Genome-wide association identi!es candidate genes that infuence the human electroencephalogram

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.

Transcript

1. 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 |
   whole-genome association
   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
   imagingincomparativelysmallcohorts(4).Therefore,animportant
   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.
   Results
   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: chodg@mail.nih.gov.
   This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
   1073/pnas.0908134107/-/DCSupplemental.
   www.pnas.org/cgi/doi/10.1073/pnas.0908134107 PNAS | May 11, 2010 | vol. 107 | no. 19 | 8695–8700
   GENETICS
   

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2. (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
   EEG frequency
   band SNP Location Position Gene
   HWE
   P value
   Plains Indians
   minor allele
   frequency
   Plains Indians
   P value
   Plains Indians
   corrected P value*
   Caucasians
   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.
   

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3. 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
   (FGF19).UGDHisanintegralGolgimembraneproteininvolvedin
   posttranslational modification of extrinsic proteins, whose expres-
   sion is up-regulated in response to TGF-β and hypoxia, a risk factor
   for schizophrenia.
   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
   and σd
   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
   H1
   ), 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
   prior assumptions.
   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
   showedassociationintherangeofP=1×10−5–1.22×10−7.Itislikely
   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
   )θ
   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
   )α power
   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
   GENETICS
   

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4. Golgi Transport Candidates Among Subthreshold Signals. Analysis of
   thegeneontologyforall candidategenessuggestedthataproportion
   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.
   Discussion
   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.
   

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5. 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.
   ThePlainsIndiansareapopulationisolate,offeringadvantagesof
   reducedgeneticandenvironmentalheterogeneity.Asdescribed,this
   isarelativelynonadmixedtribe,withmedianEuropeanadmixtureof
   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.
   Methods
   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
   with log10
   EEG power was performed on 322 individuals (137 male, 185 fe-
   male) for whom EEG was available. Replication genotyping was performed by
   5′-exonuclease assay.
   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
   geneticrelatednesswasestimatedusingtheGCprogram(47).Tocorrectforfamily
   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
   SGIP1markers,rs6588207andrs10889635.Bayesfactors(BF)werecalculatedusing
   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
   GENETICS
   

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6. 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
   GenomeDiversityCellLinePanel(http://www.cephb.fr/HGDP-CEPH-Panel).PHASE
   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
   and Alcoholism.
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7. Supporting Information Appendix.
   Participant Recruitment
   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.
   

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8. 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.
   

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9. log10
   θ log10
   α log10
   β
   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.
   

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10. Supplementary Figures:
    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
    complex phenotype.
    

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11. Figure S2: Q-Q plot of expected frequency of observed p-values for association to log10
    θ
    (A), log10
    α (B) and log10
    β (C) spectral power plotted against the expected frequencies.
    

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12. 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.
    

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13. 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.
    

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14. 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.
    

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15. 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.
    

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16. Subsaharan Africa
    North Africa
    Europe
    Middle East
    Central Asia
    Far East Asia
    Oceania
    Americas
    Plains Indians
    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.
    

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17. Americas
    Plains Indians
    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.
    

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18. 0.00
    5.00
    10.00
    15.00
    20.00
    25.00
    30.00
    <10% 10-20% 20%-30% >30%
    European Admixture
    Minor Allele Freq (%)
    rs10889635
    rs6696780
    rs12145665
    rs6693416
    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.
    

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19. 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).
    

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20. 0
    5
    10
    15
    20
    25
    30
    35
    40
    45
    <10% 10-20% 20%-30% >30%
    European Ancestry (%)
    Minor Allele Freq (%)
    rs172714
    rs6817264
    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.
    

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21. References:
    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).
    7. Devlin, B., Bacanu, S., Roeder K. Genomic Control in the Extreme. Nat Genet 36,
    1129-1130 (2004).
    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).
    

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22. 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
    

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23. 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.
    Vesicle Transport
    Maximum
    p-value*
    Corrected
    p-value†
    1p31.3 SGIP1
    SH3-domain GRB2-like (endophilin)
    interacting protein 1
    2.52x10-8 4.9x10-6
    9q33.3 FAM125B
    family with sequence similarity 125,
    member B
    9.04x10-6 1.28x10-4
    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
    Receptors
    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
    1p32-p31 ROR1
    receptor tyrosine kinasx1like orphan
    receptor 1
    3.22x10-6 2.49x10-4
    1p31.1 LPHN2 latrophilin 2 3.00x10-9 2.8x10-6
    1q22-q23 RXRG retinoid X receptor, gamma 5.90x10-7 1.41x10-5
    11p15.4 OR52B5P
    olfactory receptor, family 52, subfamily B,
    member 5 pseudogene
    1.82x10-7 3.41x10-5
    17p13.3 OR3A4
    olfactory receptor, family 3, subfamily A,
    member 4
    1.04x10-6 1.01x10-5
    Golgi/ER Component
    1p31.1 ST6GALNAC3
    ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-
    galactosyl-1,3)-N-acetylgalactosaminide
    alpha-2,6-sialyltransferase 3
    8.47x10-8 3.89x10-5
    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
    16q22 CHST6
    carbohydrate (N-acetylglucosamine 6-O)
    sulfotransferase 6
    1.20x10-6 1.64x10-5
    22q.12.3 LARGE likx1glycosyltransferase 6.87x10-7 3.12x10-5
    Signal Transduction
    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
    Synaptic Function
    6p24.1 PHACTR1 phosphatase and actin regulator 1 6.65x10-6 6.52x10-5
    16q2 SNTB2
    syntrophin, beta 2 (dystrophin-associated
    protein A1, 59kDa, basic component 2)
    6.83x10-6 2.85x10-4
    Cell Cycle
    11q12.1 CDCA5 cell division associated 5 9.27x10-6 3.0x10-5
    18q21.1 ZBTB7C
    zinc finger and BTB domain containing
    7C
    2.05x10-6 6.75x10-5
    Growth Regulation
    1p31.1 NEGR1 neuronal growth regulator 1 7.19x10-7 2.6x10-6
    

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24. Ribosome Function
    1p31.1 TYW3
    tRNA-yW synthesizing protein 3 homolog
    (S. cerevisiae)
    7.39x10-6 2.4x10-3
    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
    11q13 FAU
    Finkel-Biskis-Reilly murine sarcoma virus
    (FBR-MuSV) ubiquitously expressed
    8.29x10-6 2.9x10-5
    Cell Adhesion/Signalling
    16q24.2-q24.3 CDH13 cadherin 13, H-cadherin (heart) 8.37x10-7 1.58x10-5
    Transmembrane Transporters
    11q13.1 SLC22A20 solute carrier family 22, member 20 8.54x10-6 2.23x10-5
    12q24.31-
    q24.32
    TMEM132B transmembrane protein 132B 6.17x10-6 5.1x10-5
    17p13.2 SPNS3 spinster homolog 3 (Drosophila) 1.78x10-6 1.8x10-6
    DNA Binding/Modification
    11q13.1 POLA2
    polymerase (DNA directed), alpha 2
    (70kD subunit)
    8.54x10-6 2.25x10-5
    11q23.3 MLL
    myeloid/lymphoid or mixed-lineage
    leukemia (trithorax homolog, Drosophila)
    3.18x10-6 1.18x10-4
    16q23.2 CDYL2 chromodomain protein, Y-like 2 5.50x10-6 5.79x10-5
    Misc
    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
    Unknown Function
    1p31.3 TCTEX1D1 1D1 Tctex1 domain containing 1 1.87x10-7 1.92x10-5
    2p25
    Image clone
    30348154
    7.16x10-7 2.67x10-5
    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
    

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25. 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
    structure.
    Vesicle Transport
    Maximum
    p-value*
    Corrected
    p-value†
    4q31.3 LRBA
    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
    22q11.2 CECR2
    cat eye syndrome chromosome
    region, candidate 2 2.48x10-6 2.14x10-5
    Golgi/ER Component
    1p31.1 ST6GALNAC3
    ST6 (alpha-N-acetyl-neuraminyl-
    2,3-beta-galactosyl-1,3)-N-
    acetylgalactosaminide alpha-2,6-
    sialyltransferase 3
    7.34x10-8 3.0x10-7
    22q12.2 SELM selenoprotein M 2.90x10-6 1.48x10-5
    Cell Adhesion/Signalling
    1q22-q23 IGSF9
    immunoglobulin superfamily,
    member 9
    9.23x10-6 4.06x10-5
    11q25 OPCML
    opiod binding protein/cell adhesion
    molecule-like 6.34x10-6 2.96x10-5
    Transmembrane proteins/Transporters
    1q24.1-q25.3 FAM5B
    Family with sequence similarity 5,
    member B (FAM5B)
    6.67x10-6 3.26x10-5
    1q31.2 ATP2B4
    ATPase, Ca++ transporting,
    plasma membrane 4
    3.17x10-6 1.13x10-5
    1q41 SLC30A10
    solute carrier family 30, member
    10
    6.83x10-6 1.6x10-5
    Signal Transduction
    4q26 SYNPO2 synaptopodin 2 6.16x10-6 2.29x10-5
    Metaboolic Enzyme
    4p13 LIAS lipoic acid synthetase 4.55x10-7 2.5x10-6
    4p13 UGDH UDP-glucose dehydrogenase 4.23x10-8 7.0x10-7
    DNA Binding/Modification
    12q24.31 MLXIP MLX interacting protein 5.98x10-6 3.36x10-5
    Ribosome Function
    4p13 RPL9 ribosomal protein L9 1.29x10-6 6.8x10-6
    Misc
    15q24-q25.1 PSTPIP1 proline-serine-threonine 6.14x10-6 6.5x10-5
    

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26. phosphatase interacting protein 1
    Unknown Function
    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
    

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27. 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.
    .
    Golgi/ER Component
    Maximum
    p-value*
    Corrected
    p-value†
    1q24.1 TCMO1
    transmembrane and coiled-coil
    domains 1 1.67x10-6 1.33x10-5
    Receptors
    4q33-q34 GLRA3 glycine receptor, alpha 3 1.08x10-6 1.8x10-6
    Neurotransmitter Biosynthesis
    1q24.1 ALDH9A1
    aldehyde dehydrogenase 9 family,
    member A1 1.00x10-6 9.4x10-6
    Transmembrane proteins/Transporters
    1q25 FAM5B
    Family with sequence similarity 5,
    member B (FAM5B) 3.83x10-6 1.75x10-5
    Integral Membrane Protein
    3q26.1 C3orf57
    ortholog of down regulated by
    androgen in mouse prostate
    7.92x10-6 1.07x10-5
    13q13.1 FRY1 furry homolog (Drosophila) 4.18x10-6 2.4x10-5
    Signal Transduction
    17q23-q24 AXIN2 axin 2 9.17x10-6 5.21x10-5
    

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