Investigation of 15 of the top candidate genes for late-onset Alzheimer’s disease

MRC Human Genetics Unit, The Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU UK
Human Genetics (Impact Factor: 4.82). 03/2011; 129(3):273-282. DOI: 10.1007/s00439-010-0924-2
Source: PubMed


The 12 genome-wide association studies (GWAS) published to-date for late-onset Alzheimer’s disease (LOAD) have identified
over 40 candidate LOAD risk modifiers, in addition to apolipoprotein (APOE) ε4. A few of these novel LOAD candidate genes, namely BIN1, CLU, CR1, EXOC3L2 and PICALM, have shown consistent replication, and are thus credible LOAD susceptibility genes. To evaluate other promising LOAD candidate
genes, we have added data from our large, case–control series (n=5,043) to meta-analyses of all published follow-up case–control association studies for six LOAD candidate genes that have
shown significant association across multiple studies (TNK1, GAB2, LOC651924, GWA_14q32.13, PGBD1 and GALP) and for an additional nine previously suggested candidate genes. Meta-analyses remained significant at three loci after
addition of our data: GAB2 (OR=0.78, p=0.007), LOC651924 (OR=0.91, p=0.01) and TNK1 (OR=0.92, p=0.02). Breslow–Day tests revealed significant heterogeneity between studies for GAB2 (p<0.0001) and GWA_14q32.13 (p=0.006). We have also provided suggestive evidence that PGBD1 (p=0.04) and EBF3 (p=0.03) are associated with age-at-onset of LOAD. Finally, we tested for interactions between these 15 genes, APOE ε4 and the five novel LOAD genes BIN1, CLU, CR1, EXOC3L2 and PICALM but none were significant after correction for multiple testing. Overall, this large, independent follow-up study for 15
of the top LOAD candidate genes provides support for GAB2 and LOC651924 (6q24.1) as risk modifiers of LOAD and novel associations between PGBD1 and EBF3 with age-at-onset.


Available from: Olivia Belbin
Investigation of 15 of the top candidate genes for late-onset
Alzheimer’s disease
Olivia Belbin
Minerva M. Carrasquillo
Michael Crump
Oliver J. Culley
Talisha A. Hunter
Li Ma
Gina Bisceglio
Fanggeng Zou
Mariet Allen
Dennis W. Dickson
Neill R. Graff-Radford
Ronald C. Petersen
Kevin Morgan
Steven G. Younkin
Received: 24 August 2010 / Accepted: 19 November 2010 / Published online: 4 December 2010
Ó The Author(s) 2010. This article is published with open access at
Abstract The 12 genome-wide association studies
(GWAS) published to-date for late-onset Alzheimer’s dis-
ease (LOAD) have identified over 40 candidate LOAD risk
modifiers, in addition to apolipoprotein (APOE) e4. A few of
these novel LOAD candidate genes, namely BIN1, CLU,
CR1, EXOC3L2 and PICALM, have shown consistent rep-
lication, and are thus credible LOAD susceptibility genes. To
evaluate other promising LOAD candidate genes, we have
added data from our large, case–control series (n = 5,043) to
meta-analyses of all published follow-up case–control
association studies for six LOAD candidate genes that have
shown significant association across multiple studies (TNK1,
GAB2, LOC651924, GWA_14q32.13, PGBD1 and GALP)
and for an additional nine previously suggested candidate
genes. Meta-analyses remained significant at three loci after
addition of our data: GAB2 (OR = 0.78, p = 0.007),
LOC651924 (OR = 0.91, p = 0.01) and TNK1 (OR = 0.92,
p = 0.02). Breslow–Day tests revealed significant hetero-
geneity between studies for GAB2 (p \ 0.0001) and
GWA_14q32.13 (p = 0.006). We have also provided sug-
gestive evidence that PGBD1 (p = 0.04) and EBF3
(p = 0.03) are associated with age-at-onset of LOAD.
Finally, we tested for interactions between these 15 genes,
APOE e4 and the five novel LOAD genes BIN1, CLU, CR1,
EXOC3L2 and PICALM but none were significant after
correction for multiple testing. Overall, this large, indepen-
dent follow-up study for 15 of the top LOAD candidate genes
provides support for GAB2 and LOC651924 (6q24.1)as
risk modifiers of LOAD and novel associations between
PGBD1 and EBF3 with age-at-onset.
Genome-wide association studies represent a powerful
approach for identifying putative candidate genes for com-
mon complex disorders, such as LOAD. To-date, 12 GWAS
of LOAD have been published (Coon et al. 2007; Grupe et al.
2007; Abraham et al. 2008; Bertram et al. 2008; Li et al.
2008; Beecham et al. 2009; Carrasquillo et al. 2009; Harold
et al. 2009; Lambert et al. 2009; Poduslo et al. 2009; Potkin
et al. 2009; Seshadri et al. 2010) revealing more than 40
candidate variants that modify LOAD risk independent of
APOE (and genes likely to be in linkage disequilibrium with
APOE such as TOMM40, APOC1 and APOC2). Notably,
Electronic supplementary material The online version of this
article (doi:10.1007/s00439-010-0924-2) contains supplementary
material, which is available to authorized users.
O. Belbin M. M. Carrasquillo M. Crump
O. J. Culley T. A. Hunter L. Ma G. Bisceglio F. Zou
M. Allen D. W. Dickson S. G. Younkin (&)
Department of Neuroscience, Mayo Clinic College of Medicine,
Jacksonville, FL 32224, USA
O. Belbin K. Morgan
Human Genetics, School of Molecular Medical Sciences,
University Hospital, Queen’s Medical Centre,
Nottingham NG7 2UH, UK
M. Allen
MRC Human Genetics Unit,
The Institute of Genetics and Molecular Medicine,
Western General Hospital, Edinburgh EH4 2XU, UK
N. R. Graff-Radford
Department of Neurology, Mayo Clinic College of Medicine,
Jacksonville, FL 32224, USA
R. C. Petersen
Department of Neurology and the Mayo Alzheimer Disease
Research Center, Mayo Clinic College of Medicine,
Rochester, MN 55905, USA
Hum Genet (2011) 129:273–282
DOI 10.1007/s00439-010-0924-2
Page 1
CLU is the only signal besides these four APOE-related
signals to be identified in more than one GWAS at a genome-
wide significant level (Harold et al. 2009; Lambert et al.
Follow-up case–control association studies of the 40?
candidate loci identified by GWAS are vital in order to
filter out any false-positive signals and to provide further
evidence for genetic association of the truly functional
genes. We have successfully replicated (Carrasquillo et al.
2010) the association of variants in CLU, CR1 and PI-
CALM identified by two large GWAS (Harold et al. 2009;
Lambert et al. 2009) thus providing compelling support for
these genes as true candidate LOAD genes. Two other
recently GWAS-identified signals in EXOC3L2 and BIN1
(Seshadri et al. 2010) are currently being investigated for
replication in our large case–control series.
In addition to these candidates, AlzGene (http://www. meta-analyses of all published LOAD asso-
ciation studies have revealed significant association for
six other GWAS-identified variants (TNK1, GAB2,
LOC651924, GWA_14q32.13, PGBD1 and GALP) that are
now ranked among the Top 50 LOAD candidate genes on
the AlzGene website (Bertram et al. 2006). Although, many
variants have yet to be tested in follow-up studies, these six
loci currently represent compelling GWAS signals worthy
of follow-up investigation (Bertram and Tanzi 2009).
Here we evaluate in our large case–control series
(n = 5,043) the most significant variants in TNK1, GAB2,
LOC651924, GWA_14q32.13, PGBD1 and GALP for
genetic association with LOAD, as well as nine previously
suggested candidate LOAD genes (Grupe et al. 2007). We
have performed meta-analyses of all available published
case–control series including our data and investigated the
effect of heterogeneity on the ORs between series. We also
tested for association of these 15 variants with age-at-onset
of LOAD and for epistatic interaction with APOE e4, BIN1,
Case–control subjects
The case–control series consisted of 5,043 Caucasian sub-
jects from the United States (2,455 AD, 2,588 control)
ascertained at the Mayo Clinic (1,753 AD, 2,379 controls) or
through the National Cell Repository for Alzheimer’s Dis-
ease (NCRAD: 702 AD, 209 control). All subjects ascer-
tained at the Mayo Clinic in Jacksonville, Florida (JS: 602
AD, 604 control) and at the Mayo Clinic in Rochester,
Minnesota, (RS: 553 AD, 1,399 control) were diagnosed by a
Mayo Clinic neurologist. The neurologist confirmed a clini-
cal dementia rating score of 0 for all JS and RS subjects
enrolled as controls; cases had diagnoses of possible or
probable AD made according to NINCDS-ADRDA criteria
(McKhann et al. 1984). In the autopsy-confirmed series
(AUT: 598 AD, 376 control), all brains were evaluated by Dr.
Dennis Dickson and came from the brain bank maintained at
the Mayo Clinic in Jacksonville, FL. In the AUT series the
diagnosis of definite AD was also made according to NIN-
CDS-ADRDA criteria. All AD brains analyzed in the study
had a Braak score of 4.0 or greater. Brains employed as
controls had a Braak score of 2.5 or lower but often had brain
pathology unrelated to AD and pathological diagnoses that
included vascular dementia, fronto-temporal dementia,
dementia with Lewy bodies, multi-system atrophy, amyo-
trophic lateral sclerosis, and progressive supranuclear palsy.
One AD case from each of the 702 late-onset NCRAD
families was analyzed. NCRAD AD cases were selected
based on strength of diagnosis (autopsy-confirmed, 32% [
probable, 45% [ possible, 8% [ family report, 15%); the
case with the earliest age at diagnosis was taken when several
cases had equally strong diagnoses. The 209 NCRAD con-
trols that we employed were unrelated Caucasian subjects
from the United States with a clinical dementia rating of 0,
specifically collected for inclusion in case–control series. All
individuals with an age-at-diagnosis\60 or with mutations
in PSEN1, PSEN2 or APP were removed from analyses. The
mean age-at-diagnosis, percentage that are female and per-
centage that possess at least one copy of the APOE e4 allele
for each series are shown in Online Resource 1.
DNA isolation
For the JS and RS samples, DNA was isolated from whole
blood using an AutoGen instrument (AutoGen, Inc, Holl-
iston, MA). The DNA from AUT samples was extracted
from cerebellum using Wizard
Genomic DNA Purifica-
tion Kits (Promega Corp., Madison, WI). DNA from the
RS and AUT series was scarce, so samples from these two
series were subjected to whole genome amplification using
the Illustra GenomiPhi V2 DNA Amplification Kit (GE
Healthcare Bio-Sciences Corp., Piscataway, NJ).
Genotyping of variants
All genotyping was performed on 384-well plate formats
containing on average eight (min = 4, max = 14) negative
controls per plate. We ensured that each plate had a mixture
of cases and controls. Positive controls were not included on
these plates. GAB2 and PGBD1 variants were genotyped at
the Mayo Clinic in Jacksonville using TaqMan
Genotyping Assays in an ABI PRISM
7900HT Sequence
Detection System with 384-Well Block Module from
Applied Biosystems, California, USA. The genotype data
was analyzed using the SDS software version 2.2.2
274 Hum Genet (2011) 129:273–282
Page 2
(Applied Biosystems, California, USA). Genotype infor-
mation for BIN1 (rs744373), EXOC3L2 (rs597668), MYH13
(rs2074877), PCK1 (rs8192708) and TRAK2 (rs1302344),
were available from our GWAS (Carrasquillo et al. 2009).
All other variants were genotyped using SEQUENOM’s
MassArray iPLEX technology (SEQUENOM Inc, San
Diego, CA) following the manufacturer’s instructions.
Genotype calls were made using the default post-processing
calling parameters in SEQUENOM’s Typer 4.0 software,
followed by visual inspection to remove genotype calls that
were obviously erroneous based on the presence or absence
of allele peaks in an individual sample’s spectrogram, to
check that the boundaries of the genotype clusters were
non-overlapping and finally to ensure that samples between
clusters were not called. Genotyping probe sequences are
shown in Online Resource 2. All variants passed the p value
cut-off (p [ 0.001) for deviation from Hardy–Weinberg
equilibrium as suggested by Wigginton et al. when inves-
tigating [1,000 samples (Wigginton et al. 2005). Geno-
typing of variants in CLU, PICALM and CR1 have been
reported previously (Carrasquillo et al. 2010).
Statistical analyses
All statistics were performed using StatsDirect v2.5.8
software. Variants were analyzed for association with
LOAD by logistic regression (additive/allelic dosage,
dominant and recessive models). When included in the
analysis, covariates were sex, age at diagnosis/entry, and
APOE e4?/-. Meta-analyses were performed for each
individual series including all available published case–
control series from Caucasian populations for these vari-
ants. Genotype counts for previously published studies
were obtained either directly from the publication or from
the AlzGene website. Summary ORs and 95% CI were
calculated using the DerSimonian and Laird (1986) ran-
dom-effects model. Breslow–Day tests were used to test for
heterogeneity between series. Since age-at-onset of LOAD
did not follow a Gaussian distribution, association of the
minor allele at each variant with age-at-onset was per-
formed using a Mann–Whitney U test. Tests for epistatic
interaction were performed using the Synergy Factor Excel
spreadsheet made available by Cortina-Borja et al. (2009).
Odds ratios were calculated based on a dominant model for
the minor allele, i.e. major allele homozygotes versus
heterozygotes and minor allele homozygotes and, in the
case of APOE e4, no-e4 versus at least one copy of e4.
We have genotyped 15 candidate LOAD variants initially
identified by GWA studies. Genotype and allele counts for
these variants in our complete series are shown in Online
Resource 3. We first focused on six variants highlighted by
Bertram and Tanzi (2009) as the most compelling GWAS
signals worthy of follow-up investigation. The initial GWAS
findings for these six variants are shown in Table 1a. In order
to directly compare our results with these previous studies,
we tested for association with LOAD in our case–control
series by logistic regression using an additive/allelic dosage
model (Table 1b). Although all variants showed significant
association with LOAD (all p
\ 0.0003) in the initial stud-
ies, no variants were significantly associated with LOAD risk
in our study (all p [ 0.07). Furthermore, only two variants
had ORs in the same direction [GAB2; Reiman OR = 0.55
(Reiman et al. 2007), Mayo OR = 0.94 and LOC651924;
Grupe OR = 0.86 (Grupe et al. 2007), Mayo OR = 0.94]. It
must be noted that the Mayo series were larger in number
than the initial studies and therefore, in principle, had greater
power to detect these associations.
In order to determine whether the data from our series
would confound the significant meta-analysis reported by
AlzGene, we performed our own meta-analyses including
our data using the DerSimonian and Laird random-effects
model (Table 2). Given that none of the six variants
highlighted in the Bertram and Tanzi (2009) showed sig-
nificant association with risk of LOAD in our case–control
series, it is not surprising that the overall ORs for all six
variants were closer to 1 when our data (Table 2b) were
included (Table 2c) compared to the meta-analyses of all
previous studies (Table 2a). However, while significance
was diminished following inclusion of our data, the meta-
analyses for TNK1 (p = 0.02), GAB2 (p = 0.007) and
LOC651924 (p = 0.01) remained significant at the
p \ 0.05 level. The remaining three loci, though not sig-
nificant had overall ORs in the same direction as the Alz-
Gene meta-analysis when our data were included.
In order to investigate heterogeneity between studies for
these variants, Breslow–Day tests were performed (shown in
Table 2; Fig. 1). Forest plots of the OR and 95% CI for each
series are shown in Fig. 1. Two variants (GAB2, p \ 0.0001;
GALP, p = 0.03) showed significant heterogeneity in the
previously published data (Table 2a), while in our data
(Table 2b) only GAB2 showed significant heterogeneity
(p =
0.0002). Overall (Table 2c; Fig. 1), GAB2 showed the
most significant heterogeneity (p \ 0.0001) followed by
GWA_14q32.13 (p = 0.006) and GALP (p = 0.05).
Since some genetic variants may exert dominant or
recessive effects we also performed logistic regression
using these models and corrected for sex, age at diagnosis/
entry, and APOE e4?/- as covariates (Online Resource 4).
Although LOC651924 (OR = 0.85, p = 0.03) and
GWA_14q32.13 (OR = 1.18, p = 0.01) gave significant
ORs under a dominant model, neither would survive
Bonferroni correction for the 60 tests performed.
Hum Genet (2011) 129:273–282 275
Page 3
Table 1 Replication results for genetic association of six candidate LOAD loci identified by GWAS
Gene Variant Study (# pops) Total N (AD:CON) MAF (AD:CON) OR 95% CI p
(a) Initial study
TNK1 rs1554948 Grupe (5) 3,913 (1,828:2,085) 0.454:0.497 0.84
0.77–0.92 6 9 10
GAB2 rs10793294 Reiman (3) 1,411 (644:767) 0.190:0.300 0.55
0.46–0.65 1 9 10
LOC65192 4 rs6907175 Grupe (5) 3,913 (1,828:2,085) 0.461:0.498
0.77–0.96 3 9 10
GWA_14q32.13 rs11622883 Grupe (5) 3,913 (1,828:2,085) 0.422:0.465 0.84
0.77–0.93 9 9 10
PGBD1 rs3800324 Grupe (5) 3,913 (1,828:2,085) 0.048:0.034 1.43 1.13–1.80 3 9 10
GALP rs3745833 Grupe (5) 3,913 (1,828:2,085) 0.387:0.345 1.20 1.09–1.32 5 9 10
(b) Mayo
TNK1 rs1554948 Mayo (4) 5,043 (2,455:2,588) 0.453:0.448 1.02 0.94–1.10 0.62
GAB2 rs10793294 Mayo (4) 5,043 (2,455:2,588) 0.214:0.224 0.94 0.86–1.03 0.20
LOC65192 4 rs6907175 Mayo (4) 5,043 (2,455:2,588) 0.487:0.473 0.94 0.87–1.02 0.15
GWA_14q32.13 rs11622883 Mayo (4) 5,043 (2,455:2,588) 0.455:0.437 1.07 0.99–1.16 0.07
PGBD1 rs3800324 Mayo (4) 5,043 (2,455:2,588) 0.043:0.047 0.91 0.75–1.10 0.32
GALP rs3745833 Mayo (4) 5,043 (2,455:2,588) 0.365:0.364 1.00 0.93–1.09 0.93
Association results from logistic regression using an additive/allelic dosage model and no correction for covariates for each variant are shown
from (a) the initially published study and from (b) our data. Study; first author named on initial publication; (Grupe et al. 2007; Reiman et al.
# pops number of independent series tested, MAF minor allele frequency
OR is shown for minor allele whereas the initial study reported association for the major allele
Excludes monomorphic series UK3
Table 2 Meta-analysis of the six loci for association with LOAD in
(a) previously published studies (Grupe et al. 2007; Reiman et al.
2007; Li et al. 2008; Feulner et al. 2009; Figgins et al. 2009; Sleegers
et al. 2009) (b) our data and (c) overall; including all published
studies in Caucasian series and our data
Gene Variant # pops. Total N (AD:CON) OR 95% CI p Breslow–Day p
(a) AlzGene meta-analysis
TNK1 rs1554948 6 5,932 (2,837:3,095) 0.86 0.80–0.93 0.0002 0.37
GAB2 rs10793294 5 4,029 (2,142:1,887) 0.69 0.54–0.88 0.003 0.0009
LOC651924 rs6907175 6 5,932 (2,837:3,095) 0.89 0.82–0.96 0.005 0.39
GWA_14q32.13 rs11622883 6 5,932 (2,837:3,095) 0.88 0.80–0.97 0.01 0.18
PGBD1 rs3800324 7 10,815 (5,156:5,659) 1.21 1.02–1.44 0.03 0.53
GALP rs3745833 6 5,932 (2,837:3,095) 1.13 1.00–1.29 0.06 0.03
(b) Mayo data meta-analysis
TNK1 rs1554948 4 5,043 (2,455:2,588) 1.00 0.92–1.09 0.99 0.61
GAB2 rs10793294 4 5,043 (2,455:2,588) 0.89 0.68–1.16 0.40 0.0002
LOC651924 rs6907175 4 5,043 (2,455:2,588) 0.94 0.83–1.07 0.37 0.08
GWA_14q32.13 rs11622883 4 5,043 (2,455:2,588) 1.09 0.99–1.20 0.07 0.07
PGBD1 rs3800324 4 5,043 (2,455:2,588) 0.87 0.70–1.06 0.17 0.76
GALP rs3745833 4 5,043 (2,455:2,588) 1.02 0.94–1.12 0.64 0.39
(c) Overall meta-analysis
TNK1 rs1554948 10 10,975 (5,272:5,932) 0.92 0.85–0.98 0.02 0.13
GAB2 rs10793294 9 9,072 (4,597:4,475) 0.78 0.64–0.93 0.007 \0.0001
LOC651924 rs6907175 10 10,975 (5,272:5,932) 0.91 0.85–0.98 0.01 0.15
GWA_14q32.13 rs11622883 10 10,975 (5,272:5,932) 0.96 0.88–1.06 0.44 0.006
PGBD1 rs3800324 11 15,858 (7,611:8,247) 1.07 0.92–1.24 0.36 0.25
GALP rs3745833 10 10,975 (5,272:5,932) 1.08 0.99–1.17 0.07 0.05
# pops. number of independent series tested, Breslow–Day p p value for series heterogeneity Breslow–Day tests
276 Hum Genet (2011) 129:273–282
Page 4
We next tested for association of the variants with age-
at-onset in the 2,455 LOAD patients from our case–control
series (Table 3). The only variant to show association at the
p \ 0.05 level was PGBD1 (p = 0.04) where the minor
allele was associated with an age-at-onset 1 year earlier
than the major allele, although this weak association would
not survive Bonferroni correction (p \ 0.003).
In addition to these six loci, we had genotype informa-
tion available for nine variants (EBF3, LMNA, BCR, UBD,
THEM5, CTSS, TRAK2, MYH13 and PCK1) identified by
Grupe et al. (2007). The genotype counts, case–control
association, meta-analyses, alternative models, association
with age-at-onset and epistatic interactions can be found in
Online Resources 3–8. In summary, although all nine
variants were associated with LOAD in the initial study (all
p \ 0.001), logistic regression of our data using an additive/
allelic dosage model and no covariates revealed one variant
(rs13022344 in TRAK2) significantly associated with
LOAD in our series (OR = 0.86, p = 0.02) but in the
opposite direction to that reported in the initial study
(OR = 1.07, p = 0.001). Notably, EBF3 (p = 0.04),
THEM5 (p = 0.03), CTSS (p = 0.03) and TRAK2 (p =
0.02) were associated with LOAD risk under a recessive
model whilst correcting for covariates, although these
associations would not survive Bonferroni correction for the
60 tests performed (p \ 0.0008). None of these nine
Fig. 1 Forest plots for meta-analysis for each variant. ORs (boxes)
and 95% CI (whiskers) are plotted for each series and shown on the
right of each plot. Combined OR is the overall OR calculated by the
meta-analysis using a random-effects model. p values from Breslow–
Day tests of heterogeneity are included at the top of each plot
Table 3 Association of six loci with age-at-onset in LOAD patients
Gene Variant N Mean (SD) Upvalue
Maj Min
TNK1 rs1554948 2,442 78.3 (7.7) 78.4 (7.7) 616,938.5 0.85
GAB2 rs10793294 2,416 78.4 (7.6) 78.5 (7.7) 692,338.5 0.68
LOC651924 rs6907175 2,443 78.8 (7.9) 78.3 (7.6) 520,939.0 0.12
GWA_14q32.13 rs11622883 2,447 78.4 (7.3) 78.4 (7.8) 623,344.0 0.93
PGBD1 rs3800324 2,435 78.5 (7.6) 77.5 (7.7) 244,760.0 0.04
GALP rs3745833 2,446 78.4 (7.7) 78.0 (7.7) 339,685.0 0.43
Degrees of freedom (df) and number of samples included in the analyses (N) are given for each variant. Mean age-at-onset and standard deviation
(SD) are shown for each group defined by possession of two copies of the major allele (Maj) or at least one copy of the minor allele (Min). The
p value provides the significance level for the U statistic from a Mann–Whitney U test comparing mean age-at-onset between the two groups
Hum Genet (2011) 129:273–282 277
Page 5
variants were significant at the p \ 0.05 level following
meta-analyses either before or after addition of our data
(Online Resources 6). Overall, Breslow–Day tests revealed
genetic heterogeneity for six (LMNA p = 0.0004, BCR
p = 0.04, THEM5 p = 0.01, PCK1 p = 0.003, CTSS
p = 0.02 and TRAK2 p = 0.0002) of the nine variants.
None of these nine variants were associated with age-at-
onset (Online Resources 7) after Bonferroni correction,
however, EBF3 showed nominally significant association
with a later age-at-onset of 0.8 years (79.0 years) compared
to the major allele (78.2 years; p = 0.03).
In order to determine whether these 15 variants interact
with other strong LOAD candidates to modify risk for
LOAD, we tested for epistatic interaction between the
variants studied here and the strongest known LOAD risk
factor, APOE e4, as well as the top GWAS-identified
variants for which we had genotype information available;
BIN1 (rs744373), CLU (rs11136000), CR1 (rs3818361),
EXOC3L2 (rs597668) and PICALM (rs3851179). The
results for all 105 tests performed are shown in Online
Resource 8. There were seven interactions that were sig-
nificant at the p B 0.05 level, which are shown in Table 4,
however, none would survive Bonferroni correction for the
105 tests performed. Further investigation of these possible
epistatic interactions in multiple, independent studies is
required in order to determine whether there is true synergy
between the variants.
This study used the largest sample size to-date to investigate
15 of the top AlzGene hits (AlzGene, accessed October
2010) which were originally identified in a LOAD GWAS.
Meta-analyses remained significant at three loci after addi-
tion of our data: GAB2 (rs10793294, OR = 0.78, p =
0.007), LOC651924 (rs6907175, OR = 0.91, p = 0.01) and
TNK1 (rs1554948, OR = 0.92, p = 0.02). Although our
data alone provided no support for an association of TNK1
with LOAD (OR = 1.00, p = 0.99), the AlzGene meta-
analyses odds ratios for both GAB2
(0.69) and LOC651924,
(0.89) were well replicated in our series (OR = 0.89 and
0.94, respectively) albeit that neither variant was significant
(p = 0.40 and 0.37, respectively). We also investigated nine
additional variants (in EBF3, LMNA, BCR, THEM5, PCK1,
MYH13, CTSS, UBD and TRAK2) identified by Grupe et al.
but found no significant associations following meta-
Our meta-analyses which include nine independent
studies comprising 9,072 individuals provide good evidence
that GAB2 (rs10793294 OR = 0.78, p = 0.007) is a genuine
candidate LOAD locus. These data are further supported by a
recent family-based study (Schjeide et al. 2009a), which
revealed significant association of another GAB2 variant
(rs7101429) in 399 families (p = 0.002), thus strengthening
the evidence for GAB2. In consideration of this association of
GAB2 with LOAD in families, we performed our logistic
regression (additive model) analyses again on the total
dataset with the 112 NCRAD LOAD patients with a family
history of AD removed. We found that removing these
samples gave a comparable association to our initial analyses
(all samples: n = 4,969, OR = 0.94, p = 0.20; no family
history: n = 4,857, OR = 0.95, p = 0.36; data not shown).
We also found a comparable association of GAB2 with age-
at-onset after removal of these samples (all samples:
n = 2,416, U = 692,338.5, p = 0.68; no family history:
n = 2,304, U = 628,587, p = 0.76; data not shown).
Notably, in another family-based study by the same group,
eight variants included in this manuscript (GALP,
GWA_14q32.13, LMNA, LOC651924, MYH13, PCK1,
PGBD1, TNK1) were tested but failed to show association
with LOAD in 457 families (Schjeide et al. 2009b); this is
compatible with our meta-analyses of variants in
GWA_14q32.13 (rs11622883), PGBD1 (rs3800324) and
GALP (rs3745833) which revealed no association with
Our meta-analyses also provided evidence that
LOC651924 is a true candidate locus (OR = 0.91, p =
0.01). Although only one out of the nine series studied
revealed significant association (p \0.05), the effect of the
variants were in the same direction (with comparable ORs)
in seven of the series. As a result, the meta-analysis
revealed significant association thus supporting the evi-
dence for LOC651924 as a LOAD candidate.
The 15 variants we analyzed showed remarkable across-
study heterogeneity. The Breslow–Day p values for the
initial, Mayo follow-up and overall meta-analyses of the 15
variants we analyzed are summarized in Table 2 and Online
Resource 6. Overall, meta-analysis of the variants in four
genes (GAB2, TRAK2, LMNA and PCK1) gave Breslow–
Day p values ranging from \0.0001 to 0.002 that are sig-
nificant even after Bonferroni correction for 15 variants
analyzed (p \ 0.003). The variants in seven genes had
nominally significant or highly suggestive Breslow–Day
p values that ranged from 0.01 to 0.06, and the variants in
the three remaining genes had Breslow–Day p values of
0.12 to 0.25. Thus, our analysis of 15 promising LOAD
variants suggests that LOAD variants may often show
noteworthy series to series heterogeneity. If the heteroge-
neity we observed is real and if it occurs as frequently as our
data suggest, then many genetic variants may influence
LOAD susceptibility in a way that depends on genetic and/
or environmental factors that vary from series to series.
It is now clear that, apart from the well-known APOE
alleles, common genetic variants have only weak associa-
tion with LOAD. Whether many of these variants have
278 Hum Genet (2011) 129:273–282
Page 6
Table 4 Epistatic interactions between LOAD candidate genes significant at the p B 0.05 level (total number of tests = 105)
v1 v2 (a) TRAK2 9 CR1 (b) TRAK2 9 BIN1 (c) LOC651924 9 PICALM (d) EXOC3L2 9 APOE
--265 344 Ref 235 474 Ref 174 79 Ref 724 1,616 Ref
?-254 459 0.72 137 182 1.52 506 356 0.65 123 311 0.88
-?134 205 0.85 283 341 1.67 192 175 0.50 1,324 531 5.57
??185 239 1.00 160 220 1.47 635 553 0.52 253 74 7.63
Total 838 1,247 815 1,217 1,507 1,163 2,424 2,532
Synergy factor 1.65 (1.1–2.4); p = 0.007 0.58 (0.4–0.8); p = 0.004 1.62 (1.1–2.4); p = 0.01 1.55 (1.1–2.2); p = 0.02
v1 v2 (e) EXOC3L2 9 PICALM (f) UBD 9 EXOC3L2 (g) TRAK2 9 EXOC3L2
--257 323 Ref 142 270 Ref 249 389 Ref
?-138 180 0.96 382 559 1.30 281 444 0.99
-?275 512 0.68 99 120 1.57 147 157 1.46
??170 226 0.95 204 284 1.37 158 247 1.00
Total 840 1,241 827 1,233 835 1,237
Synergy factor 1.45 (1.0–2.1); p = 0.05 0.67 (0.4–1.0); p = 0.05 0.69 (0.5–1.0); p = 0.05
Genotype counts are given for LOAD patients and controls (CON). Counts are stratified by each combination of two variants; for each combination, variant 1 (v1) is the variant in the gene listed
first and (v2) is the variant in the gene listed second; The total counts for LOAD patients and controls are given underneath the stratified counts. Odds ratios (OR) for each stratified group
compared to the referent OR, synergy factor with 95% confidence intervals in parentheses and p value for the interaction are also provided; Variants are as follows; TRAK2;rs13022344,
CR1;rs3818361, BIN1;rs744373, LOC651924;rs6907175, PICALM;rs3851179, EXOC3L2;rs597668, APOE;e4, PICALM;rs3851179,
Ref OR for individuals carrying the major allele at both variants
? refers to individuals carrying at least one copy of the minor allele at that variant, - refers to individuals carrying the major allele only
Hum Genet (2011) 129:273–282 279
Page 7
odds ratios that truly vary because they depend on envi-
ronmental and/or genetic factors that differ from series to
series is currently unclear. What is clear is that variants of
this type are likely to be missed if genetic association
studies focus exclusively on replicable associations that
become highly significant when many series are combined.
To find important susceptibility alleles with effects that
vary from series, it may be necessary to consider and to
understand variants that show significant association in
some series and highly significant heterogeneity on meta-
analysis even though meta-analysis provides no evidence
for association.
It is important to recognize that spurious heterogeneity
can occur owing to publication bias wherein only those
series that, by chance, have false-positive results are pub-
lished. When these series are combined with follow-up
series with ORs that vary randomly around 1.0, Breslow–
Day testing can show significant, but misleading evidence
of heterogeneity. One way to mitigate this problem is to
determine if, when initial series are eliminated, the follow-
up studies show heterogeneity. In the current study, the
variants in GAB2 and in LMNA had Breslow–Day p values
of 0.0002 and 0.002 in the Mayo follow-up series alone
that retain significance even after Bonferroni correction for
15 variants tested. It is worth noting that both the GAB2
and LMNA variants also showed significant heterogeneity
in the initial studies with p values of 0.0009 and 0.01,
respectively and in the overall meta-analysis with p values
of \0.0001 and 0.0004, respectively. Since it appears that
two of the 15 variants we studied showed true series to
series heterogeneity, it seems appropriate to consider that
the heterogeneity observed for many of the other variants
may also be real.
One interesting cause of heterogeneity occurs when the
‘heterogeneous’ variant is merely a tag for the truly
functional variant (or multiple rare variants each with
strong functional effects) and the degree of linkage dis-
equilibrium between these variants differs between series
leading to weaker and/or opposing effects. When this is the
case, variants (e.g., those in GAB2 and GWA_14q32.13)
that show significant heterogeneity between multiple,
large, case–control series could be used to identify candi-
date regions for targeted sequencing and haplotype analysis
that resolves the heterogeneity thereby identifying func-
tional variants that show replicable, significant association.
We also investigated whether the 15 variants were
associated with age-at-onset. Although we have suggestive
evidence that PGBD1 and EBF3 may be associated with
age-at-onset and that LMNA may interact with APOE e4,
due to the multiple tests performed and the relatively weak
p values obtained (all p [ 0.02), we suggest that further
investigation into these findings is required in order to
determine whether these were merely due to chance or if
they represent true associations.
Finally, we tested for pairwise interactions between the
15 variants evaluated in this study as well as with APOE
e4, BIN1 (rs744373), CLU (rs11136000), CR1 (rs3818361),
EXOC3L2 (rs597668) and PICALM (rs3851179). Seven
pairs showed nominally significant synergy factors (p val-
ues ranging from 0.007 to 0.05), but none remained sig-
nificant after correction for the 105 tests performed
(p \ 0.0005). It is possible that many epistatic interactions
exist between LOAD genes, of which relatively few com-
binations have been tested here. We therefore propose that
future studies of candidate LOAD genes apply tests for
epistasis with other candidate genes in order to identify
otherwise hidden interactions that could contribute greater
risk than any gene individually.
Overall, this study represents a thorough, independent
follow-up study of 15 of the top LOAD candidate genes, in
a large case–control series and provides further evidence
for the association of GAB2 and LOC651924 (6q24.1) with
LOAD. In addition, we have provided suggestive evidence
that, in our series, two genes (PGBD1 and EBF3) are
potentially associated with age-at-onset of LOAD.
The experiments described in this manuscript comply
with the current laws of the United States of American
where they were performed. Approval was obtained from
the ethics committee or institutional review board of each
institution responsible for the ascertainment and collection
of samples (Mayo Clinic College of Medicine, Jackson-
ville, FL and Mayo Clinic College of Medicine, Rochester,
MN, USA). Written informed consent was obtained for all
individuals that participated in this study.
Acknowledgments Samples from the National Cell Repository for
Alzheimer’s Disease (NCRAD) were used in this study. We thank
contributors, including the Alzheimer’s Disease Centers who col-
lected samples used in this study, as well as patients and their fami-
lies, whose help and participation made this work possible. This work
was supported by grants from the US National Institutes of Health,
NIA R01 AG18023 (N.R.G.-R., Steven G. Younkin); Mayo Alzhei-
mer’s Disease Research Center, P50 AG16574 (R.C.P., D.W.D.,
N.R.G.-R., Steven G. Younkin); Mayo Alzheimer’s Disease Patient
Registry, U01 AG06576 (R.C.P.); and US National Institute on
Aging, AG25711, AG17216, AG03949 (D.W.D.). Samples from the
National Cell Repository for Alzheimer’s Disease (NCRAD), which
receives government support under a cooperative agreement grant
(U24AG21886) awarded by the National Institute on Aging (NIA),
were used in this study. This project was also generously supported by
the Robert and Clarice Smith Postdoctoral Fellowship (M.M.C.);
Robert and Clarice Smith and Abigail Van Buren Alzheimer’s
Disease Research Program (R.C.P., D.W.D., N.R.G.-R.; Steven G.
Younkin) and by the Palumbo Professorship in Alzheimer’s Disease
Research (Steven G. Younkin). K.M. is funded by the Alzheimer’s
Research Trust and the Big Lottery Fund
Conflict of interest The authors declare that they have no conflict
of interest.
280 Hum Genet (2011) 129:273–282
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  • Source
    • "Even in hypothesis-free genome-wide association studies (GWAS) of AD, when testing of gene-gene interactions has been incorporated, it has been restricted to interactions between APOE and other risk loci with known main effect associations. Belbin et al. (2011) investigated interactions among 21 LOAD candidate and confirmed risk genes, including APOE, BIN1, CLU, CR1, and PICALM but failed to detect any interactions with disease status or age-atonset that were significant after correction for multiple testing (Belbin et al., 2011). Similarly, Carrasquillo et al. (2011) failed to identify significant interactions between variants in BIN1 and other LOAD risk genes, including APOE, CLU, CR1, and PICALM (Carrasquillo et al., 2011). "
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    • "A genome-wide association study of family-based case-control studies suggested that PGBD1 is involved in AD [15,16]. In addition, although weak (the minor allele was associated with an age of onset 1 year earlier than the major allele), a single nucleotide polymorphism (SNP) in PGBD1, rs3800324, has been shown to be associated with the age of onset in AD, as a risk modifier in a meta-analysis study [17]. However, no Japanese AD patients were examined in any of these studies. "
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