Human brain arousal in the resting state: a genome-wide association study

Abstract

Arousal affects cognition, emotion, and behavior and has been implicated in the etiology of psychiatric disorders. Although environmental conditions substantially contribute to the level of arousal, stable interindividual characteristics are well-established and a genetic basis has been suggested. Here we investigated the molecular genetics of brain arousal in the resting state by conducting a genome-wide association study (GWAS). We selected N = 1877 participants from the population-based LIFE-Adult cohort. Participants underwent a 20-min eyes-closed resting state EEG, which was analyzed using the computerized VIGALL 2.1 (Vigilance Algorithm Leipzig). At the SNP-level, GWAS analyses revealed no genome-wide significant locus (p < 5E-8), although seven loci were suggestive (p < 1E-6). The strongest hit was an expression quantitative trait locus (eQTL) of TMEM159 (lead-SNP: rs79472635, p = 5.49E-8). Importantly, at the gene-level, GWAS analyses revealed significant evidence for TMEM159 (p = 0.013, Bonferroni-corrected). By mapping our SNPs to the GWAS results from the Psychiatric Genomics Consortium, we found that all corresponding markers of TMEM159 showed nominally significant associations with Major Depressive Disorder (MDD; 0.006 ≤ p ≤ 0.011). More specifically, variants associated with high arousal levels have previously been linked to an increased risk for MDD. In line with this, the MetaXcan database suggests increased expression levels of TMEM159 in MDD, as well as Autism Spectrum Disorder, and Alzheimer's Disease. Furthermore, our pathway analyses provided evidence for a role of sodium/calcium exchangers in resting state arousal. In conclusion, the present GWAS identifies TMEM159 as a novel candidate gene which may modulate the risk for psychiatric disorders through arousal mechanisms. Our results also encourage the elaboration of the previously reported interrelations between ion-channel modulators, sleep-wake behavior, and psychiatric disorders.

Vigilance Effects in Resting-State fMRI

“…There is growing evidence that disregulation of arousal is associated with a variety of mental disorders such as depression, autism, and schizophrenia (Boutros et al, 2008;Razavi et al, 2013;Sander et al, 2016;Jawinski et al, 2019). In a genomewise association study, Jawinski et al (2019) found an association between resting-state vigilance levels (as assessed with EEG) and genetic markers for major depressive disorder, autism spectrum disorder, and Alzheimer's disease. In parallel, there has been widespread use of rsfMRI to study disease-related alterations in resting-state brain activity and connectivity.…”

Fatigue in Cancer and Neuroinflammatory and Autoimmune Disease: CNS Arousal Matters

“…Such hyperarousal is a frequent finding in medicated and unmedicated patients with MD [ 24 , 25 , 26 , 27 ], and it has been shown to correlate with symptom severity in MD patients [ 27 ]. In addition, at the gene level, a genome-wide association study (GWAS) revealed significant evidence of an association of brain arousal with TMEM159, with other GWAS analyses and gene expression data suggesting a role of TMEM159 in MD [ 28 ]. Other indicators of hyperarousal in MD, such as prolonged sleep onset latencies [ 29 , 30 ], increased heart rate and skin conductance levels [ 31 ], or hyperactivity of the hypothalamic-pituitary-adrenal (HPA)-axis [ 32 ], are in line with the view that arousal may play a significant pathophysiological role in MD.…”

The Big Five personality traits and CNS arousal in the resting state

“…Physiological data collection and processing was carried out as previously described (Jawinski et al, 2019(Jawinski et al, , 2015. EEG assessments were conducted according to a standardized operating procedure.…”

“…The assessment of brain arousal was carried out as described elsewhere (Jawinski et al, 2019(Jawinski et al, , 2017. EEG-vigilance served as indicator for brain arousal and was measured using the Brain Vision Analyzer add-on VIGALL 2.1 (https://www.deutsche-depressionshilfe.de/forschungszentrum/aktuellestudien/vig all-vigilance-algorithm-leipzig-2-1; Hegerl et al, 2016).…”