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Psychophysiology. Author manuscript; available in PMC 2015 December 01.
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Published in final edited form as:
Psychophysiology. 2014 December ; 51(12): 1246–1258. doi:10.1111/psyp.12345.
Heritability and molecular-genetic basis of the P3 event-related
brain potential: A genome-wide association study
STEPHEN M. MALONE, UMA VAIDYANATHAN, SAONLI BASU, MICHAEL B. MILLER, MATT
MCGUE, and WILLIAM G. IACONO
Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
Abstract
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P3 amplitude is a candidate endophenotype for disinhibitory psychopathology, psychosis, and
other disorders. The present study is a comprehensive analysis of the behavioral- and moleculargenetic basis of P3 amplitude and a P3 genetic factor score in a large community sample (N =
4,211) of adolescent twins and their parents, genotyped for 527,829 single nucleotide
polymorphisms (SNPs). Biometric models indicated that as much as 65% of the variance in each
measure was due to additive genes. All SNPs in aggregate accounted for approximately 40% to
50% of the heritable variance. However, analyses of individual SNPs did not yield any significant
associations. Analyses of individual genes did not confirm previous associations between P3
amplitude and candidate genes but did yield a novel association with myelin expression factor 2
(MYEF2). Main effects of individual variants may be too small to be detected by GWAS without
larger samples.
Descriptors
P300; Endophenotype; Genome-wide association study; Gene-based tests; Heritability; GCTA;
Molecular genetics
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P3 (or P300) amplitude is often considered a robust endophenotype for disinhibited
psychopathology, such as antisocial behavior, disruptive disorders, such as attention deficit
hyperactivity disorder (ADHD) and conduct disorder, and substance use disorders (SUDs)
Address correspondence to: Stephen M. Malone, University of Minnesota, 75 East River Road, Minneapolis, MN 55455, USA.
smalone@umn.edu.
Supporting Information
Additional supporting information may be found in the online version of this article:
Table S1: Parameter estimates from moderated phenotypic common factor analysis.
Table S2: Top SNP associations with P3 amplitude.
Table S3: Top SNP associations with the P3 genetic factor score.
Table S4: SNP associations for P3-specific candidate SNPs.
Table S5: SNP associations for endophenotype-general candidate SNPs.
Table S6: Results of VEGAS gene-based tests of P3-specific candidate genes.
Table S7: Results of VEGAS gene-based tests of endophenotype-general candidate genes.
Table S8: Results of VEGAS gene-based tests of COGS endophenotype candidate genes.
Figure S1: Distribution of P3 amplitude residuals.
Figure S2: Distribution of genetic factor score residuals.
Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors.
Any queries (other than missing material) should be directed to the corresponding author for the article.
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(Hesselbrock, Begleiter, Porjesz, O’Connor, & Bauer, 2001; Iacono, Carlson, Malone, &
McGue, 2002; Iacono & Malone, 2011; Porjesz et al., 2005). We have recently discussed
endophenotypic properties of P3 (Iacono & Malone, 2011), and we refer the interested
reader to this paper for details. In brief, P3 amplitude can be measured reliably (Hall et al.,
2009; Turetsky et al., 2007; van Beijsterveldt, van Baal, Molenaar, Boomsma, & de Geus,
2001), it is stable over the course of development (Carlson & Iacono, 2006; van
Beijsterveldt et al., 2001), and reliable individual differences in developmental trajectories
are observed (Carlson & Iacono, 2006; Hill et al., 2013). P3 amplitude is heritable, with a
meta-analysis reporting a heritability of .60 (van Beijsterveldt & van Baal, 2002). This is
nicely illustrated by the finding that the correlation between P3 amplitude recorded over
parietal cortex in one hemisphere in a monozygotic (MZ) twin and P3 amplitude from the
homologous site in the other hemisphere of his or her co-twin is approximately as large as
the correlation within the same individual (Katsanis, Iacono, McGue, & Carlson, 1997). In
addition, the genetic influence on P3 amplitude appears stable over time in adolescence (van
Beijsterveldt et al., 2001) and into early adulthood (Carlson & Iacono, 2006).
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There are numerous reports of associations between P3 amplitude and the externalizing
spectrum of disinhibitory disorders, including alcoholism and alcohol abuse (Baguley et al.,
1997; L. O. Bauer, 2001a; Carlson, Katsanis, Iacono, & Mertz, 1999; Chen et al., 2007;
Cohen, Wang, Porjesz, & Begleiter, 1995; Costa et al., 2000; George, Potts, Kothman,
Martin, & Mukundan, 2004; Glenn, Parsons, & Smith, 1996; Justus, Finn, & Steinmetz,
2001; Koskinen et al., 2011; Malone, Iacono, & McGue, 2001; Rodriguez Holguin, Porjesz,
Chorlian, Polich, & Begleiter, 1999; Steinhauer, Hill, & Zubin, 1987; Yoon, Iacono,
Malone, & McGue, 2006), drug abuse or dependence (Attou, Figiel, & Timsit-Berthier,
2001; L. O. Bauer, 2001a; Biggins, MacKay, Clark, & Fein, 1997; Carlson et al., 1999;
Gamma, Brandeis, Brandeis, & Vollenweider, 2005), smoking and nicotine dependence
(Anokhin et al., 2000; Iacono et al., 2002), antisocial personality disorder (Barratt, Stanford,
Kent, & Felthous, 1997; L. O. Bauer, O’Connor, & Hesselbrock, 1994; Costa et al., 2000;
Hesselbrock, Bauer, O’Connor, & Gillen, 1993; Iacono, Malone, & McGue, 2003; Malone
et al., 2001), conduct disorder (L. O. Bauer & Hesselbrock, 1999, 2001; Kim, Kim, &
Kwon, 2001), and ADHD (Banaschewski et al., 2003; Johnstone & Barry, 1996; Szuromi,
Czobor, Komlosi, & Bitter, 2011; Yoon, Iacono, Malone, Bernat, & McGue, 2008). The
association between P3 amplitude and symptoms of different externalizing disorders can be
accounted for by a single latent dimension (Patrick et al., 2006) and is due to shared genetic
influences (Hicks et al., 2007).
Several studies have observed P3 amplitude reductions in first-degree relatives of
individuals with an externalizing disorder (Begleiter, Porjesz, Bihari, & Kissin, 1984;
Carlson & Iacono, 2008; Carlson, Iacono, & McGue, 2002; Gabrielli & al., 1982;
Hesselbrock et al., 1993; Hill, Steinhauer, Zubin, & Baughman, 1988; Iacono et al., 2002;
Polich, Pollock, & Bloom, 1994; van der Stelt, Geesken, Gunning, Snel, & Kok, 1998), as
well as in abstinent former substance abusers (L. O. Bauer, 2001b; Branchey, BuydensBranchey, & Horvath, 1993; Fein & Chang, 2006; Realmuto, Begleiter, Odencrantz, &
Porjesz, 1993). Moreover, P3 amplitude predicts the subsequent development of
externalizing psychopathology or behavior (Berman, Whipple, Fitch, & Noble, 1993;
Carlson, Iacono, & McGue, 2004; Gao, Raine, Venables, & Mednick, 2013; Habeych,
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Charles, Sclabassi, Kirisci, & Tarter, 2005; Hill, Steinhauer, Lowers, & Locke, 1995; Iacono
et al., 2002; Perlman, Markin, & Iacono, 2013).
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Although the status of P3 amplitude as an endophenotype is perhaps strongest in relation to
externalizing disorders, P3 amplitude reductions have also been observed in other
psychiatric disorders, such as borderline personality (Houston, Ceballos, Hesselbrock, &
Bauer, 2005), which shares some features with the externalizing spectrum. A large number
of studies have examined associations between P3 amplitude and schizophrenia and risk for
schizophrenia (Jeon & Polich, 2003), with P3 amplitude commonly considered both a state
and trait marker of the disease (Ford, 1999; Mathalon, Ford, & Pfefferbaum, 2000; Turetsky
et al., 2007). P3 amplitude is also reduced among patients with bipolar disorder and their
relatives (Hall et al., 2009; Turetsky et al., 2007). Associations with major depression are
inconsistent and appear to reflect primarily state characteristics, although at least one study
has reported reduced amplitude in offspring of parents with major depression (Y. Zhang,
Hauser, Conty, Emrich, & Dietrich, 2007). In addition, reduced P3 amplitude has been
reported in neurodegenerative diseases such as Alzheimer’s disease (Gooding & Aminoff,
1992; Polich & Corey-Bloom, 2005). Understanding the molecular-genetic basis of P3
amplitude is thus of broad clinical interest.
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P3 amplitude appears to reflect the modulation of attention by noradrenergic activity
originating in the locus coeruleus (Nieuwenhuis, Aston-Jones, & Cohen, 2005), which may
facilitate a process whereby computations conducted in the hippocampal formation result in
an updated representation of stimulus context (Donchin, 1981) in association cortex in the
temporal-parietal junction (Polich & Criado, 2006). This permits the organism to classify a
stimulus as relevant to behavior (e.g., a button press to stimuli designated as targets) or as
familiar. The latter allows use of P3 amplitude as a probe of recognition memory in
detecting deception (Iacono & Patrick, 2014). The process of information transfer from
short- to long-term storage is reflected in the so-called remembered word effect, whereby
words in a study session that are subsequently recalled elicit larger P3 amplitudes than
words that are not successfully recalled (e.g., Fabiani, Karis, & Donchin, 1986). Although
several neural areas are implicated in P3 generation (cf. Mulert et al., 2004), the relative
uniformity of P3 latency across the scalp suggests that the P3 represents activity of a
distributed neural circuit (Nieuwenhuis et al., 2005).
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The search for genetic markers associated with P3 amplitude has met limited success.
Linkage studies have implicated regions of several chromosomes, principally 4, 6, 7, and 12
(Begleiter et al., 1998; Hill et al., 2004; Williams et al., 1999; Wright et al., 2008; H. Zhang,
Zhong, & Ye, 2005). However, linkage analysis by itself can only highlight relatively large
segments on a given chromosome. A number of candidate gene studies have been
conducted, especially in recent years. These have produced several leads but no well
replicated findings (Berman et al., 2006; Blackwood & Muir, 2004; Bramon et al., 2008;
Chen et al., 2010; Decoster et al., 2012; Hill et al., 1998; Johnson et al., 1997; Lin, Yu,
Chen, Tsa, & Hong, 2001; Shaikh et al., 2013). For instance, several studies have
investigated dopamine genes, but these have mostly produced null findings or specific
interactions with gender, risk status, or other genes (Berman et al., 2003; Garcia-Garcia,
Barceló, Clemente, & Escera, 2011; Hill, 2000; Strobel et al., 2004). Dopamine genes may
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be especially relevant to P3 amplitude recorded at frontal sites (Gallinat et al., 2003, 2007;
Heitland, Kenemans, Oosting, Baas, & Bocker, 2013; Mulert et al., 2006), which would be
consistent with the finding that dopamine depletion is associated with reduced frontal P3
(Neuhaus et al., 2009).
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Several recent studies from the Collaborative Studies on the Genetics of Alcoholism
(COGA) have examined stimulus-related activity in the theta and delta frequency bands
during the P3 time window, an alternative method of quantifying P3-related activity. Initial
studies consisted of genome-wide linkage analysis, which uses markers consisting of
polymorphisms varying either in sequence or size in samples comprising families. If a
marker is coinherited with a trait, the two are said to be linked, and the gene that influences
the trait is thought to be located near the marker. Because such markers tend to be widely
spaced, linkage analysis is limited to identifying a relatively large chromosomal region
containing genetic markers related to a phenotype. However, finding a “hot spot” can be
followed up by analysis of SNPs or candidate genes located in the region. Such analyses
have yielded significant results for the CHRM2 gene encoding a muscarinic acetylcholine
receptor (Jones et al., 2006) and the GRM8 gene encoding a glutamate receptor (Chen et al.,
2009). A genome-wide association study (GWAS) of P3-related theta activity at a frontal
site reported association with a serotonin receptor gene, HT7 (Zlojutro et al., 2011), one
SNP in which was also associated with alcohol dependence. A second, family-based GWAS
of frontal theta activity reported associations with several SNPs in the gene KCNJ6 (Kang et
al., 2012), which encodes a potassium channel involved in the function of dopaminergic,
cholinergic, GABAergic, and glutamatergic synapses. These findings accord with the notion
that cholinergic and GABAergic activity influence P3 amplitude, perhaps by modulating the
activity of glutamate (Frodl-Bauch, Bottlender, & Hegerl, 1999; Kenemans & Kähkönen,
2010).
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P3-like waves have been elicited in animals, and the amplitude of the P3 response is reduced
in strains of mice selectively bred to show a preference for alcohol relative to other strains
(Ehlers & Somes, 2002). Findings of reduced P3 in animal models of alcoholism risk bolster
the notion that P3 amplitude is an endophenotype for alcoholism and related
psychopathology. That P3 is so ubiquitous but at the same time associated with heritable
individual differences also suggests that a “common-disease [phenotype], common-variant”
model of inheritance is likely applicable. The genotyping arrays used in GWAS primarily
assess common variants, defined most often as those that occur in at least 1–5% of the
population, and have permitted discovery of association between such variants and common
diseases. However, there are no published GWASs on P3 amplitude itself, a surprising gap
in the literature in view of the extensive interest in the genetic basis of this endophenotype
that is apparent from reviewing the literature.
To address this gap, we examined in the present investigation 527,829 single nucleotide
polymorphisms (SNPs) in a large population-based sample of adolescent and adult
participants from three independent cohorts of the Minnesota Center for Twin and Family
Research (MCTFR). The analysis plan for all GWASs in this special issue is described in
depth in Iacono, Malone, Vaidyanathan, and Vrieze (2014). In brief, we used a four-pronged
approach: estimate the heritability of P3 amplitude using twin and twin-family biometric
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models, estimate the total genetic variance in P3 amplitude accounted for by all SNPs in
aggregate by means of genome-wide complex trait analysis (GCTA; Yang, Lee, Goddard, &
Visscher, 2011), assess associations between each individual SNP and P3 amplitude in a
GWAS, and assess associations between individual genes and P3 amplitude by aggregating
the effect of all SNPs in a gene using VEGAS, a versatile gene-based test for association
studies (Liu et al., 2010). Analyses of individual SNPs and genes comprised both purely
atheoretical analyses of the whole genome as well as more targeted analyses of candidate
genetic variants.
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Our primary measure was of P3 amplitude at a midline parietal site (Pz). In addition, we
took advantage of having recordings from two additional electrodes over lateral parietal
scalp and the fact that our sample consisted of nuclear twin families to estimate genetic
factor scores. Given standard assumptions behind the latent variable models used to
decompose total variance in P3 amplitude into its additive genetic and environmental
sources, observed phenotypic scores can be transformed into genetic and environmental
factor scores (cf. Boomsma, Molenaar, Orlebeke, Rao, & Vogler, 1990). Because it is by
definition based solely on the additive genetic influence on P3 amplitude, a P3 genetic factor
score is arguably a more appropriate target for GWAS than measured P3 amplitude. We
expected it to provide greater power, relative to P3 amplitude, to detect the influence of
individual genetic variants on the P3 response.
Method
Participants
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As described in Iacono et al. (2014), the sample is a subset of the larger sample in a recent
family-based GWAS of substance abuse and related psychopathology conducted at the
Minnesota Center for Twin and Family Research (MCTFR; McGue et al., 2013; M. B.
Miller et al., 2012). Participants for the present investigation are from the older and younger
cohorts and enrichment samples of the Minnesota Twin Family Study (MTFS; Iacono,
Carlson, Taylor, Elkins, & McGue, 1999; Keyes et al., 2009; McGue et al., 2013). The
cohort-sequential nature of the MTFS design is such that the two age cohorts of twins
participate at partially overlapping assessment ages. The sample for this investigation was
based on the age-17 laboratory assessment of twins and all parents who had completed an
identical laboratory assessment. (See Iacono et al., 2014, for further details.) Participants in
MCTFR studies gave written consent or assent, if under the age of 18, to participate in the
initial study as well as to allow data used in GWASs to be placed in a public repository to be
shared with other researchers.
The sample is broadly representative ethnically of the state of Minnesota during the relevant
birth years; it is thus predominantly Caucasian (96%). To avoid population stratification,
which confounds genetic analyses if allele frequencies and mean levels of a phenotype both
vary by different subpopulations, we limited this study to Caucasian subjects, based on selfreported ethnicity corroborated by principal component analysis (PCA) of genotype data
(Iacono et al., 2014). The mean age was 17.7 (range, 16.6–20.0) for adolescent participants
and 44.6 (range, 28.4–65.3) for the parents. Fifty-seven subjects were excluded for serious
head injury, neurological disorders, use of alcohol or illicit drug the day of the assessment,
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medication likely to affect psychophysiological responses, and not refraining from taking
medications for ADHD, such as methylphenidate, as was requested of the twins (Iacono et
al., 2014). We excluded an additional 126 for reasons specific to the data used in this
particular report: recording problems, poor task performance (less than 75% accuracy), or
insufficient data (fewer than 30 artifact-free sweeps). The final sample consisted of 4,211
individuals, 2,439 adolescents (1,180 males) and 1,772 adults (1,200 males) from 1,637
families. The majority of families were MZ twin families (1,053, or 64%).
Experimental Task
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The Begleiter rotated heads task (Begleiter et al., 1984) served to elicit event-related
potentials (ERPs). Subjects viewed 240 stimuli presented one at a time on a computer
display in a Bernoulli sequence. A 500-ms baseline interval preceded stimulus onset.
Stimulus duration was 100 ms. Responses were monitored during a response window of 1.5
s and a random intertrial interval drawn from a uniform distribution of 1–2 s. Superior views
of stylized “heads” consisting of an oval, the nose, and one ear served as target stimuli (n =
80), with the head rotated 180° on half the trials. The subject’s task was to press a button on
either the left or right arm of their chair to indicate whether they had viewed a left- or rightear head. Stimuli for the remaining trials consisted of plain ovals (n = 160), which required
no response.
EEG Recording
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ERP data were collected over the course of more than 20 years using two different systems.
For older- and younger-cohort MTFS participants1 (74% of the sample),
electroencephalogram (EEG) was recorded via Grass Neurodata 12 systems (256-Hz
sampling rate, pass-band from .01 to 30 Hz with a roll-off of 6 dB). For each trial, 2 s of
EEG, including a 500-ms prestimulus baseline, were written to disk. Hardware constraints
limited the number of signals recorded to three: one from midline parietal scalp and two
from left and right parietal cortex, respectively. Signals were referred to linked ear
electrodes. Eye blinks and other eye movements were recorded by means of a transverse
electrode arrangement, with one superior to the eye and one next to the outer canthus. For
MTFS enrichment sample (ES) participants, a BioSemi ActiveTwo system was used to
collect continuously recorded EEG data with a sampling rate of 1024 Hz. Stimulus delivery
was controlled by a script written in E-Prime software 1.1 (Psychology Software Tools,
Pittsburgh, PA) to mimic the original program and pass event triggers to the recording
system. ActiveTwo amplifiers are DC coupled, and signals were low-pass filtered using a
digital 5th-order Bessel antialiasing sinc filter with a cutoff frequency (3-dB attenuation) of
205 Hz. ActiveTwo signals are monopolar.
ERP Processing
Data processing was conducted in MATLAB (The Mathworks, Natick, MA) using identical
methods for both systems, based on functions in the Psychophysiology Toolbox http://
sourceforge.net/projects/psychophys/ and custom scripts. BioSemi data were transformed to
1There were 50 exceptions to this, with 39 ES fathers tested in the Grass lab and 11 MTFS fathers tested in the BioSemi lab.
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be comparable to the original data, as described in the online supporting information. Notes
recorded at the time the data were collected guided us in visually identifying problematic
data that might need to be excluded. In addition, specific trials containing transient artifacts
and excessively small or large voltage deflections were tagged for exclusion by computer
algorithm. To increase reliability of peak selection, target ERPs were low-pass filtered with
a cutoff frequency of 10 Hz using a finite impulse response (FIR) filter with least-squared
error to minimize the contribution of higher frequencies (Losada, 2004). A computer
algorithm selected the largest peak within a window between 300 and 600 ms as the P3.
Outliers with respect to amplitude and latency were identified and their data visually
screened to determine whether outlyingness was due to problems necessitating subject
exclusion. In addition, we identified multivariate outliers using the three parietal electrodes
and a robust version of Mahalanobis distance from the robustbase package (Rousseeuw et
al., 2011) in the statistical computing environment (R Development Core Team, 2010). The
corresponding data were examined visually, and the algorithm’s selection overridden if
necessary.
Molecular Genetic Data
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The pipeline for extracting and processing DNA as well as steps taken to ensure quality
control are described in detail in Iacono et al. (2014) and in Miller et al. (2012). PCA was
conducted on genotypes of non-Caucasian subjects using EIGENSTRAT (Price et al., 2006)
in order to identify the major dimensions of genetic variation. Scores on the first 10
components were subsequently used in all analyses in order to control confounding due to
any residual population stratification in allele frequencies (cf. Price et al., 2006).
Statistical Analyses
Our primary dependent measure was P3 amplitude at the midline parietal location (Pz).
Generation (twin or parent), gender, chronological age, a dummy variable representing
recording system (BioSemi or Grass) in order to account for possible mean differences
between them, and the 10 PCs from EIGENSTRAT served as covariates in subsequent
analyses. Additive SNP effects were modeled, with each SNP represented as a count of the
number of minor alleles.
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In addition, we examined genetic factor scores (Boomsma et al., 1990) derived from a
biometric common pathway (Kendler, Heath, Martin, & Eaves, 1987) or psychometric
factors (McArdle & Goldsmith, 1990) model, with P3 amplitude at the three parietal
locations as indicators of a common factor, representing what is shared by the three P3
amplitude measures. Unique factors captured electrode-specific influences, including noise.
Preliminary moderated factor analyses (D. J. Bauer & Hussong, 2009) indicated that the four
primary covariates (age, gender, generation, and recording system) were significant
influences on the common factor mean. In addition, gender and generation influenced both
the unique and common factor variances. (Table S1 provides parameter estimates from the
preliminary factor analyses, and the supporting information provides additional detail
concerning the model.) Thus, our final biometric model allowed for gender and generation
effects on common factor and unique variances, with effects on the factor mean
accommodated through adjusting P3 measures for the four covariates (age, gender,
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generation, and recording system). Figure 1 illustrates the model for one individual,
although data from all family members are used in estimating the model.
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Biometric heritability—The amount of heritable variance in P3 amplitude and the P3
common factor was estimated using standard biometric approaches to modeling twin-family
data (Neale, Boker, Xie, & Maes, 2003) and conducted using the OpenMx package for R
(Boker et al., 2011). Our approach and the logic of biometric model fitting are described in
Iacono et al. (2014). We fit models to twin data as well as data from the entire family. For
both endophenotypes, models allowed for three latent factors, which influence (“cause”) the
endophenotype: additive genetic influences (A), common environmental influences (C), and
unique, or unshared, environmental influences (E). Parameter estimates from the common
pathway model, which is described in more detail in the supporting information, were used
to derive genetic factor scores. Because genetic factor scores and genotype are identical for
MZ twins, only one twin from each MZ pair was used in analysis of these scores. The
correlation between the two (covariate-adjusted) P3 endophenotypes was .925.
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SNP heritability—We used GCTA (Yang et al., 2011) to estimate the proportion of
variance in P3 amplitude accounted for by the combined additive effect of all SNPs on the
Illumina genotyping array (or in linkage disequilibrium [LD] with them). In a sample of
genetically unrelated individuals, the degree to which any two are phenotypically similar
must be due to the specific genetic variants they share. GCTA estimates genotypic similarity
in the form of a genetic relatedness matrix (GRM), somewhat akin to a correlation matrix
representing pairwise genetic similarity. In samples comprising families, Yang and
colleagues (Yang, Lee, Goddard, & Visscher, 2013) have recommended filtering the sample
on the basis of genetic relatedness, using several thresholds and looking for consistency
across the resulting estimates. We used thresholds of .025, .05, and .10, which remove all
but distant relatives. The same covariates were used as in all other analyses (age, gender,
generation, recording system, and the 10 PCs from EIGENSTRAT). Because LD can bias
SNP heritability estimates upward (Speed, Hemani, Johnson, & Balding, 2012), we repeated
these analyses after weighting SNPs by local LD patterns using LDAK software (http://
dougspeed.com/ldak). Yang and colleagues have more recently recommended using the
entire sample when it consists of closely related subjects, and estimating the magnitude of
genetic influence while simultaneously modeling the environmental influences family
members share (the C latent variable in biometric models). This provides an estimate of
genetic influence unconfounded by shared environmental effects (as well as an estimate of
such effects). In addition to this, we conducted the same analysis without modeling shared
environmental influences (i.e., without any threshold of genetic relatedness). The difference
between the two provides a simple indication of the magnitude of such effects (which is also
estimated directly by GCTA).
SNP effects: Genome-wide scan—Analyses of the association between each SNP in
turn and our endophenotypes were conducted by means of the R package for rapid feasible
generalized least squares (RFGLS; Li, Basu, Miller, Iacono, & McGue, 2011), a
computationally efficient form of generalized least squares (GLS) developed for this
purpose. GLS is useful with correlated data, such as the correlation that exists when subjects
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are nested in families (see Iacono et al., 2014, for details). Our sample comprised MZ and
DZ twin families. In addition, the 74 stepparents in the sample (70 of them male) were
treated as families of one. The conventional genome-wide significance threshold of 5 × 10−8
was used.
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SNP effects: Candidate SNPs—Using the results of our genome-wide scan, we
examined associations between each endophenotype and two target sets of specific
candidate SNPs implicated in previous studies of P3 or P3-related activity (N = 183; P3specific candidate SNPs) or in recent meta-analyses of disorders associated with the
endophenotypes examined in this special issue (N = 1,180; endophenotype-general candidate
SNPs). The latter included alcohol and drug dependence, cocaine abuse, smoking and
nicotine dependence, ADHD, schizophrenia, bipolar disorder, and major depression, or
related phenotypes, such as heavy drinking or excessive consumption, and the personality
characteristic of excitement seeking (Iacono et al., 2014). SNPs in the candidate sets but not
on the Illumina array were imputed (Iacono et al., 2014). Analyses of imputed SNPs used
allele dosage as the independent variable, which is a count of the minor allele weighted by
the posterior probability of each genotype. We used Bonferroni-corrected significance
thresholds for both sets, with significance criteria of 2.73 × 10−4 and 4.24 × 10−5 for P3specific and endophenotype-general SNPs, respectively.
Gene effects: Genome-wide scan—Gene-based tests can be a powerful alternative to
tests of individual SNPs, especially when there are several causal SNPs in a gene. It is
possible in such a circumstance that the p values might not be small enough to be
distinguishable from noise. We conducted gene-based tests of 17,601 autosomal genes
available in VEGAS (Liu et al., 2010). VEGAS aggregates the effects of all SNPs within a
gene by converting the p values for each SNP into a chi-squared statistic and summing these
into a single score, which is adjusted for LD between the SNPs (see Iacono et al., 2014). In
order to capture SNPs with regulatory functions and SNPs in LD with those in the gene
proper, VEGAS includes SNPs spanning a small region on each end of the gene. Because
the p values were produced by RFGLS, they accurately reflect the clustered nature of our
sample. A threshold of 2.84 × 10−6 was used for determining statistical significance, which
corrects for the number of different genes.
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Gene effects: Candidate genes—In analyses similar to our analyses of candidate
SNPs, we evaluated three sets of candidate genes: 18 that have been implicated in P3
amplitude or related measures (P3-specific candidate genes, see first column of Table S4 for
a list); 204 genes that are likely relevant to understanding the endophenotypes examined in
this special issue because they are part of the major neurotransmitter and neuromodulator
systems (dopamine, noradrenaline, acetylcholine, GABA, glutamate, and serotonin), they
are part of the endogenous cannabinoid and opioid systems, or they are involved in
metabolizing nicotine and alcohol (endophenotype-general candidate genes, see first column
of Table S5); and 92 genes identified by the Consortium on the Genetics of Schizophrenia as
related to similar endophenotypes (COGS candidate genes, see first column of Table S6).
Bonferroni correction was used to determine the significance of genes in each set, with
thresholds of 2.78 × 10−3, 2.45 × 10−5, and 5.43 × 10−4 for the three sets, respectively.
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Results
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Descriptive statistics are provided in Table 2 in Iacono et al. (2014) in this issue. Mean
amplitudes were 5% to 11% larger for females than males and 61% to 72% larger for
adolescents than adults. Plots of the distribution of each endophenotype indicated that the
assumption in regression analysis of normally distributed scores was reasonable (see Figures
S1 and S2 in supporting information).
Heritability from Biometric Models
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Table 1 gives family correlations produced by RFGLS for P3 amplitude and phenotypic
factor scores from the common pathway model, which was the basis for estimating genetic
factor scores. The pattern of correlations suggests substantial genetic influence and little
shared environmental influence. This was confirmed by the results of biometric modelfitting analyses, summarized in Table 2. Heritability estimates for both endophenotypes (P3
amplitude and the P3 common factor) were substantial, indicating that between half and
nearly two thirds of the variance in them was due to additive genetic influence. Heritability
estimates were somewhat larger in magnitude for the common factor. Point estimates of C
were nonzero in estimates obtained from twin data, although the confidence interval
included 0. This indicated that shared environment is likely not a significant influence.
SNP Heritability
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In order to obtain a subsample of unrelated individuals, we filtered the sample using genetic
relatedness thresholds of .025, .05, and .1 (Yang et al., 2013), the most stringent of which
corresponds approximately to the relationship between third or fourth cousins. Results for
each subsample are given in Table 3, which also presents results for the same subsamples
using a GRM based on SNPs weighted by LD patterns (Speed et al., 2012). The standard
errors are large, a consequence of the fact that unrelated individuals are used, which also
reduced the sample approximately in half. SNP heritability estimates vary somewhat, as
would be expected due to sampling error across the subsamples, but are relatively consistent.
The median estimates were .29 and .27 for P3 amplitude and the genetic factor score derived
from parameters of the biometric common factor model, respectively. Standard errors in
some cases were larger than the point estimates, making strong inferences ill advised. Table
3 also includes SNP heritability estimates for each endophenotype in the full sample (i.e.,
without imposing a threshold of relatedness, appearing in the row labeled “None” in the
table), which is largely driven by the phenotypic relationships among family members and
approximates the sum of factors that give rise to phenotypic resemblance (A and C) in Table
2. We also used the method of Yang and colleagues to model, and thereby control, shared
environmental influence in family data while estimating the magnitude of genetic effects
(see the column labeled “GCTA-Family”). Because there is by definition no such shared
environmental influence in the genetic factor score, this was estimated only for P3
amplitude. Despite the fact that this second method accounts for C, it produced a point
estimate that was identical to two decimal places (.57).
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SNP Effects: Genome-Wide Scan
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Figure 2 presents the Q-Q plot for P3 amplitude, while Figure 3 presents the Q-Q plot for
the genetic factor score. Substantial deviation from the expected line representing the null
distribution can indicate inflated (or deflated) power, such as might result from population
stratification. This was not the case, an inference corroborated by the genomic control
statistics, which were close to 1 for both measures (with 1 indicating that the observed
values conform exactly to expectation under the null hypothesis). Our analytic approach
therefore appears to have appropriately accommodated the lack of independence in our
family data, and there was no meaningful residual ethnic stratification. There was also no
evidence for significant associations. None of the SNP effects on either P3 or the genetic
factor score was genome-wide significant (p values > 5 × 10−8). An apparent excess of small
p values, which appear as large values of −log10(p), was evident in the Q-Q plots, especially
for P3 amplitude. These subthreshold p values may indicate that there are true associations
hidden in the GWAS signal. We list the results for all SNPs with p values less than 10−4 in
Tables S2 and S3 for P3 amplitude and the genetic factor score, respectively. Ninety-six
SNP associations with P3 amplitude produced p values less than 10−4, whereas
approximately 50 would be expected by chance (ignoring LD, which creates a correlation
among SNPs). However, the degree of overlap between endophenotypes with respect to the
specific SNPs producing small p values was limited (24 in all), especially considering the
magnitude of the phenotypic correlation between the two.
Manhattan plots, which are presented in Figure 4 for P3 and in Figure 5 for the genetic
factor score, order p values by location on each chromosome, thereby providing information
about where in the genome variants with small p values occur. Although small p values
(large −log10[p]) appear to cluster somewhat on a few chromosomes, on balance there is
little evidence of significant or even suggestive associations.
SNP Effects: Candidate SNPs
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Table S4 in the supporting information lists the results for P3-specific candidate SNPs. Two
SNPs out of 176 were nominally significant for P3 amplitude (p < .05), while one SNP—a
different one—yielded a p value less than .05 for the genetic factor score. None of the p
values was close to the Bonferroni-corrected threshold of 2.73 × 10−4. Table S5 gives results
for SNPs in our endophenotype-general candidate SNP set of 1,180. None of the observed p
values approached the Bonferroni-corrected threshold of 4.24 × 10−5; the smallest, for P3
amplitude, was 5.38 × 10−4.
Gene Effects: Genome-Wide Scan
A comprehensive evaluation of 17,601 autosomal genes provided by VEGAS yielded a
genome-wide significant association (α = 2.87 × 10−6) with both endophenotypes for
MYEF2, myelin expression factor 2, p ≤6.79 × 10−7, a gene on chromosome 15. The protein
encoded by MYEF2 is a repressor of transcription of the myelin basic protein gene (MBP).
Gene Ontology annotations related to MYEF2 include RNA binding and nucleotide binding.
The next smallest p value was 7.50 × 10−5.
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Gene Effects: Candidate Genes
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VEGAS summary statistics for P3-specific candidate genes are presented in Table S6. Test
statistics and associated p values for the 204 endophenotype-general candidate genes appear
in Table S7, and summary statistics for the 92 candidate genes associated with
endophenotypes for schizophrenia are presented in Table S8. None of the genes in these
three candidate gene sets yielded p values that survive the respective Bonferroni-corrected
threshold for each set.
Discussion
Additive Genetic Variance in P3 Amplitude
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This study is the first published GWAS of amplitude of the P3 wave, the most widely
studied ERP measure and a strong candidate endophenotype for disinhibitory behavior and
psychopathology in particular (Iacono & Malone, 2011; Porjesz et al., 2005). We conducted
biometric analyses in our sample of MZ and DZ twin families to determine the extent to
which variation in P3 amplitude reflects heritable individual differences. Estimates of
heritability from ACE models ranged from .50 to .66 for the two phenotypes, which is
consistent with results of a meta-analysis that estimated P3 heritability as approximately .60
(van Beijsterveldt & van Baal, 2002). GCTA analyses provide estimates of SNP heritability,
or phenotypic variance due to the measured genetic variants on our genotyping array (or
variants in LD with them). Using several thresholds of pairwise genetic relatedness to select
unrelated subjects from our family sample based on a weighted and unweighted GRM, we
obtained median estimates of SNP heritability of .29 for P3 amplitude and .27 for genetic
factor scores. This represents approximately 40% to 50% of the heritable variance in each
trait. These estimates are imprecise; 95% confidence intervals around them are necessarily
large when derived from genetically unrelated individuals. Nevertheless, results of GCTA
analyses indicate that much of the additive genetic influence in both endophenotypes is due
to common genetic variants. GCTA that accounted for shared environmental influences
within families and GCTA with the full sample (without a threshold of genetic relatedness)
produced nearly identical estimates (.571 and .570, respectively). These numbers cannot be
considered SNP heritability estimates, because they are driven by all factors that cause
highly related individuals to have similar values of P3 amplitude, such as nonadditive
genetic influences and rare variants that are not tagged by the SNPs on the genotyping array.
However, the fact that they were virtually identical indicates that shared environmental
influences were minimal. This is consistent with the fact that the 95% confidence interval
around the estimate of C in biometric models of both twin data and family data included 0.
Analysis of SNPs
Despite evidence from biometric analyses and GCTA that additive genetic influences on P3
amplitude and the P3 genetic factor score are substantial and due in large part to common
variants, we failed to obtain genome-wide significant associations with any individual SNPs
for either endophenotype, including those in our sets of candidate SNPs selected for having
been reported to be associated with P3 amplitude or P3-related activity in previous genomewide studies or because they are hypothesized to be relevant to disorders associated with our
endophenotypes.
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Analysis of Genes
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Genome-wide analysis of all autosomal genes produced one significant finding for both
endophenotypes that survived Bonferroni correction. Myelin expression factor 2 (MYEF2) is
a transcriptional repressor of myelin basic protein, which codes for a major constituent of
the myelin sheath surrounding oligodendrocytes and Schwann cells in the central nervous
system. In addition to increasing the velocity of the conduction of action potentials along
axons, myelin is important for facilitating long-range connections among brain regions. P3
appears to be produced by a distributed neural circuit, and myelin may facilitate coherent
activity in this circuit. This is a novel finding, although we are unable to find any previous
links to P3 or related measures, which makes independent replication especially important.
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Analyses of the 92 schizophrenia endophenotype candidate genes from COGS and the 204
candidate genes we hypothesized might be related to all the endophenotypes examined in
this special issue did produce a handful of associations that were nominally significant for
both endophenotypes (p < .05). However, these were few in number, and none survived
Bonferroni correction. Our failure to find strong evidence of associations with
neurotransmitter genes in the endophenotype-general candidate set of 204 is disappointing
given empirical and conceptual evidence that P3 amplitude depends critically on several
major neurotransmitters. We also did not corroborate previous findings regarding P3
amplitude or related phenotypes, such as event-related theta power. Although several
associations were nominally significant (p < .05), this was not the case for both
endophenotypes, despite the fact that they were very highly correlated.
Lack of Agreement with Previous Studies
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That we failed to confirm recent findings from genome-wide studies might stem from the
fact that the majority of recent studies reporting positive findings have primarily examined
event-related theta power at frontal sites. P3 amplitude consists largely of stimulus-locked
activity in the delta and theta frequency ranges (Başar, Başar-Eroğlu, Karaka, & Schürmann,
1999; Kolev, Demiralp, Yordanova, Ademoglu, & Isoglu-Alkaç, 1997), and one might
expect some overlap in SNP associations. However, the correlation between frontal theta
power and parietal P3 amplitude may not be large enough to be reflected in significant SNP
associations. Previous genome-wide findings have also been based on the COGA sample
(Chen et al., 2009, 2010; Jones et al., 2006; Kang et al., 2012; Zlojutro et al., 2011), which
comprises alcoholic probands and relatives from families with a dense history of alcoholism.
Although the density of alcohol dependence in our general population sample is not
comparable to its density in COGA, problematic alcohol use is quite prevalent in the
MCTFR in general (Hicks, Schalet, Malone, Iacono, & McGue, 2011; McGue et al., 2013),
including MTFS twins (Hamdi & Iacono, 2014). Ascertainment in COGA on such high
levels of genetic susceptibility is likely to increase the relative importance of rare variants
with large effects in genetic analyses. It also may amplify the genetic signal common to P3
amplitude and alcoholism risk more than the P3-specific signal, which is small in
population-based samples such as ours (Hicks et al., 2007).
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Limitations
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Of course, the present investigation suffers from limitations. One is the number of electrodes
used for recording P3 amplitude—one, or three in the case of the common factor approach.
Although P3 amplitude is typically greatest at the site we used (Pz) and much of the relevant
research establishing its status as an endophenotype has also used this site, several recent
positive findings for individual SNPs or candidate genes, whether of P3 amplitude or eventrelated theta activity, have been for recordings over frontal brain regions. Our use of two
different age cohorts, although allowing us to maximize sample size, may have obscured
true effects that are expressed differently in late adolescence compared to adulthood. Even
with the cohorts combined, the sample was small by current GWAS standards, if not when
this project was first undertaken. We did not use bioinformatic methods that are designed to
use additional information, such as knowledge about biological pathways and gene
expression, to mine the p values produced by GWAS for patterns, or methods for selecting
subsets of SNPs, although the approach we adopted is a reasonable starting point.
Conclusions
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The aggregate additive effect of all SNPs accounted for a little less than 30% of the variance
in P3 amplitude and the genetic factor score, which is between 40% and 60% of the heritable
variation in these measures. Approximately half the heritable variation thus appears to be
due to common genetic variants. Nevertheless, we did not obtain any statistically significant
associations between endophenotypes and individual SNPs, and the association with
MYEF2, despite being genome-wide significant, has no precedent in the literature and awaits
replication. Moreover, although the P3 genetic factor score, because it specifically reflects
the additive genetic variance in P3 amplitude, would seem to be advantageous in GWAS, its
usefulness consisted primarily in allowing us to assess the degree of correspondence
between analysis results for the two endophenotypes.
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Endophenotypes have offered the promise of assisting scientists in identifying genes for
psychiatric disorders, so much so that Miller and Rockstroh (2013) characterized the time
since the publication of Gottesman and Gould’s influential paper in 2003 as the “decade of
the endophenotype.” Against this backdrop, our failure to find genome-wide significant
SNPs is disappointing. Null findings in individual GWA studies are commonplace, however.
Assuming a two-tailed test at p < 5 × 10−8 and an effective sample size of 2,790, based on
an intraclass correlation of .31 from a simple linear mixed model analysis with a random
family-level intercept, we had 80% power to detect effects accounting for 1.4% of the
variance in our phenotypes (Gauderman & Morrison, 2006). This is larger than the typical
effect size in GWAS findings for quantitative traits (Visscher, Brown, McCarthy, & Yang,
2012). Although requiring one to argue that failing to reject the null hypothesis constitutes
positive evidence, our findings support a polygenic model of inheritance, in which complex
traits reflect the additive influence of many SNPs, each with very small effect. If the genetic
influence on P3 amplitude truly conforms to such a model, we were underpowered to detect
it. Much larger sample sizes than ours are necessary.
Due to the expense of collecting psychophysiological or similar measures, it is unlikely that
large enough samples will be available to permit detecting variants that account for more
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than a trivial proportion of the variance (de Geus, 2010). Thus, what endophenotypes may
offer in psychiatric genetics is less the promise of helping to identify novel genetic variants
as helping scientists understand the neurocognitive characteristics of psychiatric disorders
(Hall & Smoller, 2010). Alternative methods may be useful for increasing the sensitivity of
genome-wide scans, such as Bayesian and network-based methods that make use of
additional information or penalized regression approaches such as the lasso (Hastie et al.,
2009), which permits selecting subsets of relevant SNPs. However, it may also be that
different conceptualizations of the problem are required (de Geus, 2010; Hall & Smoller,
2010). For instance, trying to identify specific genetic influence on a phenotype, even an
endophenotype, from a single cross-sectional snapshot may be somewhat akin to trying to
understand the effects of gravity on a falling object while it is frozen in midair. Genes are
expressed in particular environments over the course of development, yet our analytic
approach ignores this interplay. Considering developmental trajectories may be fruitful. For
now, however, the molecular-genetic basis of P3 amplitude remains to be determined.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
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Acknowledgments
We are grateful to Dragana Vidovic, Micah Hammer, and Jennifer Donnelly for their tireless assistance with
organizing, processing, and screening all the data. This research was supported by NIH grants DA 024417, DA
05147, AA 09367, DA 13240, DA 036216, and AA015621.
References
NIH-PA Author Manuscript
Anokhin AP, Vedeniapin AB, Sirevaag EJ, Bauer LO, O’Connor SJ, Kuperman S, Rohrbaugh JW.
The P300 brain potential is reduced in smokers. Psychopharmacology. 2000; 149:409–413.
[PubMed: 10867969]
Attou A, Figiel C, Timsit-Berthier M. Opioid addiction: P300 assessment in treatment by methadone
substitution. Neurophysiologie Clinique. 2001; 31:171–180. [PubMed: 11488228]
Baguley IJ, Felmingham KL, Lahz S, Gordan E, Lazzaro I, Schotte DE. Alcohol abuse and traumatic
brain injury: Effect on event-related potentials. Archives of Physical Medicine & Rehabilitation.
1997; 78:1248–1253. [PubMed: 9365356]
Banaschewski T, Brandeis D, Heinrich H, Albrecht B, Brunner E, Rothenberger A. Association of
ADHD and conduct disorder—Brain electrical evidence for the existence of a distinct subtype.
Journal of Child Psychology and Psychiatry and Allied Disciplines. 2003; 44:356–376.
Barratt ES, Stanford MS, Kent TA, Felthous A. Neuropsychological and cognitive
psychophysiological substrates of impulsive aggression. Biological Psychiatry. 1997; 41:1045–
1061. [PubMed: 9129785]
Başar E, Başar-Eroğlu C, Karakaş S, Schürmann M. Are cognitive processes manifested in eventrelated gamma, alpha, theta and delta oscillations in the EEG? Neuroscience Letters. 1999;
259:165–168. [PubMed: 10025584]
Bauer DJ, Hussong AM. Psychometric approaches for developing commensurate measures across
independent studies: Traditional and new models. Psychological Methods. 2009; 14:101–
125.10.1037/a0015583 [PubMed: 19485624]
Bauer LO. CNS recovery from cocaine, cocaine and alcohol, or opioid dependence: A P300 study.
Clinical Neurophysiology. 2001a; 112:1508–1515. [PubMed: 11459691]
Psychophysiology. Author manuscript; available in PMC 2015 December 01.
MALONE et al.
Page 16
NIH-PA Author Manuscript
NIH-PA Author Manuscript
NIH-PA Author Manuscript
Bauer LO. Predicting relapse to alcohol and drug abuse via quantitative electroencephalography.
Neuropsychopharmacology. 2001b; 25:332–340.10.1016/S0893-133X(01)00236-6 [PubMed:
11522462]
Bauer LO, Hesselbrock VM. P300 decrements in teenagers with conduct problems: Implications for
substance abuse risk and brain development. Biological Psychiatry. 1999; 46:263–272. [PubMed:
10418702]
Bauer LO, Hesselbrock VM. CSD/BEM localization of P300 sources in adolescents “at-risk”:
Evidence of frontal cortex dysfunction in conduct disorder. Biological Psychiatry. 2001; 50:600–
608. [PubMed: 11690595]
Bauer LO, O’Connor S, Hesselbrock VM. Frontal P300 decrements in antisocial personality disorder.
Alcoholism: Clinical and Experimental Research. 1994; 18:1300–1305.
Begleiter H, Porjesz B, Bihari B, Kissin B. Event-related brain potentials in boys at risk for
alcoholism. Science. 1984; 225:1493–1496. [PubMed: 6474187]
Begleiter H, Porjesz B, Reich T, Edenberg HJ, Goate A, Blangero J, Polich J. Quantitative trait loci
analysis of human event-related brain potentials: P3 voltage. Electroencephalography and Clinical
Neurophysiology/Evoked Potentials Section. 1998; 108:244–250.
Berman SM, Noble EP, Antolin T, Sheen C, Conner BT, Ritchie T. P300 development during
adolescence: Effects of DRD2 genotype. Clinical Neurophysiology. 2006; 117:649–659.10.1016/
j.clinph.2005.11.012 [PubMed: 16426891]
Berman SM, Ozkaragoz T, Noble EP, Antolin T, Sheen C, Siddarth P, Ritchie T. Differential
associations of sex and D2 dopamine receptor (DRD2) genotype with negative affect and other
substance abuse risk markers in children of alcoholics. Alcohol. 2003; 30:201–210.10.1016/
j.alcohol.2003.06.002 [PubMed: 13679114]
Berman SM, Whipple SC, Fitch RJ, Noble EP. P3 in young boys as a predictor of adolescent substance
use. Alcohol. 1993; 10:69–76. [PubMed: 8447968]
Biggins CA, MacKay S, Clark W, Fein G. Event-related potential evidence for frontal cortex effects of
chronic cocaine dependence. Biological Psychiatry. 1997; 42:472–485. [PubMed: 9285083]
Blackwood DH, Muir WJ. Clinical phenotypes associated with DISC1, a candidate gene for
schizophrenia. Neurotoxicity Research. 2004; 6:35–41. [PubMed: 15184103]
Boker S, Neale M, Maes H, Wilde M, Spiegel M, Brick T, Fox J. OpenMx: An open source extended
structural equation modeling framework. Psychometrika. 2011; 76:306–317.10.1007/
s11336-010-9200-6 [PubMed: 23258944]
Boomsma D, Molenaar PCM, Orlebeke JF, Rao DC, Vogler GP. Estimation of individual genetic and
environmental factor scores. Genetic Epidemiology. 1990; 7:83–91. [PubMed: 2184093]
Bramon E, Dempster E, Frangou S, Shaikh M, Walshe M, Filbey FM, Murray R. Neuregulin-1 and the
P300 waveform—A preliminary association study using a psychosis endophenotype.
Schizophrenia Research. 2008; 103:178–185. [PubMed: 18571900]
Branchey MH, Buydens-Branchey L, Horvath TB. Event-related potentials in substance-abusing
individuals after long-term abstinence. American Journal of Addictions. 1993; 2:141–148.
Carlson SR, Iacono WG. Heritability of P300 amplitude development from adolescence to adulthood.
Psychophysiology. 2006; 43:470–480. [PubMed: 16965609]
Carlson SR, Iacono WG. Deviant P300 amplitude development in males is associated with paternal
externalizing psychopathology. Journal of Abnormal Psychology. 2008; 117:910–923.10.1037/
a0013443 [PubMed: 19025236]
Carlson SR, Iacono WG, McGue M. P300 amplitude in adolescent twins discordant and concordant for
alcohol use disorders. Biological Psychology. 2002; 61:203–227. [PubMed: 12385676]
Carlson SR, Iacono WG, McGue M. P300 amplitude in non-alcoholic adolescent twin pairs who
become discordant for alcoholism as adults. Psychophysiology. 2004; 41:841–844. [PubMed:
15563337]
Carlson SR, Katsanis J, Iacono WG, Mertz AK. Substance dependence and externalizing
psychopathology in adolescent boys with small, average, or large P300 event-related potential
amplitude. Psychophysiology. 1999; 36:583–590. [PubMed: 10442026]
Chen AC, Manz N, Tang Y, Rangaswamy M, Almasy L, Kuperman S, Schuckit MA. Singlenucleotide polymorphisms in corticotropin releasing hormone receptor 1 gene (CRHR1) are
Psychophysiology. Author manuscript; available in PMC 2015 December 01.
MALONE et al.
Page 17
NIH-PA Author Manuscript
NIH-PA Author Manuscript
NIH-PA Author Manuscript
associated with quantitative trait of event-related potential and alcohol cependence. Alcoholism:
Clinical and Experimental Research. 2010; 34:988–996.
Chen AC, Porjesz B, Rangaswamy M, Kamarajan C, Tang Y, Jones KA, Begleiter H. Reduced frontal
lobe activity in subjects with high impulsivity and alcoholism. Alcoholism: Clinical and
Experimental Research. 2007; 31:156–165.10.1111/j.1530-0277.2006.00277.x
Chen AC, Tang Y, Rangaswamy M, Wang JC, Almasy L, Foroud T, Porjesz B. Association of single
nucleotide polymorphisms in a glutamate receptor gene (GRM8) with theta power of event-related
oscillations and alcohol dependence. American Journal of Medical Genetics B: Neuropsychiatric
Genetics. 2009; 150B:359–368.10.1002/ajmg.b.30818
Cohen HL, Wang W, Porjesz B, Begleiter H. Auditory P300 in young alcoholics: Regional response
characteristics. Alcoholism: Clinical and Experimental Research. 1995; 19:469–475.
Costa L, Bauer L, Kuperman S, Porjesz B, O’Connor S, Hesselbrock V, Begleiter H. Frontal P300
decrements, alcohol dependence, and antisocial personality disorder. Biological Psychiatry. 2000;
47:1064–1071. [PubMed: 10862806]
de Geus EJ. From genotype to EEG endophenotype: a route for post-genomic understanding of
complex psychiatric disease? Genome Medicine. 2010; 2:1–4. [PubMed: 20193046]
Decoster J, De Hert M, Viechtbauer W, Nagels G, Myin-Germeys I, Peuskens J, van Winkel R.
Genetic association study of the P300 endophenotype in schizophrenia. Schizophrenia Research.
2012; 141:54–59.10.1016/j.schres.2012.07.018 [PubMed: 22910404]
Donchin E. Presidential address, 1980. Surprise! … Surprise? Psychophysiology. 1981; 18:493–513.
[PubMed: 7280146]
Ehlers CL, Somes C. Long latency event-related potentials in mice: Effects of stimulus characteristics
and strain. Brain Research. 2002; 957:117–128. [PubMed: 12443987]
Fabiani M, Karis D, Donchin E. P300 and recall in an incidental memory paradigm.
Psychophysiology. 1986; 23:298–308. [PubMed: 3749410]
Fein G, Chang M. Visual P300s in long-term abstinent chronic alcoholics. Alcoholism: Clinical and
Experimental Research. 2006; 30:2000–2007.
Ford JM. Schizophrenia: The broken P300 and beyond. Psychophysiology. 1999; 36:667–682.
[PubMed: 10554581]
Frodl-Bauch T, Bottlender R, Hegerl U. Neurochemical substrates and neuroanatomical generators of
the event-related P300. Neuropsychobiology. 1999; 40:86–94.10.1159/000026603 [PubMed:
10474063]
Gabrielli WF Jr, Mednick SA, Volavka J, Pollock VE, Schulsinger F, Itil TM. Electroencephalograms
in children of alcoholic fathers. Psychophysiology. 1982; 19:404–407. [PubMed: 7122778]
Gallinat J, Bajbouj M, Sander T, Schlattmann P, Xu K, Ferro EF, Winterer G. Association of the
G1947A COMT (Val(108/158)Met) gene polymorphism with prefrontal P300 during information
processing. Biological Psychiatry. 2003; 54:40–48.10.1016/S0006-3223(02)01973-X [PubMed:
12842307]
Gallinat J, Gotz T, Kalus P, Bajbouj M, Sander T, Winterer G. Genetic variations of the NR3A subunit
of the NMDA receptor modulate prefrontal cerebral activity in humans. Journal of Cognitive
Neuroscience. 2007; 19:59–68.10.1162/jocn.2007.19.1.59 [PubMed: 17214563]
Gamma A, Brandeis D, Brandeis R, Vollenweider FX. The P3 in ‘ecstasy’ polydrug users during
response inhibition and execution. Journal of Psychopharmacology. 2005; 19:504–512. [PubMed:
16166188]
Gao Y, Raine A, Venables PH, Mednick SA. The association between P3 amplitude at age 11 and
criminal offending at age 23. Journal of Clinical Child and Adolescent Psychology. 2013; 42:120–
130. [PubMed: 22963083]
Garcia-Garcia M, Barceló F, Clemente IC, Escera C. COMT and ANKK1 gene–gene interaction
modulates contextual updating of mental representations. NeuroImage. 2011; 56:1641–1647.
[PubMed: 21352928]
Gauderman, WJ.; Morrison, JM. QUANTO 1.1: A computer program for power and sample size
calculations for genetic-epidemiology studies. 2006. Retrieved from http://hydra.usc.edu/gxe
George MR, Potts G, Kothman D, Martin L, Mukundan CR. Frontal deficits in alcoholism: An ERP
study. Brain and Cognition. 2004; 54:245–247. [PubMed: 15050784]
Psychophysiology. Author manuscript; available in PMC 2015 December 01.
MALONE et al.
Page 18
NIH-PA Author Manuscript
NIH-PA Author Manuscript
NIH-PA Author Manuscript
Glenn SW, Parsons OA, Smith LT. ERP responses to target and nontarget visual stimuli in alcoholics
from VA and community treatment programs. Alcohol. 1996; 13:85–92. [PubMed: 8837941]
Gooding DS, Aminoff MJ. Evaluation of dementia by event-related potentials. Journal of Clinical
Neurophysiology. 1992; 9:521–525. [PubMed: 1464678]
Gottesman II, Gould TD. The endophenotype concept in psychiatry: Etymology and strategic
intentions. American Journal of Psychiatry. 2003; 160:636–645. [PubMed: 12668349]
Habeych ME, Charles PJ, Sclabassi RJ, Kirisci L, Tarter RE. Direct and mediated associations
between P300 amplitude in childhood and substance use disorders outcome in young adulthood.
Biological Psychiatry. 2005; 57:76–82. [PubMed: 15607303]
Hall MH, Schulze K, Rijsdijk F, Kalidindi S, McDonald C, Bramon E, Sham P. Are auditory P300 and
duration MMN heritable and putative endophenotypes of psychotic bipolar disorder? A Maudsley
Bipolar Twin and Family Study. Psychological Medicine. 2009; 39:1277–1287.10.1017/
S0033291709005261 [PubMed: 19250581]
Hall MH, Smoller JW. A new role for endophenotypes in the GWAS era: Functional characterization
of risk variants. Harvard Review of Psychiatry. 2010; 18:67–74.10.3109/10673220903523532
[PubMed: 20047462]
Hamdi NR, Iacono WG. Lifetime prevalence and co-morbidity of externalizing disorders and
depression in prospective assessment. Psychological Medicine. 2014; 44:315–324.10.1017/
S0033291713000627 [PubMed: 23590946]
Hastie, T.; Tibshirani, R.; Friedman, J.; Hastie, T.; Friedman, J.; Tibshirani, R. The elements of
statistical learning. Vol. 2. New York, NY: Springer; 2009.
Heitland I, Kenemans JL, Oosting RS, Baas JM, Bocker KB. Auditory event-related potentials (P3a,
P3b) and genetic variants within the dopamine and serotonin system in healthy females.
Behavioural Brain Research. 2013; 249:55–64.10.1016/j.bbr.2013.04.013 [PubMed: 23619133]
Hesselbrock V, Bauer L, O’Connor S, Gillen R. Reduced P300 amplitude in relation to family history
of alcoholism and antisocial personality disorder among young men at risk for alcoholism. Alcohol
and Alcoholism Supplement. 1993; 2:95–100.
Hesselbrock V, Begleiter H, Porjesz B, O’Connor S, Bauer L. P300 event-related potential amplitude
as an endophenotype of alcoholism—Evidence from the collaborative study on the genetics of
alcoholism. Journal of Biomedical Science. 2001; 8:77–82. [PubMed: 11173979]
Hicks BM, Bernat E, Malone SM, Iacono WG, Patrick CJ, Krueger RF, McGue M. Genes mediate the
association between P3 amplitude and externalizing disorders. Psychophysiology. 2007; 44:98–
105.10.1111/j.1469-8986.2006.00471.x [PubMed: 17241145]
Hicks BM, Schalet BD, Malone SM, Iacono WG, McGue M. Psychometric and genetic architecture of
substance use disorder and behavioral disinhibition measures for gene association studies.
Behavior Genetics. 2011; 41:459–475.10.1007/s10519-010-9417-2 [PubMed: 21153693]
Hill SY. Biological phenotypes associated with individuals at high risk for developing alcohol-related
disorders: Part 1. Addiction Biology. 2000; 5:5–22.10.1080/13556210071234 [PubMed:
20575816]
Hill SY, Jones BL, Holmes B, Steinhauer SR, Zezza N, Stiffler S. Cholinergic receptor gene (CHRM2)
variation and familial loading for alcohol dependence predict childhood developmental trajectories
of P300. Psychiatry Research. 2013; 209:504–511.10.1016/j.psychres.2013.04.027 [PubMed:
23747232]
Hill SY, Locke J, Zezza N, Kaplan B, Neiswanger K, Steinhauer SR, Xu J. Genetic association
between reduced P300 amplitude and the DRD2 dopamine receptor A1 allele in children at high
risk for alcoholism. Biological Psychiatry. 1998; 43:40–1.10.1016/S0006-3223(97)00203-5
[PubMed: 9442343]
Hill SY, Shen S, Zezza N, Hoffman EK, Perlin M, Allan W. A genome wide search for alcoholism
susceptibility genes. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics.
2004; 128:102–113.
Hill SY, Steinhauer S, Lowers L, Locke J. Eight-year longitudinal follow-up of P300 and clinical
outcome in children from high-risk for alcoholism families. Biological Psychiatry. 1995; 37:823–
827. [PubMed: 7647169]
Psychophysiology. Author manuscript; available in PMC 2015 December 01.
MALONE et al.
Page 19
NIH-PA Author Manuscript
NIH-PA Author Manuscript
NIH-PA Author Manuscript
Hill SY, Steinhauer SR, Zubin J, Baughman T. Event-related potentials as markers for alcoholism risk
in high density families. Alcoholism: Clinical and Experimental Research. 1988; 12:545–554.
Houston RJ, Ceballos NA, Hesselbrock VM, Bauer LO. Borderline personality disorder features in
adolescent girls: P300 evidence of altered brain maturation. Clinical Neurophysiology. 2005;
116:1424–1432.10.1016/j.clinph.2005.01.013 [PubMed: 15978505]
Iacono WG, Carlson SR, Malone SM, McGue M. P3 event-related potential amplitude and the risk for
disinhibitory disorders in adolescent boys. Archives of General Psychiatry. 2002; 59:750–757.
[PubMed: 12150652]
Iacono WG, Carlson SR, Taylor J, Elkins IJ, McGue M. Behavioral disinhibition and the development
of substance use disorders: Findings from the Minnesota Twin Family Study. Development and
Psychopathology. 1999; 11:869–900. [PubMed: 10624730]
Iacono WG, Malone SM. Developmental endophenotypes: Indexing genetic risk for substance abuse
with the P300 brain event-related potential. Child Development Perspectives. 2011; 5:239–
247.10.1111/j.1750-8606.2011.00205.x [PubMed: 22247735]
Iacono WG, Malone SM, McGue M. Substance use disorders, externalizing psychopathology, and
P300 event-related potential amplitude. International Journal of Psychophysiology. 2003; 48:147–
178. [PubMed: 12763572]
Iacono WG, Malone SM, Vaidyanathan U, Vrieze SI. Genome-wide scans of genetic variants for
psychophysiological endophenotypes: A methodological overview. Psychophysiology. 2014 (in
press).
Iacono, WG.; Patrick, CJ. Employing polygraph assessment. In: Weiner, IB.; Otto, RK., editors. The
handbook of forensic psychology. 4. Hoboken, NJ: John Wiley & Sons; 2014.
Jeon YW, Polich J. Meta-analysis of P300 and schizophrenia: Patients, paradigms, and practical
implications. Psychophysiology. 2003; 40:684–701. [PubMed: 14696723]
Johnson JP, Muhleman D, MacMurray J, Gade R, Verde R, Ask M, Comings DE. Association between
the cannabinoid receptor gene (CNR1) and the P300 event-related potential. Molecular Psychiatry.
1997; 2:169–171. [PubMed: 9106243]
Johnstone SJ, Barry RJ. Auditory event-related potentials to a two-tone discrimination paradigm in
attention deficit hyperactivity disorder. Psychiatry Research. 1996; 64:179–192. [PubMed:
8944396]
Jones KA, Porjesz B, Almasy L, Bierut L, Dick D, Goate A, Begleiter H. A cholinergic receptor gene
(CHRM2) affects event-related oscillations. Behavior Genetics. 2006; 36:627–639. [PubMed:
16823639]
Justus AN, Finn PR, Steinmetz JE. P300, disinhibited personality, and early-onset alcohol problems.
Alcoholism: Clinical & Experimental Research. 2001; 25:1457–1466.
Kang SJ, Rangaswamy M, Manz N, Wang JC, Wetherill L, Hinrichs T, Dick D. Family-based
genome-wide association study of frontal theta oscillations identifies potassium channel gene
KCNJ6. Genes, Brain and Behavior. 2012; 11:712–719.
Katsanis J, Iacono WG, McGue MK, Carlson SR. P300 event-related potential heritability in
monozygotic and dizygotic twins [published erratum appears in Psychophysiology 1998 Jan;35(1):
133]. Psychophysiology. 1997; 34:47–58. [PubMed: 9009808]
Kendler KS, Heath AC, Martin NG, Eaves LJ. Symptoms of anxiety and symptoms of depression:
Same genes, different environments? Archives of General Psychiatry. 1987; 44:451–457.
[PubMed: 3579496]
Kenemans JL, Kähkönen S. How human electrophysiology informs psychopharmacology: From
bottom-up driven processing to top-down control. Neuropsychopharmacology. 2010; 36:26–51.
[PubMed: 20927044]
Keyes MA, Malone SM, Elkins IJ, Legrand LN, McGue M, Iacono WG. The enrichment study of the
Minnesota Twin Family Study: Increasing the yield of twin families at high risk for externalizing
psychopathology. Twin Research and Human Genetics. 2009; 12:489–501.10.1375/twin.12.5.489
[PubMed: 19803776]
Kim MS, Kim JJ, Kwon JS. Frontal P300 decrement and executive dysfunction in adolescents with
conduct problems. Child Psychiatry and Human Development. 2001; 32:93–106. [PubMed:
11758881]
Psychophysiology. Author manuscript; available in PMC 2015 December 01.
MALONE et al.
Page 20
NIH-PA Author Manuscript
NIH-PA Author Manuscript
NIH-PA Author Manuscript
Kolev V, Demiralp T, Yordanova J, Ademoglu A, Isoglu-Alkaç Ü. Time-frequency analysis reveals
multiple functional components during oddball P300. NeuroReport. 1997; 8:2061–2065. [PubMed:
9223102]
Koskinen SM, Ahveninen J, Kujala T, Kaprio J, O’Donnell BF, Osipova D, Rose RJ. A longitudinal
twin study of effects of adolescent alcohol abuse on the neurophysiology of attention and
orienting. Alcoholism: Clinical and Experimental Research. 2011; 35:1339–1350.
Li X, Basu S, Miller MB, Iacono WG, McGue M. A rapid generalized least squares model for a
genome-wide quantitative trait association analysis in families. Human Heredity. 2011; 71:67–
82.10.1159/000324839 [PubMed: 21474944]
Lin CH, Yu YW, Chen TJ, Tsa SJ, Hong CJ. Association analysis for dopamine D2 receptor Taq1
polymorphism with P300 event-related potential for normal young females. Psychiatric Genetics.
2001; 11:165–168. [PubMed: 11702060]
Liu JZ, Mcrae AF, Nyholt DR, Medland SE, Wray NR, Brown KM. A versatile gene-based test for
genome-wide association studies. American Journal of Human Genetics. 2010; 87:139–145.
[PubMed: 20598278]
Losada, RA. Practical FIR filter design in MATLAB. White paper. 2004. Retrieved from http://
www.mathworks.com/matlabcentral/fileexchange/19880-digital-filters-with-matlab
Malone SM, Iacono WG, McGue M. Event-related potentials and comorbidity in alcohol-dependent
adult males. Psychophysiology. 2001; 38:367–376. [PubMed: 11352124]
Mathalon DH, Ford JM, Pfefferbaum A. Trait and state aspects of P300 amplitude reduction in
schizophrenia: A retrospective longitudinal study. Biological Psychiatry. 2000; 47:434–449.
[PubMed: 10704955]
McArdle JJ, Goldsmith HH. Alternative common factor models for multivariate biometric analyses.
Behavior Genetics. 1990; 20:569–608. [PubMed: 2288547]
McGue M, Zhang Y, Miller MB, Basu S, Vrieze S, Hicks B, Iacono WG. A genome-wide association
study of behavioral disinhibition. Behavior Genetics. 2013; 43:363–373. [PubMed: 23942779]
Miller GA, Rockstroh B. Endophenotypes in psychopathology research: Where do we stand? Annual
Review of Clinical Psychology. 2013; 9:177–213.10.1146/annurev-clinpsy-050212-185540
Miller MB, Basu S, Cunningham J, Eskin E, Malone SM, Oetting WS, McGue M. The Minnesota
Center for Twin and Family Research genome-wide association study. Twin Research and Human
Genetics. 2012; 15:767–774. [PubMed: 23363460]
Mulert C, Juckel G, Giegling I, Pogarell O, Leicht G, Karch S, Rujescu D. A Ser9Gly polymorphism
in the dopamine D3 receptor gene (DRD3) and event-related P300 potentials.
Neuropsychopharmacology. 2006; 31:1335–1344.10.1038/sj.npp.1300984 [PubMed: 16395310]
Mulert C, Pogarell O, Juckel G, Rujescu D, Giegling I, Rupp D, Möller HJ. The neural basis of the
P300 potential. European Archives of Psychiatry and Clinical Neuroscience. 2004; 254:190–198.
[PubMed: 15205974]
Neale, MC.; Boker, SM.; Xie, G.; Maes, HH. Mx: Statistical modeling. 6. Richmond, VA: Department
of Psychiatry, Virginia Commonwealth University; 2003.
Neuhaus AH, Goldberg TE, Hassoun Y, Bates JA, Nassauer KW, Sevy S, Malhotra AK. Acute
dopamine depletion with branched chain amino acids decreases auditory top-down event-related
potentials in healthy subjects. Schizophrenia Research. 2009; 111:167–173.10.1016/j.schres.
2009.03.023 [PubMed: 19356906]
Nieuwenhuis S, Aston-Jones G, Cohen JD. Decision making, the P3, and the locus coeruleus–
norepinephrine system. Psychological Bulletin. 2005; 131:510–532. [PubMed: 16060800]
Patrick CJ, Bernat EM, Malone SM, Iacono WG, Krueger RF, McGue M. P300 amplitude as an
indicator of vulnerability to externalizing psychopathology in adolescent males.
Psychophysiology. 2006; 43:84–92.10.1111/j.1469-8986.2006.00376.x [PubMed: 16629688]
Perlman G, Markin A, Iacono WG. P300 amplitude reduction is associated with early-onset and lateonset pathological substance use in a prospectively studied cohort of 14 year-old adolescents.
Psychophysiology. 2013; 50:974–982.10.1111/psyp.12081
Polich J, Corey-Bloom J. Alzheimers disease and P300: Review and evaluation of task and modality.
Current Alzheimer Research. 2005; 2:515–525. [PubMed: 16375655]
Psychophysiology. Author manuscript; available in PMC 2015 December 01.
MALONE et al.
Page 21
NIH-PA Author Manuscript
NIH-PA Author Manuscript
NIH-PA Author Manuscript
Polich J, Criado JR. Neuropsychology and neuropharmacology of P3a and P3b. International Journal
of Psychophysiology. 2006; 60:172–185. [PubMed: 16510201]
Polich J, Pollock VE, Bloom FE. Meta-analysis of P300 amplitude from males at risk for alcoholism.
Psychological Bulletin. 1994; 115:55–73. [PubMed: 8310100]
Porjesz B, Rangaswamy M, Kamarajan C, Jones KA, Padmanabhapillai A, Begleiter H. The utility of
neurophysiological markers in the study of alcoholism. Clinical Neurophysiology. 2005;
116:993–1018.10.1016/j.clinph.2004.12.016 [PubMed: 15826840]
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components
analysis corrects for stratification in genome-wide association studies. Nature Genetics. 2006;
38:904–909. [PubMed: 16862161]
R Development Core Team. R: A language and environment for statistical computing. R: Foundation
for Statistical Computing; Vienna, Austria: 2010. Retrieved from http://www.R-project.org
Realmuto G, Begleiter H, Odencrantz J, Porjesz B. Event-related potential evidence of dysfunction in
automatic processing in abstinent alcoholics. Biological Psychiatry. 1993; 33:594–601. [PubMed:
8329490]
Rodriguez Holguin S, Porjesz B, Chorlian DB, Polich J, Begleiter H. Visual P3a in male alcoholics
and controls. Alcoholism: Clinical & Experimental Research. 1999; 23:582–591.
Rousseeuw, P.; Crous, C.; Todorov, V.; Ruckstuhl, A.; Salibian-Barrera, M.; Verbeke, T.; Maechler,
M. robustbase: Basic Robust Statistics. 2011. (Version R package version 0.7–3). Retrieved from
http://CRAN.R-project.org/package=robustbase
Shaikh M, Hall MH, Schulze K, Dutt A, Li K, Williams I, Bramon E. Effect of DISC1 on the P300
waveform in psychosis. Schizophrenia Bulletin. 2013; 39:161–167.10.1093/schbul/sbr101
[PubMed: 21878470]
Speed D, Hemani G, Johnson MR, Balding DJ. Improved heritability estimation from genome-wide
SNPs. American Journal of Human Genetics. 2012; 91:1011–1021.10.1016/j.ajhg.2012.10.010
[PubMed: 23217325]
Steinhauer SR, Hill SY, Zubin J. Event-related potentials in alcoholics and their first-degree relatives.
Alcohol. 1987; 4:307–314. [PubMed: 3620100]
Strobel A, Debener S, Anacker K, Muller J, Lesch KP, Brocke B. Dopamine D4 receptor exon III
genotype influence on the auditory evoked novelty P3. NeuroReport. 2004; 15:2411–
2415.10.1097/00001756-200410250-00022 [PubMed: 15640766]
Szuromi B, Czobor P, Komlosi S, Bitter I. P300 deficits in adults with attention deficit hyperactivity
disorder: A meta-analysis. Psychological Medicine. 2011; 41:1529–1538.10.1017/
S0033291710001996 [PubMed: 20961477]
Turetsky BI, Calkins ME, Light GA, Olincy A, Radant AD, Swerdlow NR. Neurophysiological
endophenotypes of schizophrenia: The viability of selected candidate measures. Schizophrenia
Bulletin. 2007; 33:69–94.10.1093/schbul/sbl060 [PubMed: 17135482]
van Beijsterveldt CEM, van Baal GCM. Twin and family studies of the human electroencephalogram:
A review and a meta-analysis. Biological Psychology. 2002; 61:111–138. [PubMed: 12385672]
van Beijsterveldt CEM, van Baal GCM, Molenaar PCM, Boomsma DI, de Geus EJC. Stability of
genetic and environmental influences on P300 amplitude: A longitudinal study in adolescent
twins. Behavior Genetics. 2001; 31:533–543. [PubMed: 11838531]
van der Stelt O, Geesken R, Gunning WB, Snel J, Kok A. P3 scalp topography to target and novel
visual stimuli in children of alcoholics. Alcohol. 1998; 15:119–136. [PubMed: 9476958]
Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. American Journal
of Human Genetics. 2012; 90:7–24.10.1016/j.ajhg.2011.11.029 [PubMed: 22243964]
Williams JT, Begleiter H, Porjesz B, Edenberg HJ, Foroud T, Reich T, Blangero J. Joint multipoint
linkage analysis of multivariate qualitative and quantitative traits. II. Alcoholism and eventrelated potentials. American Journal of Human Genetics. 1999; 65:1148–1160.10.1086/302571
[PubMed: 10486334]
Wright MJ, Luciano M, Hansell NK, Montgomery GW, Geffen GM, Martin NG. QTLs identified for
P3 amplitude in a non-clinical sample: Importance of neurodevelopmental and neurotransmitter
genes. Biological Psychiatry. 2008; 63:864–873.10.1016/j.biopsych.2007.09.002 [PubMed:
17949694]
Psychophysiology. Author manuscript; available in PMC 2015 December 01.
MALONE et al.
Page 22
NIH-PA Author Manuscript
NIH-PA Author Manuscript
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: A tool for genome-wide complex trait analysis.
American Journal of Human Genetics. 2011; 88:76–82.10.1016/j.ajhg.2010.11.011 [PubMed:
21167468]
Yang, J.; Lee, SH.; Goddard, ME.; Visscher, PM. Genome-wide complex trait analysis (GCTA):
Methods, data analyses, and interpretations. In: Gondro, C.; van der Werf, J.; Hayes, B., editors.
Genome-wide association studies and genomic prediction. Vol. 1019. New York, NY: Humana
Press; 2013. p. 215-236.2013/06/13 ed
Yoon HH, Iacono WG, Malone SM, Bernat EM, McGue M. The effects of childhood disruptive
disorder comorbidity on P3 event-related brain potentials in preadolescents with ADHD.
Biological Psychology. 2008; 79:329–336.10.1016/j.biopsycho.2008.08.001 [PubMed:
18762228]
Yoon HH, Iacono WG, Malone SM, McGue M. Using the brain P300 response to identify novel
phenotypes reflecting genetic vulnerability for adolescent substance misuse. Addictive
Behaviors. 2006; 31:1067–1087.10.1016/j.addbeh.2006.03.036 [PubMed: 16644137]
Zhang H, Zhong X, Ye Y. Multivariate linkage analysis using the electrophysiological phenotypes in
the COGA alcoholism data [Supplement 1]. BMC Genetics. 2005; 6:S118.10.1186/1471-2156-6S1-S118 [PubMed: 16451575]
Zhang Y, Hauser U, Conty C, Emrich HM, Dietrich DE. Familial risk for depression and P3b
component as a possible neurocognitive vulnerability marker. Neuropsychobiology. 2007;
55:14–20. [PubMed: 17556848]
Zlojutro M, Manz N, Rangaswamy M, Xuei X, Flury-Wetherill L, Koller D, Kuperman S. Genomewide association study of theta band event-related oscillations identifies serotonin receptor gene
HTR7 influencing risk of alcohol dependence. American Journal of Medical Genetics Part B:
Neuropsychiatric Genetics. 2011; 156:44–58.
NIH-PA Author Manuscript
Psychophysiology. Author manuscript; available in PMC 2015 December 01.
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Figure 1.
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Illustration of the common pathway model for deriving genetic and environmental factor
scores. P3 amplitude at three sites (P3, Pz, and P4) is due to the influence of a common
factor, F, as well as site-specific or unique influences (U1–U3). The factor loadings, λ1 to λ3,
are estimates of the magnitude of the influence of F on the three measurements. To identify
the model, the variance of F is fixed at 1 and the factor mean is fixed at 0. Under the model,
amplitude at a given electrode site, j, equals P3j = αj + λj F + uj, where αj are the intercepts
for the amplitude measures (equivalent to the intercept in linear regression) and uj is the
unique (residual) influence on each amplitude measure. The common factor, F, is itself
influenced (caused) by additional latent factors: A, representing additive genetic influence;
C, representing common environmental influence; and E, representing specific
environmental influence. Using family data, in which genetic and environmental
correlations among family members are known, the magnitude of each latent variable’s
influence on F can be estimated, given standard assumptions. Factor variances are fixed at
one (not shown), so the total variance in F can be represented as a2 + c2 + e2, using standard
tracing rules for path analysis. Because our interest is in the common genetic influence on F,
we did not decompose the unique factors into A and C in addition to E.
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Figure 2.
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Q-Q plot for SNP associations with P3 amplitude. The 45° line gives the expected value
under the null distribution. The area shaded in gray corresponds to the 95% acceptance
region. Median and mean genomic control values are given in the inset in the upper left. N
refers to the number of SNPs, which is 10 fewer than the number of SNPs on the array
because there was no variation for 10 SNPs in this sample. Q-Q plots in GWAS give the
observed p values against the expected p values under the null distribution of no association,
although the additive inverse of the common log of p values (−log10[p value]) is used in
order to emphasize small p values. Because the vast majority of SNPs are not expected to be
associated with a given phenotype, observed p values should conform closely to their
expected values, falling on or very close to the 45° line depicted.
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Figure 3.
Q-Q plot for SNP associations with the genetic factor score. The 45° line gives the expected
value under the null distribution of no association. The area shaded in gray corresponds to
the 95% acceptance region. Median and mean genomic control values are given in the inset
in the upper left. N refers to the number of SNPs that were actually polymorphic in this
sample, which is smaller than the P3 sample because subjects without amplitude values for
all three parietal electrodes were dropped. Q-Q plots in GWAS give the observed p values
against the expected p values under the null distribution, although the additive inverse of the
common log of p values (−log10[p value]) is used in order to emphasize small p values.
Because the vast majority of SNPs are not expected to be associated with a given phenotype,
observed p values should conform closely to their expected values, falling on or very close
to the 45° line depicted.
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Figure 4.
Manhattan plot of individual SNP associations with P3 amplitude. Manhattan plots also
depict the distribution of −log10(p values) but are ordered by SNP location on a
chromosome, which provides information about the location of any SNPs associated with
small p values. The horizontal line at 7.3 indicates the genome-wide significance level
(5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate
“suggestive” significance.
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Figure 5.
Manhattan plot of individual SNP associations with the genetic factor score. Manhattan plots
also depict the distribution of −log10(p values) but are ordered by SNP location on a
chromosome, which provides information about the location of any SNPs associated with
small p values. The horizontal line at 7.3 indicates the genome-wide significance level
(5E-08). The horizontal line at 5 indicates E-05, which is sometimes used to indicate
“suggestive” significance.
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Table 1
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Within-Family Correlations for P3 Amplitude
Pair
P3 amplitude
Common factor
MZ twins
.636
.662
DZ twins
.387
.413
Father-offspring
.193
.201
Mother-offspring
.257
.259
Mother-father
.005
.016
Note. “Factor” represents the common factor used for deriving genetic factor scores. P3 amplitude at the three parietal electrodes served as
indicators of the factor (cf. Figure 1). All amplitude measures were adjusted for effects of age, sex, generation, recording system, and PCs from
EIGENSTRAT.
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Table 2
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Heritability Estimates from Biometric Model-Fitting Analyses
Data
Family
Twins
Measure
A
C
E
P3 amplitude
.602 (.556–.643)
.000 (.000–.025)
.398 (.357–.442)
Common factor
.658 (.612–.699)
.000 (.000–.024)
.342 (.301–.386)
P3 amplitude
.497 (.324–.660)
.134 (.000–.295)
.369 (.331–.412)
Common factor
.537 (.359–.713)
.150 (.000–.317)
.313 (.276–.355)
Note. Point estimates are provided for each variance component, with 95% confidence intervals in parentheses. These are standardized and sum to
1. Data = ACE model was estimated based on the entire family or only the MZ and DZ twins; A = additive genetic influence; C = common or
shared environmental influence; E = unique or unshared environmental influence.
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Table 3
P3 amplitude
Threshold
Genetic factor score
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Unweighted GRM
Weighted GRM
.025
.190 (.186)
.251 (.256)
GCTA-Family
.274 (.199)
.249 (.321)
.050
.300 (.181)
.410 (.244)
.324 (.194)
.241 (.309)
.100
.279 (.179)
.366 (.240)
.370 (.193)
.273 (.305)
None
.570 (.020)
N/A
1.000 (.019)
N/A
.571 (.041)
Unweighted GRM
Weighted GRM
MALONE et al.
SNP Heritability of P3 Amplitude from GCTA Analyses
Note. Standard errors associated with each GCTA are in parentheses. Sample sizes for P3 range from 1,991 to 2,054 for the three subsets and equaled 4,166 for the full sample. They ranged from 1,806 to
1,852 for the genetic factor score, with 3,125 in the full sample. Threshold = genetic relatedness threshold used for selecting unrelated individuals; None = no threshold was imposed and all subjects were
included; Unweighted GRM = raw GRM; Weighted GRM = weights based on LD patterns to discount those SNPs in high LD (Speed et al., 2012). This is not used in the full sample, because the method
was designed for samples of unrelated individuals or samples containing a small number of large pedigrees (Doug Speed, e-mail communication, May 4, 2014). GCTA-Family = all subjects used to
estimate the total genetic variance related to P3 amplitude while simultaneously modeling, and thus controlling statistically, shared environmental influences. This was not relevant to the genetic factor
score, which, by definition, is not influenced by the family environment.
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