DAPK1 variants are associated with Alzheimer’s
disease and allele-specific expression
Yonghong Li1,*, Andrew Grupe1, Charles Rowland1, Petra Nowotny2, John S.K. Kauwe2,
Scott Smemo2, Anthony Hinrichs2, Kristina Tacey1, Timothy A. Toombs1, Shirley Kwok1,
Joseph Catanese1, Thomas J. White1, Taylor J. Maxwell2, Paul Hollingworth3,
Richard Abraham3, David C. Rubinsztein4, Carol Brayne5, Fabienne Wavrant-De Vrie `ze6,
John Hardy6, Michael O’Donovan3, Simon Lovestone7, John C. Morris2, Leon J. Thal8,
Michael Owen3, Julie Williams3and Alison Goate2
1Celera Diagnostics, 1401 Harbor Bay Parkway, Alameda, CA 94502, USA,2Departments of Psychiatry, Neurology,
Genetics and Biology, Washington University, St Louis, MO 63110, USA,3Department of Psychological Medicine and
Biostatistics and Bioinformatics Unit, Cardiff University, Wales College of Medicine, Cardiff CF14 4XN, UK,
4Department of Medical Genetics, Cambridge Institute for Medical Research, Cambridge CB2 2XY, UK,5Department
of Public Health and Primary Care, Institute of Public Health, Cambridge CB2 2SR, UK,6National Institute on Aging,
Bethesda, MD 20892, USA,7Departments of Old Age Psychiatry and Neuroscience, MRC Centre for
Neurodegeneration Research, King’s College London, Institute of Psychiatry, London SE5 8AF, UK and8Department
of Neurosciences, University of California, San Diego, CA 92093, USA
Received May 2, 2006; Revised July 7, 2006; Accepted July 11, 2006
Genetic factors play an important role in the etiology of late-onset Alzheimer’s disease (LOAD). We tested
gene-centric single nucleotide polymorphisms (SNPs) on chromosome 9 and identified two SNPs in the
death-associated protein kinase, DAPK1, that show significant association with LOAD. SNP rs4878104 was
significantly associated with LOAD in our discovery case–control sample set (WU) and replicated in each
of two initial validation case–control sample sets (P < 0.05, UK1, SD). The risk-allele frequency of this SNP
showed a similar direction in three other case–control sample sets. A meta-analysis of the six sample
sets combined, totaling 2012 cases and 2336 controls, showed an allelic P-value of 0.0016 and an odds
ratio (OR) of 0.87 (95%CI: 0.79–0.95). Minor allele homozygotes had a consistently lower risk than major
allele homozygotes in the discovery and initial two replication sample sets, which remained significant in
the meta-analysis of all six sample sets (OR 5 0.7, 95%CI: 0.58–0.85), whereas the risk for heterozygous sub-
jects was not significantly different from that of major allele homozygotes. A second SNP, rs4877365, which
is in high linkage disequilibrium with rs4878104 (r25 0.64), was also significantly associated with LOAD
(meta P 5 0.0017 in the initial three sample sets). Furthermore, DAPK1 transcripts show differential allelic
gene expression, and both rs4878104 and rs4877365 were significantly associated with DAPK1 allele-specific
expression (P 5 0.015 to <0.0001). These data suggest that genetic variation in DAPK1 modulates suscepti-
bility to LOAD.
Late-onset Alzheimer’s disease (LOAD) is the most common
type of dementia. The prevalence of AD is increasing because
of a longer lifespan. In addition to age, genetic variation is
thought to play a major role in the etiology of LOAD, and
is likely to be modulated through complex gene–gene and
gene–environment co-actions and interactions. Variation in
the apolipoprotein E (APOE) gene contributes to both risk
and age at disease onset of LOAD (1,2). Although whole
# 2006 The Author(s)
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Human Molecular Genetics, 2006, Vol. 15, No. 17
Advance Access published on July 17, 2006
genome linkage scans of LOAD have implicated candidate
chromosomal regions (3), other susceptibility genes have not
been firmly established.
Chromosome 9 is one of the several prominent chromo-
somes where one or more linkage peaks have been observed
by multiple whole genome scans (4–8). A number of genes
on this chromosome, including UBQLN1 and ABCA1, have
been reported to be associated with LOAD (9–11), but these
findings remain controversial (12–16). To identify genetic
variants associated with LOAD on chromosome 9, we
scanned single nucleotide polymorphisms (SNPs) across the
entire chromosome using DNA samples collected from
LOAD patients and similar non-demented individuals. The
scan was not based on linkage disequilibrium (LD) of
markers across the whole chromosome but rather a targeted,
gene-centric approach; SNPs were selected from dbSNP and
the Celera human genome SNP database. Putative functional
SNPs [non-synonymous, UTR, predicted transcription factor
binding site (TFBS) and exon splice silencer SNPs] were
selected first, followed by other types of gene-centric SNPs.
To reduce the likelihood of identifying spurious associations,
we employed up to six independently collected LOAD
case–control sample sets of Caucasian descent (Table 1).
Genotyping and analysis were performed in several phases
including a hypothesis-generating discovery phase in one
sample set, followed by a hypothesis-validation phase in two
other independent sample sets. This led us to identify two
SNPs, rs4878104 and rs4877365, in the death-associated
protein kinase 1 (DAPK1) that are significantly associated
with LOAD. SNP rs4878104 was then evaluated in three
other independently collected LOAD case–control sample
sets (UK2, UK3, Linkage). We also assessed the potential bio-
logical consequences of the LOAD-associated SNPs and
found that these genetic variants may directly or indirectly
modulate allele-specific expression of DAPK1.
DAPK1 variants are associated with LOAD
In the discovery phase, we genotyped 674 SNPs in 347 genes
in one sample set (Fig. 1A and Supplementary Material,
Fig. S1); 642 SNPs were genotyped first in the WU sample
set and 32 SNPs first in the UK1 sample set. Forty-seven of
these SNPs met a nominally significant threshold (P , 0.05)
for association with LOAD based upon a x2test for allelic
association (Fig. 1A). In the validation phase, we genotyped
these 47 markers in two other sample sets. Three markers
were replicated in the combined validation sample sets
(allelic P , 0.05, one-sided; Table 2, Supplementary Material,
Tables S1 and S2), including rs4878104, which was signifi-
cantly associated with LOAD in each of the three sample
sets (Table 2). A meta-analysis of all three sample sets
showed an allelic P-value of 0.0006 and an odds ratio (OR)
of 0.79 (95%CI: 0.69–0.90), suggesting that the minor allele
is protective. However, the genotypic analysis suggests that
while homozygotes for the minor allele are at reduced risk
(OR ¼ 0.55, CI: 0.4–0.74), heterozygous subjects are not at
significantly different risk from subjects homozygous for the
major allele (Fig. 1B). The most significant associated
marker, rs4878104, is located in intron 2 of the DAPK1
gene, which maps to 9q22 and is located directly under a pre-
viously reported LOAD linkage peak (7,10). In addition, two
other markers from the chromosome 9 scan were replicated
in the combined validation sample set and remained signifi-
cant in the meta-analysis of all three sample sets (Supplemen-
tary Material, Tables S1 and S2). One marker, rs2018621, in
protein-O-mannosyltransferase 1 (POMT1), was significant
in one of the validation sample sets. The allele frequency of
the marker is relatively low, with 3.1% in controls and 5.3%
in cases. Another marker, rs2274159, in DFNB31 was not sig-
nificant in either of the two validation sample sets, individu-
ally, but reached significance when the two were combined.
Defects in DFNB31 cause hereditary non-syndromic recessive
hearing loss. The POMT1 and DFNB31 markers are 44.2 and
27.0 Mbp distal from the DAPK1 marker, respectively, and
share little LD (data not shown).
Because of the stronger association of the DAPK1 variant
with LOAD, and since DAPK1 is an excellent biological can-
didate gene (see discussion section) within a previously ident-
ified linkage region, we focused our further study on DAPK1
by genotyping the most significant marker in other sample sets
and fine-mapping the DAPK1 region. We examined rs4878104
in three other LOAD sample sets, including two case–control
sample sets from the UK and one US sample set generated by
selecting one case per family from our genetic linkage sample
and similar controls without dementia collected in the St Louis
area. In these sample sets, we did not find significant evidence
for association with LOAD (Table 2), however, the direction-
ality of the risk allele was the same as in the initial three
Table 1. Sample set characteristics
Sample set Sample size
Country of origin AAO
ApoE4 allele frequency (%)
AAO, age at disease onset for cases in average + SD; AAE, age at exam for controls in average +SD.
Human Molecular Genetics, 2006, Vol. 15, No. 17 2561
sample sets. A meta-analysis of rs4878104 with all five repli-
cation sample sets remained significant (P ¼ 0.014) and the
meta-analysis of all six samples showed a P-value of
0.0016. rs4878104 did not show a significant interaction
with APOE e4 presence or absence in any of the six sample
sets, individually or together.
We examined the LD structure of the region containing
rs4878104 in HapMap (HapMap public release #16c.1;
http://www.hapmap.org). The marker, rs4878104, is within a
58.5 kb region of high LD from rs913778 to rs888333,
which encompasses part of intron 2 of the DAPK1 gene and
a recently predicted gene LOC643284, which is homologous
to 40S ribosomal protein S29 (Fig. 1C). To fine map this
region, we identified 14 tagging SNPs with r2, 0.8. One of
the tagging SNPs was rs4878104 itself and another appeared
to have a SNP nearby that prevented us from developing an
assay. We therefore genotyped the remaining 12 tagging
SNPs in the WU sample set (Supplementary Material,
Table S3). One tagging marker, rs4877365, showing a r2of
0.68 (in cases þ controls) with rs4878104, was also signifi-
cantly associated with LOAD (P ¼ 0.020) in the WU sample
set and replicated in the combined initial two validation
sample sets. This marker was significant in the SD sample
set and trended to significance in the UK1 sample set
(Table 2). Similar to rs4878104, it showed a stronger effect
under a recessive model. The meta-analysis of all the three
sample sets showed a significant association for this marker
(P ¼ 0.0017, allelic). Two other SNPs in this LD block
showed r2. 0.5 with rs4878104; one of these SNPs,
rs7036598, showed significant association in the exploratory
sample set, but was not replicated in either of the initial repli-
cation sample sets.
We performed a permutation-based haplotype analysis
using the program PHASE. Six SNPs were identified using
Tag ‘n’Tell (http://snp.cgb.ki.se/tagntell/)
rs871495, rs7036598, rs3128519, rs3128521, rs4878104),
which tag the haplotypes present at a frequency .1% in our
combined samples. This analysis failed to detect significant
association with disease (P ¼ 0.35). Placing haplotypes into
their evolutionary context may increase biological information
(17). To take advantage of this increase in information, we
used tree scanning, a method, which incorporates the evol-
utionary history of the haplotypes, to analyze our data. Tree
scanning in this region failed to detect significant association
with LOAD. Inspection of the network suggests that at least
13 of the 16 SNPs used in this analysis (including two SNPs
whichwere significant in
(mutations at the same site are observed in independent
parts of the network i.e. the mutation has occurred multiple
times in evolutionary history).
A causal SNP that has experienced homoplasy should be
significant when tested alone (all haplotypes grouped by
state of the SNP). It should also show evidence for effects at
each individual branch marked by the SNP if the comparison
is made in the immediate area around the branch, provided the
sample size is sufficient. In contrast, if the SNP is in LD with a
second causal SNP, then the individual branches will only
exhibit association if the second causal allele is also present
on the haplotype branch. This hypothesis was tested with an
alternative approach described in the methods. Markers
rs4877365 and rs4878104 were significant in the single SNP
analyses; both are inferred to have experienced homoplasy
and occur at the same three regions of the tree (Fig. 1D).
Using these regions, the tree can be collapsed into five haplo-
type groups. Each of the three groups with the GC haplotype
(groups 2, 3, 5) were individually tested against group 1,
which has the AT haplotype and is internal to the others.
Two of the three comparisons to group 1 were nominally
Figure 1. (A) Association with LOAD for exploratory markers on chromo-
some 9. P-values of 674 SNPs, their physical positions (Celera assembly
R27) and the distribution of annotated genes (bar graph) are shown. A separate
line denotes P ¼ 0.05. Linkage peak regions are noted along with references.
The arrow points to rs4878104 in DAPK1, rs2274159 in DFNB31 and
rs2018621 in POMT1, respectively (from left to right). (B) rs4878104 geno-
type and LOAD risk. Odds ratios and confidence intervals of minor allele
homozygous (TT) and heterozygous (TC) subjects in each individual sample
set and the three sample sets combined (All). TT_WU: 0.53 (0.33–0.86);
TC_WU: 0.92 (0.68–1.26); TT_SD: 0.57 (0.34–0.96); TC_SD: 0.80 (0.58–
1.11); TT_UK1: 0.55 (0.36–0.84); TC_UK1: 0.94 (0.73–1.22); TT_All:
0.55 (0.40–0.74); TC_All: 0.90 (0.74–1.09). (C) DAPK1 gene structure,
HapMap LD diagram and positions of the disease-associated and expression
markers. (D) Haplotype analysis based upon 16 SNPs in a single haplotype
block containing the associated SNPs in DAPK1. Each rectangle represents
a haplotype group. Mutations at rs4877365 are labeled as ‘G $ A’ and
‘C $ T’ for rs4878104. The haplotypes have been grouped to illustrate the
key transitions involving rs4877365 and rs4878104. P-values were calculated
by Fisher’s exact test from cases–controls in the adjacent haplotype groups.
N ¼ number of chromosomes. The specific haplotypes represented in each
group will be provided upon request to the authors.
2562Human Molecular Genetics, 2006, Vol. 15, No. 17
significant (group 2 and group 5 or groups 4 and 5; Fig. 1D)
with a highly significant Fisher’s combined probabilities test
(P ¼ 0.0054 for groups 2, 3 and 5; P ¼ 0.0058 for groups 2,
3 and 4 þ 5). The non-significant P-value of group 3 compared
with group 1 is probably because of the small sample size
(N ¼ 17). Group 4 only has a change at rs4877365, and was
not significantly different from group 5 or group 1, but exhib-
ited an intermediate ratio of cases to controls.
SNPs associated with LOAD are also associated with
DAPK1 allele-specific expression
Because of the intronic nature of the significant SNPs, we
sought to determine whether the disease-associated variants
have a direct effect (cis) on DAPK1 transcript levels. This
information can be obtained directly by measuring the relative
expression level of the allele 1 specific transcript and the allele
2 specific transcript. Because this method measures both
allele-specific transcripts in the same sample, the measurement
is much less impacted by biological variation that occurs when
comparing expression levels between different samples. In
addition, this method does not require normalization to a
house-keeping gene because the transcript level in each
sample should be identical for allele 1 and allele 2, unless
there is a cis-controlling factor that is present on only one
allele/haplotype and changes its relative expression level
(e.g. a mutation in a repressor element on allele 1/haplotype
1 leads to an expression ratio that is higher for allele 1 than
allele 2). Therefore, allele-specific expression analysis is
much more sensitive to small relative changes in expression
levels, whereas across sample comparisons include much
more noise (biological variation between samples and techni-
cal noise from normalizing to a house-keeping gene) and can
only detect larger expression differences. We thus tested
DAPK1 for allelic expression differences and evaluated
whether rs4878104 or rs4877365 is associated with the
markers could not be used directly for this analysis because
they are located within a DAPK1 intron; therefore, we used
a two-step approach. First, we genotyped two high-frequency
SNPs that map to the DAPK1 transcript and identified 69 Cau-
casians, who were heterozygous for at least one of these
expression markers (46 for rs3118863 and 48 for rs3818584;
minor allele frequency: 0.44 for rs3118863 and 0.47 for
rs3818584). The differences in the two allele-specific tran-
scripts were as large as ?2-fold.
To determine whether the two disease-associated SNPs,
rs4878104 and rs4877365, are associated with the observed
DAPK1 allele-specific expression, we next genotyped these
two SNPs in all 69 heterozygous carriers of rs3118863
and/or rs3818584. The two expression SNPs are 72 and
129 kb away from rs4878104, the closer of the two
disease-associated markers, and do not reside in the high
LD region shared by the two disease-associated markers
(Fig. 1C and Supplementary Material, Table S4). If either
rs4878104 or rs4877365 is the sole causative element, it
would be expected that only heterozygous carriers of the
disease-associated variant show an allele-specific expression
(Fig. 2A). As shown in Figure 2B, both homozygous and het-
erozygous carriers of rs4878104 and rs4877365 showed
allele-specific differences in expression. However, the allele-
specific expression ratio was significantly higher in hetero-
zygous carriers of either rs4878104 or rs4877365 when com-
pared with homozygous carriers (P , 0.05, Fig. 2B), whereas
the genomic DNA control showed no such genotype-
dependent difference (data not shown). These results indicate
that the genotype status of both LOAD-associated SNPs
Table 2. Allelic association of the DAPK1 SNPs rs4878104 and rs4877365 with LOAD
Allelic P Allelic ORPower
SD þ UK1 þ Linkage
þ UK2 þ UK3c
WU þ SD þ UK1
þ Linkage þ UK2
SD þ UK1c
WU þ SD þ UK1d
0.79 (0.63: 0.97)
0.78 (0.62: 0.97)
0.81 (0.68: 0.97)
0.94 (0.79: 1.13)
0.88 (0.73: 1.07)
0.96 (0.82: 1.12)
0.89 (0.82: 0.96)
225934 853201234.43451067924233637.60.00160.87 (0.79: 0.95)
0.76 (0.61: 0.96)
0.79 (0.62: 0.99)
0.85 (0.70: 1.03)
0.82 (0.71: 0.95)
0.80 (0.69: 0.92)
aCounts of genotype 11, 12 and 22 and minor allele frequency (MAF).
bExploratory sample set, two-sided P-value and OR (95%CI) are shown.
cReplication sample set, one-sided P-value and OR (90%CI) are shown.
dTwo-sided P-value, Cochran Mantel and Haenzsel test, using sample set as the stratifying variable.
e11, 12 and 22 denote TT, TC and CC, respectively, for rs4878104 and 11, 12 and 22 denote AA, AG and GG, respectively, for rs4877365.
Human Molecular Genetics, 2006, Vol. 15, No. 17 2563
Our chromosome 9 scan resulted in the identification of two
SNPs, rs4878104 and rs4877365, in DAPK1 that show signifi-
cant association with LOAD. Unlike APOE that shows very
strong association with LOAD even in small studies,
rs4878104 was only marginally associated with LOAD
(allelic P meta ¼ 0.0016). However, allele frequencies in
cases and controls were consistent in five out of six sample
sets, with all six showing the same directionality of the risk
allele. Besides allele frequency, homozygosity was more con-
sistently and strongly associated with LOAD than heterozyg-
osity in the initial three sample sets and the meta-analysis of
all six sample sets. Several factors may have contributed to
the non-significant result in three of the six sample sets.
First, the original finding is likely to be an overestimate of
the actual risk and thus our calculated power to replicate the
initial result may be inflated. Based on the effect size of the
five replication sample meta-analysis, we estimate that 1950
cases and 1950 controls are required to achieve 80% power
and a one-sided significance of P ¼ 0.05. Second, the tested
marker may not reflect the causal variant thereby reducing
the power of the study. Third, genetic heterogeneity in
LOAD may contribute to the non-significant association in
some samples. Fourth, family-based association analyses
have little power when the effect size of the tested locus is
much smaller than at least one other untested locus (18).
Fifth, our haplotype analyses suggest that these SNPs are
subject to homoplasy, which can reduce the power for tests
of single SNP association. Overall, the disease association
for a complex disease is generally evaluated through a
meta-analysis of all tested sample sets, which also provides
greater power than the analysis of multiple smaller sample
sets (19,20). In this regard our result derived from six indepen-
dent sample sets, totaling over 4000 samples, suggests that
rs4878104 may be a genuine but weak risk factor for LOAD.
Another SNP, rs4877365, was significantly associated with
LOAD in two of the initial three sample sets and trended
towards significance in the third sample set. Similar to
rs4878104, this marker also showed a stronger effect under a
recessive model (data not shown). The results of our haplotype
analyses suggest that one or both of these polymorphisms may
be causal variants given that they are independently significant
on multiple haplotype backgrounds and are more significant
when these historically unrelated haplotypes are grouped
together in the individual SNP tests. However, because of
the lack of power for the contrasts between groups 3 and 1,
we cannot rule out the possibility that these variants are in
strong LD with unknown causal variants. Such causal variants
may affect the transcription of DAPK1 and/or the expression/
function of LOC643284, a recently predicted gene similar to
40S ribosomal protein S29 within this region of high LD.
Figure 2. DAPK1 allele-specific gene expression stratified by the LOAD-associated markers rs4878104 or rs4877365. (A) Allele-specific transcript ratios that are
different from one require heterozygosity of the causal variant (X,Y). Markers that show association with allele-specific expression, when stratified by genotype,
are expected to be in strong LD with the causal variant or represent the causal variant itself. Markers that are not in LD with the causal variant are unlikely to
show association with allele-specific expression. A1, A2: allele-specific expression marker. Note that the alleles (X/Y) of the causal SNP can occur on either
allele-specific transcript, if the causal SNP is not in perfect LD with the expression marker (A1/A2). (B) The ratio of allele-specific gene expression is shown for
cDNA. Two markers, rs3818584 and rs3118863, were analyzed to measure allelic gene expression (Hom: homozygotes; Het, heterozygotes). A Mann–Whitney
test was performed to assess the association. The relative expression was calculated as 2DCt(DCtwas determined by subtracting the smaller Ctvalue of one allele
PCR reaction from the larger Ctvalue of the other allele PCR reaction).
2564 Human Molecular Genetics, 2006, Vol. 15, No. 17
DAPK1 is a Ca2þ/calmodulin-dependent serine/threonine
kinase that plays a pro-apoptotic role in the programmed
cell death cascade, including neuronal apoptosis. DAPK1 is
predominantly expressed in the brain and lung: in embryonic
rat brain DAPK1 mRNA is present at high levels in the cer-
ebral cortex, cerebellar Purkinje cells and hippocampus, but
is largely restricted to the hippocampus in adult rat brain
(21). Western blot analysis detected DAPK1 protein in rat
cortex, hippocampus and olfactory bulb, but not in cerebellum,
hindbrain or mesencephalon (22). This expression pattern is
particularly relevant to AD, because the hippocampus and
cortex are the most severely affected brain regions, whereas
regions of low expression, such as cerebellum and basal
ganglia are less affected in AD (23). Increased DAPK1
kinase activity or expression has been observed in neuronal
cell death (24), and neurons lacking DAPK1 are less suscep-
tible to apoptotic insults in cell culture and knockout animal
models (25,26). DAPK1 is a transcriptional target of p53
(27), and is expected to play an important role in
p53-mediated apoptosis; p53 expression is increased in the
brains of AD patients (28) and the intracellular domain of
amyloid precursor protein directly regulates p53 expression
(29). In addition, a recent study has reported that DAPK1
kinase activity-deficient mice are more efficient learners and
have better spatial memory than wild-type mice (30).
DAPK1 is therefore an excellent positional and biological can-
didate gene for AD.
Our functional study revealed that DAPK1 expression
shows allelic imbalance, a phenomenon (31) that is seen for
genetic risk factors in other complex diseases such as
calpain 10 and type 2 diabetes (32), and may explain the
genetic association of DAPK1 with LOAD. In addition, our
study shows that the genotypes of the two intronic
LOAD-associated SNPs are significantly associated with
DAPK1 allele-specific expression, although they are unlikely
to be the sole cis-acting variants. Because of the intronic
nature of the disease-associated SNPs, we cannot determine
which allele is associated with increased expression. Never-
theless, this association suggests that these SNPs may interact
with other unidentified polymorphic cis-acting regulatory
factors to influence the level of DAPK1 transcripts. We
cannot exclude the possibility that they are in high LD with
other polymorphic cis-acting elements governing DAPK1 tran-
scription. Regardless of the molecular mechanism of this regu-
lation, considering that DAPK1 allele-specific expression
predicts variation in DAPK1 protein/activity and thus neuronal
apoptotic potential, allele-specific expression of DAPK1 var-
iants provide a plausible explanation linking the genetic
association with LOAD to a disease-relevant functional
outcome. This hypothesis needs to be tested by correlating
DAPK1 brain expression with genotype. However, expression
of DAPK1 can be induced during neuronal apoptosis (21),
which could potentially mask the effect of allele-specific
expression of DAPK1 when samples frompatients with different
degrees of apoptosis are assayed. Therefore, such an experiment
should be ideally performed with tissues from normal brains.
An increasing body of evidence suggests that apoptosis may
play a role in AD etiology. For example, changes in presenilin
expression or activity have been associated with apoptotic
phenotypes in cell-based models (33) and animal knock-down
experiments (33,34). Genetic evidence for sporadic AD, such
as the disease associations with DAPK1, GAPD (35) and
LOC439999 (36) variants, also point to apoptosis as a
In summary, these results, together with the existing data on
the biological functions of DAPK1 (21,22,24,30,37,38),
strongly implicate DAPK1 as one of the genetic factors affect-
ingsusceptibility to LOAD.
(i) common SNPs in DAPK1 are significantly associated
with LOAD, (ii) DAPK1 is in a previously reported linkage
peak, (iii) DAPK1 is a pro-apoptotic mediator in the pro-
grammed cell death pathway, (iv) DAPK1 is highly expressed
in brain, particularly adult hippocampus and cortex, and abol-
ition of DAPK1 kinase activity enhances learning and
memory, and (v) DAPK1 transcripts show allelic expression
differences and the SNPs associated with LOAD risk may
directly or indirectly modulate this allele-specific gene
expression. DAPK1 genotype/activity may influence risk for
AD by influencing the cell number in the hippocampus and/
or by influencing the response to environmental stimuli such
as amyloid beta. As a small molecule inhibitor of DAPK1
has been described for potential use in the treatment of
ischemia-induced acute brain injury (37) and for attenuating
neuronal damage in a chronic infusion model of amyloid
beta toxicity (38), this compound or other classes of DAPK1
inhibitors may be candidates for clinical development and
testing in AD.
MATERIALS AND METHODS
Six LOAD case–control sample sets of Caucasian descent,
collected with informed consent/assent from the participating
individuals and approvals from the participating institutions,
were used in this collaborative study (Table 1). The three
sample sets that were used for discovery and initial replication
are the WU sample set, obtained through the Washington Uni-
versity Alzheimer’s Disease Research Center (ADRC) patient
registry, the SD sample set obtained from the ADRC of the
University of California, San Diego and the UK1 sample set
obtained from the Medical Research Council Late Onset AD
Genetic Resource that included samples from Cardiff Univer-
sity, Wales School of Medicine, King’s College London and
Cambridge University. These samples have been used in our
samples, the UK2 and UK3 samples, have not been previously
described. The same ascertainment and diagnostic instruments
and criteria were used in the collection of all UK samples.
An additional LOAD sample set (‘linkage sample set’) was
generated by selecting one case per family from our genetic
linkage sample of affected sib pairs and matching them to a
corresponding number of Caucasian, non-demented controls
collected in St Louis (these controls are independent of the
controls used in the exploratory sample above) (Table 1).
Genotyping of SNPs was performed by allele-specific real-
time PCR for individual samples using primers designed and
Human Molecular Genetics, 2006, Vol. 15, No. 17 2565
validated in-house (42). Previous analyses showed that the
accuracy of our genotyping is better than 99%, as determined
by internal comparisons of differentially designed assays for
the same marker and comparisons for the same marker
across different groups (35). Linkage cases and controls
were genotyped using SequenomTMtechnology by Washing-
Genotyping: Hardy–Weinberg equilibrium was evaluated
using an exact test as described by Weir (‘Genetic Data Analy-
sis II’, Sinauer Associates, Sunderland MA, 1996, 2nd
edition). Tests for allelic association of SNPs with disease
status were carried out using the x2test or a Fisher’s exact
test where cell sizes were ,5. Markers with minor allele fre-
quency of ?2% in either cases or controls were analyzed.
Meta analyses: When combining data from different sample
sets, association was assessed using the method of Cochran
Mantel and Haenzsel using sample set as the stratifying vari-
Haplotype analyses: We estimated haplotypes of the high
LD region containing rs4878104 and rs4877365 using
16 SNPs (four exploratory and 12 fine mapping markers).
For the haplotype analyses, all SNPs were genotyped in
each of the three sample sets. Haplotypes were estimated for
each population separately. Our previous analyses of these
populations provided no evidence of stratification within or
between these samples (35). To maximize our power to esti-
mate the haplotype network and detect association, we com-
bined the samples for subsequent analyses. Haplotypes were
estimated using the software PHASE (44,45). A set of 95%
plausible haplotype trees was estimated using statistical parsi-
mony in the program TCS (46,47). A permutation-based hap-
lotype analysis using PHASE was performed. Because of
computational constraints, we used Tag ‘n’ Tell (http://snp.
cgb.ki.se/tagntell/) to identify a subset of SNPs, which tag
the haplotypes present at a frequency .1% in our combined
samples. Association with LOAD was tested by tree scanning
using unique haplotypes observed at least five times in the
dataset (17). Tree scanning uses the phylogenetic network to
define tests based on each branch of the tree. Each branch rep-
resents a pooling of haplotypes: haplotypes on one side of the
branch are pooled together and define one allele, whereas the
haplotypes on the other side are pooled to define a separate
allele. This results in a biallelic locus that can be tested for
association with the phenotype. Tree scanning incorporates a
permutation-based multiple test correction and was modified
to deal with case–control data (48).
In cases where a SNP is causal and subject to homoplasy,
tree scanning may fail to detect association. A causal SNP
that is subject to homoplasy should be significant when
tested alone (all haplotypes grouped by state of the SNP). It
should also show evidence for effects at each individual
branch marked by the SNP if the comparison is conditioned
upon other mutations at that SNP, provided the sample size
is sufficient. These tests are performed by comparing the
number of cases and controls in each haplotype group
(defined by mutations at the SNP of interest) using Fisher’s
exact test on a 2?2 contingency table.
DAPK1 allele-specific expression study
DNA and total RNA were extracted from peripheral blood
mononuclear cells of normal blood donors. A cDNA library
was prepared from total RNA using the High Capacity
cDNA Archive Kit (ABI). For DAPK1, two high-frequency
exonic SNPs, rs3118863 or rs3818584, were used to
measure allele-specific gene expression. High-frequency
markers were selected to increase the number of heterozy-
gotes. Allele-specific expression assays were carried out on
cDNA samples with the same real-time PCR procedure as
described for genotyping. The same primers were used for
genotyping and allele-specific expression assays.
For the examination of allele-specific expression, 92 indi-
vidual samples of genomic DNA were first genotyped for
two expression markers, rs3118863 or rs3818584. Sixty-nine
donors of Caucasian descent, heterozygous for rs3118863 or
rs3818584, were then examined for allele-specific gene
expression. cDNA was arrayed in quadruplicates or duplicates
onto 384-well plates, together with appropriate PCR controls,
and were run on an ABI-7900 real-time PCR system under
standard conditions. Genomic DNA was also arrayed onto
the same plate as a control. The relative expression of both
alleles for each expression marker was determined by sub-
tracting the smaller Ct value of one allele PCR reaction
from the larger Ct value of another allele PCR reaction
(dCt). The ratio of two allele-specific transcripts was calcu-
lated as 2dCt(i.e. a one-cycle difference in our real-time
PCR-based assay results in a 2-fold relative difference). For
the statistical analysis, dCt values were obtained as an
average of two to four reactions for each sample and data
point (SE ¼ 0.11 for each assay, averaged across all
samples). As a control, the dCtvalues were also obtained
with heterozygous genomic DNA, which theoretically should
equal zero [actual: 20.05 (+0.13) for rs3118863 and 20.02
(+0.13) for rs3818584; average dCt(+SD)].
For testing the relationship between disease-associated var-
iants and allele-specific expression, the individuals who are
heterozygous of the expression markers were then genotyped
for the two DAPK1 disease-associated markers. The allele-
specific expression level measured by the expression marker
was thenstratifiedby the
disease-associated markers (homozygote or heterozygote).
The Mann–Whitney test was used to assess whether hetero-
zygosity and homozygosity of the LOAD-associated SNPs
are significantly associated with the allele-specific expression
ratio. As a control, the same test was performed for the exper-
imentally determined genotype ratio in heterozygous genomic
DNA (P ¼ 0.45–0.95, data not shown). Statistical significance
was calculated separately for each combination of expression
marker versus disease-associated marker.
Supplementary Material is available at HMG Online.
We thank the families/individuals for their invaluable partici-
pation in this study, John Sninsky and Sam Broder for
2566 Human Molecular Genetics, 2006, Vol. 15, No. 17
stimulating discussions, and our colleagues at Celera Diagnos-
tics for providing expert technical support. We acknowledge
Mary Coats and Elizabeth Grant for coordinating the
Washington University material, Mary Sundsmo for coordi-
nating the University of California, San Diego, case material,
Pamela Moore and Dragana Turic for providing clinical/DNA
samples from the MRC UK Genetic Resource for LOAD.
Funding for this work was partly provided by the National
Institute of Health [Alzheimer’s Disease Research Center
Grants P50 AG05681 (J.C.M.), P50 AG05131 (L.T.); RO1
AG16208 (A. Goate) and PO1 AG03991 (J.C.M.)], the
Medical Research Council, UK (J.W., M.O., M.O’D. and
S.L.), and the Alzheimer’s Research Trust (J.W., M.O.,
M.O’D. and S.L.). P.N. was partly supported by Missouri’s
J.S.K.K. is a Ford Foundation Predoctoral Fellow and was
supported by NIH training grant T32 HG00045. T.J.M. was
supported by MICORTEX and NIH grant GM065509. J.H.
was supported by the NIH intramural program and also by
the VERUM Foundation (DIADEM project). D.C.R. is a
Wellcome Trust Senior Research Fellow in Clinical Science.
Conflict of Interest statement. Some of the authors (Y.L.,
A. Grupe, C.R., K.T., T.A.T., S.K., J.C., T.J.W.) are employed
by Celera Diagnostics. A. Goate received research funding
from and was a consultant to Celera Diagnostics. M.O. and
J.W. received research funding from Celera Diagnostics.
1. Meyer, M.R., Tschanz, J.T., Norton, M.C., Welsh-Bohmer, K.A.,
Steffens, D.C., Wyse, B.W. and Breitner, J.C. (1998) APOE genotype
predicts when–not whether–one is predisposed to develop Alzheimer
disease. Nat. Genet., 19, 321–322.
2. Strittmatter, W.J., Saunders, A.M., Schmechel, D., Pericak-Vance, M.,
Enghild, J., Salvesen, G.S. and Roses, A.D. (1993) Apolipoprotein E:
high-avidity binding to beta-amyloid and increased frequency of type 4
allele in late-onset familial Alzheimer disease. Proc. Natl Acad. Sci. USA,
3. Pastor, P. and Goate, A.M. (2004) Molecular genetics of Alzheimer’s
disease. Curr. Psychiat. Rep., 6, 125–133.
4. Pericak-Vance, M.A., Grubber, J., Bailey, L.R., Hedges, D., West, S.,
Santoro, L., Kemmerer, B., Hall, J.L., Saunders, A.M., Roses, A.D. et al.
(2000) Identification of novel genes in late-onset Alzheimer’s disease.
Exp. Gerontol., 35, 1343–1352.
5. Curtis, D., North, B.V. and Sham, P.C. (2001) A novel method of
two-locus linkage analysis applied to a genome scan for late onset
Alzheimer’s disease. Ann. Hum. Genet., 65, 473–481.
6. Myers, A., Wavrant De-Vrieze, F., Holmans, P., Hamshere, M., Crook, R.,
Compton, D., Marshall, H., Meyer, D., Shears, S., Booth, J. et al. (2002)
Full genome screen for Alzheimer disease: stage II analysis. Am. J. Med.
Genet., 114, 235–244.
7. Blacker, D., Bertram, L., Saunders, A.J., Moscarillo, T.J., Albert, M.S.,
Wiener, H., Perry, R.T., Collins, J.S., Harrell, L.E., Go, R.C. et al. (2003)
Results of a high-resolution genome screen of 437 Alzheimer’s disease
families. Hum. Mol. Genet., 12, 23–32.
8. Olson, J.M., Goddard, K.A. and Dudek, D.M. (2002) A second locus for
very-late-onset Alzheimer disease: a genome scan reveals linkage to 20p
and epistasis between 20p and the amyloid precursor protein region.
Am. J. Hum. Genet., 71, 154–161.
9. Wollmer, M.A., Streffer, J.R., Lutjohann, D., Tsolaki, M., Iakovidou, V.,
Hegi, T., Pasch, T., Jung, H.H., Bergmann, K., Nitsch, R.M. et al. (2003)
ABCA1 modulates CSF cholesterol levels and influences the age at onset
of Alzheimer’s disease. Neurobiol. Aging, 24, 421–426.
10. Bertram, L., Hiltunen, M., Parkinson, M., Ingelsson, M., Lange, C.,
Ramasamy, K., Mullin, K., Menon, R., Sampson, A.J., Hsiao, M.Y. et al.
(2005) Family-based association between Alzheimer’s disease and
variants in UBQLN1. N. Engl. J. Med., 352, 884–894.
11. Katzov, H., Chalmers, K., Palmgren, J., Andreasen, N., Johansson, B.,
Cairns, N.J., Gatz, M., Wilcock, G.K., Love, S., Pedersen, N.L. et al.
(2004) Genetic variants of ABCA1 modify Alzheimer disease risk and
quantitative traits related to beta-amyloid metabolism. Hum. Mutat., 23,
12. Li, Y., Tacey, K., Doil, L., van Luchene, R., Garcia, V., Rowland, C.,
Schrodi, S., Leong, D., Lau, K., Catanese, J. et al. (2004) Association of
ABCA1 with late-onset Alzheimer’s disease is not observed in a case–
control study. Neurosci. Lett., 366, 268–271.
13. Brouwers, N., Sleegers, K., Engelborghs, S., Bogaerts, V., van Duijn,
C.M., Paul De Deyn, P., Van Broeckhoven, C. and Dermaut, B. (2006)
The UBQLN1 polymorphism, UBQ-8i, at 9q22 is not associated with
Alzheimer’s disease with onset before 70 years. Neurosci. Lett., 392,
14. Kamboh, M.I., Minster, R.L., Feingold, E. and Dekosky, S.T. (2005)
Genetic association of ubiquilin with Alzheimer’s disease and related
quantitative measures. Mol. Psychiat., 11, 273–279.
15. Slifer, M.A., Martin, E.R., Haines, J.L. and Pericak-Vance, M.A. (2005)
The ubiquilin 1 gene and Alzheimer’s disease. N. Engl. J. Med., 352,
16. Smemo, S., Nowotny, P., Hinrichs, A.L., Kauwe, J.S., Cherny, S.,
Erickson, K., Myers, A.J., Kaleem, M., Marlowe, L., Gibson, A.M. et al.
(2006) Ubiquilin 1 polymorphisms are not associated with late-onset
Alzheimer’s disease. Ann. Neurol., 59, 21–26.
17. Templeton, A.R., Maxwell, T., Posada, D., Stengard, J.H., Boerwinkle, E.
and Sing, C.F. (2005) Tree scanning: a method for using haplotype trees in
phenotype/genotype association studies. Genetics, 169, 441–453.
18. Li, M., Boehnke, M. and Abecasis, G.R. (2006) Efficient study designs for
test of genetic association using sibship data and unrelated cases and
controls. Am. J. Hum. Genet., 78, 778–792.
19. Lohmueller, K.E., Pearce, C.L., Pike, M., Lander, E.S. and
Hirschhorn, J.N. (2003) Meta-analysis of genetic association studies
supports a contribution of common variants to susceptibility to common
disease. Nat. Genet., 33, 177–182.
20. Skol, A.D., Scott, L.J., Abecasis, G.R. and Boehnke, M. (2006) Joint
analysis is more efficient than replication-based analysis for two-stage
genome-wide association studies. Nat. Genet., 38, 209–203.
21. Yamamoto, M., Takahashi, H., Nakamura, T., Hioki, T., Nagayama, S.,
Ooashi, N., Sun, X., Ishii, T., Kudo, Y., Nakajima-Iijima, S. et al. (1999)
Developmental changes in distribution of death-associated protein kinase
mRNAs. J. Neurosci. Res., 58, 674–683.
22. Tian, J.H., Das, S. and Sheng, Z.H. (2003) Ca2þ-dependent
phosphorylation of syntaxin-1A by the death-associated protein (DAP)
kinase regulates its interaction with Munc18. J. Biol. Chem., 278,
23. Budson, A.E. and Price, B.H. (2005) Memory dysfunction.
N. Engl. J. Med., 352, 692–699.
24. Yamamoto, M., Hioki, T., Ishii, T., Nakajima-Iijima, S. and Uchino,
S. (2002) DAP kinase activity is critical for C(2)-ceramide-induced
apoptosis in PC12 cells. Eur. J. Biochem., 269, 139–147.
25. Pelled, D., Raveh, T., Riebeling, C., Fridkin, M., Berissi, H.,
Futerman, A.H. and Kimchi, A. (2002) Death-associated protein (DAP)
kinase plays a central role in ceramide-induced apoptosis in cultured
hippocampal neurons. J. Biol. Chem., 277, 1957–1961.
26. Schori, H., Yoles, E., Wheeler, L.A., Raveh, T., Kimchi, A. and
Schwartz, M. (2002) Immune-related mechanisms participating in
resistance and susceptibility to glutamate toxicity. Eur. J. Neurosci., 16,
27. Martoriati, A., Doumont, G., Alcalay, M., Bellefroid, E., Pelicci, P.G. and
Marine, J.C. (2005) dapk1, encoding an activator of a p19ARF-p53-
mediated apoptotic checkpoint, is a transcription target of p53. Oncogene,
28. de la Monte, S.M., Sohn, Y.K. and Wands, J.R. (1997) Correlates of p53-
and Fas (CD95)-mediated apoptosis in Alzheimer’s disease. J. Neurol.
Sci., 152, 73–83.
29. Costa, C.A., Sunyach, C., Pardossi-Piquard, R., Sevalle, J., Vincent, B.,
Boyer, N., Kawarai, T., Girardot, N., St George-Hyslop, P. and
Checler, F. (2006) Presenilin-dependent gamma-secretase-mediated
control of p53-associated cell death in Alzheimer’s disease. J. Neurosci.,
Human Molecular Genetics, 2006, Vol. 15, No. 172567
30. Yukawa, K., Tanaka, T., Bai, T., Li, L., Tsubota, Y., Owada-Makabe, K., Download full-text
Maeda, M., Hoshino, K., Akira, S. and Iso, H. (2006) Deletion of the
kinase domain from death-associated protein kinase enhances spatial
memory in mice. Int. J. Mol. Med., 17, 869–873.
31. Pastinen, T. and Hudson, T.J. (2004) Cis-acting regulatory variation in the
human genome. Science, 306, 647–650.
32. Horikawa, Y., Oda, N., Cox, N.J., Li, X., Orho-Melander, M., Hara, M.,
Hinokio, Y., Lindner, T.H., Mashima, H., Schwarz, P.E. et al. (2000)
Genetic variation in the gene encoding calpain-10 is associated with type
2 diabetes mellitus. Nat. Genet., 26, 163–175.
33. Ye, Y. and Fortini, M.E. (1999) Apoptotic activities of wild-type and
Alzheimer’s disease-related mutant presenilins in Drosophila
melanogaster. J. Cell. Biol., 146, 1351–1364.
34. Campbell, W.A., Yang, H., Zetterberg, H., Baulac, S., Sears, J.A., Liu, T.,
Wong, S.T., Zhong, T.P. and Xia, W. (2006) Zebrafish lacking Alzheimer
presenilin enhancer 2 (Pen-2) demonstrate excessive p53-dependent
apoptosis and neuronal loss. J. Neurochem., 96, 1423–1440.
35. Li, Y., Nowotny, P., Holmans, P., Smemo, S., Kauwe, J.S., Hinrichs, A.L.,
Tacey, K., Doil, L., van Luchene, R., Garcia, V. et al. (2004) Association
of late-onset Alzheimer’s disease with genetic variation in multiple
members of the GAPD gene family. Proc. Natl Acad. Sci. USA, 101,
36. Grupe, A., Li, Y., Rowland, C., Nowotny, P., Hinrichs, A.L., Smemo, S.,
Kauwe, J.S., Maxwell, T.J., Cherny, S., Doil, L. et al. (2006) A scan of
chromosome 10 identifies a novel locus showing strong association with
late-onset Alzheimer disease. Am. J. Hum. Genet., 78, 78–88.
37. Velentza, A.V., Wainwright, M.S., Zasadzki, M., Mirzoeva, S.,
Schumacher, A.M., Haiech, J., Focia, P.J., Egli, M. and Watterson, D.M.
(2003) An aminopyridazine-based inhibitor of a pro-apoptotic protein
kinase attenuates hypoxia-ischemia induced acute brain injury. Bioorg.
Med. Chem. Lett., 13, 3465–3470.
38. Craft, J.M., Watterson, D.M., Frautschy, S.A. and Van Eldik, L.J. (2004)
Aminopyridazines inhibit beta-amyloid-induced glial activation and
neuronal damage in vivo. Neurobiol. Aging, 25, 1283–1292.
39. Busby, V., Goossens, S., Nowotny, P., Hamilton, G., Smemo, S.,
Harold, D., Turic, D., Jehu, L., Myers, A., Womick, M. et al. (2004)
Alpha-T-catenin is expressed in human brain and interacts with the Wnt
signaling pathway but is not responsible for linkage to chromosome 10 in
Alzheimer’s disease. Neuromol. Med., 5, 133–146.
40. Li, Y., Hollingworth, P., Moore, P., Foy, C., Archer, N., Powell, J.,
Nowotny, P., Holmans, P., O’Donovan, M., Tacey, K. et al. (2005)
Genetic association of the APP binding protein 2 gene (APBB2) with late
onset Alzheimer disease. Hum. Mutat., 25, 270–277.
41. Li, Y., Rowland, C., Tacey, K., Catanese, J., Sninsky, J., Hardy, J.,
Powell, J., Lovestone, S., Morris, J.C., Thal, L. et al. (2005) The
BDNF Val66Met polymorphism is not associated with late onset
Alzheimer’s disease in three case–control samples. Mol. Psychiat.,
42. Germer, S., Holland, M.J. and Higuchi, R. (2000) High-throughput SNP
allele-frequency determination in pooled DNA samples by kinetic PCR.
Genome Res., 10, 258–266.
43. Agresti, A. (1990) Categorical Data Analysis. John Wiley & Sons.
44. Stephens, M. and Donnelly, P. (2003) A comparison of Bayesian methods
for haplotype reconstruction from population genotype data. Am. J. Hum.
Genet., 73, 1162–1169.
45. Stephens, M., Smith, N.J. and Donnelly, P. (2001) A new statistical
method for haplotype reconstruction from population data. Am. J. Hum.
Genet., 68, 978–989.
46. Clement, M., Posada, D. and Crandall, K.A. (2000) TCS: a computer
program to estimate gene genealogies. Mol. Ecol., 9, 1657–1659.
47. Templeton, A.R., Weiss, K.M., Nickerson, D.A., Boerwinkle, E. and
Sing, C.F. (2000) Cladistic structure within the human Lipoprotein lipase
gene and its implications for phenotypic association studies. Genetics,
48. Nowotny, P., Hinrichs, A.L., Smemo, S., Kauwe, J.S., Maxwell, T.,
Holmans, P., Hamshere, M., Turic, D., Jehu, L., Hollingworth, P. et al.
(2005) Association studies between risk for late-onset Alzheimer’s disease
and variants in insulin degrading enzyme. Am. J. Med. Genet. B:
Neuropsychiatr. Genet., 136, 62–68.
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