GWAS of EEG oscillations unveils genetic pleiotropy between brain structure, function, and behavior
Presented During:
Monday, June 24, 2024: 5:45 PM - 7:00 PM
COEX
Room:
Grand Ballroom 101-102
Poster No:
855
Submission Type:
Abstract Submission
Authors:
Philippe Jawinski1, Jacquelyn Meyers2, José Morosoli Garcia3, Sarah Medland4, ENIGMA-EEG Consortium5, Paul Thompson6, Dirk Smit7
Institutions:
1Humboldt-Universität zu Berlin, Berlin, Germany, 2Downstate Health Sciences University, Brooklyn, NY, 3QIMR Berghofer Medical Research Institute, Herston, Australia, 4QIMR Berghofer Medical Research Institute, Brisbane, Australia, 5Cross-Instutional, Worldwide, 6University of Southern California, Los Angeles, CA, 7University of Amsterdam, Amsterdam, Noord-Holland
First Author:
Co-Author(s):
Introduction:
Oscillations in neuronal brain activity play a crucial role in information processing and have been studied extensively as biological markers of human behavior and psychopathology [1]. A century ago, in 1924, Hans Berger's discovery marked the inception of a transformative era in neuroscience, leading to crucial advancements in our understanding of brain function and the corresponding behavioral phenomena [2]. Twin studies have demonstrated that individual differences in EEG oscillations are strongly driven by genetic factors [3]. However, our understanding of their molecular genetic architecture is still very limited. Here, we conducted a genome-wide association study (GWAS) of resting-state EEG oscillations to discover associated genomic loci and to examine the pleiotropic relationships with other complex traits, i.e., the links with brain structure and mental illness.
Methods:
We conducted to our best knowledge the largest GWAS of resting-state EEG to date, combining data from 9 cohorts with a total N = 14,361 participants. EEGs were recorded during a three- to five-minute eyes-closed resting-state condition. We used harmonized analysis protocols to examine the power of the EEG frequency bands alpha, beta, delta, theta, and broadband at the vertex site, as well as alpha power and alpha peak frequency at occipital leads. GWAS analyses were run in RAREMETALWORKER [4] followed by cross-cohort meta-analysis in METAL [5]. We used LD score regression [6] and pleioFDR [7] to investigate a shared genetic basis with other complex traits, including MRI-derived brain structure variables [8] and psychiatric disorders [9]. Variant discoveries were annotated using positional and functional mapping strategies based on RefSeq [10], GTEx [11], and other omics databases. Finally, we estimated the degree of polygenicity of EEG oscillations via genetic effect size distribution analyses implemented in GENESIS [12].
Results:
SNP-based heritability estimates ranged from 14-27% (SE: 3.7%). We discovered two genome-wide significant loci: an intergenic region at 13q12.3 (p = 6.6e-09), and a known schizophrenia risk locus in an intron of FANCA at 16q24.3 (p = 1.4e-08). Both loci were associated with alpha peak frequency. We identified 32 additional likely associated loci by leveraging pleiotropy with psychiatric traits such as schizophrenia, major depression, and bipolar disorder. Of these loci, 27 have a nearest gene that is protein-coding, and 24 are known GTEx expression quantitative trait loci (eQTLs). Using genetic correlations, we demonstrate a shared genetic basis between EEG power and MRI-derived cerebral white matter volume (rG = -0.33, p = 3.0e-07) and cortical surface area (rG = 0.26, p = 2.0e-04), with the top regional associations implicating the orbitofrontal, anterior cingulate, and precuneus surface area. Genetic correlations also indicated an overlap with generalized epilepsy, neuroticism, and loneliness. Polygenicity analyses revealed an estimated number of ~5.5k (SE: 2.7k) underlying variants contributing to the SNP-based heritability of EEG oscillations, which is lower when compared to estimates derived for height (12.5k; SE: 1.3k) and neuroticism (1.62k; SE: 2.2k).
Conclusions:
Our results suggest that common genetic variation substantially affects EEG oscillations, with a genetic architecture that overlaps those of brain structure variables. In addition, our results provide support for genetic pleiotropy with psychiatric and neurological traits. Genetic effect size distribution analyses unveiled a relatively low degree of polygenicity, indicating a rapid increase of discoveries in future studies with larger sample sizes. In sum, our study supports twin studies on the strong heritability of brain oscillations, identifies novel gene loci, and reveals evidence for pleiotropic associations between MRI-derived brain variables, brain electrophysiology, and neurological and psychiatric disorders.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Genetics:
Genetic Association Studies 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Electroencephaolography (EEG)
Psychiatric Disorders
Other - Genetics; Brain oscillations
1|2Indicates the priority used for review
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