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Genetic load is associated with hypothalamic-pituitary-adrenal axis dysregulation in macaques
B Ferguson1, J E Hunter2, J Luty1
1Division of Neurosciences, Oregon National Primate Research Center, Oregon Health & Sciences University, Beaverton, OR, USA.
Genes, Brain, and Behavior
|September 25, 2012
Summary
Genetic variants in serotonin and cortisol pathways influence hypothalamic-pituitary-adrenal (HPA) axis regulation. Combining multiple risk genes may better predict HPA axis dysregulation and associated psychiatric disorders.
Area of Science:
- Neuroendocrinology
- Behavioral Genetics
- Psychiatric Genetics
Background:
- Hypothalamic-pituitary-adrenal (HPA) axis dysregulation is linked to neuropsychiatric disorders like PTSD, MDD, schizophrenia, and alcohol abuse.
- Gene variants in cortisol, serotonin, and opioid pathways are associated with HPA axis dysfunction.
Purpose of the Study:
- To characterize gene polymorphisms in neurotransmitter pathways and their interaction with HPA axis activity.
- To assess the effect of dexamethasone (DEX) suppression response on adrenocorticotropic hormone (ACTH) and cortisol levels.
Main Methods:
- Dexamethasone (DEX) suppression test was performed on 62 male rhesus macaques to measure ACTH and cortisol suppression.
- Monkeys with high and low ACTH suppression were genotyped for polymorphisms in five genes: rhCRH, rhTPH2, rhMAOA, rhSLC6A4, and rhOPRM.
- Statistical analyses were used to identify associations between gene variants and HPA axis activity.
Main Results:
- ACTH suppression levels showed broad distribution, unlike robust cortisol suppression.
- Significant associations were found between three variants (rhCRH-2610C>T, rhTPH2 2051A>C, and rh5-HTTLPR) and DEX suppression of ACTH.
- An additive effect of risk genotypes from these three loci was detected, correlating with blunted ACTH response (P=0.0009).
Conclusions:
- Multiple risk alleles in serotonin and cortisol signaling pathway genes may offer better prediction of HPA axis dysregulation risk.
- This polygenic approach may improve the understanding and prediction of associated psychiatric disorders.
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