Hodgkinson CA, Enoch MA, Srivastava V, Sankararaman S, Yamani G, Yuan Q, Zhou Z, Albaugh B, White K, Shen PH, Goldman D. (2010) Genome-wide association analysis identifies candidate genes that influence the human electroencephalogram. Proceedings of the National Academy of Sciences, 107(19), 8695-8700.
Genome-wide association identiﬁes candidate genes
that inﬂuence the human electroencephalogram
Colin A. Hodgkinsona,1, Mary-Anne Enocha, Vibhuti Srivastavaa, Justine S. Cummins-Omana, Cherisse Ferriera,
Polina Iarikovaa, Sriram Sankararamanb, Goli Yaminia, Qiaoping Yuana, Zhifeng Zhoua, Bernard Albaughc,
Kenneth V. Whitea, Pei-Hong Shena, and David Goldmana
aLaboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, Rockville, MD 20852; bComputer Science Department, University of
California, Berkeley, CA 94720; and cCenter for Human Behavior Studies, Weatherford, OK 73096
Edited* by Raymond L. White, University of California, Emeryville, CA, and approved March 31, 2010 (received for review July 23, 2009)
Complex psychiatric disorders are resistant to whole-genome
analysis due to genetic and etiological heterogeneity. Variation in
resting electroencephalogram (EEG) is associated with common,
complex psychiatric diseases including alcoholism, schizophrenia,
and anxiety disorders, although not diagnostic for any of them. EEG
traits for an individual are stable, variable between individuals, and
moderately to highly heritable. Such intermediate phenotypes
appear to be closer to underlying molecular processes than are
clinical symptoms, and represent an alternative approach for the
identiﬁcation of genetic variation that underlies complex psychiat-
ric disorders. We performed a whole-genome association study on
alpha (α), beta (β), and theta (θ) EEG power in a Native American
cohort of 322 individuals to take advantage of the genetic and en-
vironmental homogeneity of this population isolate. We identiﬁed
three genes (SGIP1, ST6GALNAC3, and UGDH) with nominal associ-
ation to variability of θ or α power. SGIP1 was estimated to account
for 8.8% of variance in θ power, and this association was replicated
in US Caucasians, where it accounted for 3.5% of the variance.
Bayesian analysis of prior probability of association based upon
earlier linkage to chromosome 1 and enrichment for vesicle-related
transport proteins indicates that the association of SGIP1 with θ
power is genuine. We also found association of SGIP1 with alcohol-
ism, an effect that may be mediated via the same brain mechanisms
accessed by θ EEG, and which also provides validation of the use of
EEG as an endophenotype for alcoholism.
alcoholism | electroencephalogram | endophenotype | genetics |
Genetic studies of behavior and psychiatric disorders are ham-
pered by etiologic heterogeneity of these complex phenotypes.
Addiction vulnerability arises from both internalizing (emotional)
and externalizing (dyscontrol) behavioral dimensions (1), and both
of these broad aspects of behavior are strongly inﬂuenced by early
life trauma and other gene/environment interactions (2). Etiologic
heterogeneity dilutes power to detect genetic effects, and is a reason
for failures to detect and replicate genome-wide associations
(GWAS) in complex disorders. Increasing sample size does not
remove underlying heterogeneity and can introduce additional
confounds. In neuropsychiatry, these considerations have led to the
use of intermediate phenotypes (or endophenotypes) that are her-
itable, relevant to disease, and have good measurement properties
and assay variation more closely related to gene function (3) as
surrogates to probe the underlying biology of complex disorders.
Risk genes for schizophrenia have recently been identiﬁed using
quantitative variables derived from functional magnetic resonance
strategy to increase power for GWAS of psychiatric disorders might
be the use of endophenotypes.
The resting electroencephalogram (EEG) is a safely and in-
expensively obtained phenotype relevant to normal behavioral
variation and to psychiatric disease. The EEG recorded at the
scalp is the sum of postsynaptic currents of synchronously depo-
larized, radially oriented pyramidal cells in the cerebral cortex, and
reﬂects rhythmic electrical activity of the brain. EEG patterns
dynamically and quantitatively index cortical activation, cognitive
function, and state of consciousness. EEG traits were among the
original intermediate phenotypes in neuropsychiatry, having been
ﬁrst recorded in humans in 1924 by Hans Berger, who documented
the α rhythm, seen maximally during states of relaxation with eyes
closed, and supplanted by faster β waves during mental activity.
EEG can be used clinically for the evaluation and differential di-
agnosis of epilepsy and sleep disorders, differentiation of en-
cephalopathy from catatonia, assessment of depth of anesthesia,
prognosis in coma, and determination of brain death (5, 6). EEG
also has moderate predictive value for personality variation and
psychiatric disease including depression (7), bipolar disorder (8),
attention-deﬁcit/hyperactivity disorder (9), and obsessive-com-
pulsive disorder (10). Increased β power is associated with both
alcoholism and family history of alcoholism (11, 12), θ power is
altered in alcoholics (13–15), and reduced α power has been as-
sociated with a family history of alcoholism and with alcoholism
with comorbid anxiety disorders (16, 17). However, the EEG is not
clinically useful for diagnosis of any speciﬁc psychiatric disorder.
The stability and heritability of the EEG make it suitable for
genetic analysis. Under standardized conditions the resting EEG
is a stable trait in healthy adults, with high test-retest correlations
[e.g., 0.7 even at >10 months (18)]. The marked interindividual
variability in the resting EEG spectral band power is largely
genetically determined and heritability of EEG spectral power is
uniformly high for all wave forms (19, 20).
We have performed GWAS in a sample of Plains American
Indians, in which α (8–13 Hz), β (13–30 Hz), and θ (3–8 Hz) EEG
spectral power are moderately heritable with high test-retest cor-
relations over 2 years (15). Notably, this sample represents
a population isolate evidencing a small but, as it turned out, useful
degree of European admixture, and is genetically distinct from
other Native American populations. In this dataset, common fa-
milial traits such as alcoholism and other psychiatric disorders do
not themselves generate statistical signals approaching genome-
wide signiﬁcance. However, we were able to identify a set of genes
and possible pathways that affect α and θ EEG wave forms that are
relevant to some of these same complex behavioral traits. Two of
the gene associations were replicated in a US Caucasian dataset.
Genome-Wide Signiﬁcant Loci for Resting EEG Power. Five separate
genomic regions, three for θ power [Fig. 1A, all on chromosome
Author contributions: C.A.H., M.-A.E., and D.G. designed research; C.A.H., M.-A.E., J.S.C.-O.,
C.F., P.I., Z.Z., B.A., and K.V.W. performed research; S.S. contributed new reagents/analytic
tools; C.A.H., M.-A.E., V.S., J.S.C.-O., C.F., P.I., G.Y., Q.Y., Z.Z., P.-H.S., and D.G. analyzed data;
and C.A.H., M.-A.E., V.S., and D.G. wrote the paper.
The authors declare no conﬂict of interest.
*This Direct Submission article had a prearranged editor.
1To whom correspondence should be addressed. E-mail: [email protected]
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
www.pnas.org/cgi/doi/10.1073/pnas.0908134107 PNAS | May 11, 2010 | vol. 107 | no. 19 | 8695–8700
(chr) 1] and two for α power (Fig.1B,chr 1 and 4), showed genome-
wide signiﬁcant association (P < 1.23 × 10−7) to a single EEG trait
(Table 1). No genome-wide signiﬁcant associations were observed
for β power (Fig. 1C) or for the complex psychiatric diagnosis of
alcohol use disorder (SI Text), demonstrating that random ge-
nome-wide signiﬁcant signals are not generated for common fa-
milial traits in a dataset of this size. Quantile-quantile plots show
an excess of low P values for θ power but little or no excess for α
power or β power (SI Text). Due to the population structure of the
Plains Indians there exist close family relationships between study
participants conﬁrmed by λ values of 1.3 (α and θ) and 1.1 (β), and
these could lead to spurious association. Empirically derived P
values (Table 1) that correct for family structure for nominally
signiﬁcant single-nucleotide polymorphisms (SNPs) (P < 1 × 10−5)
remained suggestive of association, although no marker remained
signiﬁcant after Bonferroni correction.
Genomic Regions Associated with θ Power. The three genomic
regions associated with θ power are all located at chr 1p31, each
identifying a single candidate gene (Table 1). In Plains Indians,
four signiﬁcant (and two subthreshold) alleles are represented on
two closely related low-frequency haplotypes (SI Text) that span
almost the whole of the SH3-domain GRB2-like (endophilin)
-interacting protein 1 gene (SGIP1), and an additional three sub-
threshold markers lie within another haplotype block that extends
3′ from SGIP1 into the adjacent Tctex1-containing 1 gene
(TCTEX1D1). The association with SGIP1 accounts for 8.8% of
observed variation in θ power. Two missense polymorphisms in
exon 7 of SGIP1, rs17490057 and rs7526812, can be excluded as the
functional locus because of their distributions within haplotypes.
Four haplotypes, including a common one (frequency = 0.24)
carrying the increased θ power-associated alleles, are present in
HapMap Caucasians (SI Text). A single marker signiﬁcant for θ
power was found in both ST6 (α-N-acetylneuraminyl-2,-3-β-ga-
lactosyl-1,3)-N-acetylgalactosamine-α-2,6-sialyltransferase 3 (ST-
6GALNAC3: rs6696780) and latrophilin 2 (LPHN2: rs12145665).
ST6GALNAC3 is an integral Golgi membrane protein which
catalyzes the transfer of sialic acids to carbohydrate groups on
glycoproteins and glycolipids. LPHN2 is a G-protein-coupled re-
ceptor related to the receptor that binds black widow (Latrodectus)
spider venom in synaptic membranes (21).
Genomic Regions Associated with α Power. Two genomic regions
show signiﬁcant association to α power. Although the ST6GAL-
NAC3 gene overlaps with our ﬁndings for θ power, the two α
power-associated markers (rs410076 and rs172714) both lie within
the third intron of ST6GALNAC3 and are in linkage disequilib-
rium with each other, but not with the θ power-associated
rs6696780 located more than 169 kb away. The signals for asso-
ciation to α and θ power are therefore likely to be independent in
this gene region. α power was also associated with UDP-glucose
dehydrogenase gene (UGDH) through two markers, rs7667766
and rs6817264, that are located in introns 1 and 9, respectively. An
additional three SNPs adjacent to this region that show sub-
Fig. 1. Manhattan plots for resting EEG spectral power: θ (A), α (B), and β (C).
The three plots show association (−log10
P value) (y axis) for individual SNPs
(uncorrected for family structure) against chromosome position (x axis).
Threshold P values of 1 × 10−7 (**) and 1 × 10−5 (*) are indicated by dotted lines.
Table 1. Markers with signiﬁcant association to EEG traits accompanied by subthreshold markers within signiﬁcant regions
band SNP Location Position Gene
corrected P value*
Theta rs6588207 1p31.3 66839146 SGIP1 0.5972 0.03 4.24 × 10−8 7.1 × 10−6 0.056
Theta rs10889635 1p31.3 66848163 SGIP1 0.5526 0.03 2.52 × 10−8 4.9 × 10−6 0.013
Theta rs6656912 1p31.3 66856259 SGIP1 1 0.02 4.24 × 10−7 1.61 × 10−5 ND
Theta rs6681460 1p31.3 66895645 SGIP1 0.4659 0.03 7.30 × 10−8 1.3 × 10−5 ND
Theta rs10789215 1p31.3 66923773 SGIP1 1 0.03 8.51 × 10−8 1.2 × 10−5 NS
Theta rs2146904 1p31.3 66932924 SGIP1 1 0.03 4.37 × 10−7 3.63 × 10−5 ND
Theta rs536410 1p31.3 66963672 SGIP1 0.8271 0.04 1.69 × 10−7 1.43 × 10−5 ND
Theta rs2483704 1p31.3 66967941 SGIP1 1 0.04 3.63 × 10−7 2.59 × 10−5 ND
Theta rs2916 1p31.3 66989285 TCTEX1D1 0.6424 0.03 1.87 × 10−7 1.92 × 10−5 ND
Theta rs6696780 1p31.1 76504495 ST6GALNAC3 0.2083 0.02 8.47 × 10−8 3.89 × 10−5 ND
Alpha rs410076 1p31.1 76774219 ST6GALNAC3 0.6564 0.35 1.03 × 10−7 1.1 × 10−6 NS
Alpha rs172714 1p31.1 76787701 ST6GALNAC3 0.2563 0.35 7.34 × 10−8 3.0 × 10−7 NS
Theta rs1245665 1p31.1 81800834 LPHN2 1 0.02 3.00 × 10−9 7.1 × 10−6 NS
Alpha rs2608830 4p13 39135549 RPL9/LIAS 0.5768 0.11 1.29 × 10−6 6.8 × 10−6 ND
Alpha rs2259073 4p13 39135549 UGDH 0.4204 0.11 4.55 × 10−7 2.5 × 10−6 ND
Alpha rs2687964 4p13 39151469 UGDH 0.6905 0.10 4.54 × 10−6 1.39 × 10−5 ND
Alpha rs7667766 4p13 39172667 UGDH 0.6632 0.11 7.04 × 10−8 8.0 × 10−7 NS
Alpha rs6827264 4p13 39194505 UGDH 0.5355 0.11 4.23 × 10−8 7.0 × 10−7 NS
HWE, Hardy-Weinberg equilibrium; ND, not determined; NS, not signiﬁcant.
*Empirical P values correcting for participant relatedness.
8696 | www.pnas.org/cgi/doi/10.1073/pnas.0908134107 Hodgkinson et al.
threshold association to α power are located in the 5′-regulatory
region of UGDH, and in the adjacent lipoic acid synthetase (LIAS)
and ribosomal protein-like 9 (RPL9) genes. All ﬁve SNPs are within
a single haplotype block extending almost 1 Mb (SI Text) and
encompassing UGDH, LIAS, and RPL9, along with klotho β (KLB),
a component of the receptor for ﬁbroblast growth factor 19
posttranslational modiﬁcation of extrinsic proteins, whose expres-
sion is up-regulated in response to TGF-β and hypoxia, a risk factor
Validation of SGIP1 Association. We estimated posterior probabili-
ties of association (PPAs) for the top two hits in SGIP1: rs6588207
and rs10889635. Posterior probability of association is based on
Bayesian method and can be interpreted directly as a probability
(22). The log10
(BF) for the combination of associated markers was
4.11 and for rs10889635 was 4.40 [σa
as described by Servin
and Stephens (23) for phenotypes normally distributed across
genotypes]. We assumed a value of 10−4 for π (prior probability of
), achoice based on earlier evidence oflinkage of EEG variation
to this chr 1 region in the Collaborative Study on the Genetics of
Alcoholism (COGA) dataset, and nominally signiﬁcant enrich-
ment for θ power-associated proteins that mediate vesicle trans-
port (P = 0.040) based on gene ontology. A moderate PPA of 0.56
was observed for the region whereas rs10889635 showed a PPA of
0.72, indicating that this region is associated with θ power among
Plains Indians, justifying analysis of these markers in an in-
dependent sample after consideration of PPAs under deﬁned
Replication in the United States. Caucasians. The ﬁve genome-wide
signiﬁcant regions were tested in an independent US Caucasian
dataset. Association of increased θ power to the G allele of
rs10889635 within SGIP1 was replicated (P = 0.013) (Fig. 2A),
accounting for 3.5% of the variance in θ power in this population,
although the association does not remain signiﬁcant after correc-
tion for multiple testing. Within SGIP1, the A allele of rs6588207,
signiﬁcant in Plains Indians, showed a similar trend (P = 0.056) but
did not reach signiﬁcance. No ST6GALNAC3, LPHN2, or UGDH
markers showed association in Caucasians.
Subthreshold Associations with θ and α Power. Other genomic regions
that several represent chance ﬁndings, because either no annotated
gene is present in the region or the gene would not appear to affect
neuronal reactivity (SI Text). All potential candidate genes identiﬁed
foreach EEGspectral power(at signiﬁcant orsubthreshold level)are
listed by function in SI Text.
Fig. 2. Replication of EEG/SNP associations in Caucasians. (A) One-way analysis of variance (ANOVA) for rs6588207 and rs10889635 in SGIP1, against (−log10
power averaged across posterior electrodes. The G allele of rs10889635 showed association to increased θ power in both Plains Indians (P = 2.52 × 10−8) and
Caucasians (P = 0.013). The A allele of rs6588207 showed association to increased θ power in Plains Indians (P = 4.24 × 10−8) with a similar trend in Caucasians
(P = 0.056). (B) One-way ANOVA for rs261900 in BICD1, against (−log10
) α power in Plains Indians (at the P3 electrode) and Caucasians (−log10
averaged across posterior electrodes. The T allele showed association to increased α power in both Plains Indians (P = 9.76 × 10−9) and Caucasians (P = 0.0023).
Hodgkinson et al. PNAS | May 11, 2010 | vol. 107 | no. 19 | 8697
Golgi Transport Candidates Among Subthreshold Signals. Analysis of
of the genes are involved in similar cellular processes (SI Text). Like
SGIP1, four of these genes, BICD1, FAM125B, ANKRD27, and
C11orf2, are either involved in retrograde Golgi transport or at least
contain a domain similar to those found in vesicular transport pro-
teins. Additionally, RTN1, RPH3AL, and CECR2 are involved in
vesicle-mediated transport and neuroendocrine processes. BICD1
(bicaudal D homolog 1) is particularly interesting in that although
there was only subthreshold association [rs261900: P = 4.32 × 10−7
(uncorrected)/4.5 × 10−6 (empirical)] to α power across posterior
scalp electrodes, the T allele of rs261900 is also associated (P =
0.0023) with increased α power in Caucasians (Fig. 2B).
β EEG Power. Several candidate genes associated to β power have
functions that may affect neuronal electrical activity neurons.
These include the α3 glycine receptor gene (GLRA3), a brain-
expressed synaptic receptor that inhibits neuroexcitability, with
four SNPs in a single haplotype block. rs12027066 is near ALD9A
(aldehyde dehydrogenase, 9 family, member A1, an enzyme in-
volved in GABA synthesis). Genetic variation in GABA receptors
has been associated with disorders for which EEG has been pro-
posed as an intermediate phenotype, including alcoholism (24)
and schizophrenia (25).
SGIP1 Association with θ Power Is Revealed by Admixture Mapping.
The Plains Indian population was selected because of its charac-
teristics as a population isolate with low admixture (European
admixture; mean 5.23% and median 1.6%) and is genetically dis-
tinct even from other Indian tribes (SI Text). However, despite the
isolate character of Plains Indians, analysis of the θ power asso-
ciation on chr 1 suggests that this signal was detected via admixture
mapping, making it feasible to identify the introgressed European-
derived chromosomal region and extended haplotypes harboring
the putative functional locus. The minor allele frequency (MAF)
in Plains Indians for the signiﬁcant SNP rs10889635 is low (>0.03)
compared with Caucasians (MAF = 0.41). When MAF is plotted
against European ethnic factor score, a measure of European
ancestry, the frequency of the minor G allele increases with in-
creasing European admixture (SI Text), an effect seen also for θ
power-associated markers in ST6GALNAC3 and LPHN2, and to
a lesser extent in the more distant SEP15 gene. Localized admix-
ture for the region surrounding SGIP1 on chr 1 was assessed forthe
17 Plains Indians carrying the G allele of rs10889635 and 9 Plains
Indians with low European admixture (mean = 1.3%, median =
1.0%). In all carriers of the G allele, at least one copy of SGIP1 was
European-derived (Fig. 3). The relatively small size of the com-
puted blocks of admixture suggests that introduction of European
DNA into this population is not recent (26). The association of
SGIP1 to θ power is unlikely to be a stratiﬁcation artifact because
there is no correlation between degree of European ancestry and
EEG power in any of the frequency bands (SI Text), and because
the association at SGIP1 was replicated in Caucasians. Instead,
this appears to be a serendipitous example of admixture mapping
within a GWAS. The nonreplicating associations of LPHN2 and
ST6GALNAC3 to θ power are likely to arise as a consequence of
this admixture, and probably do not represent true associations.
For α power, European ancestry does not predict the associated
minor allele frequencies in ST6GALNAC3 and UGDH (SI Text),
indicating these associations may be valid but population-speciﬁc.
EEG as an Endophenotype for Alcoholism. To evaluate whether
SGIP1 is a candidate gene for alcoholism, in addition to EEG
variation we analyzed signiﬁcant SGIP1 markers for association
with alcohol use disorders (AUD) in the Plains Indian cohort
(Table 2). Alleles signiﬁcant (but subthreshold) for association
with increased θ power were underrepresented in individuals with
AUD. Reduced θ power in alcoholics had been reported for this
dataset (15). The present data indicate a relationship between
SGIP1 genotype and AUD. Signiﬁcant SNPs at other loci did not
show association to AUD except for SNPs at LPHN2 and
ST6GALNAC3 that were linked to rs1088935 by their common
European ancestry. Moreover, strength of these associations
weakened with increasing distance from SGIP1.
This GWAS in Plains American Indians identiﬁed two chro-
mosomal regions, both previously implicated in linkage studies,
containing multiple candidate genes for variability in resting
EEG power. SGIP1 and ST6GALNAC3 lie within a linkage peak
on chr 1p (95–115 cM), linked to β2 EEG spectral power (16–20
Hz) in the COGA (27, 28). UGDH, LIAS, RPL9, and KLB lie in
a region of chr 4 previously linked to EEG in this Plains Indian
dataset (15), together with an additional Native American pop-
ulation (29) and in the COGA dataset (30). Additionally, the
study has identiﬁed BICD1 located on chr 12p11 as a candidate
gene for resting α power variability.
Linkage studies have identiﬁed other chromosome regions po-
tentially harboring genes involved in EEG variability. Low-voltage
α power mapped to chr 20q (31). Convergence of linkage was
found for α, β, and θ resting EEG power to chr 5q13-14 in this
Plains Indian dataset, suggesting common loci that regulate EEG
across the frequency spectrum (15).This ﬁnding was not replicated
in this GWAS, most likely because of the relatively small effect of
this locus on EEG power. In the present study, failure to replicate
associations of ST6GALNAC3 and UGDH in Caucasians may
reﬂect cross-population heterogeneity in loci inﬂuencing the
EEG. With regard to this point, the haplotype block structure of
the chr 4 region containing UGDH is more complex in Caucasians
compared with Plains Indians, and it is possible that the associa-
tion signal arises from variation in other genes in the region in-
cluding KLB and LIAS. KLB is an interesting candidate because
FGF-19 signaling affects cortical development (32) and voltage-
gated Na+ channels, thereby altering neuronal reactivity (33).
The EEG, although an intermediate phenotype, is itself a com-
plex trait reﬂecting the rhythmic electrical activity of the brain,
which is probably modulated by many loci. Discovery of a single
locus accounting for 8.8% of variance in θ EEG is therefore for-
tuitous. In US Caucasians this locus accounts for 3.5% of variance,
the diminution due to the increased frequency of the informative
alleles and relative haplotype frequencies.
Fig. 3. Local admixture of chr 1 region (61.47–70.76 Mb) containing SGIP1 in
17 Plains Indians (E) carrying the θ power-associated G allele of rs10889635
compared with 9 Plains Indians (L) with low European admixture. The analysis
shows a common European chromosomal segment at SGIP1 in all Plains Indi-
ans carrying the G allele of rs10889635, indicating that the association signal
(P = 2.52 × 10−8) is in part due to admixture at the SGIP1 locus. The only Plains
Indian homozygous for the G allele of rs10889635 is indicated (†).
8698 | www.pnas.org/cgi/doi/10.1073/pnas.0908134107 Hodgkinson et al.
The data from the signiﬁcant and subthreshold genes suggest
that many of the genes involved in determining EEG variability are
involved in intracellular transport processes rather than cell-sur-
face receptor activity, although two subthreshold genes were in-
volved in glycine ion channel and GABAergic function. SGIP1 is
directly involved in vesicle formation at the plasma membrane
through interactions with phospholipids and EPS15, an adaptor
protein. Knockdown of SGIP1 expression reduces clathrin-medi-
ated endocytosis (34). Alterations in the efﬁciency of endocytosis
might change neuronal activity via receptor turnover or neuro-
transmitter uptake. The rabphilin 3A-like (without C2 domains)
gene (RPHAL) generated only a modest P value [6.2 × 10−6 (un-
corrected)/1.2 × 10−5 (empirical)] but is involved in exocytosis and
vesicle transport, and is on chr 17 located within a linkage peak
identiﬁed for the maximum-drinks phenotype in the COGA study
(35). Coincidentally, D17S1308, which gave the strongest linkage
signal, maps adjacent to the vacuolar protein-sorting 53 homolog
gene (VPS53), a gene involved in retrograde vesicle trafﬁcking in
the late Golgi. BICD1 is also involved in vesicle trafﬁcking and is
essential for retrograde Golgi-to-endoplasmic reticulum transport
via interaction with the adaptor protein Rab6a. Disruption of
BICD1/Rab6a interaction uncouples vesicles from the dynein-
dynactin transport machinery, resulting in the accumulation of
vesicles at the cell periphery (36). BICD1 also appears to have an
important function in nuclear positioning in neurons during de-
velopment. The involvement of BICD1 with EEG variability is
further suggested because BICD1 interacts directly with LIS1 (37),
a protein that also interacts directly with DISC1 (38). Variation at
DISC1 has been associated with alteration in the P300-evoked
potential, another phenotype of neuronal reactivity (39).
Two other subthreshold genes, cadherin 13 (CDH13) and ﬁbro-
blast growth factor 14 (FGF14), were identiﬁed in a GWAS for
nicotine dependence (40) and more recently in a GWAS for alcohol
dependence (41). FGF14 potentially alters neuronal reactivity
through interaction with voltage-gated Na+ channels (33). Analysis
of response to contextual imagery in cigarette smokers suggested
that alteration of EEG activity may be a common feature associated
with craving (42). The adaptor-related protein complex 2, α sub-
unit2 gene (AP2A2), which encodes another protein involved in
endocytosis, is located close to D11S1984, which generated the
maximum linkage signal to alcohol dependence in a southwestern
Native American population (29). Although it appears that our
main ﬁndings are for genes that solely inﬂuence EEG power, con-
vergences of ﬁndings at the subthreshold level to previous results in
addictions suggest that by using an intermediate phenotype we po-
tentially identiﬁed genes that have a general inﬂuence on addiction.
1.5%. Although it is a family-based sample (SI Text), we were able to
correct our analyses for the relatedness of the participants.
In conclusion, this GWAS suggests that neuronal excitability
in the brain is determined in part by the ability to recycle neu-
ronal membrane components. Replication of association of
SGIP1 to θ power validates the use of the GWAS approach on
intermediate phenotype datasets of modest size. The success of
this study in identifying novel genes and cellular processes in
determining EEG traits demonstrates that if there are loci of
moderate effect size, which is more likely for an intermediate
phenotype in a population isolate, it may be unnecessary to have
datasets numbering in the thousands, and that the analysis of
accurately measured, heritable traits in relatively homogeneous
populations is an informative alternative in the genetic analysis
of complex psychiatric diseases and behavior.
Participant Recruitment. Plains American Indians. Participants were recruited
from a tribe in rural Oklahoma (15) (fully described in SI Text). Written in-
formed consent was obtained according to a human research protocol ap-
proved by the Tribal Council and the human research committee of the
National Institute on Alcohol Abuse and Alcoholism. EEG was obtained from
359 people. The alcohol use disorder (AUD) cohort comprised 225 cases and
156 controls. AUD was deﬁned as DSM-III alcohol abuse (without dependence)
(222 individuals) or DSM-III alcohol dependence (3 individuals).
The replication dataset (described in SI Text) consisted of 185 European
Americans (104 women and 81 men: 61 healthy controls and 124 individuals
with a variety of DSM-III diagnoses) recruited in Bethesda, MD (43). Written
informed consent was obtained via a human research protocol approved by
the NIAAA institutional review board.
EEG Acquisition and Analysis. Details of EEG methods and analysis are in Enoch
et al. (15) and SI Text. Resting EEG was recorded using a ﬁtted electrode cap
in a ﬁeld setting while the subject was seated with eyes closed and relaxed in
a darkened noise-bafﬂed room. To balance data quality against participant
time, data were collected from six scalp electrodes (one frontal, two occip-
ital, three parietal) selected to maximize information for α and θ power.
Spectral power was determined for θ (3–8 Hz), α (8–13 Hz), and β (13–30 Hz),
and log-transformed to normalize the distribution. For each frequency band,
power was averaged for the ﬁve posterior electrodes to reduce variability
due to individual electrode measurement and was justiﬁed by high corre-
lations between α and θ power at the selected electrodes (15), coherence
between these signals (44), and the higher θ and α activity in posterior and
occipital regions (45). These data were used for association (see SI Text for
means and ranges). No outliers were removed.
Genotyping. Samples (399) were genotyped using the Illumina HapMap550K.
Fourteen were discarded due to low genotype call rate (<97%), and four were
discarded after gender and allele transmission testing. Genotypes were called
using BeadStudio 3.2 (Illumina). Thirty-ﬁve replicate pairs showed average
genotype reproducibility of 0.99994. Following quality control, association
EEG power was performed on 322 individuals (137 male, 185 fe-
male) for whom EEG was available. Replication genotyping was performed by
Statistical Analysis. Association was tested using the standard linear regression
model in PLINK (46). SNPs with MAF < 1% or a call rate < 90% were excluded,
leaving 405,281 SNPs. Threshold for association was set at <1.23 × 10−7 using the
Bonferroni method. Posthoc analyses using sex and age as covariates both in-
dividually and together did not inﬂuence associations. P value inﬂation due to
structure, QFAM family-based association tests for SNPs determined to be nomi-
nally signiﬁcant were performed within PLINK with 107 rounds of permutation,
yielding empirical P values. Following GWAS, we estimated PPAs for the two top
Bayesian IMputation-Based Association Mapping (http://quartus.uchicago.edu/
∼yguan/bimbam). PPAs werecalculated as described in Stephens andBalding(22).
Enrichment in gene ontology terms was detected using GOTM (48).
Local Admixture. Localized admixture on chr 1 was detected using Local
Ancestry in adMixed Populations (LAMP) (49) v2.0 with an r2 cutoff of 0.95 to
minimize exclusion of informative SNPs due to variable LD between Euro-
Table 2. Comparison of minor allele frequency for seven SGIP1 SNPs between individuals with alcohol use disorder
and controls shows signiﬁcant underrepresentation of alleles associated with increased θ power
rs6588207 rs10889635 rs66881460 rs10789215 rs2146904 rs536410 rs2483704
Alcohol use disorder 0.018 0.018 0.018 0.018 0.018 0.020 0.018
Controls 0.054 0.051 0.045 0.039 0.051 0.067 0.061
P value, 1 degree of freedom 0.005 0.009 0.028 0.076 0.009 0.001 0.002
χ2 7.843 6.78 4.824 3.139 6.87 10.903 10.024
Hodgkinson et al. PNAS | May 11, 2010 | vol. 107 | no. 19 | 8699
peans and American Indians. Computational runs using generation time
estimates of 3, 5, 7, and 9 yielded similar estimates of local admixture. A
recombination rate of 1 × 10−8 and seed α values of 0.04 (Europeans) and
0.96 (American Indians) were used.
Population Substructure and Admixture Determination. Ancestry informative
markers (186) (50) were typed in the EEG samples and the HGDP-CEPH Human
Structure 2.2 (http://pritch.bsd.uchicago.edu/software.html) was run using ances-
try informative marker data from the EEG and CEPH datasets simultaneously to
identify population substructure and compute individual ethnic factor scores.
ACKNOWLEDGMENTS. We thank the study participants for their contribu-
tions, and Longina Akhtar and Elisa Moore for technical assistance. This study
was funded by the intramural program of National Institute on Alcohol Abuse
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Supporting Information Appendix.
Plains American Indians
Participants were recruited from a Plains Indian tribe living in rural Oklahoma. The
dataset is fully described in Enoch et al.1,2,3. Exclusion criteria included a history of
severe head injury with loss of consciousness, epilepsy, seizures, stroke, brain tumors,
neurological disease, current use of psychotropic medications, chronic drug use, positive
breath test for alcohol and clinical alcohol withdrawal symptoms at time of testing. Blind-
rated DSM-III-R lifetime psychiatric diagnoses were derived from the Schedule for
Affective Disorders and Schizophrenia-Lifetime Version (SADS-L). A total of 225
individuals had a diagnosis of Alcohol Use Disorder (AUD) (101 women, mean (SD) age
= 41.8 (12.1) yrs; 124 men, mean (SD) age = 41.0 (11.4) yrs). There were 156 non-
alcoholics (112 women, mean (SD) age = 44.5 (16.3) yrs; 44 men, mean (SD) age = 39.6
(18.5) yrs. Forty five participants had anxiety disorders (26 women, 19 men). AUD was
defined as DSM-III alcohol abuse (without dependence) or DSM-III alcohol dependence.
Only 3 individual included in the AUD diagnostic group have alcohol abuse (without
dependence). Almost all of the participants had lifetime exposure to alcohol.
Within the 322 GWAS sample the number of relative pairs at different degrees of
relationships for 303 participants were as follows: 1st degree; 286: 2nd degree; 820: 3rd
degree: 38311: 4th degree: 3019. For 19 individuals there was no known relationship to
any other participant. >97% of the relative pairs are that the 3rd degree, 4th degree, or
unrelated degree of relationship.
North American Caucasians
This cohort comprised 185 volunteers (81 male, 104 female), all Caucasians living in the
North East of the USA4,5. The mean (S.D.) ages of these participants were: women - 43.2
(15.8) yrs, and men - 40.8 (16.9) yrs. The group included 61 healthy controls (31 male,
30 female), and 124 with a variety of DSM-III diagnoses (often co-morbid) which
included AUD, depression, specific phobia and anxiety disorders4,5.
EEG acquisition and analysis
Full details are provided in Enoch et al, 20081. EEG data were collected at an improved
setting to allow access for the Native American study group. Participants were seated in a
sound-dampened, darkened room. The resting EEG was recorded with the subjects’ eyes
closed for 3 minutes. EEG signals were recorded using a fitted cap (Electro-Cap
International Inc, Eaton, OH) with pure tin electrodes in a six channel montage: FZ
(frontal-central), P3, PZ, P4 (parietal-central, left and right) and O1, O2 (occipital left and
right) with reference to balanced sternovertebral electrodes. Data collection was
performed in the field and therefore data were only gathered for six electrodes to reduce
the time required for each subject whilst maintaining data quality. The electrodes were
selected because α power is maximal posteriorly, and to obtain frontal θ & β EEG. Data
quality was confirmed by comparison to repeat measurements made after two years. To
monitor electro-oculographic artifacts electrodes were applied below and lateral to the
left eye. An observer monitored the participant and EEG tracing for signs of drowsiness
(alpha wave drop-out) or movement. The session was repeated if substantial drowsiness,
sleep or movement was detected. FPZ was used as the ground electrode. EEG signals
were continuously digitized at a sampling rate of 200 Hz. Quantitative spectral analysis
was then performed6. Data records were partitioned into consecutive 512-point subunits
(2.56 sec each) autoregressively filtered to remove low frequency subharmonics and Fast
Fourier transformed to produce power spectrum estimates in 0.39 Hz steps for the three
frequency domains: 3 – 8 Hz, 8 – 13 Hz and 13 – 30 Hz. The absolute spectral power in
each of the three frequency bands at each of the six electrode locations was log10
transformed to normalize the distribution. The strong cross correlation of the spectral
power obtained for the five posterior electrodes1,5 allowed us to average log10 spectral
wave power for these five electrodes. This reduced the number of primary analyses
performed and helps to reduce artifacts arising from random statistical variation.
Mean 4.119 4.113 3.269
Standard Deviation 0.368 0.359 0.263
Range 3.378-5.300 3.234-5.216 2.613-4.22
Table 1: Summary of mean values and ranges for log10 α, β & θ spectral power in the
Plains Indian Dataset used in the GWAS study.
Figure S1: (A) Manhattan plot for p values for association to alcohol use disorder in the
Plains Indian dataset using the Illumina 550K array. Threshold p-values 1x10-5 (*) are
indicated by dotted lines. P-values are not corrected for family structure. (B) Quantile-
quantile plot of expected frequency of p-values plotted against the observed frequencies
for association to Alcohol Use Disorder shows no enrichment of low p-values for this
Figure S2: Q-Q plot of expected frequency of observed p-values for association to log10
α (B) and log10
β (C) spectral power plotted against the expected frequencies.
Figure S3: Diagram showing the LD structure (D’) and haplotypes for the SGIP1 region (66.797Kb-66.961Kb) of
Chromosome 1 the Plains Indians (A) and HapMap Caucasians (B). D’>0.9 is shown as red shading in the LD plot.
Markers showing genome-wide significance for association to increased log10θ spectral power (rs6588207, rs10889635,
rs6681460, rs10789215 from left to right) are shown (*). The alleles associated with increased theta (red boxes) at
genome-wide significance are present on only two low frequency haplotypes. The two mis-sense polymorphisms
(rs17490057: E112Q and rs7526812: R131K) present in SGIP1 exon 7 are shown (blue boxes). The two haplotype
carrying the alleles associated with increased theta found in Plains Indians are marked (†), and only differ at one marker
(blue box – E112Q mis-sense polymorphism). Haplotype frequencies were estimated by Haploview 4.0 and are shown at
the right of each haplotype. Yellow shading indicates the alleles not found in Plains Indians for other markers that
distinguish two additional Caucasian haplotypes (including a common haplotype – freq=0.24) that carry the alleles
associated with increased theta power.
Figure S4: Diagram showing the LD structure (D’) of the Chromosome 4 region in the Plains Indians
(39,108Kb-39,234Kb) containing the KLB, RPL9, LIAS and UGDH genes (shown above LD plot). D’>0.9
is indicated by red shading. Markers with genome-wide (*) and sub-threshold (*) significance are
indicated. Alleles associated with increased alpha power are shown (red boxes). Haplotype frequencies are
shown to the right of each haplotype.
Figure S5: Genes identified with association at genome-wide significant or sub-threshold
level for theta power arranged according to gene function or subcellular compartment.
Gene function and/or location was determined based upon the NCBI Entrez Gene entry
and PubMed entries for each gene.
Figure S6: Genes identified with association at genome-wide significant or sub-threshold
level for alpha power arranged according to gene function or subcellular compartment.
Gene function and/or location was determined based upon the NCBI Entrez Gene entry
and PubMed entries for each gene.
Far East Asia
Figure S7A: Eigenvectors plotted for 186 AIMs genotyped in the Plains Indians and the
51 world-wide populations of the CEPH diversity panel9, showing that the Plains
Indians cluster with other groups from the Americas, and are distinct from European,
African and Asian populations.
Figure S7B Eigenvectors plotted for 186 AIMs in Plains Indians and CEPH diversity
panel populations from the Americas which show that although closely related to other
populations within the Americas, including another North American Indian tribe (South
West Indians) that the Plains Indians represent a distinct population.
<10% 10-20% 20%-30% >30%
Minor Allele Freq (%)
Figure S8: Effect of Admixture on Association: Loci showing genome-wide significant
association to increased theta power were clustered at chromosome 1p, and the
distribution of the associated alleles for SGIP1, ST6GALNAC3 and LPHN2 to a few
individuals suggested that the association signals are linked, and potentially arise due to
admixture. Analysis of European ancestry with the theta power associated markers in
SGIP1 (rs10889635), ST6GALNAC3 (rs6696780), LPHN2 (rs12145665) and SEP15
(rs6693416), covering a 20Mbase region shows that there is an apparent correlation
between genotype at these loci and increasing European ancestry.
Figure S9: Bivariate analysis of -log10θ(i), -log10 10
α (ii) and –log β (iii) power against
European admixture component shows that there is no correlation between degree of
admixture and EEG power for θ(r2=0.00152), α (r2 2
=0.0042) or β(r =0.00039).
<10% 10-20% 20%-30% >30%
European Ancestry (%)
Minor Allele Freq (%)
Figure S10: Analysis of ST6GALNAC3 and UGDH markers (rs172714 and rs6817264
respectively) associated with increasing alpha power shows no similar relationship of
increasing minor allele frequency with increasing European ancestry. These data suggest
that the genome-wide significant association signals from these two loci do not arise as a
result of admixture.
1. Enoch MA, Shen PH, Ducci F, Yuan Q, Liu J, White KV, Albaugh B,
Hodgkinson CA, Goldman D. Common genetic origins for EEG, alcoholism and
anxiety: the role of CRH-BP. PLoS ONE. 3 (10) :e3620 (2008).
2. Enoch MA, Schwartz L, Albaugh B, Virkkunen & Goldman D. Dimensional
Anxiety Mediates Linkage of GABRA2 Haplotypes with Alcoholism. Am J Med
Genet B Neuropsychiatr Genet. 141B (6):599-607 (2006).
3. Enoch MA, Waheed JF, Harris CR, Albaugh B, Goldman D. Sex Differences in
the Influence of COMT Val158Met on Alcoholism and Smoking in Plains
American Indians. Alcohol Clin Exp Res. 30 (3):399-406 (2006).
4. Enoch MA, White KV, Harris CR, Robin RW, Ross J, Rohrbaugh JW, et al.
Association of low-voltage alpha EEG with a subtype of alcohol use disorders.
Alcohol Clin Exp Res. 23, 1312- 1319 (1999).
5. Enoch MA, Rohrbaugh JW, Davis EZ, Harris CR, Ellingson RJ, Andreason P,
Moore V, Varner JL, Brown GL, Eckardt MJ, et al. Relationship of genetically
transmitted alpha EEG traits to anxiety disorders and alcoholism. Am J Med
Genet. 1995 Oct 9;60(5):400-8. (1995).
6. Coppola R. Isolating low frequency activity EEG spectrum analysis.
Eectroencephalogr Clin Neurophysiol. 46, 224-6 (1979).
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8. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich
D.Principal components analysis corrects for stratification in genome-wide
association studies. Nat Genet. 38, 904-909 (2006).
9. Addictions biology: haplotype-based analysis for 130 candidate genes on a single
array. Hodgkinson CA, Yuan Q, Xu K, Shen PH, Heinz E, Lobos EA, et al
Alcohol Alcohol. 43(5):505-515 (2008).
Table2: SNP markers from chromosome 1 included in haplotype analysis of Figure S3.
Marker number corresponds to numbering in Figure S3A.
Marker # SNP Chr1: Position Alleles
46 rs1988415 66797467 G:A
47 rs1338188 66802450 C:T
48 rs1975523 66809463 A:C
49 rs963140 66810164 T:G
50 rs1159851 66812831 G:T
51 rs10889633 66817218 G:T
52 rs1867631 66818714 A:C
53 rs1445573 66820030 G:A
54 rs10128053 66820545 A:G
55 rs11208917 66835973 C:A
56 rs12023176 66836119 C:T
57 rs6588207 66839143 G:A
58 rs10889635 66848163 A:G
59 rs6656912 66856259 C:T
60 rs6588212 66859469 A:G
61 rs6664698 66862006 G:A
62 rs4655643 66862687 C:A
63 rs6696812 66872897 C:T
64 rs17490057 66881865 G:C
65 rs7526812 66881923 G:A
66 rs6689755 66885638 A:G
67 rs1925341 66887672 G:A
68 rs1925342 66887818 A:C
69 rs9659684 66890492 A:G
70 rs6681460 66895645 G:A
71 rs1570814 66897531 C:T
72 rs12094526 66898702 G:A
73 rs6692318 66906914 T:C
74 rs1325264 66907823 T:G
76 rs6689451 66915440 G:A
77 rs11208944 66916150 G:A
78 rs11208946 66919227 T:C
79 rs10789215 66923773 C:T
80 rs1408852 66927916 C:A
82 rs2146905 66939481 C:A
83 rs3738167 66943854 G:T
84 rs9633417 66944000 C:A
85 rs609707 66949571 T:C
86 rs510771 66951234 C:A
Table 3: Gene Hits for Theta Power (3-8Hz): Regions with no annotated gene have been
excluded. Maximum p-values shown are uncorrected* and corrected† for family structure.
SH3-domain GRB2-like (endophilin)
interacting protein 1
family with sequence similarity 125,
11q13 C11orf2 chromosome 11 open reading frame2 9.17x10-6 2.44x10-5
17p13.3 RPH3AL rabphilin 3A-like (without C2 domains) 6.20x10-6 1.2x10-5
19q13.11 ANKRD27 ankyrin repeat domain 27 (VPS9 domain) 5.60x10-6 3.15x10-5
1p36.11 IL22RA1 interleukin 22 receptor, alpha 1 4.43x10-6 4.47x10-5
1p36.1-34.3 OPRD1 15Kb from OPRD1 8.05x10-6 1.47x10-4
receptor tyrosine kinasx1like orphan
1p31.1 LPHN2 latrophilin 2 3.00x10-9 2.8x10-6
1q22-q23 RXRG retinoid X receptor, gamma 5.90x10-7 1.41x10-5
olfactory receptor, family 52, subfamily B,
member 5 pseudogene
olfactory receptor, family 3, subfamily A,
1p22.3 SEP15 15 kDa selenoprotein 1.39x10-7 2.44x10-4
11q13 TM7SF2 transmembrane 7 superfamily member 2 8.29x10-6 2.9x10-5
carbohydrate (N-acetylglucosamine 6-O)
22q.12.3 LARGE likx1glycosyltransferase 6.87x10-7 3.12x10-5
2q22.2 ARHGAP15 Rho GTPase activating protein 15 4.58x10-6 7.54x10-5
6q23 SGK1 serum/glucocorticoid regulated kinase 1 5.53x10-6 1.97x10-3
13q34 FGF14 fibroblast growth factor 14 8.99x10-6 3.85x10-5
6p24.1 PHACTR1 phosphatase and actin regulator 1 6.65x10-6 6.52x10-5
syntrophin, beta 2 (dystrophin-associated
protein A1, 59kDa, basic component 2)
11q12.1 CDCA5 cell division associated 5 9.27x10-6 3.0x10-5
zinc finger and BTB domain containing
1p31.1 NEGR1 neuronal growth regulator 1 7.19x10-7 2.6x10-6
tRNA-yW synthesizing protein 3 homolog
3p21.3 LARS2 leucyl-tRNA synthetase 2, mitochondrial 9.23x10-6 6.91x10-5
11q13 MRPL49 mitochondrial ribosomal protein L49 8.29x10-6 2.9x10-5
Finkel-Biskis-Reilly murine sarcoma virus
(FBR-MuSV) ubiquitously expressed
16q24.2-q24.3 CDH13 cadherin 13, H-cadherin (heart) 8.37x10-7 1.58x10-5
11q13.1 SLC22A20 solute carrier family 22, member 20 8.54x10-6 2.23x10-5
TMEM132B transmembrane protein 132B 6.17x10-6 5.1x10-5
17p13.2 SPNS3 spinster homolog 3 (Drosophila) 1.78x10-6 1.8x10-6
polymerase (DNA directed), alpha 2
myeloid/lymphoid or mixed-lineage
leukemia (trithorax homolog, Drosophila)
16q23.2 CDYL2 chromodomain protein, Y-like 2 5.50x10-6 5.79x10-5
1q21-q25 KIRREL kin of IRRE like (Drosophila) 6.36x10-6 8.57x10-5
10q23 ANXA11 annexin A11 5.40x10-6 2.52x10-5
17pter-p13 ASPA aspartoacylase (Canavan disease) 8.71x10-7 5.7x10-6
1p31.3 TCTEX1D1 1D1 Tctex1 domain containing 1 1.87x10-7 1.92x10-5
10q26.13 C10orf122 Chromosome 10 open reading frame 122 8.82x10-6 1.69x10-4
11q13 ZNHIT2 zinc finger, HIT type 2 8.29x10-6 2.9x10-5
11q23.3 C11orf60 chromosome 11 open reading frame 60 7.71x10-6 2.49x10-4
12q24.3 Hs637708 3.12x10-6 1.14x10-4
Table 4: Gene Hits for Alpha Power (8-13Hz): Regions with no annotated gene have
been excluded. Maximum p-values shown are uncorrected* and corrected† for family
LPS-responsive vesicle trafficking,
beach and anchor containing 7.94x10-6 3.79x10-5
12p11.2-p11.1 BICD1 bicaudal D homolog 1 (Drosophila) 4.32x10-7 2.6x10-6
14q21.1 TTC6 tetrapeptide repeat domain 7.32x10-6 4.58x10-5
14q23.1 RTN1 reticulon 1 3.64x10-6 1.1x10-5
cat eye syndrome chromosome
region, candidate 2 2.48x10-6 2.14x10-5
22q12.2 SELM selenoprotein M 2.90x10-6 1.48x10-5
opiod binding protein/cell adhesion
molecule-like 6.34x10-6 2.96x10-5
Family with sequence similarity 5,
member B (FAM5B)
ATPase, Ca++ transporting,
plasma membrane 4
solute carrier family 30, member
4q26 SYNPO2 synaptopodin 2 6.16x10-6 2.29x10-5
4p13 LIAS lipoic acid synthetase 4.55x10-7 2.5x10-6
4p13 UGDH UDP-glucose dehydrogenase 4.23x10-8 7.0x10-7
12q24.31 MLXIP MLX interacting protein 5.98x10-6 3.36x10-5
4p13 RPL9 ribosomal protein L9 1.29x10-6 6.8x10-6
15q24-q25.1 PSTPIP1 proline-serine-threonine 6.14x10-6 6.5x10-5
phosphatase interacting protein 1
1q32.3 Hs553186 8.65x10-6 7.18x10-5
1q41 LOC725510 4.88x10-7 1.6x10-6
20q13.32 ZNF831 zinc finger protein 831 4.74x10-6 1.97x10-5
Table 5: Gene Hits for Beta Power (13-30Hz): Regions: *TCOM1 and ALDH9A1 are
closely located on chromosome 1 and represent two candidates identified by the same set
of four SNPs. Two regions on chromosome 6 and 8 had no identifiable transcript located
close to the sub-threshold SNP(s). Maximum p-values shown are uncorrected* and
corrected† for family structure.
transmembrane and coiled-coil
domains 1 1.67x10-6 1.33x10-5
4q33-q34 GLRA3 glycine receptor, alpha 3 1.08x10-6 1.8x10-6
aldehyde dehydrogenase 9 family,
member A1 1.00x10-6 9.4x10-6
Family with sequence similarity 5,
member B (FAM5B) 3.83x10-6 1.75x10-5
Integral Membrane Protein
ortholog of down regulated by
androgen in mouse prostate
13q13.1 FRY1 furry homolog (Drosophila) 4.18x10-6 2.4x10-5
17q23-q24 AXIN2 axin 2 9.17x10-6 5.21x10-5