, MAOA, MAOB, SLC6A4, TH, and TPH2)

play a role in the neurotransmission system.

The remaining four genes

(CRHR1, CRHR2, NR3C1, and POMC)

are involved in the neuroendocrine system [8].

Thus, this significant association strongly suggests that

NR3C1 may be involved in biological mechanisms with CFS.

The NR3C1 gene encodes the protein for the glucocorticoid receptor,

which is expressed in almost every cell in the body and

regulates genes that control a wide variety of functions

including the development, energy metabolism, and immune response of the organism.

A previous animal study has observed that

age increases the expression of the glucocorticoid receptor in neural cells, and

increases in glucocorticoid receptor expression in human skeletal muscle cells

have been suggested to contribute to the etiology of the metabolic syndrome.

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.

J Transl Med. 2009 Sep 22;7(1):81. [Epub ahead of print]

Huang LC, Hsu SY, Lin E.

BACKGROUND:

In the studies of genomics,

it is essential

to select

a small number of genes

that are more significant

than the others

for the association studies

of disease susceptibility.

In this work,

our goal was

to compare computational tools

with and

without feature selection

for predicting chronic fatigue syndrome (CFS)

using genetic factors

such as single nucleotide polymorphisms (SNPs).

METHODS:

We employed

the dataset

that was original to

the previous study by the CDC Chronic Fatigue Syndrome Research Group.

To uncover relationships

between CFS and

SNPs,

we applied

three classification algorithms

including naive Bayes,

the support vector machine algorithm, and

the C4.5 decision tree algorithm.

Furthermore,

we utilized

feature selection methods

to identify

a subset

of influential SNPs.

One was

the hybrid feature selection approach

combining the chi-squared and

information-gain methods.

The other was the wrapper-based feature selection method.

RESULTS:

The naive Bayes model

with the wrapper-based approach

performed maximally

among predictive models

to infer the disease susceptibility

dealing with

the complex relationship

between CFS and

SNPs.

CONCLUSION:

We demonstrated that

our approach is

a promising method

to assess

the associations

between CFS and

SNPs.

PMID: 19772600 [PubMed - as supplied by publisher]

full-text:

http://www.translational-medicine.com/content/pdf/1479-5876-7-81.pdf