Open Journal of Genetics, 2013, 3, 243-252 OJGen
http://dx.doi.org/10.4236/ojgen.2013.34027 Published Online December 2013 (http://www.scirp.org/journal/ojgen/)
Family-based association analysis of alcohol dependence
implicates KIAA0040 on Chromosome 1q in multiplex
alcohol dependence families
Shirley Y. Hill*, Bobby L. Jones, Nicholas Zezza, Scott Stiffler
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, USA
Received 27 September 2013; revised 10 October 2013; accepted 11 November 2013
Copyright © 2013 Shirley Y. Hill et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Background: A previous genome-wide linkage study
of alcohol dependence in multiplex families found a
suggestive linkage result for a region on Chromosome
1 near microsatellite markers D1S196 and D1S2878.
The KIAA0040 gene has been mapped to this region
(1q24 - q25). A recent genome-wide association study
using SAGE (the Study of Addiction: Genetics and
Environment) and COGA (Collaborative Study on
the Genetics of Alcoholism) found five SNPs within
the KIAA0040 gene significantly associated with al-
cohol dependence. A meta-analysis using data from
these sources also found the KIAA0040 gene signifi-
cantly associated with alcohol dependence. Methods:
Using family data consisting of 1000 individuals with
phenotypic data (762 with both phenotype and DNA),
finer mapping of a 0.3 cM region that included the
KIAA0040 gene and a flanking gene was undertaken
using SNPs with minor allele frequency (MAF) ≥ 0.15
and pair-wise linkage disequilibrium (LD) of r2 < 0.8
using the HapMap CEU population. Results: Signifi-
cant FBAT p-values were observed for six SNPs, four
within the KIAA0040 gene (rs2269650, rs2861158,
rs1008459, rs2272785) and two adjacent to KIAA0040
(rs10912899 and rs3753555). Five haplotype blocks of
varying size were identified using HAPLOVIEW.
Analysis using the haplotype-based test function of
FBAT revealed one two-SNP block (rs1008459-
rs2272785) associated with alcohol dependence. This
block showed a pattern of transmission in which one
haplotype, CT, with a frequency of 0.577 was found
to be over-transmitted to affected offspring (p = 0.017)
while another haplotype, AG, with a frequency of
0.238 was found to be under-transmitted to affected
offspring (p = 0.006). A three-SNP block (rs1008459-
rs2272785-rs375355) showed an overall significant
association (p = 0.011) with alcohol dependence with
the haplotype ACT over-transmitted to affected off-
spring (p = 0.016) and the haplotype GAG under-
transmitted (p = 0.002). Conclusions: Family-based
association analysis shows the KIAA0040 gene sig-
nificantly associated with alcohol dependence. The
potential importance of the KIAA0040 gene for AD
risk is currently unknown. However, the present re-
sults support earlier findings from a genome-wide as-
sociation study.
Keywords: KIAA0040; Alcohol Dependence; Multiplex
Families
1. INTRODUCTION
Alcohol dependence is a complex disorder that is char-
acterized by psychological and physical dependence and
is often accompanied by chronic consumption of haz-
ardous levels of ethanol. Excessive use of alcohol is the
third leading cause of preventable death [1] in the US.
The economic and social costs have been estimated to be
$184 billion due to alcohol-related accidents, lost pro-
ductivity, incarceration and other alcohol related morbid-
ity [2]. In spite of the fact that the use of alcohol is quite
common, a smaller proportion of the population drink in
sufficient quantity and with associated health, family and
work-related problems to be considered alcohol depend-
ent AD. The one-year prevalence of AD in the US is
3.8% [3]. The lifetime prevalence of AD has been esti-
mated at 12.5% [4]. Prevalence among male respondents
ages 15 - 54 has been reported to be higher with 20.1%
of men and 8.2% of women meeting criteria for alcohol
dependence [5]. There is now evidence that those indi-
viduals with the greatest propensity for AD may carry an
increased genetic risk for developing alcohol depend-
*Corresponding author.

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 244
ence.
Although there is considerable heritability for alcohol
dependence (0.49 - 0.64) in males [6,7] and females
(0.56 - 0.59), [8,9] few genes have been identified that
reliably confer susceptibility. However, studies employ-
ing well-designed sampling strategies over sample fami-
lies with a high density of cases have revealed important
clues for gene finding as seen in the Collaborative Study
on the Genetics of Alcoholism (COGA) studies [10,11].
Genome-wide association (GWAS) studies have also
revealed potentially important loci but require large sam-
ples to detect loci having genome-wide significance. A
meta-analysis of two GWAS studies of alcohol depend-
ence totaling 4979 cases and controls has identified three
loci with statistical significance of p < 5 × 10−7 [12].
In a genome-wide scan of multiplex families ascer-
tained through a pair of affected probands [13], we found
evidence for linkage in multiple chromosomal regions.
The present report is based on efforts to follow up on
linkage findings for a region on Chromosome 1q23.3 to
1q25.1 that includs a maximal LOD score of 3.46 (p =
0.002) at marker D1S196 and at an adjacent marker
D1S2878 with a LOD value of 3.45 (p = 0.002). A pre-
vious follow-up of this region revealed significant fam-
ily-based association for the astrotactin neuronal protein
(ASTN1) gene [14]. Two nearby genes, KIAA0040 and
TNN have been mapped to 1q25.1 and have been re-
cently identified in genome-wide association analyses as
being significantly related to alcohol dependence [12,15].
Accordingly, a study of the TNN/KIAA0040 region was
undertaken.
2. MATERIALS AND METHODS
2.1. Study Sample
Written consent was obtained from all members of the
multiplex families who participated in the study after the
nature and purpose of the study was fully explained to
them. The consent forms used in the study were ap-
proved by the University of Pittsburgh Institutional Re-
view Board.
2.2. Multiplex Families
Multiplex families were selected on the basis of the
presence of a pair of alcohol dependent brothers or sis-
ters. The probands were selected from among individuals
in treatment for alcohol dependence in the Pittsburgh
area. Probands were eligible if they met DSM-III criteria
for AD and had a same sex sibling who similarly met
criteria for AD. Families were excluded if the probands
or any first-degree relative were considered to have a
primary diagnosis of drug dependence (preceded alcohol
dependence onset by at least 1 year), or the proband or
first-degree relative met criteria for schizophrenia, or a
recurrent major depressive disorder. Probands and rela-
tives with mental retardation or physical illness preclud-
ing participation were excluded. Complete details re-
garding participant selection may be seen in Hill et al.
[13]. The majority of probands (80%) had three or more
siblings who contributed DNA, consented to a clinical
interview, and provided family history. These large sib-
ships resulted in a total of 648 sib pairs within the pro-
band generation. Across the generations, an average of
5.7 individuals per family was genotyped.
2.3. Generation I and II Diagnoses
All proband pairs and their cooperative relatives (siblings
and parents) were personally interviewed using a struc-
tured psychiatric interview (Diagnostic Interview Sched-
ule [DIS]). The DIS provides good reliability and valid-
ity [16] for alcohol dependence and alcohol abuse by
DSM-III and IIIR criteria [17,18] the diagnostic criteria
in place when the study began. The DIS also provides an
alcoholism diagnosis by Feighner Criteria [19].
2.4. Generation III—Young Adult Assessment
for DSM-IV Diagnoses
With the initiation of a third generation follow-up, off-
spring who had reached their 19th birthday were as-
sessed using the Composite International Diagnostic In-
terview (CIDI) [20] to determine the presence or absence
of a DSM-IV Axis I diagnosis. The CIDI-SAM (Sub-
stance Abuse Module) [21] was also administered in or-
der to determine quantity, frequency, and pattern of drug
and alcohol use. Interrater reliability for interviewers on
the diagnostic instruments used in this study exceeded
90%.
2.5. SNP Selection
Previously, we carried out a genome-wide linkage analy-
sis finding potentially important linkage results for mul-
tiple regions including Chromosome 1 [13]. Our study
included genotyping in a 26.6 cM region on Chromo-
some 1 that centered on the microsatellite marker
D1S196. A LOD score of 3.46 was obtained using a bi-
nary alcohol dependence phenotype and including rele-
vant covariates (age, gender and the personality variable
Constraint). Constraint from the Multidimensional Per-
sonality Questionnaire measures tendencies to inhibit
impulse expression, rejection of unconventional behavior,
and risk taking and with genetic variance of 0.58 in twins
reared apart [22].
In order to investigate the region further, SNPs were
chosen with minor allele frequency (MAF) ≥ 0.15 and
pair-wise linkage disequilibrium (LD) of r2 < 0.8 using
the HapMap CEU population at approximately 1 cM
intervals in this region. The genotyping and analysis was

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 245
completed in three stages. First, we focused on a 19 cM
region extending from rs7522166 to rs2816187. This
region, bounded by these SNPs was chosen because
rs7522166 is 7 cM proximal to D1S196 and rs2816187 is
13 cM distal to D1S196. We genotyped 18 SNPs at ap-
proximately 1 cM intervals in this region. Analysis of
these 18 SNPs revealed the greatest statistical signifi-
cance for rs228008 located in the ASTN1 gene. Finer
mapping of this gene at an average distance of 28.9 kb
using twelve additional SNPs confirmed the significant
result obtained for rs228008 [14]. Because two nearby
genes, KIAA0040 and TNN (1q25.1) have shown highly
significant association with alcohol dependence in a
GWAS study [15], a study of this region was planned. A
total of 18 SNPs were selected for genotyping with 9
SNPs selected to cover a 0.3 cM region extending from
rs12094153 to rs3753555 covering the TNN/KIAA0040
region at intervals of no greater than 5 kb, with 8 SNPS
selected for their presence within the KIAA0040 gene.
The SNPs selected for presence within the gene were
chosen based on the reports of Wang et al. [12] and Zuo
et al. [15]. Specifically, from among the SNPs evaluated
by Zuo et al. [15], the five SNPs having the best statis-
tical significance were chosen (rs6701037, rs6425323,
rs1057302, rs1057239, and rs1894709). These SNPs
were also reported to be significantly related to alcohol
dependence in the Wang et al. [12] meta-analysis.
2.6. DNA Isolation and Genotyping
Genomic DNA was extracted from whole blood with a
second aliquot prepared for EBV transformation and
cryopreservation. PCR conditions were as described in
Hill et al. [13]. Genotyping was completed on a Biotage
PSQ 96 MA Pyrosequencer (Biotage AB, Uppsala, Swe-
den). Each polymorphism was analyzed by PCR ampli-
fication incorporating a biotinylated primer. Thermal
cycling included 45 cycles at an annealing temperature
of 60˚C. The Biotage workstation was used to isolate the
biotinylated single strand from the double strand PCR
products. The isolated product was then sequenced using
the complementary sequencing primer.
2.7. Quality Control
SNP genotyping quality control involved ongoing moni-
toring of SNP signals provided by Qiagen software.
Output is provided using three categories for each SNP:
pass, fail and check. Data analysis was performed for
only those signals meeting the “pass” criterion. Signals
that failed or were returned as needing further checking
were rerun. If after 3 attempts the SNP did not meet the
“pass” criterion, it was eliminated from the analysis and
another SNP chosen as a replacement.
2.8. Statistical Methods
The sample included 133 pedigrees consisting of 1000
individuals (49% male and 51% female). Among the
1000 subjects, 542 were affected, 436 were unaffected,
and 22 had unknown status.
2.9. Mendelian Inconsistency
The PedCheck program [23] was used to evaluate indi-
vidual SNPs for Mendelian inconsistencies based on the
pedigree structures. As a result of the evaluation, 36
marker genotypes from among the 13,656 were coded as
missing to resolve the reported inconsistencies.
2.10. Hardy-Weinberg Equilibrium (HWE)
Estimates of population allele frequencies were calcu-
lated using MENDEL version 11 [24]. Files required by
the MENDEL program were generated via the program
Mega2 [25]. Marker allele frequencies were tested for
departures from Hardy-Weinberg equilibrium using the
allele frequency option in MENDEL. None of the 18
SNPs analyzed were found to have p-values below the
Bonferroni adjusted threshold (<0.003) that would indi-
cate significant HWE departures.
2.11. Genetic Maps
The Genetic Map Interpolator (GMI) software [26] was
used to retrieve current physical map positions from En-
sembl (Ensembl 68). These physical positions were then
used to linearly interpolate genetic map positions based
on the Rutgers Combined Linkage-Physical Map [27,28].
2.12. Family-Based Association Test (FBAT)
Transmission rates of marker alleles were examined us-
ing the family-based association test program, FBAT
[29,30], assuming an additive genetic model with robust
variance estimation (-e option) to account for the relat-
edness. This family-based method is a generalization of
the transmission disequilibrium test (TDT) [31], which
provides a valid test of association even if admixture is
present. FBAT converts each pedigree into nuclear fami-
lies, which are then treated as independent families for
the test statistic calculation. Informative families con-
sisting of parent-child trios are utilized in the FBAT
analysis.
Generation I and II individuals were coded as affected
if they met criteria for alcohol dependence by DSM-III
criteria. Generation III individuals were coded as af-
fected if they met criteria for alcohol abuse or depend-
ence, or drug abuse or dependence. Choice of this
broader phenotype for the third generation was based on
the greater prevalence of drug use disorders in the third

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 246
generation. Also, Generation I and II individuals had
been selected for the family study based on the presence
of primary alcohol dependence (if drug dependence was
presence it must have followed the alcohol dependence
diagnosis by one year). However, analyses were also
conducted using an alcohol abuse/dependence phenotype
for the third generation.
2.13. Gamete-Competition (GC)
We also considered the gamete-competition model [32],
a generalization of the transmission disequilibrium test
(TDT), to investigate association of marker alleles with
alcohol dependence. The gamete-competition model can
be used to test for differences in transmission of marker
alleles to affected individuals.
2.14. Haplotype Analysis
Linkage disequilibrium (LD) analysis was performed
using the HAPLOVIEW program version 4.2 [33]. The
LD block structure was defined by calculating D’ values
pairwise between SNPs. SNP haplotype blocks were cre-
ated using the HAPLOVIEW default block determination
method [34]. Additionally, a sliding window approach
was used to identify two and three SNP blocks in order
to insure that any blocks within larger haplotype blocks
could be analyzed. Haplotype blocks were investigated
for family-based association with affected status. A
within-family association analysis between alcohol de-
pendence and the revealed haplotypes was performed
using haplotype FBAT [35] assuming an additive genetic
model and using a robust estimate of variance (-e op-
tion).
3. RESULTS
3.1. Association Results
Analysis of 18 SNPs covering a 68.8 Kb region on
Chromosome 1 extending from rs12094153 to rs3753555
revealed six SNPs associated with alcohol dependence
with significant FBAT p values (rs2269650, rs2861158,
rs1008459, rs2272785, rs10912899, rs3753555). The
SNP showing the most significant association with alco-
hol dependence affected status was rs1008459 (FBAT p
= 0.006) located within intron 2 of KIAA0040. Four
SNPs are within the KIAA0040 gene. Two of these were
also found to be significant using Gamete Competition
(GC) analyses. Results for the FBAT and GC analyses
are summarized in Table 1. LocusZoom [36] was used to
generate a plot of the association test results (Figure 1).
3.2. Haplotype Analysis
Five haplotype blocks were identified by HAPLOVIEW.
Pairwise linkage disequilibrium between the SNPs and
the LD block structure are shown in Figure 2. Haplotype
analyses were performed using two alternative pheno-
types for Generation III (see Table 2). The gender dis-
tribution by generation may be seen in Table 3. Results
of the haplotype analysis can be seen in Table 4. One
two SNP block (Block 5) which consisted of one SNP
within the KIAA0040 gene (rs2272785) and an adjacent
SNP (rs3753555) showed an association with affected
status with a p value of 0.041 using a broader SUD pheno-
type and 0.034 when restricted to alcohol abuse or depend-
ence only. In Block 5, the haplotype CT with a frequency
of 0.577, was found to be over-transmitted to affected
offspring (p = 0.017) while the haplotype block AG with
a frequency of 0.238, was found to be under-transmitted
Figure 1. Association plot (−log10 of the p values from FBAT)
for SNPs within 120 kb of rs1008459, the SNP with the maxi-
mum association observed.
Figure 2. Linkage disequilibrium analysis was performed using
HAPLOVIEW (version 4.2). The block structure was defined
by calculating D’ values pairwise between SNPs. One two SNP
block was identified containing rs2272785 and rs3753555 that
showed statistical significance with alcohol dependence. The
haplotype CT with a frequency of 0.577 was found to be over-
transmitted to affected offspring (p = 0.017). Block AG with
frequency of 0.238, was under-transmitted to affected offspring
(p = 0.006).

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 247

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 248
Table 2. Diagnostic status by generation.
Age Mean ± SD Affected UnaffectedUnknown Total Genotyped
Generation I
Alcohol Dependence 59.4 (9.1) 115 127 6 248 121
Generation II
Alcohol Dependence 36.4 (7.8) 335 162 6 503 392
Generation III
SUD Phenotype
Alcohol Abuse/Dependence
Phenotype
23.9 (5.8)
92
47
147
195
10
7
249
249
248
248
Total 542 436 22 1000 761
Three individuals from an ancestral generation (great grandparents of third generation subject) were also genotyped, but are not included in the tables.
Table 3. Gender distribution by generation for individuals with phenotype.
Male Female Total
Generation I 124 124 248
Generation II 250 253 503
Generation III 115 134 249
Total 490 511 1000
Table 4. Haplotype analysis.
Block Overall
Markers Haplotype Freq. p-valuea p-valueb p-valuea p-valueb
rs2272785-rs3753555 C-T 0.577 0.017 0.044 0.041 0.034
A-G 0.238 0.006 0.009
rs1008459-rs2272785-rs3753555 A-C-T 0.585 0.016 0.043 0.012 0.011
G-A-G 0.205 0.002 0.003
aAffected status for Generation III includes any SUD; bAffected status for Generation III is alcohol dependence only.
to affected offspring (p = 0.006). This analysis was first
performed using affected status for Generation III to in-
clude any SUD (alcohol abuse or dependence, or drug
abuse or dependence). Re-analysis using alcohol de-
pendence only as the affected phenotype for Generation
III resulted in minor alterations in significance (p = 0.044
and p = 0.009, respectively).
None of the other four blocks identified by HAP-
LOVIEW were found to be associated with alcohol de-
pendence.
Based on results from our sliding window analysis, we
find an association for alcohol dependence for a three-
SNP block that includes the previously identified two-
SNP block and includes rs1008459 and rs2272785 along
with rs3753555. This three-SNP block showed a overall
significant association (p = 0.012) with SUD with an
individual p-value for GAG of 0.002. This result ob-
tained with third generation offspring coded as affected,
whether alcohol dependent or having SUD, was con-
firmed using third generation codes as affected when
only alcohol dependence was present showed an overall
probability of (p = 0.011) and a haplotype specific p-
value for GAG of 0.003.
4. DISCUSSION
Within-family association (FBAT and GC) analyses were
performed for 18 SNPs in a region of Chromosome 1.
Based on the FBAT within-family association analyses,
our results suggest that variation in the KIAA0040 gene
is associated with risk for alcohol dependence in families
with multiple cases of alcohol dependence. These results
support findings from a genome-wide association study
[15] and a meta-analysis that includs data from studies
that utilizes both case/control and within family associa-
tion analyses for alcohol dependence for SNPs within the
KIAA0040 gene [12].
Zuo et al. [15] reported significant results for five
SNPs within the KIAA0040 gene (rs10572239, rs-
1894709, rs6701037, rs6425323, rs1057302). It is note-
worthy that this GWAS study also finds one SNP having

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 249
Cis-Acting regulatory effects and the rs2269650 SNP
with a p value of 8.6 × 10−5. This SNP showed signifi-
cant results in the present family-based association anal-
ysis as well. However, the five top ranked SNPs reported
in Zuo et al. [15] were not significant in our within-fam-
ily association analysis. Two of these SNPs lie in the
region proximal to the KIAA0040 gene (rs6701037 and
rs6425323) and distal to the TNN gene while three other
SNPs lie in Exon 5 (rs1057302 and rs1057239) and in-
tron 4 (rs1894709). Consideration of the meta-analysis of
family data provided by Wang et al. [12] shows a repli-
cation in the present family data for one SNP (rs1008459)
with a reported FBAT value of 0.0367. However, it
should be noted that three SNPs (rs6701037, rs2269655,
and rs6425323) that reached genome-wide significance
in the analysis of Wang et al. [12] were not significant in
the present study. However, our results for rs10912899
which lies between two of these SNPs, rs6701037 and
rs6425323, did show significance (p = 0.02) based on our
FBAT analysis.
Haplotype analysis revealed one two-SNP block with
a p-value of 0.006 and one three-SNP block with a p-
value of 0.002. The Zuo et al. [15] SNPs showing ge-
nomewide significance appear to cluster in Exon 5,
whereas the current results also suggest the importance
of Exon 4 (rs2861158) and intron 2 where we found a
two-SNP and a three-SNP haplotype respectively with
p-values suggesting their importance.
The biological significance of the current findings is
unknown because the KIAA0040 gene encodes a protein
whose function is unknown. There is evidence that the
KIAA0040 protein product may represent one of the
tumor antigens expressed on colorectal cancer cells and
recognized by tumor reactive T-cells (CT28 line) [37].
The KIAA0040 gene is flanked by two genes TNN and
TNR with a plausible role in alcohol dependence. A pre-
vious study has reported significance of SNPs within the
TNN gene and AD [15]. Because the TNN gene lies only
8.9 kb from KIAA0040, the TNN gene is of interest.
Also, the TNN gene encodes a protein, tenascin-N,
which is involved in neurite outgrowth and cell migra-
tion in the hippocampus [38]. Weaker evidence for a role
of the TNN gene in AD was seen in the present analysis
with two SNPs (rs12563833 and rs1018829) showing
marginal FBAT significance.
The present results should be considered in the context
of some limitations. Although this study represents a
follow-up on a linkage peak originally reported for this
region of Chromosome 1, the peak observed was rela-
tively large [13]. Because the peak was broad, it may be
expected that a number of genes are within this peak.
KIAA0040 was not included in our original planned
analysis as its function has not been defined. However,
KIAA0040 has shown genome-wide significance for
alcohol dependence in a large case/control data set [15].
Another issue concerns whether or not the present
family-based findings did, indeed, replicate the top
GWAS SNPs reported by Zuo et al. [15] and Wang et al.
[12]. Of the five SNPs reported as reaching genome-wide
significance, two SNPs, rs10912899 and rs2269650, in
the present study were within 300 - 400 base pairs of two
SNPs reported by Zuo et al. [15] and Wang et al. [12] to
have genome-wide significance (rs6701037 and rs-
1057302). It is noteworthy that Wang et al. [12] report-
ing on their meta-analytic family study data were not
able to completely replicate individual SNPs from the
GWAS findings. Of the four SNPs reported by Wang et
al. [12], one was not significant, and two had nominal p-
values, though one was highly significant.
The issue of representativeness of our findings may be
considered a limitation. The multiplex families on which
the present report is based were ascertained through af-
fected sib pairs. Multiplex families appear to differ from
alcohol dependent families in the general population by
having greater transmission of alcohol dependence
across generations. Follow-up of third generation off-
spring from multiplex families shows an exceptionally
high rate of AD and associated substance use by young
adulthood [39,40]. This suggests that multiplex family
samples may provide an efficient means of identifying
genes because of the greater likelihood that genes may be
segregating within these families that confer greater sus-
ceptibility to early onset alcohol dependence and related
substance use disorders [41].
Another possible limitation of our results is that some
of the diagnoses were based on DSM-III criteria and
others on DSM-IV. The DSM-III system was the current
system in place when Generation I and II was recruited.
The definition of alcohol dependence provided by DSM-
III requires the presence of tolerance and physical de-
pendence. With the initiation of a longitudinal follow-up
for Generation III, subjects were assessed using DSM-IV
criteria. These criteria require three or more symptoms
within a 12-month period that may include tolerance or
physical dependence but do not require the presence of
these symptoms if other symptoms are present. Accord-
ingly, the third generation may have met criteria for al-
cohol dependence based on social or occupational im-
pairment, use in spite of physical impairment, persistent
desire to use, or inability to cut down or control use.
An additional limitation of our analysis was the need
to include a broader phenotypic definition for the Gen-
eration III individuals. While it would be possible to
code any Generation III individual as unaffected if they
did not meet criteria for alcohol dependence, it appeared
that this would incorrectly reflect individual’s addiction
susceptibility where significant abuse or dependence on
drugs was present. An additional analysis in which the

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 250
third generation offspring was coded for alcohol de-
pendence only (yes/no) provided essentially the same
results.
Because the power to detect association increases with
the number of observations available for related indi-
viduals [42], the family-based association analysis was
strengthened by having a larger number of family mem-
bers that includes three generations. However, the
younger age (mean of 24 years) of the third generation
family members means that not all have moved through
the period of risk. As a result, some individuals coded as
not dependent may eventually convert to affected status.
However, in spite of this limitation we observed a sig-
nificant relationship between SNPs within the KIAA-
0040 gene and alcohol dependence suggesting that this
gene may have clinical importance in the etiology of
alcohol dependence.
A comment is needed regarding the family-based ap-
proach which was taken in this report. Risch and Meri-
kangas [43] were among the first to suggest that associa-
tion studies are sometimes more powerful than linkage
analyses. Since that time, there has been a shift toward
large genome-wide association studies (GWAS) instead
of family-based methods where sample sizes are more
modest. Some have questioned whether GWAS methods
that are designed to detect common rather than rare
variants will explain a substantial portion of heritability
in psychiatric disorders [44]. This view has been ampli-
fied by others who argue that GWAS may detect com-
mon variants with statistically significant results but only
modest population attributable risk in comparison to fo-
cused investigations of families where genes can be
found with high predictive value [45]. Perhaps, the use of
multiple statistical genetic methods is preferable when
characterizing the genetic underpinnings of complex
phenotypes such as alcohol dependence. Simulations
carried out using linkage, case-control association and
family-based tests have shown that each method has
limitations that may be handled best by the use of multi-
ple methods [46].
In summary, the present results using family-based
association found evidence that the KIAA0040 gene is
related to risk for alcohol dependence and supports the
GWAS results offered by Zuo et al. [15]. Future work is
needed to uncover the function of this gene and its po-
tential role in the risk/protection from alcohol depend-
ence.
5. ACKNOWLEDGEMENTS
The authors wish to acknowledge the continued support of Daniel E.
Weeks, Ph.D. in data analysis and interpretation. Also, we wish to
acknowledge the contribution of the family members who have con-
tributed DNA and continue to be involved in clinical follow up. Also,
we want to acknowledge the contributions of many clinical staff in
assessing research participants to determine phenotypic data. A special
thanks to Brian Holmes for assistance in manuscript preparation. The
financial support for the study reported in this manuscript was provided
by NIAAA Grants AA018289, AA05909, AA08082, and AA015168 to
SYH. The authors have no conflicts of interest to report.
REFERENCES
[1] Mokdad, A.H., Marks, J.S., Stroup, D.F. and Gerberding,
J.L. (2004) Actual causes of death in the United States,
2000. JAMA, 291, 1238-1245.
http://dx.doi.org/10.1001/jama.293.3.293
[2] Harwood, H. (2000) Updating estimates of the economic
costs of alcohol abuse in the United States: Estimates,
update methods, and data. Report prepared by The Lewin
Group for the National Institute on Alcohol Abuse and
Alcoholism. Based on estimates, analyses, and data re-
ported in Harwood H, Fountain D, and Livermore G. The
economic costs of alcohol and drug abuse in the United
States 1992. Report prepared for the National Institute on
Drug Abuse and the National Institute on Alcohol Abuse
and Alcoholism, National Institutes of Health, Depart-
ment of Health and Human Services. NIH Publication No.
98-4327. Rockville, MD: National Institutes of Health.
[3] Grant, B.F., Dawson, D.A., Stinson, F.S., Chou, S.P., Du-
four, M.C. and Pickering, R.P. (2004) The 12-month pre-
valence and trends in DSM-IV alcohol abuse and depen-
dence: United States, 1991-1992 and 2001-2002. Drug
Alcohol Dependence, 74, 223-234.
http://dx.doi.org/10.1016/j.drugaldep.2004.02.004
[4] Hasin, D.S., Stinson, F.S., Ogburn, E. and Grant, B.F.
(2007) Prevalence, correlates, disability, and comorbidity
of DSM-IV alcohol abuse and dependence in the United
States: results from the National Epidemiologic Survey
on alcohol and related conditions. Archives of General
Psychiatry, 64, 830-842.
http://dx.doi.org/10.1001/archpsyc.64.7.830
[5] Kessler, R.C., Crum, R.M., Warner, L.A., Nelson, C.B.,
Schulenberg, J. and Anthony, J. (1997) Lifetime co-oc-
currence of DSM-III-R alcohol abuse and dependence
with other psychiatric disorder in the national comorbid-
ity survey. Archives of General Psychiatry, 54, 313-321.
http://dx.doi.org/10.1001/archpsyc.1997.0183016003100
5
[6] Caldwell, C.B. and Gottesman, I.I., (1991) Sex differ-
ences in the risk for alcoholism: A twin study. Behavior
Genetics, 21, 563.
http://dx.doi.org/10.1007/BF01066682
[7] Heath, A.C., Bucholz, K.K., Madden, P.A.F., Dinwiddie,
S.H., Slutske, W.S., et al. (1997) Genetic and environ-
mental contributions to alcohol dependence risk in a na-
tional twin sample: Consistency of findings in women
and men. Psychological Medicine, 27, 1381-1396.
http://dx.doi.org/10.1017/S0033291797005643
[8] Prescott, C.A., Aggen, S.H. and Kendler, K.S. (1999) Sex
differences in the sources of genetic liability to alcohol
abuse and dependence in a population based sample of
US twins. Alcoholism: Clinical and Experimental Re-
search, 23, 1136-1144.

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 251
http://dx.doi.org/10.1111/j.1530-0277.1999.tb04270.x
[9] Kendler, K.S., Heath, A.C., Neale, M.C., Kessler, R.C.
and Eaves, L.J., (1992) A population-based twin study of
alcoholism in women. JAMA, 268, 1877-1882.
http://dx.doi.org/10.1001/jama.1992.03490140085040
[10] Reich, T., Edenberg, H.J., Goate, A., Williams, J.T., Rice,
J.P., et al. (1998) Genome-wide search for genes affect-
ing the risk for alcohol dependence. American Journal of
Medical Genetics Part B: Neuropsychiatr Genet, 81, 207-
215.
http://dx.doi.org/10.1002/(SICI)1096-8628(19980508)81:
3<207::AID-AJMG1>3.0.CO;2-T
[11] Edenberg, H.J., Dick, D.M., Xuei, X., Tian, H., Almasy,
L., et al. (2004) Variations in GABRA2, encoding the
alpha 2 subunit of the GABA(A) receptor, are associated
with alcohol dependence and with brain oscillations. The
American Journal of Human Genetics, 74, 705-714.
http://dx.doi.org/10.1086/383283
[12] Wang, K.S., Liu, X., Zhang, Q., Pan, Y., Aragam, N. and
Zeng, M. (2011) A meta-analysis of two genome-wide
association studies identifies 3 new loci for alcohol de-
pendence. Journal of Psychiatric Research, 45, 1419-
1425. http://dx.doi.org/10.1016/j.jpsychires.2011.06.005
[13] Hill, S.Y., Shen, S., Zezza, N., Hoffman, E.K., Perlin, M.
and Allan, W. (2004) A genome-wide search for alcohol-
ism susceptibility genes. Am J Med Genet, Part B, 128B,
102-113. http://dx.doi.org/10.1002/ajmg.b.30013
[14] Hill, S.Y., Weeks, D.E., Jones, B.L., Zezza, N. and Stif-
fler, S. (2012) ASTN1 and Alcohol Dependence: Family-
Based Association Analysis in Multiplex Alcohol De-
pendence Families. American Journal of Medical Genet-
ics Part B, 159B, 445-455.
http://dx.doi.org/10.1002/ajmg.b.32048
[15] Zuo, L., Gelernter, J., Zhang, C.K., Zhao, H., Lu, L., et al.
(2012) Genome-Wide association study of alcohol depen-
dence implicates KIAA0040 on Chromosome 1q. Neuro-
psychopharmacology, 37, 557-566.
http://dx.doi.org/10.1038/npp.2011.229
[16] Helzer, J.E., Robins, L.N., McEvoy, L.T., Spitznagel,
E.L., Stoltzman, R.K., et al. (1985) A comparison of cli-
nical and diagnostic interview schedule diagnoses. Physi-
cian reexamination of lay-interviewed cases in the gene-
ral population. Archives of General Psychiatry, 42, 657-
666.
http://dx.doi.org/10.1001/archpsyc.1985.0179030001900
3
[17] American Psychiatric Association (1980) Diagnostic and
Statistical Manual of Mental Disorders (DSM-III). Wash-
ington, DC.
[18] American Psychiatric Association (1987) Diagnostic and
Statistical Manual of Mental Disorders-Revised (DSM-
III-R). Washington, DC.
[19] Feighner, J.P., Robins, E., Guze, S.B., Woodruff, R.A. Jr.,
Winokur, G. and Munoz, R. (1972) Diagnostic criteria for
use in psychiatric research. Archives of General Psychia-
try, 26, 57-63.
http://dx.doi.org/10.1001/archpsyc.1972.0175019005901
1
[20] Janca, A., Robins, L.N., Cottler, L.B. and Early, T.S.
(1992) Clinical observation of assessment using the
Composite International Diagnostic Interview (CIDI). An
analysis of the CIDI field trials B wave II at the St. Louis
site. The British Journal of Psychiatry, 160, 815-818.
http://dx.doi.org/10.1192/bjp.160.6.815
[21] Cottler, L.B., Robins, L.N. and Helzer, J.E. (1989) The
reliability of the CIDI-SAM: A comprehensive substance
abuse interview. British Journal of Addiction, 84, 801-
814.
http://dx.doi.org/10.1111/j.1360-0443.1989.tb03060.x
[22] Tellegen, A., Lykken, D.T., Bouchard, T.J., Wilcox, K.J.,
Segal, N.L. and Rich, S. (1988) Personality similarity in
twins reared apart and together. Journal of Personality
and Social Psychology, 54, 1031-1039.
http://dx.doi.org/10.1037/0022-3514.54.6.1031
[23] O’Connell, J.R. and Weeks, D.E. (1998) PedCheck: A
program for identifying genotype incompatibilities in lin-
kage analysis. The American Journal of Human Genetics,
63, 259-266. http://dx.doi.org/10.1086/301904
[24] Lange, K., Cantor, R., Horvath, S., Perola, M., Sabatti, C.,
et al. (2001) Mendel version 4.0: A complete package for
the exact genetic analysis of discrete traits in pedigree
and population data sets. The American Journal of Hu-
man Genetics, 69, 504.
[25] Mukhopadhyay, N., Almasy, L., Schroeder, M., Mulvihill,
W.P. and Weeks, D.E. (2005) Mega2: data-handling for
facilitating genetic linkage and association analyses. Bio-
informatics, 21, 2556-2557.
http://dx.doi.org/10.1093/bioinformatics/bti364
[26] Mukhopadhyay, N., Tang, X. and Weeks, D.E. (2010)
Genetic Map Interpolator. Annual Meeting of the Ameri-
can Society of Human Genetics, Washington, D.C.
[27] Kong, X., Murphy, K., Raj, T., He, C., White P.S. and
Matise, T.C. (2004) A combined linkage-physical map of
the human genome. The American Journal of Human
Genetics, 75, 1143-1148.
http://dx.doi.org/10.1086/426405
[28] Matise, T.C., Chen, F., Chen, W., De La Vega F.M.,
Hansen, M., et al. (2007) A second-generation combined
linkage physical map of the human genome. Genome Re-
search, 17, 1783-1786.
http://dx.doi.org/10.1101/gr.7156307
[29] Laird, N.M., Horvath, S. and Xu, X. (2000) Implement-
ing a unified approach to family-based tests of associa-
tion. Genetic Epidemiology, 19, S36-42.
http://dx.doi.org/10.1002/1098-2272(2000)19:1+<::AID-
GEPI6>3.0.CO;2-M
[30] Rabinowitz, D. and Laird, N. (2000) A unified approach
to adjusting association tests for population admixture
with arbitrary pedigree structure and arbitrary missing
marker information. Human Heredity, 504, 227-233.
http://dx.doi.org/10.1159/000022918
[31] Spielman, R.S., McGinnis, R.E. and Ewens, W.J. (1993)
Transmission test for linkage disequilibrium: The insulin
gene region and insulin-dependent diabetes mellitus
(IDDM). The American Journal of Human Genetics, 52,
506-516.
[32] Sinsheimer, J.S., Blangero, J. and Lange, K. (2000) Ga-
mete-competition models. The American Journal of Hu-

S. Y. Hill et al. / Open Journal of Genetics 3 (2013) 243-252 252
man Genetics, 66, 1168-1172.
http://dx.doi.org/10.1086/302826
[33] Barrett, J.C., Fry, B., Maller, J. and Daly, M.J. (2005)
HAPLOVIEW: Analysis and visualization of LD and
haplo-type maps. Bioinformatics, 21, 263-265.
http://dx.doi.org/10.1093/bioinformatics/bth457
[34] Gabriel, S.B., Schaffner, S.F., Nguyen, H., Moore, J.M.,
Roy, J., et al. (2002) The structure of haplotype blocks in
the human genome. Science, 296, 2225-2229.
http://dx.doi.org/10.1126/science.1069424
[35] Horvath, S., Xu, X., Lake, S.L., Silverman, E.K., Weiss,
S.T. and Laird, N.M. (2004) Family-based tests for asso-
ciating haplotypes with general phenotype data: Applica-
tion to asthma genetics. Genetic Epidemiology, 26, 61-69.
http://dx.doi.org/10.1002/gepi.10295
[36] Pruim, R.J., Welch, R.P., Sanna, S., Teslovich, T.M.,
Chines, P.S., et al. (2010) LocusZoom: Regional visuali-
zation of genome-wide association scan results. Bioin-
formatics, 26, 2336-2337.
http://dx.doi.org/10.1093/bioinformatics/btq419
[37] Peng, W., Wang, H.Y., Miyahara, Y., Peng, G. and Wang,
R.-F. (2008) Tumor-associated galectin-3 modulats the
function of tumor-reactive T cells. Cancer Research, 68,
7228-7236.
http://dx.doi.org/10.1158/0008-5472.CAN-08-1245
[38] Neidhardt, J., Fehr, S., Kutsche, M., Lohler, J. and
Schachner, M. (2003) Tenascin-N: Characterization of a
novel member of the tenascin family that mediates neu-
rite repulsion from hippocampal explants. Molecular and
Cellular Neuroscience, 23, 193-209.
http://dx.doi.org/10.1016/S1044-7431(03)00012-5
[39] Hill, S.Y., Shen, S., Locke-Wellman J., Matthews, A.G.
and McDermott, M. (2008) Psychopathology in offspring
from multiplex alcohol dependence families: A prospec-
tive study during childhood and adolescence. Psychiatry
Research, 160, 155-166.
http://dx.doi.org/10.1016/j.psychres.2008.04.017
[40] Hill, S.Y., Tessner, K.D., McDermott, M.D. (2011) Psy-
chopathology in offspring from families of alcohol de-
pendent female probands: A prospective study. Journal of
Psychiatric Research, 45, 285-294.
http://dx.doi.org/10.1016/j.jpsychires.2010.08.005
[41] Hill, S.Y. (2010) Neural Plasticity, human genetics, and
risk for alcohol dependence. In: Reilly, M.T. and Lov-
inger, D.M., Eds., Functional Plasticity and Genetic
Variation, Academic Press, 53-94.
http://dx.doi.org/10.1016/S0074-7742(10)91003-9
[42] Sahana, G., Guldbrandtsen, B., Janss, C. and Ott, J. (2010)
Comparison of association mapping methods in a com-
plex pedigree population. Genetic Epidemiology, 34, 455-
462. http://dx.doi.org/10.1002/gepi.20499
[43] Risch, N. and Merikangas, K. (1996) The future of ge-
netic studies of complex human diseases. Science, 273,
1516-1517.
http://dx.doi.org/10.1126/science.273.5281.1516
[44] Maher, B. (2008) Personal genomes: The case of missing
heritability. Nature, 456, 18-21.
http://dx.doi.org/10.1038/456018a
[45] Mitchell, K.J. and Porteous, D.J. (2009) GWAS for psy-
chiatric disease: Is the framework built on a solid founda-
tion. Molecular Psychiatry, 14, 740-745.
http://dx.doi.org/10.1038/mp.2009.17
[46] Suarez, B.K., Culverhouse, R., Jin, C.H. and Hinrichs, A.
(2007) Linkage, case-control association, and family-
based association tests for complex disorders. BMS Pro-
ceedings, 1, S43.
http://www.biomedcentral.com/1753-6561/1/S1/S43