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

Kenn White
December 21, 2013

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.

Kenn White

December 21, 2013
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  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: [email protected]
    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

    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

    View Slide

  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.

    View Slide

  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

    View Slide

  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

    View Slide

  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

    View Slide

  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

    View Slide

  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

    View Slide

  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

    View Slide