Heritability and molecular-genetic basis of resting EEG activity: a genome-wide association study.

paper Cited Public

Authors: Malone, Stephen M; Burwell, Scott J; Vaidyanathan, Uma; Miller, Michael B; McGue, Matt; Iacono, William G
Year: 2014
Journal: Psychophysiology
PMID: 25387704
DOI: 10.1111/psyp.12344
PMCID: PMC4262140

Figure 1

Q-Q plot for SNP associations with alpha power at occipital-parietal leads. The 45° line gives the expected value under the null distribution. The area shaded in gray corresponds to the 95% acceptance region. Median and mean genomic control values are given in the inset in the upper left. N refers to the number of SNPs, which is 8 fewer than the number of SNPs on the array because there was no variation for 8 SNPs in this sample. Q-Q plots in GWAS give the observed p-values against the expected p-values under the null distribution of no association, although the additive inverse of the common log of p-values (−log10[p]) is used in order to emphasize small p-values. Because the vast majority of SNPs are not expected to be associated with a given phenotype, observed p-values should conform closely to their expected values, falling on or very close to the 45° line. The gray region in each plot depicts the 95% confidence region (null acceptance region).

Figure 10

Manhattan plot of individual SNP associations with beta power at Cz. Manhattan plots also depict the distribution of −log10(p) but are ordered by SNP location on a chromosome, which provides information about the location of any SNPs associated with small p-values. The horizontal line at 7.3 indicates the genome-wide significance level (5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate “suggestive” significance.

Figure 11

Manhattan plot of individual SNP associations with theta power at Cz. Manhattan plots also depict the distribution of −log10(p) but are ordered by SNP location on a chromosome, which provides information about the location of any SNPs associated with small p-values. The horizontal line at 7.3 indicates the genome-wide significance level (5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate “suggestive” significance.

Figure 12

Manhattan plot of individual SNP associations with delta power at Cz. Manhattan plots also depict the distribution of −log10(p) but are ordered by SNP location on a chromosome, which provides information about the location of any SNPs associated with small p-values. The horizontal line at 7.3 indicates the genome-wide significance level (5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate “suggestive” significance.

Figure 13

Manhattan plot of individual SNP associations with total power at Cz. Manhattan plots also depict the distribution of −log10(p) but are ordered by SNP location on a chromosome, which provides information about the location of any SNPs associated with small p-values. The horizontal line at 7.3 indicates the genome-wide significance level (5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate “suggestive” significance.

Figure 14

Manhattan plot of individual SNP associations with alpha peak frequency at occipital-parietal leads. Manhattan plots also depict the distribution of −log10(p) but are ordered by SNP location on a chromosome, which provides information about the location of any SNPs associated with small p-values. The horizontal line at 7.3 indicates the genome-wide significance level (5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate “suggestive” significance.

Figure 15

Associations between individual SNPs within or near GABRA2 and beta power. SNPs were imputed with reference to 1000 Genomes haplotypes (see text for additional detail). p-values produced by RFGLS analyses of the dosages for these imputed SNPs are expressed as −log10(p). SNP positions are from GRCh37 build 37 (hg19). Raw p-values are plotted in the upper panel. rs279858, which tags a haplotype associated with problematic alcohol use (Enoch, 2008), is indicated in black. The lower panel gives results are from a follow-up analysis that adjusts for effects of this SNP as an additional covariate, to determine to what extent the elevated p-values in the upper panel likely represent a single signal.

Figure 2

Q-Q plot for SNP associations with alpha power at Cz. The 45° line gives the expected value under the null distribution. The area shaded in gray corresponds to the 95% acceptance region. Median and mean genomic control values are given in the inset in the upper left. N refers to the number of SNPs, which is 8 fewer than the number of SNPs on the array because there was no variation for 8 SNPs in this sample. Q-Q plots in GWAS give the observed p-values against the expected p-values under the null distribution of no association, although the additive inverse of the common log of p-values (−log10[p]) is used in order to emphasize small p-values. Because the vast majority of SNPs are not expected to be associated with a given phenotype, observed p-values should conform closely to their expected values, falling on or very close to the 45° line. The gray region in each plot depicts the 95% confidence region (null acceptance region).

Figure 3

Q-Q plot for SNP associations with beta power at Cz. The 45° line gives the expected value under the null distribution. The area shaded in gray corresponds to the 95% acceptance region. Median and mean genomic control values are given in the inset in the upper left. N refers to the number of SNPs, which is 8 fewer than the number of SNPs on the array because there was no variation for 8 SNPs in this sample. Q-Q plots in GWAS give the observed p-values against the expected p-values under the null distribution of no association, although the additive inverse of the common log of p-values (−log10[p]) is used in order to emphasize small p-values. Because the vast majority of SNPs are not expected to be associated with a given phenotype, observed p-values should conform closely to their expected values, falling on or very close to the 45° line. The gray region in each plot depicts the 95% confidence region (null acceptance region).

Figure 4

Q-Q plot for SNP associations with theta power at Cz. The 45° line gives the expected value under the null distribution. The area shaded in gray corresponds to the 95% acceptance region. Median and mean genomic control values are given in the inset in the upper left. N refers to the number of SNPs, which is 8 fewer than the number of SNPs on the array because there was no variation for 8 SNPs in this sample. Q-Q plots in GWAS give the observed p-values against the expected p-values under the null distribution of no association, although the additive inverse of the common log of p-values (−log10[p]) is used in order to emphasize small p-values. Because the vast majority of SNPs are not expected to be associated with a given phenotype, observed p-values should conform closely to their expected values, falling on or very close to the 45° line. The gray region in each plot depicts the 95% confidence region (null acceptance region).

Figure 5

Q-Q plot for SNP associations with delta power at Cz. The 45° line gives the expected value under the null distribution. The area shaded in gray corresponds to the 95% acceptance region. Median and mean genomic control values are given in the inset in the upper left. N refers to the number of SNPs, which is 8 fewer than the number of SNPs on the array because there was no variation for 8 SNPs in this sample. Q-Q plots in GWAS give the observed p-values against the expected p-values under the null distribution of no association, although the additive inverse of the common log of p-values (−log10[p]) is used in order to emphasize small p-values. Because the vast majority of SNPs are not expected to be associated with a given phenotype, observed p-values should conform closely to their expected values, falling on or very close to the 45° line. The gray region in each plot depicts the 95% confidence region (null acceptance region).

Figure 6

Q-Q plot for SNP associations with total power at Cz. The 45° line gives the expected value under the null distribution. The area shaded in gray corresponds to the 95% acceptance region. Median and mean genomic control values are given in the inset in the upper left. N refers to the number of SNPs, which is 8 fewer than the number of SNPs on the array because there was no variation for 8 SNPs in this sample. Q-Q plots in GWAS give the observed p-values against the expected p-values under the null distribution of no association, although the additive inverse of the common log of p-values (−log10[p]) is used in order to emphasize small p-values. Because the vast majority of SNPs are not expected to be associated with a given phenotype, observed p-values should conform closely to their expected values, falling on or very close to the 45° line. The gray region in each plot depicts the 95% confidence region (null acceptance region).

Figure 7

Q-Q plot for SNP associations with alpha peak frequency at occipital-parietal leads. The 45° line gives the expected value under the null distribution. The area shaded in gray corresponds to the 95% acceptance region. Median and mean genomic control values are given in the inset in the upper left. N refers to the number of SNPs, which is 8 fewer than the number of SNPs on the array because there was no variation for 8 SNPs in this sample. Q-Q plots in GWAS give the observed p-values against the expected p-values under the null distribution of no association, although the additive inverse of the common log of p-values (−log10[p]) is used in order to emphasize small p-values. Because the vast majority of SNPs are not expected to be associated with a given phenotype, observed p-values should conform closely to their expected values, falling on or very close to the 45° line. The gray region in each plot depicts the 95% confidence region (null acceptance region).

Figure 8

Manhattan plot of individual SNP associations with alpha power at occipital-parietal leads. Manhattan plots also depict the distribution of −log10(p) but are ordered by SNP location on a chromosome, which provides information about the location of any SNPs associated with small p-values. The horizontal line at 7.3 indicates the genome-wide significance level (5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate “suggestive” significance.

Figure 9

Manhattan plot of individual SNP associations with alpha power at Cz. Manhattan plots also depict the distribution of −log10(p) but are ordered by SNP location on a chromosome, which provides information about the location of any SNPs associated with small p-values. The horizontal line at 7.3 indicates the genome-wide significance level (5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate “suggestive” significance.

#	Section	Preview
40	Method — Statistical analyses — Gene effects: Genome-wide scan	We used VEGAS (Liu et al., 2010) to conduct gene-based tests of all 17,601 genes identified by…
41	Method — Statistical analyses — Gene effects: Candidate genes	We evaluated three sets of candidate genes. The first comprised a small number of candidate genes (n…
42	Method — Statistical analyses — Gene effects: Candidate genes	The second candidate set consisted of 204 candidate genes that are likely relevant to understanding…
43	Results	Table 2 in Iacono et al. (2014) in this issue presents descriptive statistics for all measures. The…
44	Results — Heritability from biometric models	Family correlations from RFGLS, which have been adjusted for effects on mean levels of the various…
45	Results — Heritability from biometric models	those based only on twins, especially for the higher frequencies and total power. The remaining…
46	Results — SNP heritability	SNP heritability estimates are presented in Table 3. Values are provided for each threshold of…
47	Results — SNP heritability	Finally, we include estimates based on the entire sample, whether by not imposing a threshold of…
48	Results — SNP effects: Genome-wide scan	Figures 1–7 consist of Q-Q plots for each of the EEG measures of observed p-values against…
49	Results — SNP effects: Candidate SNPs	We specifically examined 18 SNPs identified by Hodgkinson and colleagues (2010); none of these…
50	Results — Gene effects: Genome-wide scan	Our gene-based test of 17,601 genes on autosomal chromosomes yielded two genes associated with delta…
51	Results — Gene effects: candidate genes	RFGLS analyses of APOE ε4 risk status indicated that although risk was associated with reduced…
52	Results — Gene effects: candidate genes	The association between beta power and the GABRA2 gene produced the smallest p-value. Because there…
53	Results — Gene effects: candidate genes	and the present study. Although all SNPs were imputed in our data, with a single exception…
54	Results — Gene effects: candidate genes	GABRA2 contains two large haplotype blocks, and a synonymous SNP in the exon of the GABRA2 gene,…
55	Results — Gene effects: candidate genes	Analyses of the 204 genes in our set of endophenotype-general candidate genes produced one…
56	Discussion — Biometric and SNP heritability of EEG parameters	Biometric model-fitting analyses indicated that a substantial proportion of the variation in the…
57	Discussion — Biometric and SNP heritability of EEG parameters	GCTA analyses indicated that, although point estimates varied somewhat across endophenotypes, all…
58	Discussion — Biometric and SNP heritability of EEG parameters	lowest frequencies, especially the theta band. This difference between higher and lower frequencies…
59	Discussion — Analysis of individual variants	Despite the findings that the various measures are likely substantially influenced by additive…

Name	Type
1000 Genomes Project	cohort
acetylcholine	drug
Active withdrawal local	cohort
additive genetic influences	cohort
ADHD	phenotype
adolescent participants	cohort
adolescents	cohort
adults	cohort
aging	phenotype
alcohol	phenotype
alcohol dependence	phenotype
Alcohol_dependence	phenotype
alcohol dependence with comorbid antisocial behavior local	phenotype
alcoholism	phenotype
alcohol use and abuse local	phenotype
alpha activity	phenotype
alpha band	phenotype
alpha EEG power local	phenotype
alpha oscillations	phenotype
alpha peak frequency	phenotype
alpha power	phenotype
Alpha rhythm peak frequency local	phenotype
Alzheimer's disease	phenotype
Alzheimer’s disease	phenotype
antisocial personality disorder	phenotype
anxiety	phenotype
anxious arousal local	phenotype
apoE	gene
APOE ε4	gene
ascending reticular formation local	anatomy
asymmetry in EEG activity local	phenotype
autism	phenotype
autism spectrum disorders	phenotype
basal forebrain neurons local	anatomy
behavioral phenotypes	phenotype
behavior problems	phenotype
benzodiazepines	drug
beta activity	phenotype
beta power	phenotype
Biometric Heritability Estimates local	phenotype
bipolar disorder	phenotype
cannabinoids	drug
Caucasian participants	cohort
cerebral hemispheres	anatomy
CEU haplotypes local	cohort
CEU sample of Caucasians local	cohort
Children with autism local	cohort
cholinergic pathways local	drug
cholinergic projections local	drug
Clu	gene
CLU variant local	variant
COGA sample	cohort
cognition	phenotype
cognitive processes	phenotype
Cognitive state local	phenotype
combined age cohorts local	cohort
common variants	cohort
Community volunteers local	cohort
COMT	gene
COMT Val158Met	gene
conduct disorder	phenotype
corticosterone	drug
CRH-BP local	gene
CRHBP	gene
Cz	anatomy
Cz electrode	anatomy
decision-making	phenotype
DEFA4 local	gene
DEFA6 local	gene
delta power	phenotype
depression	phenotype
disinhibitory psychopathology local	phenotype
Disinhibitory psychopathology local	phenotype
dopamine	drug
drinking-related behaviors local	phenotype
drowsiness	phenotype
drug dependence	phenotype
early onset of alcohol dependence local	phenotype
EEG	phenotype
EEG abnormalities local	phenotype
EEG_endophenotype local	phenotype
EEG frequency ranges local	phenotype
EEG measures local	phenotype
EEG parameter local	phenotype
EEG parameters	phenotype
EEG slow waves local	phenotype
EEG-specific candidate SNPs local	variant
Emotion	phenotype
endophenotype	phenotype
endophenotype-general candidate SNPs	variant
ES sample	cohort
externalizing disorders	phenotype
eye (superior electrode) local	anatomy
families	cohort
Family data local	cohort
first-degree relatives	cohort
four-member families local	cohort
frontal cortex	anatomy
GABA	phenotype
GABAA receptor	drug
GABAB receptor gene local	gene
GABAB receptor gene polymorphism local	variant
Gabra1	gene
GABRA2	gene
gene	gene
Generation Cohort local	cohort
genes	gene
Genetic Relatedness local	phenotype
glutamate	drug
HapMap	cohort
Head injury	phenotype
healthy controls	cohort
heavy drinking	phenotype
Heavy marijuana use local	phenotype
heritability	phenotype
higher frequencies local	phenotype
High frequency EEG power local	phenotype
high frequency oscillations	phenotype
high-risk offspring	cohort
hippocampus	anatomy
Hippocampus volume local	phenotype
Hodgkinson 2010 SNPs local	variant
Htr3b	gene
hyperactivity	phenotype
illicit drug use	phenotype
Illumina markers local	variant
Individuals in treatment local	cohort
individual_variant local	variant
intelligence	phenotype
late adolescence cohort local	cohort
latent externalizing dimension	phenotype
lateral parietal cortex	anatomy
linked earlobes local	anatomy
long-term potentiation	phenotype
lower frequencies local	phenotype
Low frequency EEG power local	phenotype
Low-frequency power local	phenotype
low-frequency power (delta and theta) local	phenotype
low-risk individuals	cohort
low-voltage alpha local	phenotype
LVA local	phenotype
marijuana	phenotype
McGue et al., 2013 local	cohort
MCTFR	cohort
MDMA	drug
MDMA use local	phenotype
memory	phenotype
methamphetamine	drug
Methamphetamine use local	phenotype
methylphenidate	drug
motor inhibition	phenotype
MTFS	cohort
neurological disorders	phenotype
nicotine	drug
Non-treatment samples local	cohort
Non-treatment seeking individuals local	cohort
noradrenaline	drug
noradrenergic modulation local	drug
normal controls	cohort
Normally developing children local	cohort
normal vision	phenotype
occipital cortex	anatomy
occipital-parietal local	anatomy
Occipital-parietal local	anatomy
occipital-parietal locations local	anatomy
ocular dominance columns local	anatomy
older cohort	cohort
opioid	drug
outer canthus local	anatomy
parents and offspring local	cohort
Perceptual state local	phenotype
phenotype	phenotype
posterior region	anatomy
prefrontal cortex	anatomy
present study	cohort
primary visual cortex	anatomy
psychiatric disorders	phenotype
Psychiatric Genomics Consortium	cohort
psychopathology	phenotype
psychophysiological_measure local	phenotype
relapse	phenotype
rest local	phenotype
resting EEG parameters local	phenotype
reward	phenotype
reward sensitivity	phenotype
rs279858	variant
rs511310 local	variant
sample_used_here local	cohort
schizophrenia	phenotype
selective attention	phenotype
sensorimotor beta activity local	phenotype
serotonin	drug
SGIP1	gene
shared environmental influence	phenotype
short-term memory	phenotype
sleep	phenotype
Sleepiness local	phenotype
slower alpha peak frequency local	phenotype
Slow-wave power local	phenotype
SNP	cohort
SNP heritability	phenotype
spatial working memory	phenotype
Stein et al., 2012 local	cohort
stepparents local	cohort
study cohort	cohort
substance abuse	phenotype
thalamocortical projections	anatomy
theta activity	phenotype
theta/alpha ratio local	phenotype
theta band	phenotype
theta-band activity local	phenotype
Theta-band activity local	phenotype
theta/beta ratio local	phenotype
theta oscillations	phenotype
total power	phenotype
Twin cohort	cohort
unique environmental influence local	phenotype
Val158/Met polymorphism local	variant
vertex	anatomy
women	cohort
working memory	phenotype
younger cohort	cohort

Citation	PMID	DOI	Status
Agrawal, A et al., Behavior Genetics, 2006, Association of GABRA2 with drug dependence in the collaborative study of the genetics of alcoholism sample	16622805	10.1007/s10519-006-9069-4	Primary
Allen, JJ et al., Psychophysiology, 2004, The stability of resting frontal electroencephalographic asymmetry in depression	15032992	10.1111/j.1469-8986.2003.00149.x	Cited
Anokhin, AP et al., Human Genetics, 1992, A genetic study of the human low-voltage electroencephalogram	1427795	10.1007/BF00210751	Cited
Anokhin, AP, International Journal of Psychophysiology, 2014, Genetic psychophysiology: Advances, problems, and future directions	24739435	10.1016/j.ijpsycho.2014.04.003	Cited
Barry, RJ et al., Clinical Neurophysiology, 2003, A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography	12559224	10.1016/s1388-2457(02)00362-0	Cited
Bauer, LO et al., Alcoholism, Clinical and Experimental Research, 2007, Variation in GABRA2 predicts drinking behavior in project MATCH subjects	17949392	10.1111/j.1530-0277.2007.00517.x	Cited
Bauer, LO et al., Journal of Studies on Alcohol, 1993, EEG, autonomic and subjective correlates of the risk for alcoholism	8412148	10.15288/jsa.1993.54.577	Cited
Bauer, LO, Neuropsychopharmacology, 2001, Predicting relapse to alcohol and drug abuse via quantitative electroencephalography	11522462	10.1016/S0893-133X(01)00236-6	Cited
Begic, D et al., Psychiatria Danubina, 2011, Quantitative electroencephalography in schizophrenia and depression	22075736	—	Cited
Benchenane, K et al., Current Opinion in Neurobiology, 2011, Oscillations in the prefrontal cortex: a gateway to memory and attention	21429736	10.1016/j.conb.2011.01.004	Cited
Benchenane, K et al., Neuron, 2010, Coherent theta oscillations and reorganization of spike timing in the hippocampal- prefrontal network upon learning	20620877	10.1016/j.neuron.2010.05.013	Cited
Berger, H, Archiv für Psychiatrie und Nervenkrankheiten, 1929, Über das Elektrenkephalogramm des Menschen	—	—	—
Bismark, AW et al., Biological Psychology, 2010, Polymorphisms of the HTR1a allele are linked to frontal brain electrical asymmetry	20025927	10.1016/j.biopsycho.2009.12.002	Cited
Bodenmann, S et al., Journal of Neuroscience, 2009, The functional Val158Met polymorphism of COMT predicts interindividual differences in brain alpha oscillations in young men	19726643	10.1523/JNEUROSCI.1427-09.2009	Cited
Browning, BL et al., American Journal of Human Genetics, 2009, A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals	19200528	10.1016/j.ajhg.2009.01.005	Cited
Bulgin, NL et al., Neuromolecular Medicine, 2008, Association study of theta EEG asymmetry and brain-derived neurotrophic factor gene variants in childhood-onset mood disorder	18543122	10.1007/s12017-008-8038-x	Cited
Buzsaki, G, Neuron, 2002, Theta oscillations in the hippocampus	11832222	10.1016/s0896-6273(02)00586-x	Cited
Clark, RC et al., International Journal of Psychophysiology, 2004, Spontaneous alpha peak frequency predicts working memory performance across the age span	15172130	10.1016/j.ijpsycho.2003.12.011	Cited
Clementz, BA et al., Psychophysiology, 1994, Resting EEG in first-episode schizophrenia patients, bipolar psychosis patients, and their first-degree relatives	7972603	10.1111/j.1469-8986.1994.tb01052.x	Cited
Cohen, HL et al., Alcoholism, Clinical and Experimental Research, 1991, EEG characteristics in males at risk for alcoholism	1755520	10.1111/j.1530-0277.1991.tb00613.x	Cited
Costa, L et al., Drug and Alcohol Dependence, 1997, Quantitative electroencephalographic differences associated with alcohol, cocaine, heroin and dual-substance dependence	9246556	10.1016/s0376-8716(97)00058-6	Cited
Covault, J et al., American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2004, Allelic and haplotypic association of GABRA2 with alcohol dependence	15274050	10.1002/ajmg.b.30091	Cited
David, O et al., Neuroimage, 2003, A neural mass model for MEG/EEG: coupling and neuronal dynamics	14642484	10.1016/j.neuroimage.2003.07.015	Cited
de Geus, EJ, Genome Medicine, 2010, From genotype to EEG endophenotype: a route for post-genomic understanding of complex psychiatric disease?	20828426	10.1186/gm184	Cited
Deckel, AW et al., Journal of Clinical Psychology, 1996, Antisocial personality disorder, childhood delinquency, and frontal brain functioning: EEG and neuropsychological findings	8912107	10.1002/(SICI)1097-4679(199611)52:6<639::AID-JCLP6>3.0.CO;2-F	Cited
Delorme, A et al., Journal of Neuroscience Methods, 2004, EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis	15102499	10.1016/j.jneumeth.2003.10.009	Cited
Derringer, J et al., Psychiatric Genetics, 2011, Nonreplication of an association of SGIP1 SNPs with alcohol dependence and resting theta EEG power	21317682	10.1097/YPG.0b013e32834371fd	Primary
Dick, DM et al., Alcoholism-Clinical and Experimental Research, 2006, Association between GABRA1 and drinking behaviors in the collaborative study on the genetics of alcoholism sample	16792556	10.1111/j.1530-0277.2006.00136.x	Primary
Dick, DM et al., Behavior Genetics, 2006, The role of GABRA2 in risk for conduct disorder and alcohol and drug dependence across developmental stages	16557364	10.1007/s10519-005-9041-8	Primary
Dick, DM et al., Journal of Studies on Alcohol, 2006, Marital status, alcohol dependence, and GABRA2: evidence for gene-environment correlation and interaction	16562401	10.15288/jsa.2006.67.185	Primary
Domino, EF et al., Psychopharmacology Bulletin, 1989, Further observations on the effects of subhypnotic doses of midazolam in normal volunteers	2626519	—	Cited
Drgon, T et al., American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2006, Linkage disequilibrium, haplotype and association studies of a chromosome 4 GABA receptor gene cluster: candidate gene variants for addictions	16894595	10.1002/ajmg.b.30349	Cited
Ducci, F et al., Alcohol, 2009, HTR3B is associated with alcoholism with antisocial behavior and alpha EEG power--an intermediate phenotype for alcoholism and co-morbid behaviors	19185213	10.1016/j.alcohol.2008.09.005	Cited
Edenberg, HJ et al., American Journal of Human Genetics, 2004, Variations in GABRA2, encoding the alpha 2 subunit of the GABA(A) receptor, are associated with alcohol dependence and with brain oscillations	15024690	10.1086/383283	Primary
Ehlers, CL et al., Biological Psychiatry, 1990, EEG fast frequency activity in the sons of alcoholics	2322623	10.1016/0006-3223(90)90531-6	Cited
Ehlers, CL et al., Drug and Alcohol Dependence, 2010, EEG spectral phenotypes: heritability and association with marijuana and alcohol dependence in an American Indian community study	19748744	10.1016/j.drugalcdep.2009.07.024	Cited
Ehlers, CL et al., Neuropsychopharmacology, 1991, Evaluation of EEG alpha activity in sons of alcoholics	2064719	—	Cited
Elias-Sonnenschein, LS et al., Journal of Neurology, Neurosurgery and Psychiatry, 2011, Predictive value of APOE-epsilon4 allele for progression from MCI to AD-type dementia: a meta-analysis	21493755	10.1136/jnnp.2010.231555	Cited
Engel, AK et al., Current Opinion in Neurobiology, 2010, Beta-band oscillations--signalling the status quo?	20359884	10.1016/j.conb.2010.02.015	Cited
Enoch, MA et al., Alcoholism, Clinical and Experimental Research, 1999, Association of low-voltage alpha EEG with a subtype of alcohol use disorders	10470973	—	Cited
Enoch, MA et al., American Journal of Medical Genetics B: Neuropsychiatric Genetics, 2006, Dimensional anxiety mediates linkage of GABRA2 haplotypes with alcoholism	16874763	10.1002/ajmg.b.30336	Cited
Enoch, MA et al., American Journal of Medical Genetics, 1995, Relationship of genetically transmitted alpha EEG traits to anxiety disorders and alcoholism	8546153	10.1002/ajmg.1320600510	Cited
Enoch, MA et al., PloS One, 2008, Common genetic origins for EEG, alcoholism and anxiety: the role of CRH-BP	18974851	10.1371/journal.pone.0003620	Cited
Enoch, MA et al., Psychiatric Genetics, 2003, Genetic origins of anxiety in women: a role for a functional catechol-O-methyltransferase polymorphism	12605099	10.1097/01.ypg.0000054709.85338.c3	Cited
Enoch, MA, Pharmacology Biochemistry and Behavior, 2008, The role of GABAA receptors in the development of alcoholism	18440057	10.1016/j.pbb.2008.03.007	Cited
Fehr, C et al., Psychiatric Genetics, 2006, Confirmation of association of the GABRA2 gene with alcohol dependence by subtype-specific analysis	16395124	10.1097/01.ypg.0000185027.89816.d9	Cited
Finn, PR et al., Alcoholism, Clinical and Experimental Research, 1999, Reduced EEG alpha power in the male and female offspring of alcoholics	10069554	—	Cited
Foxe, JJ et al., Frontiers in Psychology, 2011, The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention	21779269	10.3389/fpsyg.2011.00154	Cited
Gasser, T et al., Electroencephalography and Clinical Neurophysiology, 1985, Test-retest reliability of spectral parameters of the EEG	2579798	10.1016/0013-4694(85)90005-7	Cited
Ge, D et al., Genome Research, 2008, WGAViewer: software for genomic annotation of whole genome association studies	18256235	10.1101/gr.071571.107	Cited
Gilmore, CS et al., Behavior Genetics, 2010, Brain electrophysiological endophenotypes for externalizing psychopathology: a multivariate approach	20155392	10.1007/s10519-010-9343-3	Cited
Goldman, RI et al., Neuroreport, 2002, Simultaneous EEG and fMRI of the alpha rhythm	12499854	10.1097/01.wnr.0000047685.08940.d0	Cited
Golenkina, SA et al., Molekuliarnaia Biologiia, 2010, Analysis of clusterin gene (CLU/APOJ) polymorphism in Alzheimer’s disease patients and in normal cohorts from Russian populations	20873220	—	Cited
Grandy, TH et al., Neuroimage, 2013, Individual alpha peak frequency is related to latent factors of general cognitive abilities	23624490	10.1016/j.neuroimage.2013.04.059	Cited
Greenwood, TA et al., American Journal of Psychiatry, 2011, Analysis of 94 candidate genes and 12 endophenotypes for schizophrenia from the Consortium on the Genetics of Schizophrenia	21498463	10.1176/appi.ajp.2011.10050723	Cited
Gudmundsson, S et al., Clinical Neurophysiology, 2007, Reliability of quantitative EEG features	17765604	10.1016/j.clinph.2007.06.018	Cited
Harold, D et al., Nature Genetics, 2009, Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease	19734902	10.1038/ng.440	Cited
Harris, A et al., International Journal of Neuroscience, 2006, Dynamic spectral analysis findings in first episode and chronic schizophrenia	16484051	10.1080/00207450500402977	Cited
Henriques, JB et al., Journal of Abnormal Psychology, 1990, Regional brain electrical asymmetries discriminate between previously depressed and healthy control subjects	2307762	10.1037//0021-843x.99.1.22	Cited
Henriques, JB et al., Journal of Abnormal Psychology, 1991, Left frontal hypoactivation in depression	1757667	10.1037//0021-843x.100.4.535	Cited
Herning, RI et al., Annals of the New York Academy of Sciences, 2005, Neuropsychiatric alterations in MDMA users: preliminary findings	16179502	10.1196/annals.1344.003	Cited
Herning, RI et al., Biological Psychiatry, 1997, Neurophysiological signs of cocaine dependence: increased electroencephalogram beta during withdrawal	9146819	10.1016/S0006-3223(96)00258-2	Cited
Hodgkinson, CA et al., Proceedings of the National Academy of Sciences of the United States of America, 2010, Genome-wide association identifies candidate genes that influence the human electroencephalogram	20421487	10.1073/pnas.0908134107	Cited
Howie, B et al., Nature Genetics, 2012, Fast and accurate genotype imputation in genome-wide association studies through pre-phasing	22820512	10.1038/ng.2354	Cited
Iacono, WG et al., Child Development Perspectives, 2011, Developmental endophenotypes: Indexing genetic risk for substance abuse with the P300 brain event-related potential	22247735	10.1111/j.1750-8606.2011.00205.x	Cited
Iacono, WG et al., Development and Psychopathology, 1999, Behavioral disinhibition and the development of substance use disorders: Findings from the Minnesota Twin Family Study	10624730	10.1017/s0954579499002369	Cited
Iacono, WG et al., Psychophysiology, 2014, Genome wide scans of genetic variants for psychophysiological endophenotypes: A methodological overview	25387703	10.1111/psyp.12343	Cited
Irons, DE et al., Alcoholism: Clinical and Experimental Research, 2014, Gamma-aminobutyric acid system genes -- No evidence for a role in alcohol use and abuse in a community-based sample	24460875	10.1111/acer.12352	Cited
Kaplan, RF et al., Progress in Neuro-Psychopharmacology and Biological Psychiatry, 1988, Behavioral and EEG responses to alcohol in nonalcoholic men with a family history of alcoholism	3241870	10.1016/0278-5846(88)90083-8	Cited
Keyes, MA et al., Twin Research and Human Genetics, 2009, The enrichment study of the Minnesota twin family study: increasing the yield of twin families at high risk for externalizing psychopathology	19803776	10.1375/twin.12.5.489	Cited
Klimesch, W et al., Brain Res Rev, 2007, EEG alpha oscillations: the inhibition-timing hypothesis	16887192	10.1016/j.brainresrev.2006.06.003	Cited
Klimesch, W, Brain Research: Brain Research Reviews, 1999, EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis	10209231	10.1016/s0165-0173(98)00056-3	Cited
Knott, V et al., Psychiatry Research, 2001, EEG power, frequency, asymmetry and coherence in male depression	11306251	10.1016/s0925-4927(00)00080-9	Cited
Koek, RJ et al., Journal of Affective Disorders, 1999, Hemispheric asymmetry in depression and mania. A longitudinal QEEG study in bipolar disorder	10360405	10.1016/s0165-0327(98)00171-2	Cited
Kondacs, A et al., Clinical Neurophysiology, 1999, Long-term intra-individual variability of the background EEG in normals	10574286	10.1016/s1388-2457(99)00122-4	Cited
Lambert, JC et al., Nature Genetics, 2009, Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease	19734903	10.1038/ng.439	Cited
Lee, TW et al., Brain Topography, 2012, The influence of apolipoprotein E Epsilon4 polymorphism on qEEG profiles in healthy young females: a resting EEG study	22562716	10.1007/s10548-012-0229-y	Cited
Li, X et al., Human Heredity, 2011, A rapid generalized least squares model for a genome-wide quantitative trait association analysis in families	21474944	10.1159/000324839	Cited
Liu, JZ et al., American Journal of Human Genetics, 2010, A versatile gene-based test for genome-wide association studies	20598278	10.1016/j.ajhg.2010.06.009	Cited
Loo, SK et al., Journal of the American Academy of Child and Adolescent Psychiatry, 2010, Familial clustering and DRD4 effects on electroencephalogram measures in multiplex families with attention deficit/hyperactivity disorder	20410729	—	Cited
Lopes da Silva, FH et al., Electroencephalography and Clinical Neurophysiology, 1980, Relative contributions of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis	6160987	10.1016/0013-4694(80)90011-5	Cited
Lydall, GJ et al., Neuroscience Letters, 2011, Genetic association study of GABRA2 single nucleotide polymorphisms and electroencephalography in alcohol dependence	21683760	10.1016/j.neulet.2011.05.240	Cited
Massar, SA et al., International Journal of Psychophysiology, 2013, Resting-state EEG theta activity and risk learning: sensitivity to reward or punishment?	24184042	10.1016/j.ijpsycho.2013.10.013	Cited
McGue, M et al., Behavior Genetics, 2013, A genome-wide association study of behavioral disinhibition	23942779	10.1007/s10519-013-9606-x	Cited
McLoughlin, G et al., American Journal of Medical Genetics B: Neuropsychiatric Genetics, 2013, In search of biomarkers in psychiatry: EEG-based measures of brain function	24273134	10.1002/ajmg.b.32208	Cited
Miller, MB et al., Twin Research and Human Genetics, 2012, The Minnesota Center for Twin and Family Research genome-wide association study	23363460	10.1017/thg.2012.62	Cited
Muthukumaraswamy, SD et al., Proceedings of the National Academy of Sciences of the United States of America, 2009, Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans	19416820	10.1073/pnas.0900728106	Cited
Neale, MC et al., Mx: Statistical modeling, 2003	—	—	—
Newton, TF et al., Clinical Neurophysiology, 2004, Association between quantitative EEG and neurocognition in methamphetamine-dependent volunteers	14706488	10.1016/s1388-2457(03)00314-6	Cited
Obrist, WD, Brain function in old age, 1979, Electroencephalographic changes in normal aging and dementia	—	—	—
Payne, L et al., Journal of Cognitive Neuroscience, 2013, Attention-modulated alpha-band oscillations protect against intrusion of irrelevant information	23530921	10.1162/jocn_a_00395	Cited
Petit, D et al., Journal of Psychosomatic Research, 2004, Sleep and quantitative EEG in neurodegenerative disorders	15172204	10.1016/j.jpsychores.2004.02.001	Cited
Pivik, RT et al., Psychophysiology, 1993, Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts	8248447	10.1111/j.1469-8986.1993.tb02081.x	Cited
Pizzagalli, DA, Handbook of psychophysiology, 2007, Electroencephalography and high-density electrophysiological source localization	—	—	—
Pollock, VE et al., Alcoholism, Clinical and Experimental Research, 1995, Personality and EEG beta in older adults with alcoholic relatives	7771661	10.1111/j.1530-0277.1995.tb01470.x	Cited
Pollock, VE et al., Archives of General Psychiatry, 1983, The EEG after alcohol administration in men at risk for alcoholism	6870482	10.1001/archpsyc.1983.01790070047006	Cited
Pollock, VE et al., Biological Psychiatry, 1990, Quantitative, waking EEG research on depression	2183880	10.1016/0006-3223(90)90591-o	Cited
Pollock, VE et al., Electroencephalography and Clinical Neurophysiology, 1991, Reliability of topographic quantitative EEG amplitude in healthy late-middle-aged and elderly subjects	1713548	10.1016/0013-4694(91)90152-t	Cited
Ponomareva, NV et al., Frontiers in Aging Neuroscience, 2013, Age-dependent effect of Alzheimer’s risk variant of CLU on EEG alpha rhythm in non-demented adults	24379779	10.3389/fnagi.2013.00086	Cited
Ponomareva, NV et al., Neurobiology of Aging, 2008, EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease	17293007	10.1016/j.neurobiolaging.2006.12.019	Cited
Porjesz, B et al., Proceedings of the National Academy of Sciences of the United States of America, 2002, Linkage disequilibrium between the beta frequency of the human EEG and a GABA(A) receptor gene locus	11891318	10.1073/pnas.052716399	Primary
Posthuma, D et al., Behavior Genetics, 2001, Are smarter brains running faster? Heritability of alpha peak frequency, IQ, and their interrelation	11838534	10.1023/a:1013345411774	Cited
Price, AL et al., Nature Genetics, 2006, Principal components analysis corrects for stratification in genome-wide association studies	16862161	10.1038/ng1847	Cited
Propping, P et al., Human Genetics, 1981, Genetic disposition to alcoholism. An EEG study in alcoholics and their relatives	10819022	10.1007/BF00278854	Cited
R: A language and environment for statistical computing, 2010	—	—	—
Rangaswamy, M et al., Alcoholism, Clinical and Experimental Research, 2003, Theta power in the EEG of alcoholics	12711923	10.1097/01.ALC.0000060523.95470.8F	Primary
Rangaswamy, M et al., Biological Psychiatry, 2002, Beta power in the EEG of alcoholics	12372655	10.1016/s0006-3223(02)01362-8	Primary
Rangaswamy, M et al., International Journal of Psychophysiology, 2004, Resting EEG in offspring of male alcoholics: beta frequencies	14962576	10.1016/j.ijpsycho.2003.09.003	Primary
Ranlund, S et al., Schizophrenia Research, 2014, Resting EEG in psychosis and at-risk populations - A possible endophenotype?	24486144	10.1016/j.schres.2013.12.017	Cited
Ray, WJ et al., Science, 1985, EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes	3992243	10.1126/science.3992243	Cited
Rommelse, NNJ et al., Neuroscience and Biobehavioral Reviews, 2011, A review on cognitive and brain endophenotypes that may be common in autism spectrum disorder and attention-deficit/hyperactivity disorder and facilitate the search for pleiotropic genes	21382410	10.1016/j.neubiorev.2011.02.015	Cited
Rousseeuw, P et al., robustbase: Basic Robust Statistics (Version R package version 0.7-3), 2011	—	—	—
Saletu-Zyhlarz, GM et al., Alcohol and Alcoholism, 2004, Differences in brain function between relapsing and abstaining alcohol-dependent patients, evaluated by EEG mapping	15082461	10.1093/alcalc/agh041	Cited
Salinsky, MC et al., Electroencephalography and Clinical Neurophysiology, 1991, Test-retest reliability in EEG frequency analysis	1718711	10.1016/0013-4694(91)90203-g	Cited
Schutter, DJLG et al., Cognitive Brain Research, 2005, Electrophysiological ratio markers for the balance between reward and punishment	15878265	10.1016/j.cogbrainres.2005.04.002	Cited
Smit, CM et al., International Journal of Psychophysiology, 2006, Genetic variation of individual alpha frequency (IAF) and alpha power in a large adolescent twin sample	16338015	10.1016/j.ijpsycho.2005.10.004	Cited
Smit, DJ et al., Biological Psychology, 2007, The relation between frontal EEG asymmetry and the risk for anxiety and depression	16875773	10.1016/j.biopsycho.2006.06.002	Cited
Smit, DJ et al., Psychophysiology, 2005, Heritability of background EEG across the power spectrum	16364064	10.1111/j.1469-8986.2005.00352.x	Cited
Snyder, SM et al., Journal of Clinical Neurophysiology, 2006, A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder	17016156	10.1097/01.wnp.0000221363.12503.78	Cited
Soyka, M et al., Journal of Psychiatric Research, 2008, GABA-A2 receptor subunit gene (GABRA2) polymorphisms and risk for alcohol dependence	17207817	10.1016/j.jpsychires.2006.11.006	Cited
Speed, D et al., American Journal of Human Genetics, 2012, Improved heritability estimation from genome-wide SNPs	23217325	10.1016/j.ajhg.2012.10.010	Cited
Sponheim, SR et al., Biological Psychiatry, 2000, Clinical and biological concomitants of resting state EEG power abnormalities in schizophrenia	11094142	10.1016/s0006-3223(00)00907-0	Cited
Sponheim, SR et al., Psychophysiology, 1994, Resting EEG in first-episode and chronic schizophrenia	8146253	10.1111/j.1469-8986.1994.tb01023.x	Cited
Sponheim, SR et al., Schizophrenia Research, 2003, Sensitivity and specificity of select biological indices in characterizing psychotic patients and their relatives	12892855	10.1016/s0920-9964(02)00385-7	Cited
Stam, CJ, Electroencephalography: basic principles, clinical applications, and related fields, 2005, Dementia and EEG	—	—	—
Stein, JL et al., Nature Genetics, 2012, Identification of common variants associated with human hippocampal and intracranial volumes	22504417	10.1038/ng.2250	Cited
Steriade, M et al., Electroencephalography and Clinical Neurophysiology, 1990, Basic mechanisms of cerebral rhythmic activities	1701118	10.1016/0013-4694(90)90001-z	Cited
Stewart, JL et al., Journal of Abnormal Psychology, 2010, Resting frontal EEG asymmetry as an endophenotype for depression risk: sex-specific patterns of frontal brain asymmetry	20677839	10.1037/a0019196	Cited
Struve, FA et al., Drug and Alcohol Dependence, 1999, Topographic quantitative EEG sequelae of chronic marihuana use: a replication using medically and psychiatrically screened normal subjects	10529019	10.1016/s0376-8716(99)00029-0	Cited
Tallman, JF et al., Annual Review of Neuroscience, 1985, The GABA-ergic system: a locus of benzodiazepine action	2858999	10.1146/annurev.ne.08.030185.000321	Cited
Tang, Y et al., International Journal of Psychophysiology, 2007, Genetic influences on bipolar EEG power spectra	17383039	10.1016/j.ijpsycho.2007.02.004	Primary
Tesche, CD et al., Proceedings of the National Academy of Sciences of the United States of America, 2000, Theta oscillations index human hippocampal activation during a working memory task	10639180	10.1073/pnas.97.2.919	Cited
Tierney, AL et al., PloS One, 2012, Developmental trajectories of resting EEG power: an endophenotype of autism spectrum disorder	22745707	10.1371/journal.pone.0039127	Cited
van Beijsterveldt, CE et al., American Journal of Human Genetics, 1996, Heritability of human brain functioning as assessed by electroencephalography	8644716	—	Cited
Venables, NC et al., Schizophrenia Bulletin, 2009, Genetic and disorder-specific aspects of resting state EEG abnormalities in schizophrenia	18381357	10.1093/schbul/sbn021	Cited
Villafuerte, S et al., Genes, Brain and Behavior, 2013, Impulsiveness mediates the association between GABRA2 SNPs and lifetime alcohol problems	23566244	10.1111/gbb.12039	Cited
Vogel, F, EEG-EMG Zeitschrift für Elektroenzephalographie, Elektromyographie und Verwandte Gebiete, 1986, Principles and significance of genetically-induced variability of the normal human EEG	3100270	—	Cited
Volk, DW et al., Cerebral Cortex, 2002, Reciprocal alterations in pre- and postsynaptic inhibitory markers at chandelier cell inputs to pyramidal neurons in schizophrenia	12217970	10.1093/cercor/12.10.1063	Cited
Vrieze, SI et al., Psychophysiology, 2014, Genetic associations of nonsynonymous exonic variants with psychophysiological endophenotypes	25387709	10.1111/psyp.12349	Cited
Whittington, MA et al., International Journal of Psychophysiology, 2000, Inhibition-based rhythms: experimental and mathematical observations on network dynamics	11102670	10.1016/s0167-8760(00)00173-2	Cited
Wiesel, TN et al., Journal of Neurophysiology, 1965, Comparison of the effects of unilateral and bilateral eye closure on cortical unit responses in kittens	5883730	10.1152/jn.1965.28.6.1029	Cited
Winterer, G et al., Behavior Genetics, 2003, Association analysis of exonic variants of the GABA(B)-receptor gene and alpha electroencephalogram voltage in normal subjects and alcohol-dependent patients	12645817	10.1023/a:1021043315012	Cited
Winterer, G et al., Psychiatry Research, 1998, Quantitative EEG (QEEG) predicts relapse in patients with chronic alcoholism and points to a frontally pronounced cerebral disturbance	9579706	10.1016/s0165-1781(97)00148-0	Cited
Yang, J et al., American Journal of Human Genetics, 2011, GCTA: a tool for genome-wide complex trait analysis	21167468	10.1016/j.ajhg.2010.11.011	Cited
Yang, J et al., Genome-Wide Association Studies and Genomic Prediction, 2013, Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations	23756893	10.1007/978-1-62703-447-0_9	Cited

In this knowledge base

Title	Year	PMID
A genome wide association study of fast beta EEG in families of European ancestry.	2017	28040410
An endophenotype approach to the genetics of alcohol dependence: a genome wide association study of fast beta EEG in families of African ancestry.	2017	28070124
Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci.	2017	27995817
In search of rare variants: preliminary results from whole genome sequencing of 1,325 individuals with psychophysiological endophenotypes.	2014	25387710

External

Title	Authors	Journal	Year	Link
ADHD polygenic risk predicts neural signatures of cognitive control: Evidence from midfrontal theta dynamics.	Aydin Ü et al.	—	2026	→
Individual alpha peak predicts clinical response in TMS therapy for depression: An independent replication.	Baradits M et al.	—	2026	→
Number of Alpha Peaks in the Electroencephalogram Is Associated With Clinical Phenotype and Copy Number Variants in Youths With Autism.	Arutiunian V et al.	—	2026	→
Depression and anxiety are increased in autism and ADHD: Evidence from a young adult community-based sample.	Capp S et al.	—	2025	→
qEEG Neuromarkers of Complex Childhood Trauma in Adolescents.	Marcu GM et al.	—	2025	→
Reduced occurrence of alpha waves during resting state predicts high ADHD traits in young adults	Rodriguez-Larios J et al.	—	2025	—
Face processing in young adults with autism and ADHD: An event related potentials study.	Aydin Ü et al.	—	2023	→
A nonparametric Bayesian model for estimating spectral densities of resting-state EEG twin data.	Hart B et al.	—	2022	→
Human resting-state EEG and radiofrequency GSM mobile phone exposure: the impact of the individual alpha frequency.	Wallace J et al.	—	2022	→
Quality of life, functional impairment and continuous performance task event-related potentials (ERPs) in young adults with ADHD and autism: A twin study.	Aydin Ü et al.	—	2022	→
The Interactive Effect of Genetic and Epigenetic Variations in <i>FKBP5</i> and <i>ApoE</i> Genes on Anxiety and Brain EEG Parameters.	Kuznetsova IL et al.	—	2022	→
Using multivariate endophenotypes to identify psychophysiological mechanisms associated with polygenic scores for substance use, schizophrenia, and education attainment.	Harper J et al.	—	2022	→
Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity.	Smit DJA et al.	—	2021	→
Genome-Wide Scan for Five Brain Oscillatory Phenotypes Identifies a New QTL Associated with Theta EEG Band.	Rebelo MÂ et al.	—	2020	→
Discovering heritable modes of MEG spectral power.	Leppäaho E et al.	—	2019	→
Human brain arousal in the resting state: a genome-wide association study.	Jawinski P et al.	—	2019	→
Electrophysiological activity is associated with vulnerability of Internet addiction in non-clinical population.	Wang GY et al.	—	2018	→
Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity.	Smit DJA et al.	—	2018	→
Heritability of Sleep EEG Topography in Adolescence: Results from a Longitudinal Twin Study.	Markovic A et al.	—	2018	→
Toward a reliable, automated method of individual alpha frequency (IAF) quantification.	Corcoran AW et al.	—	2018	→
A genome wide association study of fast beta EEG in families of European ancestry.	Meyers JL et al.	—	2017	→
An endophenotype approach to the genetics of alcohol dependence: a genome wide association study of fast beta EEG in families of African ancestry.	Meyers JL et al.	—	2017	→
GABA<sub>A</sub> receptor subtype involvement in addictive behaviour.	Stephens DN et al.	—	2017	→
Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci.	Liu M et al.	—	2017	→
What can time-frequency and phase coherence measures tell us about the genetic basis of P3 amplitude?	Malone SM et al.	—	2017	→
Whole genome sequence association and ancestry-informed polygenic profile of EEG alpha in a Native American population.	Peng Q et al.	—	2017	→
Brain Networks and α-Oscillations: Structural and Functional Foundations of Cognitive Control.	Sadaghiani S et al.	—	2016	→
Electrophysiological Endophenotypes for Schizophrenia.	Owens EM et al.	—	2016	→
One-year developmental stability and covariance among oddball, novelty, go/no-go, and flanker event-related potentials in adolescence: A monozygotic twin study.	Burwell SJ et al.	—	2016	→
Cracking the brain's genetic code.	Thompson PM	—	2015	→
Longitudinal stability and predictive utility of the visual P3 response in adults with externalizing psychopathology.	Yoon HH et al.	—	2015	→
Risky Business: Pathways to Progress in Biologically Informed Studies of Psychopathology.	Agrawal A et al.	—	2015	→
The importance of endophenotypes in schizophrenia research.	Braff DL	—	2015	→
The Power of Theory, Research Design, and Transdisciplinary Integration in Moving Psychopathology Forward.	Vaidyanathan U et al.	—	2015	→
Decomposing P300 to identify its genetic basis.	Ford JM	—	2014	→
Genetic associations of nonsynonymous exonic variants with psychophysiological endophenotypes.	Vrieze SI et al.	—	2014	→
Genome-wide scans of genetic variants for psychophysiological endophenotypes: a methodological overview.	Iacono WG et al.	—	2014	→
Heritability and molecular genetic basis of antisaccade eye tracking error rate: a genome-wide association study.	Vaidyanathan U et al.	—	2014	→
In search of rare variants: preliminary results from whole genome sequencing of 1,325 individuals with psychophysiological endophenotypes.	Vrieze SI et al.	—	2014	→