Glypican Gene GPC5 Participates in the Behavioral Response to Ethanol: Evidence from Humans, Mice, and Fruit Flies.
ABSTRACT Alcohol use disorders are influenced by many interacting genetic and environmental factors. Highlighting this complexity is the observation that large genome-wide association experiments have implicated many genes with weak statistical support. Experimental model systems, cell culture and animal, have identified many genes and pathways involved in ethanol response, but their applicability to the development of alcohol use disorders in humans is undetermined. To overcome the limitations of any single experimental system, the analytical strategy used here was to identify genes that exert common phenotypic effects across multiple experimental systems. Specifically, we (1) performed a mouse linkage analysis to identify quantitative trait loci that influence ethanol-induced ataxia; (2) performed a human genetic association analysis of the mouse-identified loci against ethanol-induced body sway, a phenotype that is not only comparable to the mouse ethanol-ataxia phenotype but is also a genetically influenced endophenotype of alcohol use disorders; (3) performed behavioral genetic experiments in Drosophila showing that fly homologs of GPC5, the member of the glypican gene family implicated by both the human and mouse genetic analyses, influence the fly's response to ethanol; and (4) discovered data from the literature demonstrating that the genetically implicated gene's expression is not only temporally and spatially consistent with involvement in ethanol-induced behaviors but is also modulated by ethanol. The convergence of these data provides strong support to the hypothesis that GPC5 is involved in cellular and organismal ethanol response and the etiology of alcohol use disorders in humans.
Article: Response to alcohol in daughters of alcoholics: a pilot study and a comparison with sons of alcoholics.[show abstract] [hide abstract]
ABSTRACT: Drinking, but not alcohol-dependent, 18-29-year-old daughters of alcoholics (n = 38) from the Collaborative Study on the Genetics of Alcoholism were compared to 75 family-history-positive (FHP) men from the same families, and 68 family-history-negative (FHN) male controls. Subjects received 0.75 ml/kg of ethanol (for women), 0.9 ml/kg of ethanol (for men), and placebo, each of which was consumed over 8 min on different occasions. The breath-alcohol concentrations (BrAC) and reactions to alcohol [using the Subjective High Assessment Scale (SHAS) and body sway measures] were evaluated over 210 min. The results indicate that, despite slightly higher BrAC values for the FHP men, on the SHAS the FHP women and the FHP men demonstrated significantly lower scores than the FHN male controls, although the values for FHP men and women did not differ. On body sway, the FHP men showed evidence of less alcohol-related increases than FHN men, and there was a trend in the same direction for FHP women, but only early in the session (e.g. at 60 min). Pilot data for 11 FHN women revealed higher scores for both SHAS and body sway at 60 min, compared to FHP women, but, perhaps reflecting the small number of subjects, the family history differences were not significant. Overall, the results in FHP women resemble those for FHP men, and suggest that a low level of response to alcohol might also be a characteristic of daughters of alcoholics.Alcohol and Alcoholism 35(3):242-8. · 2.95 Impact Factor
[show abstract] [hide abstract]
ABSTRACT: The level of response (LR) to alcohol is a genetically-influenced phenotype related to the alcoholism risk. Usually measured by evaluating psychological and physiological changes that follow the administration of alcohol, the heritability of LR is estimated to be between 0.4 and 0.6, and efforts are being made to find genes related to this phenotype. This paper presents data from a family-based genome with linkage analysis focusing on alcohol challenge determinants of LR. The subjects were 18-to-29-year-old sibling pairs with at least one parent who was alcohol-dependent and who had experience with alcohol but were not yet alcohol-dependent themselves. Both members of the sibling pairs were given oral alcohol challenges (0.75-0.90 ml/kg of ethanol for females and males, respectively), with LR established using the Subjective High Assessment Scale (SHAS) and changes in body sway (BS) repeatedly over a 3.5-hr. period. Blood samples from siblings and at least one parent were genotyped using 811 microsatellite markers, with results evaluated using several related variance component approaches as implemented in SOLAR for continuous traits. In addition, association was tested using single nucleotide polymorphisms (SNPs) within the KCNMA1, HTR7 and SLC18A2 genes that may relate to a finding on chromosome 10. Data were generated from 238 sib-pairs representing 365 individuals (41.6% were males) from 165 families. The most consistent results across methods and samples were observed for SHAS on chromosome 10 between 120 and 140 cM (with a maximum LOD score of 2.6 at 122 cM), and a second region of possible interest at 173 cM (LOD = 1.2). Statistical analysis with the KCNMA1, HTR7 and SLC18A2 genes, which lie in the support region of interest revealed no evidence for association after correction for multiple comparisons. These evaluations from the largest known alcohol challenge-based genetic study to date highlight the potential importance of genes on chromosome 10 as possible contributors to the low LR to alcohol as a risk factor for alcoholism.Alcoholism Clinical and Experimental Research 12/2005; 29(11):1976-82. · 3.34 Impact Factor
[show abstract] [hide abstract]
ABSTRACT: As with other genetically complex common psychiatric and medical conditions, multiple genetic and environmental components contribute to alcohol use disorders (AUDs), which can confound attempts to identify genetic components. Intermediate phenotypes are often more closely correlated with underlying biology and have often proven invaluable in genetic studies. Level of response (LR) to alcohol is an intermediate phenotype for AUDs, and individuals with a low LR are at increased risk. A high rate of concurrent alcohol and nicotine use and dependence suggests that these conditions may share biochemical and genetic mechanisms. Genetic association studies indicate that a genetic locus, which includes the CHRNA5-CHRNA3-CHRNB4 gene cluster, plays a role in nicotine consumption and dependence. Genetic association with alcohol dependence was also recently shown. We show here that two of the markers from the nicotine studies also show an association (multiple testing corrected P < 0.025) with several LR phenotypes in a sample of 367 siblings. Additional markers in the region were analyzed and shown to be located in a 250-kb expanse of high linkage disequilibrium containing three additional genes. These findings indicate that LR intermediate phenotypes have utility in genetic approaches to AUDs and will prove valuable in the identification of other genetic loci conferring susceptibility to AUDs.Proceedings of the National Academy of Sciences 01/2009; 105(51):20368-73. · 9.68 Impact Factor
Glypican Gene GPC5 Participates in the Behavioral
Response to Ethanol: Evidence from Humans, Mice,
and Fruit Flies
Geoff Joslyn,*,1Fred W. Wolf,* Gerry Brush,* Lianqun Wu,* Marc Schuckit,†and Raymond L. White*,‡
*Ernest Gallo Clinic and Research Center, Emeryville, California 94608,†Department of Psychiatry, University of
California, San Diego, San Diego, California 92161, and‡Department of Neurology, University of California, San
Francisco, San Francisco, California 94143
ABSTRACT Alcohol use disorders are influenced by many interacting genetic and environmental factors.
Highlighting this complexity is the observation that large genome-wide association experiments have
implicated many genes with weak statistical support. Experimental model systems, cell culture and animal,
have identified many genes and pathways involved in ethanol response, but their applicability to the
development of alcohol use disorders in humans is undetermined. To overcome the limitations of any single
experimental system, the analytical strategy used here was to identify genes that exert common phenotypic
effects across multiple experimental systems. Specifically, we (1) performed a mouse linkage analysis to
identify quantitative trait loci that influence ethanol-induced ataxia; (2) performed a human genetic
association analysis of the mouse-identified loci against ethanol-induced body sway, a phenotype that is not
only comparable to the mouse ethanol-ataxia phenotype but is also a genetically influenced endopheno-
type of alcohol use disorders; (3) performed behavioral genetic experiments in Drosophila showing that fly
homologs of GPC5, the member of the glypican gene family implicated by both the human and mouse
genetic analyses, influence the fly’s response to ethanol; and (4) discovered data from the literature dem-
onstrating that the genetically implicated gene’s expression is not only temporally and spatially consistent
with involvement in ethanol-induced behaviors but is also modulated by ethanol. The convergence of these
data provides strong support to the hypothesis that GPC5 is involved in cellular and organismal ethanol
response and the etiology of alcohol use disorders in humans.
The etiologyofalcohol use disorders (AUD) involvesthe interactionof
many genetic, environmental and behavioral factors (Bierut 2011;
Goldman et al. 2005; Kimura and Higuchi 2011). Two approaches
have been taken to reduce this complexity and make the genetics of
AUDs more experimentally tractable. Animal and cell models provide
the simplest, most flexible experimental systems, but their relevance to
the human disorder is a concern. In humans, efforts have been made
to develop endophenotypes; simpler, intermediate phenotypes predic-
tive of AUD development but more directly influenced by genetic
variation (Gottesman and Gould 2003). One prominent AUD endo-
phenotype is a low level of response (LR) to ethanol. Low LR is
genetically influenced (Heath and Martin 1992; Madden et al. 1995;
Martin et al. 1981) and predicative of later AUD development (Quinn
and Fromme 2011; Schuckit and Smith 2000), and independent stud-
ies have demonstrated genetic association of alcohol dependence and
ethanol LR to several polymorphisms, including the same nicotinic
receptor locus (Joslyn et al. 2008; Wang et al. 2008).
Recent genome-wide association (GWA) studies have highlighted
the genetic complexity of AUDs. Large surveys involving hundreds to
thousands of subjects identified a single intergenic locus marked by
rs7590720 that barely reached statistical significance after correcting
for multiple tests (Bierut et al. 2010; Edenberg et al. 2010; Joslyn et al.
2010; Treutlein et al. 2009). A recent meta-analysis of over 45,000
individuals uncovered a different marker, rs6943555 in the AUTS2
gene, which just reaches statistical significance (Schumann et al. 2011).
Copyright © 2011 Joslyn et al
Manuscript received August 31, 2011; accepted for publication October 26, 2011
This is an open-access article distributed under the terms of the Creative
Commons Attribution Unported License (http://creativecommons.org/licenses/
by/3.0/), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
Supporting information is available online at http://www.g3journal.org/lookup/
1Corresponding author: Ernest Gallo Clinic and Research Center, 5858 Horton
Street, Suite 200, Emeryville, CA 94608. E-mail: firstname.lastname@example.org
Volume 1|December 2011|
Such weak association results that explain a very small proportion of
the heritability have been observed in other genetically influenced
behavioral disorders. This has been interpreted to mean that the pop-
ulation genetic susceptibility is either expressed through a large num-
ber of alleles common in the population that individually confer a very
small risk and/or alleles that confer a larger individual risk but are rare
in the population and are thus not captured by association with the
common SNPs used in GWA studies (Manolio et al. 2009; Yang et al.
2010). Theoretically, discovering such susceptibility alleles will require
very large (hundreds of thousands) subject samples, combined with
whole genome sequencing, to enable the genotyping of rare alleles. In
practice, such surveys are years in the future: the samples must be
collected; sequencing technology needs to further mature; and analysis
methods need to be further developed.
Cell and animal models used to evaluate the genetic contributions
to AUD risk also face interpretational challenges. Cell culture models
have demonstrated that gene expression and cellular signaling are
altered by exposure to alcohol [reviewed by Moonat et al. (2010)].
While such in vitro studies enable experimental dissection of cellular
ethanol response, it is not clear that the cellular stress response to
a sublethal dose of ethanol shares mechanisms with human AUDs.
Rodent models have been widely used because these animals can be
taught complex alcohol-related behaviors that can then be manipu-
lated and evaluated by pharmacological, genetic, and electrophysio-
logical techniques [reviewed by Lovinger and Crabbe (2005) and
Stuber et al. (2010)]. However, the applicability of the rodent behav-
iors to the human condition is difficult to establish. Invertebrate mod-
els of ethanol response, specifically Drosophila melanogaster and
Caenorhabditis elegans, afford greater genetic manipulability than
rodents (Wolf and Heberlein 2003), enabling detailed genetic path-
ways influencing the behavior to be constructed. However, even more
so than rodent experiments, relating invertebrate behaviors to human
ethanol response is challenging.
In this article, we use a cross-species experimental analysis
approach, reasoning that if multiple experimental models suggest
the same mechanism, we have increased our chances of discovering an
etiologically significant component of AUDs. To this end, we first
analyzed mouse and human genetic data identifying a candidate gene
putatively involved in ethanol response in both species. We then
gained further experimental support for the candidate gene by
determining whether functionally disruptive alleles of the Drosophila
homologs alter the fly’s response to ethanol. Through this approach,
we identified GPC5, a member of the glypican gene family. Glypicans
are well characterized as modulators of developmental signaling path-
ways, such as Wnts, Hedgehogs, and Bone Morphogenic Proteins
[reviewed by Selleck (2000)]. Intriguingly, evidence is now accumu-
lating that these same pathways participate not only in neural de-
velopment but also in synaptic maintenance and plasticity, leading
to the hypothesis that they are involved in the expression of behavioral
phenotypes (Budnik and Salinas 2011; Farías et al. 2010; Ille and
Sommer 2005; Marqués 2005; Okerlund and Cheyette 2011; Salinas
MATERIALS AND METHODS
Finding a phenotype that is related to alcohol dependence (AD) and
comparable across humans, mice, and flies was central to this analysis.
While AD cannot be accurately modeled in mice and flies, acute
response to ethanol ingestion is similar between these organisms, and
in humans, alcohol response is predictive of later AUD development
(Erblich and Earleywine 1999; Pollock 1992; Quinn and Fromme
2011; Schuckit and Smith 1996; Schuckit and Smith 2000; Schuckit
et al. 2000; Schuckit et al. 1996). We compared locomotor responses
to ethanol: body sway in humans, ataxia in mice, and locomotor
activity in flies.
BXD ethanol-induced ataxia QTL interval mapping
BXD recombinant inbred mouse data were retrieved and analyses
were performed using the WebQTL (www.genenetwork.org) data and
analysis suite (Wang et al. 2003). GeneNetwork is an online data
repository with integrated computational tools designed to allow users
to explore complex genetic networks by integrating data from dif-
ferent sources. The BXD resource (a small subset of the collected
data) contains over 2800 measured phenotypes, which were contrib-
uted by hundreds of scientists, all linked to genotypes, providing an
easily accessible tool to genetically map traits of interest. Phenotypes
were selected by searching the BXD phenotype database using the
search strings “ethanol AND ataxia” as well as “alcohol AND ataxia,”
From the returned list of phenotypes, those that described ataxic re-
sponse to the administration of ethanol were selected for interval
Ethanol-induced ataxia QTL were mapped using the Interval
Mapping module. A likelihood ratio statistic (LRS) was calculated for
each marker using 1000 permutations. LRS values were converted to
the log of the odds (LOD) ratio to be consistent with human genetic
reporting norms: LOD ¼ LRS/4.61.
Mouse human synteny and marker selection
Mouse chromosomal intervals yielding suggestive linkage for an
ethanol-induced ataxia phenotype had their human syntenic counter-
part identified using data collected and presented by the comparative
genomics functions of Ensembl (www.ensembl.org). The proximal-
and distal-most markers exhibiting suggestive linkage definedthe limits
of mouse ethanol ataxia QTL. Using Ensembl’s mouse genome refer-
ence NCBIM37, all genes, either wholly or partially within the defined
QTL, were identified. Using the Ensembl comparative genomics
module, the syntenic human genes (genome reference GRCh37) were
identified. Human SNPs were selected for analysis if located within
a syntenic gene as defined by its transcript plus 100 kb of flanking
sequence. All analyzed genotypes were derived from the Illumina
HumanCNV370-Duo DNA Analysis BeadChip.
The human subjects genotyped and phenotyped in this study are part
of the San Diego Sibling Pair investigation. Subjects were collected
under a protocol approved by the Human Subjects Protection
Committee of the University of California, San Diego (UCSD) and
is described in greater detail elsewhere (Schuckit et al. 2005; Wilhelm-
sen et al. 2003). Participants, ages 18–25, were selected from UCSD
students who responded to a randomly mailed questionnaire and who
met the following criteria: (1) had a minimum family size of two
siblings, male or female, 18–25 years old; (2) had consumed alcohol
but had NEVER BEEN alcohol dependent; (3) had at least one parent
who met the criteria for alcohol dependence using the Diagnostic and
Statistical Manual of Mental Disorders (DSM-IV-TR) (American Psy-
chiatric Association 2000); (4) had never met criteria for antisocial
personality disorder or any DSM-IV Axis I psychiatric condition. In
addition to collecting further questionnaire data, selected subjects were
given an alcohol challenge in a laboratory setting to measure their
responses to an approximately 0.75 ml/kg of ethanol consumed within
| G. Joslyn et al.
8–10 min (dose was weight- and sex-adjusted to produce similar
blood alcohol levels). Body sway was measured using a harness at-
tached to the chest at the level of the axilla from which two perpen-
dicular ropes extended forward and to the left side, passing over
pulleys that measured the number of centimeters of movement
per minute as gathered through three 1 min evaluations at each time
point (Schuckit and Gold 1988). Anterior-posterior body sway (BSA)
at the time of peak alcohol effect (60 min) was tested for genetic
marker association in our analyses. To reduce ethnic heterogeneity,
only Caucasian subjects were analyzed. In total, 367 subjects, 134
males and 233 females, were selected from 186 independent families:
38 singleton families; 121 two-sibling families; 23 three-sibling fami-
lies; 3 four-sibling families; and a single six-sibling family. The actual
number of subjects per marker-phenotype analysis varied slightly be-
cause of missing genotype and phenotype data.
Subjects were not consented for release to public databases. Data
may be available for sharing under a confidential agreement between
a requestor and the authors.
DNA preparation and genotyping
DNA was extracted from blood specimens within 5 days of the draw
using Gentra Puregene reagents and protocols (http://www1.qiagen.
com). Extracted DNA was quantified using the Pico Green method
(Molecular Probes/Invitrogen), and all DNA solutions were normal-
ized to a common concentration for genotyping assays. Genotyping
was carried out using the Illumina HumanCNV370-Duo DNA Anal-
ysis BeadChip. Genotype generation and quality control were per-
formed by deCODE Genotyping Service (http://www.decode.com/).
Human association analysis
Because BSA is not normally distributed (skewness and kurtosis: 1.29
and 2.28), it was corrected for non-normality using the Box-Cox
transformation (Box and Cox 1964; Venables and Ripley 2002). The
scores were then scaled to mean ¼ 0 and SD ¼ 1 to make them
comparable essentially as Z-scores.
Association tests were performed in R (R Development Core Team
2011) with the lmekin function of the kinship package (Atkinson and
Therneau 2008). This function provides a linear mixed-effects model
whereby the genetic relatedness among individuals (based on the kin-
ship coefficient) is incorporated into the covariance structure of the
random effects. This adjusts the model fit and compensates for the
fact that the siblings are related and, therefore, so are their genotypes
and phenotypes, which would otherwise violate the assumption of
independent observations in a linear regression model.
The fixed-effects portion of the model was used for testing the
association between a single SNP and BSA. The SNP was treated in R
as a factor with three levels (categories), which is similar to coding the
major homozygotes as 1/1, the heterozygotes as 0/1, and the minor
homozygotes as 0/0. For each SNP, two tests were performed to
determine if a given genotype class differed in its average phenotype
from the other genotypes. The test compared (a) the major
homozygotes (1/1) and the heterozygotes (0/1), and (b) the major
homozygotes (1/1) and the minor homozygotes (0/0). Tests (a) and
(b) were performed by testing the significance of the regression
coefficients from zero, of the heterozygote term and the minor
homozygote term, while holding the major homozygote coefficient
constant at zero. For rare SNPs, minor homozygote individuals are
often not present in the sample, in which case, only test (a) was
available for the SNP. In all cases, the Wald test was used to examine
the significance of the regression coefficients.
The large number of tests performed in the analysis required that
the nominal P-values be adjusted for multiple testing. For this, false
discovery rate (FDR) q-values were calculated using the method de-
scribed by Storey and Tibshirani (2003).
Drosophila behavioral experiments
Drosophila strains were maintained on standard cornmeal/molasses/
yeast media at 25?C and 70% humidity with an approximately 16-hr/
8-hr light/dark schedule. All strains were outcrossed for five genera-
tions to the Berlin genetic background strain carrying the w1118eye-
color marker. Strain sources were dallyMB950(MB00950: Bloomington
Stock Center), dally80(Xinhua Lin), dlpf03537(Exelixis/Harvard), and
Ethanol-stimulated locomotor activity and sedation kinetics were
determined as previously described (Kong et al. 2010; Rothenfluh
et al. 2006; Wolf et al. 2002). Ethanol vapor was mixed with a humid-
ified air stream at set ratios to achieve a final flow rate of 5.5 L/min
and was delivered to individual exposure chambers, each containing
a group of 20 genetically identical flies. For locomotor activity, flies
were filmed just prior to and during a 25 min exposure to 47% ethanol
vapor. For sedation kinetics, flies were exposed to 67% ethanol vapor,
and the number of flies that lost the ability to right themselves was
counted at 3 min intervals. All behavioral experiments were repeated
on separate days with flies derived from separate crosses.
Figure 1 Flowchart of the experimental strategy leading to the
identification of GPC5 as a participant in ethanol-induced behaviors.
Volume 1December 2011|Glypican Genes and Ethanol Response|
Quantitative PCR was carried out on reverse-transcribed RNA
from fly heads according to the manufacturer’s instructions (Applied
Biosystems) on an ABI PRISM 7900 Sequence Detection System,
using expression levels of RpL32 as a standard to normalize sample
concentrations. TaqMan probesets (Applied Biosystems) used in this
study were dally: Dm01822385_g1, dlp: Dm01798599_g1, and RpL32:
Ethanol absorption was measured by exposing groups of 25
flies to either ethanol vapor (47%) or humidified air for 30 min.
Flies were immediately frozen on dry ice, and the ethanol con-
centration in whole-fly homogenates was measured with an alcohol
dehydrogenase–based spectrophotometric assay (Diagnostic Chemi-
Data analysis followed the logic presented in Figure 1. First, mouse
ethanol response QTL were mapped in the recombinant inbred strain
BXD, a strain constructed from the parental strains C57/B6 and DBA,
which has been extensively phenotyped for alcohol-related behaviors.
We limited our analysis to ethanol-induced ataxia as our ultimate goal
was to compare the BXD result with human data. Ethanol-induced
ataxia is a phenotypic measure held in common between mouse and
human. All BXD data and QTL interval mapping were performed
using the tools available on the genenetwork.org website as described
in Materials and Methods. Twelve phenotypes comparable to the
human body sway phenotype were present in the GeneNetwork da-
tabase and were analyzed (Table 1).
Ethanol-induced ataxia QTL were mapped using the Interval
Mapping module of GeneNetwork. LRS/LOD scores were calculated
using 1000 permutations. The algorithm defines two statistical
thresholds, suggestive and significant, as defined by Lander and Kru-
glyak (1995). While no significant linkages were discovered, 10 of the
12 phenotypes defined 13 suggestive ethanol-induced ataxia QTL (Ta-
All suggestive loci had their syntenic human region tested for
genetic association to ethanol-induced anterior-posterior body sway
(BSA). Using the comparative genomics functions of Ensembl, all
human genes syntenic with the mouse loci were identified. Human
subjects had been genotyped previously using the Illumina
HumanCNV370-Duo genotyping array. Marker SNPs were chosen
for analysis based on location within the genomic limits for the
gene’s transcript plus 100 kb flanking the transcription start and
stop sites. We thus had markers spanning the transcripts of the
n Table 1 BXD phenotypes analyzed for genetic linkage
Ethanol response (2.5 g/kg ip), ataxia, screen test sensitivity measured as
the latency to fall, saline response minus ethanol response [seconds] by
K. E. Browman and colleagues
Ethanol response (dose, ip), ataxia on grid test, 2 to 10 min after injection
[errors/run] by J. C. Crabbe and colleagues
Ethanol sensitivity, initial ethanol-induced ataxia, onset threshold [mg/kg]
by E. J. Gallaher and colleagues
Ethanol response (dose route), maximal threshold to ethanol-induced ataxia
[mg/ml] by E. J. Gallaher and colleagues
Ethanol response (1.75 g/kg ip), initial sensitivity measured by blood
ethanol concentration (BEC, retrobulbar bleed) at loss of balance using a
dowel test (BEC time 0) [mg % ethanol] by S. L. Kirstein and colleagues
Ethanol response (1.75 mg/kg ip), time to ataxia measured as loss of
balance using a dowel test (Loss corresponds to BEC time 0) [min] by
S. L. Kirstein and colleagues
Ethanol response (1.75 mg/kg ip), duration of ataxia following the first
ethanol injection using a dowel test (Regain Test 1 corresponds to BEC1)
[min] by S. L. Kirstein and colleagues
Ethanol response (2 g/kg ip), acute ataxia, difference between day 3 (first
ethanol treatment) and day 2 (saline baseline) in the chronic drug group
[n grid test errors/10 min test] by T. J. Phillips and colleagues
Ethanol response (2 g/kg ip), acute ataxia measured using grid test
(Accuscan activity monitor), difference between day 11 (first and only
ethanol treatment) and day 2 (saline baseline) in the chronic saline group
[n errors/activity counts/10 min test] by T. J. Phillips and colleagues
Ethanol response (2 g/kg ip), difference in ataxia using grid test (Accuscan
activity monitor) between acute ethanol on day 3 (first ethanol treatment)
and day 2 (saline baseline) in the chronic ethanol group [n errors/activity
counts/10 min test] by T. J. Phillips and colleagues
Ethanol response (2 g/kg ip), difference in ataxia using grid test (Accuscan
activity monitor) between injection on day 11 (first and only ethanol
treatment) and day 2 (saline baseline) in the chronic saline group [errors/
activity counts/10 min test] by T. J. Phillips and colleagues
Ethanol response (1.75 g/kg ip), ataxia measured by rotarod performance,
difference from saline baseline (supplementary data to Brigman et al.) by
J. L. Brigman and colleagues
Data can be retrieved at genenetwork.org using the GeneNetwork ID. The PMID links to the primary publication describing the experimental protocol (www.ncbi.nlm.
|G. Joslyn et al.
syntenic genes plus possible regulatory sequences flanking the tran-
scribed DNA. Each marker SNP was tested for association to BSA
using a regression model. False discovery rate q-values were calcu-
lated by treating each syntenic locus as a separate hypothesis. The
results for each marker are presented in supporting information,
Table S1, and the top associated markers are presented in Table 2.
GPC5 (glypican 5) became the gene of interest due to the statistical
strength of the linkage data in mice and the association data in
humans. The BXD linkage yielded a LOD score of 3.9 (P ?
1.25E205), slightly shy of the genome-wide significance value of 4.3
(P ? 5.0E206). Similarly, the multiple test corrected significance
value for association to BSA was q ¼ 0.08 (nominal P ¼
7.54E205) very close to the standard q ¼ 0.05 threshold for signifi-
cance. Taken together, these data implicate GPC5 as a candidate QTL
that influences ethanol-induced ataxia.
To further investigate glypican involvement in ethanol response,
mutant alleles of the Drosophila glypican genes dally and dlp were
investigated to determine whether they alter Drosophila ethanol re-
sponse. The six mammalian glypican genes have two homologs in the
Drosophila genome. Evolutionarily, dally is thought to be orthologous
to the human glypican gene paralogs GPC3 and GPC5, whereas dlp is
orthologous to the other four human glypican paralogs (Filmus et al.
2008). We tested both genes in Drosophila, reasoning that homologous
gene function as measured by something as complex as behavior may
not precisely track with the predicted evolutionary lineage of a gene.
Partial loss-of-function alleles of dlp and dally were used in ethanol
behavioral assays because they were less afflicted with the develop-
mental defects and lethality caused by strong loss-of-function alleles.
Stocks containing partial loss-of-function alleles, either dlpf03537or
dallyMB950, were first outcrossed onto the Berlin genetic background
to normalize the genetic background between experimental and con-
trol flies. Each allele contains a transposon inserted into the first in-
tron (Figure 2, A and E). dlpf03537was found to be weakly viable and
sterile as a homozygote but without overt morphological or behavioral
phenotypes. Transcript levels of dlp in fly heads were reduced by 40%
in dlpf03537heterozygotes (P ¼ 0.0363, n ¼ 3 replicates, two-sample t-
test); dlpf03537failed to complement dlp1lethality, strong evidence that
dlpf03537is a dlp allele. dallyMB950was found to be homozygous viable
and fertile, and it also had no overt morphological or behavioral
phenotypes. Heads of homozygous dallyMB950flies were shown to
have a 27% reduction in dally transcript (P ¼ 0.0218, n ¼ 5 replicates,
two-sample t-test), but dallyMB950complemented the sterility and
wing vein morphology phenotypes of the strong loss-of-function allele
dally80. Given the molecular evidence that dallyMB950is an allele of
dally, a transposon inserted into the gene coupled with reduced tran-
script production, we conclude that intragenic complementation is
occurring because the dallyMB950allele provides enough function to
complement the phenotype of the strong loss-of-function allele.
Flies carrying the dallyMB950and dlpf03537alleles were evaluated for
alterations in their response to ethanol. Three assays were performed:
locomotor stimulation in response to moderate concentrations of
ethanol; sedation in response to higher concentrations of ethanol;
and the acquisition of rapid tolerance to ethanol sedation. Flies ex-
posed to a continuous stream of moderate concentration ethanol
n Table 2 Summary of BXD linkage and human association of the syntenic locus
Mouse ChromosomePhenotype Max LOD
Association q-valueHuman Genes
2101442.43 635rs6077309 0.1510
All loci with “suggestive” linkage to at least one BXD ethanol-induced ataxia phenotype are listed. The first three columns describe the BXD linkage (see Table 1 to
decipher phenotype code). If a mouse chromosome has more than one linkage signal, the loci are differentiated with a letter after the chromosome number. The next
four columns describe the association results of the human syntenic locus; q-values refer to the marker. All genes that map within 100 kb of the maximum associated
marker are listed.
Volume 1December 2011 |Glypican Genes and Ethanol Response |
vapor (47%) exhibit a characteristic pattern of locomotor activity.
When ethanol is first introduced, flies show a transient burst of loco-
motor activity—a startle response to the smell of ethanol (Figure 2, B
and F). Activity briefly returns to baseline levels, followed by increas-
ing hyperactivity that coincides with the rising internal concentrations
of ethanol (approximately 25 mM at 25 min exposure). As internal
ethanol concentration increases, flies become progressively more un-
coordinated and eventually they become sedated. Sedation sensitivity
is measured using a higher ethanol dose, 67% vapor, which results in
50% sedation after ?20 min exposure in control flies (Figure 2, D and
G). If ethanol-exposed flies are allowed to rest for 3.5 hr to metabolize
absorbed ethanol, reexposed flies take substantially longer to sedate
(?32 min for 50% sedation). This acquired resistance to ethanol
sedation is termed rapid tolerance and is due to neuro-adaptations
to the effects of ethanol intoxication (Scholz et al. 2000; Wolf and
The ethanol behavioral responses of dlpf03537and dallyMB950flies
differed significantly from controls, and they were qualitatively similar
to each other. Ethanol-induced locomotor activity in homozygous
mutant flies was the most similar response; both mutant strains, as
homozygotes, showed significantly reduced ethanol hyperactivity (Fig-
ure 2, B and F, left panels). Interestingly, heterozygous mutants
showed the opposite result of increased ethanol hyperactivity, but only
dlpf03537heterozygotes showed a significant increase (Figure 2, B and
F, right panels). Given that the expression and complementation
experiments indicate that transcript levels decrease with the number
of mutant alleles, the simple model that ethanol hyperactivity
decreases with decreasing transcript is not supported; a more complex
relationship between dally and dlp gene expression and ethanol hy-
peractivity must exist. dallyMB950and dlpf03537mutants were also sim-
ilar in their ethanol sedation sensitivity. Because of the weak viability
of dlpf03537homozygotes, only heterozygotes were generated in suffi-
cient numbers for the sedation assays. The dlpf03537heterozygotes
displayed increased sedation sensitivity (Figure 2D). dallyMB950heter-
ozygotes and homozygotes were both more sensitive to the sedating
effect of ethanol, with the homozygotes exhibiting greater sensitivity
(Figure 2G and data not shown). The two mutants differed in their
development of rapid tolerance [rapid tolerance was measured as the
difference in sedation sensitivity between an initial exposure to etha-
nol, e1, and a second exposure, e2, delivered four hours later (Figure
2C)]. While dlpf03537heterozygotes developed greater tolerance,
dallyMB950heterozygotes and homozygotes were indistinguishable
from control flies (Figure 2, D and G). Ethanol absorption was un-
altered in the mutants (Figure 2 legend), so we can conclude that the
behavioral alterations were due to different responses to the same
Our search for genes influencing AUD development first looked for
genetic linkage in mice, and we discovered 13 candidate QTL
contributing to ethanol-induced ataxia. These hypothetical QTL were
examined for genetic associations with alcohol-induced ataxia in
humans. With candidate loci as a hypothesis, we reduced the number
of statistical tests in comparison with a genome-wide association
Figure 2 Drosophila glypican
homologs regulate behavioral
responses to acute and re-
peated ethanol exposure. (A)
Simplified diagram of the dlp
locus. Coding regions are in-
dicated with shaded boxes.
The transposon insertion allele
dlpf03537is located in the first
intron. Probes for qPCR span
the last intron. (B) Ethanol-
stimulated locomotion of flies
homozygous (left) or heterozy-
gous (right) for f03537 continu-
ously exposed to 47% ethanol
vapor (0–25 min, bar on hori-
zontal axis). Control
experiments was the genetic
significance (two-sample t-test)
was assessed by comparison
of the total distance traveled
during the hyperactive phase
(2–25 min). f03537: n ¼ 5,
f03537/+: n ¼ 8. (C) Exposure
detecting ethanol rapid toler-
ance. Flies were exposed twice
to 67% ethanol vapor with 4 hr between the start of exposures 1 and 2. (D) Sedation sensitivity and tolerance of flies heterozygous for f03537.
ST50 is the time to 50% sedation, and sedation tolerance is the difference between the ST50 of exposure 2 (e2) and 1 (e1). Sensitivity and sedation
are illustrated by the horizontal bar graphs.?P ¼ 0.0206,??P ¼ 0.0022, two-sample t-test. n ¼ 9. (E) Simplified diagram of the dally locus. (F)
Ethanol-stimulated locomotion of flies homozygous (left) or heterozygous (right) for MB950 continuously exposed to 47% ethanol vapor
(0–25 min). MB950: n ¼ 7, MB950/+: n ¼ 11. (G) Sedation sensitivity and tolerance of flies homozygous for MB950.???P ¼ 0.0002, two-sample
t-test. n ¼ 7. Ethanol absorption was unaltered in dlp (control: 24.6 mM, f03537/+: 24.2 mM, P ¼ 0.8726, two-sample t-test, n ¼ 6) and dally
mutant flies (control: 29.5 mM, MB950: 31.7 mM, P ¼ 0.5231, two-sample t-test, n ¼ 4).
| G. Joslyn et al.
approach and thus increased the statistical power to detect genetic
associations at the human loci. We also obtained greater genetic
resolution and human relevance.
Human genetic association identified GPC5, a member of the
glypican gene family, to be of statistical interest with a multiple-test–
corrected FDR q-value of 0.082. We then moved to a Drosophila
model system to obtain correlative evidence in an experimental system
where mutations can be introduced and tested for their effect on
ethanol-induced behaviors. The Drosophila results supported the
mammalian genetic results by demonstrating that flies carrying mu-
tant alleles of dally or dlp, the Drosophila homologs of mammalian
glypicans, exhibit alterations in ethanol-induced locomotor activity,
ethanol-induced sedation, and rapid tolerance to ethanol-induced se-
dation. Together, these results strongly implicate GPC5 as a contribu-
tor to ethanol response.
The mammalian glypicans (GPC1 to GPC6) are heparan sulfate
proteoglycans (HSPG) linked to the cell surface via a glycosylphos-
phatidylinositol (GPI) anchor. Much of what we know about glypican
function in mammals was founded upon Drosophila experiments in-
vestigating dally and dlp, orthologs of the mammalian 3/5 subfamily
and the 1/2/4/6 subfamily, respectively [reviews in Filmus et al. (2008)
and Wu et al. (2010)]. Glypicans are involved in modulating cellular
signaling, participating in many aspects of normal morphogenesis
(including neuronal development and axon guidance) where they
regulate the signaling of the Wnt, Hedgehog, fibroblast growth factor
(FGF), and bone morphogenetic protein (BMP) ligands. Consistent
with this role in developmental signaling, dysregulation of glypicans
has been detected in various cancers (Filmus 2001).
GPC5 is developmentally expressed in a pattern consistent with its
involvement in central nervous system, limb, and kidney development
(Saunders et al. 1997). Because acute ethanol response is brain medi-
ated, only brain expression patterns in mice were considered here in
detail. Unlike the majority of glypican family members where expres-
sion in embryonic brain is much greater than in adult brain, GPC5
brain expression increases with embryonic age and is highest in adult
tissue (Luxardi et al. 2007). GPC59s embryonic brain expression is also
more spatially restricted than the other glypicans; it is limited to the
striatum primordium and ventral diencephalic wall starting about day
10 of embryogenesis (Luxardi et al. 2007). In adults, the greatest
expression is seen in the caudate nucleus, the putamen, and the hip-
pocampus (Saunders et al. 1997), structures thought to play a signifi-
cant role in drug behavior (Koob and Volkow 2010). In flies, both
dally and dlp are expressed in the nervous system during development
and in adulthood (Chintapalli et al. 2007).
In addition to being expressed in brain regions thought to play
a role in ethanol response, there is evidence that GPC5 transcription
can be modulated by ethanol. After discovering that ethanol-induced
expression of Gabra4 in mouse cortical neurons is dependent upon
the activation of heat shock factor 1 (HSF1) and its binding to an
ethanol response sequence element located between exons 1 and 2,
Pignataro et al. (2007) performed a microarray screening experiment
to identify additional genes whose transcription is activated by ethanol
via HSF1. By exposing cortical neurons to ethanol or heat and using
microarrays to identify genes whose expression was altered by the
treatment, they identified 50 genes that were transcriptionally upregu-
lated in response to ethanol and ?450 genes upregulated in response
to heat. The authors highlighted the 9 genes that responded “dramat-
ically” (upregulated 50% or greater) to both treatments, a list that
includes GPC5. Additionally, we previously found that Drosophila
dally expression was transiently increased following exposure to a se-
dating dose of ethanol (Kong et al. 2010).
Glypicans have been shown to participate in organismal de-
velopment through their interactions with the Wnt, Hedgehog, FGF,
and BMP morphogen signaling pathways (Selleck 2000). These de-
velopmental pathways have more recently been implicated not only in
neural development but also in postdevelopmental synaptic mainte-
nance and plasticity, making them credible participants in neurolog-
ical, psychiatric, and behavioral phenotypes (Budnik and Salinas 2011;
Farías et al. 2010; Ille and Sommer 2005; Marqués 2005; Okerlund and
Cheyette 2011; Salinas 2005). We hypothesize that glypicans modulate
these pathways in their neurological functions as they do in their
developmental functions. Whereas, prior to this report, there was
no experimental literature addressing glypicans’ role in behavior, there
are reports of glypicans being involved in human psychiatric condi-
tions: GPC6 is associated with a neuroticism using an age-by-SNP
interaction model (Calboli et al. 2010), and GPC1 is a member of
a schizophrenia gene network derived from a gene set enrichment
analysis of GWA data (Potkin et al. 2010).
In summary, we presented evidence that the glypican GPC5
participates in human ethanol response, an endophenotype related to
AUD risk. The strength of the evidence is derived from the
coalescence of results across different experimental systems: mouse
linkage identified the candidate locus, human genetic association
identified a single gene contained within the mouse-defined locus, and
Drosophila behavioral experiments demonstrated that mutant alleles
of the gene identified in mice and humans alter the fly’s ethanol re-
sponse. In addition to our analyses, the literature on GPC5 reports its
expression to be spatially and temporally consistent with a putative
involvement in ethanol behaviors, and moreover, its expression can be
modulated by ethanol. Finally, the known functions of glypicans as
modulators of cell signaling systems that participate in neural devel-
opment as well as synaptic maintenance and plasticity are also con-
sistent with participation in ethanol-induced behaviors. GPC5 is thus
a strong candidate as a gene involved in ethanol response and the
development of alcohol use disorders.
We thank the many scientists that have contributed data to GeneNet-
work. Christopher Walker and Andrew Lee provided laboratory
support. This research was supported by funds provided by the State
of California for medical research on alcohol and substance abuse
through the University of California, San Francisco (R.L.W.), National
Institute on Alcohol Abuse and Alcoholism (NIAAA) grants NIAAA
5R01AA018799 (F.W.W.), and NIAAA grants 05526 and 08401
American Psychiatric Association, 2000
of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Ameri-
can Psychiatric Association, Washington, D.C.
Atkinson, B., and T. Therneau, 2008
sparse matrices, and modeling data from large pedigrees. R package.
Version 1.1.1. http://cran.r-project.org.
Bierut, L. J., 2011Genetic vulnerability and susceptibility to substance de-
pendence. Neuron 69: 618–627.
Bierut, L. J., A. Agrawal, K. K. Bucholz, K. F. Doheny, C. Laurie et al.,
2010A genome-wide association study of alcohol dependence. Proc.
Natl. Acad. Sci. USA 107: 5082–5087.
Box, G. E. P., and D. R. Cox, 1964 An analysis of transformations (with
discussion). J. R. Stat. Soc. Ser. B 26: 211–252.
Budnik, V., and P. C. Salinas, 2011 Wnt signaling during synaptic devel-
opment and plasticity. Curr. Opin. Neurobiol. 21: 151–159.
Diagnostic and Statistical Manual
Kinship: mixed-effects Cox models,
Volume 1December 2011|Glypican Genes and Ethanol Response|
Calboli, F. C. F., F. Tozzi, N. W. Galwey, A. Antoniades, V. Mooser et al.,
2010A genome-wide association study of neuroticism in a population-
based sample. PLoS ONE 5: e11504.
Chintapalli, V. R., J. Wang, and J. A. T. Dow, 2007
identify better Drosophila melanogaster models of human disease. Nat.
Genet. 39: 715–720.
Edenberg, H. J., D. L. Koller, X. Xuei, L. Wetherill, J. N. McClintick et al.,
2010Genome-wide association study of alcohol dependence implicates
a region on chromosome 11. Alcohol. Clin. Exp. Res. 34: 840–852.
Erblich, J., and M. Earleywine, 1999
ated cognitive impairment during an ethanol challenge. Alcohol. Clin.
Exp. Res. 23: 476–482.
Farías, G. G., J. A. Godoy, W. Cerpa, L. Varela-Nallar, and N. C. Inestrosa,
2010Wnt signaling modulates pre- and postsynaptic maturation:
therapeutic considerations. Dev. Dyn. 239: 94–101.
Filmus, J., 2001 Glypicans in growth control and cancer. Glycobiology 11:
Filmus, J., M. Capurro, and J. Rast, 2008
Goldman, D., G. Oroszi, and F. Ducci, 2005
uncovering the genes. Nature Publishing Group 6: 521–532.
Gottesman, I. I., and T. D. Gould, 2003
psychiatry: etymology and strategic intentions. Am. J. Psychiatry 160:
Heath, A., and N. Martin, 1992 Genetic differences in psychomotor per-
formance decrement after alcohol: a multivariate analysis. J. Stud. Alcohol
Ille, F., and L. Sommer, 2005Wnt signaling: multiple functions in neural
development. Cell. Mol. Life Sci. 62: 1100–1108.
Joslyn, G., G. Brush, M. Robertson, T. L. Smith, J. Kalmijn et al., 2008
Chromosome 15q25.1 genetic markers associated with level of response
to alcohol in humans. Proc. Natl. Acad. Sci. USA 105: 20368–20373.
Joslyn, G., A. Ravindranathan, G. Brush, M. Schuckit, and R. L. White,
2010 Human variation in alcohol response is influenced by variation in
neuronal signaling genes. Alcohol. Clin. Exp. Res. 34: 800–812.
Kimura, M., and S. Higuchi, 2011 Genetics of alcohol dependence. Psy-
chiatry Clin. Neurosci. 65: 213–225.
Kong, E. C., L. Allouche, P. A. Chapot, K. Vranizan, M. S. Moore et al.,
2010Ethanol-regulated genes that contribute to ethanol sensitivity
and rapid tolerance in Drosophila. Alcohol. Clin. Exp. Res. 34: 302–
Koob, G. F., and N. D. Volkow, 2010
psychopharmacology 35: 217–238.
Lander, E., and L. Kruglyak, 1995Genetic dissection of complex traits:
guidelines for interpreting and reporting linkage results. Nature Pub-
lishing Group 11: 241–247.
Lovinger, D. M., and J. C. Crabbe, 2005
treatment target identification and insight into mechanisms. Nat. Neu-
rosci. 8: 1471–1480.
Luxardi, G., A. Galli, S. Forlani, K. Lawson, F. Maina et al., 2007
are differentially expressed during patterning and neurogenesis of early
mouse brain. Biochem. Biophys. Res. Commun. 352: 55–60.
Madden, P., A. Heath, G. Starmer, J. Whitfield, and N. Martin,
1995Alcohol sensitivity and smoking history in men and women. Al-
cohol. Clin. Exp. Res. 19: 1111–1120.
Manolio, T. A., F. S. Collins, N. J. Cox, D. B. Goldstein, L. A. Hindorff et al.,
2009Finding the missing heritability of complex diseases. Nature 461:
Marqués, G., 2005Morphogens and synaptogenesis in Drosophila. J.
Neurobiol. 64: 417–434.
Martin, N., J. Oakeshott, J. Gibson, A. Wilks, G. Starmer et al., 1981
Prodromus to a twin study of sensitivity to intoxication and alcohol
metabolism. Aust. N. Z. J. Med. 11: 140–143.
Moonat, S., B. G. Starkman, A. Sakharkar, and S. C. Pandey, 2010
roscience of alcoholism: molecular and cellular mechanisms. Cell. Mol.
Life Sci. 67: 73–88.
to major psychiatric disorders? J Neurodev Disord 3: 162–174.
Using FlyAtlas to
Children of alcoholics exhibit attenu-
Glypicans. Genome Biol. 9: 224.
The genetics of addictions:
The endophenotype concept in
Neurocircuitry of addiction. Neuro-
Laboratory models of alcoholism:
Pignataro, L., A. N. Miller, L. Ma, S. Midha, P. Protiva et al., 2007
regulates gene expression in neurons via activation of heat shock factor 1.
J. Neurosci. 27: 12957–12966.
Pollock, V., 1992 Meta-analysis of subjective sensitivity to alcohol in sons of
alcoholics. Am. J. Psychiatry 149: 1534–1538.
Potkin, S. G., F. Macciardi, G. Guffanti, J. H. Fallon, Q. Wang et al.,
2010Identifying gene regulatory networks in schizophrenia. Neuro-
image 53: 839–847.
Quinn, P. D., and K. Fromme, 2011
lenge: a quantitative review. Alcohol Clin. Exp. Res. 35: 1759–1770.
R Development Core Team, 2011R: A Language and Environment for
Statistical Computing. Available at: http://www.r-project.org/.
Rothenfluh, A., R. J. Threlkeld, R. J. Bainton, L. T.-Y. Tsai, A. W. Lasek et al.,
2006Distinct behavioral responses to ethanol are regulated by alternate
RhoGAP18B isoforms. Cell 127: 199–211.
Salinas, P. C., 2005Signaling at the vertebrate synapse: new roles for em-
bryonic morphogens? J. Neurobiol. 64: 435–445.
Saunders, S., S. Paine-Saunders, and A. D. Lander, 1997
cell surface proteoglycan glypican-5 is developmentally regulated in
kidney, limb, and brain. Dev. Biol. 190: 78–93.
Scholz, H., J. Ramond, C. M. Singh, and U. Heberlein, 2000
ethanol tolerance in Drosophila. Neuron 28: 261–271.
Schuckit, M., and E. Gold, 1988A simultaneous evaluation of multiple
markers of ethanol/placebo challenges in sons of alcoholics and controls.
Arch. Gen. Psychiatry 45: 211–216.
Schuckit, M., and T. Smith, 1996An 8-year follow-up of 450 sons of al-
coholic and control subjects. Arch. Gen. Psychiatry 53: 202–210.
Schuckit, M., and T. Smith, 2000 The relationships of a family history of
alcohol dependence, a low level of response to alcohol and six domains of
life functioning to the development of alcohol use disorders. J. Stud.
Alcohol 61: 827–835.
Schuckit, M., T. Smith, J. Kalmijn, J. Tsuang, V. Hesselbrock et al.,
2000Response to alcohol in daughters of alcoholics: a pilot study and
a comparison with sons of alcoholics. Alcohol Alcohol. 35: 242–248.
Schuckit, M., J. Tsuang, R. Anthenelli, J. Tipp, and J. Nurnberger,
1996Alcohol challenges in young men from alcoholic pedigrees and
control families: a report from the COGA project. J. Stud. Alcohol 57:
Schuckit, M. A., K. Wilhelmsen, T. L. Smith, H. S. Feiler, P. Lind et al.,
2005Autosomal linkage analysis for the level of response to alcohol.
Alcohol. Clin. Exp. Res. 29: 1976–1982.
Schumann, G., L. J. Coin, A. Lourdusamy, P. Charoen, K. H. Berger et al.,
2011Genome-wide association and genetic functional studies identify
autism susceptibility candidate 2 gene (AUTS2) in the regulation of
alcohol consumption. Proc. Natl. Acad. Sci. USA. 108: 7119–7124.
Selleck, S. B., 2000Proteoglycans and pattern formation: sugar biochem-
istry meets developmental genetics. Trends Genet. 16: 206–212.
Storey, J., and R. Tibshirani, 2003Statistical significance for genomewide
studies. Proc. Natl. Acad. Sci. USA 100: 9440–9445.
Stuber, G. D., F. W. Hopf, K. M. Tye, B. T. Chen, and A. Bonci,
2010Neuroplastic alterations in the limbic system following cocaine or
alcohol exposure. Curr Top Behav Neurosci 3: 3–27.
Treutlein, J., S. Cichon, M. Ridinger, N. Wodarz, M. Soyka et al., 2009
Genome-wide association study of alcohol dependence. Arch. Gen.
Psychiatry 66: 773–784.
Venables, W. N., and B. D. Ripley, 2002
S. Ed. 4. Springer, New York.
Wang, J., R. W. Williams, and K. F. Manly, 2003
complex trait analysis. Neuroinformatics 1: 299–308.
Wang, S., N. Ray, W. Rojas, M. V. Parra, G. Bedoya et al., 2008
patterns of genome admixture in Latin American Mestizos. PLoS Genet.
Wilhelmsen, K. C., M. Schuckit, T. L. Smith, J. V. Lee, S. K. Segall et al.,
2003 The search for genes related to a low-level response to alcohol
determined by alcohol challenges. Alcohol. Clin. Exp. Res. 27: 1041–1047.
Wolf, F. W., and U. Heberlein, 2003
J. Neurobiol. 54: 161–178.
Subjective response to alcohol chal-
Expression of the
Modern Applied Statistics with
Invertebrate models of drug abuse.
|G. Joslyn et al.
Wolf, F. W., A. R. Rodan, L. T.-Y. Tsai, and U. Heberlein, 2002
resolution analysis of ethanol-induced locomotor stimulation in Dro-
sophila. J. Neurosci. 22: 11035–11044.
Wu, Y., T. Y. Belenkaya, and X. Lin, 2010
Dally-like in Wingless/Wnt signaling and distribution. Methods Enzy-
mol. 480: 33–50.
Dual roles of Drosophila glypican
Yang, J., B. Benyamin, B. P. McEvoy, S. Gordon, A. K. Henders et al.,
2010Common SNPs explain a large proportion of the heritability for
human height. Nat. Genet. 42: 565–569.
Communicating editor: I. M. Hall
Volume 1December 2011| Glypican Genes and Ethanol Response|