Genome‐wide association analysis links multiple psychiatric liability genes to oscillatory brain activity

Abstract

Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but current understanding of specific genetic influences remains limited. We performed the largest genome‐wide association study to date of oscillatory power during eyes‐closed resting electroencephalogram (EEG) across a range of frequencies (delta 1–3.75 Hz, theta 4–7.75 Hz, alpha 8–12.75 Hz, and beta 13–30 Hz) in 8,425 subjects. Additionally, we performed KGG positional gene‐based analysis and brain‐expression analyses. GABRA2—a known genetic marker for alcohol use disorder and epilepsy—significantly affected beta power, consistent with the known relation between GABAA interneuron activity and beta oscillations. Tissue‐specific SNP‐based imputation of gene‐expression levels based on the GTEx database revealed that hippocampal GABRA2 expression may mediate this effect. Twenty‐four genes at 3p21.1 were significant for alpha power (FDR q < .05). SNPs in this region were linked to expression of GLYCTK in hippocampal tissue, and GNL3 and ITIH4 in the frontal cortex–genes that were previously implicated in schizophrenia and bipolar disorder. In sum, we identified several novel genetic variants associated with oscillatory brain activity; furthermore, we replicated and advanced understanding of previously known genes associated with psychopathology (i.e., schizophrenia and alcohol use disorders). Importantly, these psychopathological liability genes affect brain functioning, linking the genes' expression to specific cortical/subcortical brain regions.

Conflict-related medial frontal theta as an endophenotype for alcohol use disorder

“…Given the current state of the psychiatric/endophenotype behavioral genetics field, it is difficult to speculate on the specific genetic mechanisms that give rise to the AUD-theta genetic correlation. The search to identify specific genetic variants of several task-based EEG-based endophenotypes, including midfrontal theta power, has so far proven largely unsuccessful (Iacono et al., 2017; Malone, McGue, & Iacono, 2017; although see Smit et al, 2018, and Meyers et al, 2017, for genomewide association studies [GWAS] of resting state EEG oscillatory power). Evidence suggests that psychophysiological endophenotypes, like psychiatric phenotypes, are highly polygenic and genetically complex, with individual common variants explaining a very small fraction of the phenotypic variance.…”

“…Because of this, sample sizes much larger than those used in past work (e.g., >4000 individuals in Iacono, Vaidyanathan, Vrieze, & Malone, 2014; Malone et al, 2017) will be needed to increase the power to detect specific variants contributing to midfrontal theta (Iacono et al, 2017). The feasibility of that endeavor remains unclear, although multisite collaborations, such as the ENIGMA consortium (Smit et al, 2018; Thompson et al, 2014), may offer a promising way forward. Along the same lines, identifying specific variants for AUD has also been challenging.…”

Mapping causal pathways from genetics to neuropsychiatric disorders using genome‐wide imaging genetics: Current status and future directions

“…To address this, consortia have recently identified hundreds of genome‐wide significant common variant loci, each generally composed of multiple correlated SNPs, that impact human brain structure, including intracranial volume (seven loci), subcortical volumes (38 loci), ventricular volumes (seven loci), cortical surface area and thickness (150 loci), and even white matter anatomy (225 loci) . No variants have yet been identified impacting brain function as measured through fMRI, though a handful of variants reach genome‐wide significance for oscillatory brain activity as measured with electroencephalograms (two loci) …”

Discovering heritable modes of MEG spectral power

“…So far, genetic connections to brain imaging have mainly been sought by associating single‐nucleotide polymorphisms (SNPs) of predefined candidate genes with neuroimaging measurements, especially in clinical populations (Egan et al, ; Meyer‐Lindenberg, ) but increasingly also in healthy participants (Darki et al, ; Mueller, Makeig, Stemmler, Hennig, & Wacker, ; Smolka et al, ). Recently, unrestricted genome‐wide linkage and association analyses have successfully been applied to neuroimaging phenotypes, but so far mainly to fairly simple and prevalent imaging measures, such as the different cortical rhythms (Malone et al, ; Porjesz et al, ; Salmela et al, ; Smit et al, ), and auditory evoked responses (Renvall et al, ).…”