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Association analysis of dynamin-binding protein (DNMBP) on chromosome 10q with late onset Alzheimer's disease in a large caucasian UK sample.

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RESEARCH ARTICLE
Neuropsychiatric Genetics
Association Analysis of Dynamin-Binding Protein
(DNMBP) on Chromosome 10q With Late Onset
Alzheimer’s Disease in a Large Caucasian UK Sample
A.R. Morgan,1* P. Hollingworth,1 R. Abraham,1 S. Lovestone,2 C. Brayne,3 D.C. Rubinsztein,3 A. Lynch,4
B. Lawlor,4 M. Gill,4 M.C. O’Donovan,1 M.J. Owen,1 and J. Williams1
1
Department of Psychological Medicine, School of Medicine, Cardiff University, Cardiff, UK
Department of Neuroscience, Institute of Psychiatry, Kings College, London, UK
2
3
Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
4
Mercer’s Institute for Research on Aging, St. James Hospital and Trinity College, Dublin, Ireland
Received 8 January 2008; Accepted 25 March 2008
A recent scan of single nucleotide polymorphisms (SNPs) in the
region 40–107 Mb on chromosome 10q in a large Japanese
case–control cohort identified six SNPs in or near the
dynamin-binding protein gene (DNMBP) that were associated
with late onset Alzheimer’s disease (LOAD) in individuals lacking the APOE e4 allele [Kuwano et al. (2006); Hum Mol Genet
15:2170–2182]. We genotyped these six SNPs in 1,212 unrelated
Caucasian patients of UK origin with LOAD and 1,389 ethnically,
gender and age matched control subjects. We did not observe a
statistically significant association with the risk of LOAD for any
of the six SNPs in the sample as a whole. When stratifying the
sample by APOE one SNP (intergenic SNP rs11190302) was
associated with LOAD in individuals lacking the e4 allele
(genotypic P ¼ 0.027, allelic P ¼ 0.066). However this association
was in the opposite direction to that detected in the Japanese
population. It remains to be determined whether DNMBP is
associated with LOAD. 2008 Wiley-Liss, Inc.
Key words: late-onset Alzheimer’s disease; DNMBP; chromosome 10; association
INTRODUCTION
Several groups have observed linkage with risk of AD or related
measures on chromosome 10 [Kehoe et al., 1999; Bertram et al.,
2000; Ertekin-Taner et al., 2000; Myers et al., 2000, 2002; Blacker
et al., 2003; Farrer et al., 2003], and many have reported association
with different candidate genes on chromosome 10 (see the AlzGene
Database. Available at: http://www.alzgene.org [Bertram et al.,
2007]), but attempts to replicate these findings have rarely proved
consistent and the identification of the LOAD gene on this chromosome remains elusive.
Kuwano et al. [2006] performed a large scale single nucleotide
polymorphism (SNP) based association analysis in a large Japanese
case–control cohort (1,526 LOAD samples and 1,666 controls).
They looked at 1,206 SNPs in a region implicated by several linkage
2008 Wiley-Liss, Inc.
How to Cite this Article:
Morgan AR, Hollingworth P, Abraham R,
Lovestone S, Brayne C, Rubinsztein DC,
Lynch A, Lawlor B, Gill M, O’Donovan MC,
Owen MJ, Williams J. 2009. Association
Analysis of Dynamin-Binding Protein
(DNMBP) on Chromosome 10q With Late
Onset Alzheimer’s Disease in a Large
Caucasian UK Sample.
Am J Med Genet Part B 150B:61–64.
studies, between 60 and 107 Mb on chromosome 10, and identified
six SNPs associated with LOAD among individuals lacking the e4
allele. Three of the six SNPs, including the most statistically
significant (rs3740058), are located in the dynamin-binding protein
gene (DNMBP) on 10q24; the other three fall downstream of this
gene.
DNMBP (chr10:101,626,898–101,759,666) is a scaffold protein
that brings the dynamin and actin regulatory proteins together and
is concentrated at synapses in the brain. It is an excellent candidate
gene for AD because of its role in the amyloid precursor protein
(APP) recycling pathways.
Additional Supporting Information may be found in the online version of
this article.
Grant sponsor: Medical Research Council, UK; Grant sponsor: Alzheimer’s
Research Trust, UK.
*Correspondence to:
Dr. A.R. Morgan, Department of Psychological Medicine, Wales College of
Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.
E-mail: morganar1@cardiff.ac.uk
Published online 1 May 2008 in Wiley InterScience
(www.interscience.wiley.com)
DOI 10.1002/ajmg.b.30768
61
62
Here we attempted to validate the finding of Kuwano et al. in a
well characterized case–control dataset which consists of 1,212
unrelated Caucasian patients of UK origin with LOAD and 1,389
ethnically, gender and age matched control subjects.
Another effort to replicate the finding reported by Kuwano et al.
has recently been published [Minster et al., 2007]. In this study the
most significant SNP from the Japanese study (rs3740058) was
examined in large Caucasian American case–control cohort (1,030
LOAD cases, 910 controls) and the other 5 in a subset of this cohort
(298 cases, 311 controls). No associations were reported.
METHODS
Samples
Clinical data and DNA samples were collected from 1,212
individuals (70% females) with late-onset Alzheimer’s disease and
1,389 control subjects (62.4% females). Age at onset ranged from
59 to 95 years (mean ¼ 75.6 years, SD ¼ 6.84). All cases were
Caucasian, of UK origin (parents born in the UK) and diagnosed
with probable AD in accordance with the National Institute of
Neurological and Communication Disorders and Stroke and the
Alzheimer’s Disease and Related Disorders Associations (NINCDS–ADRDA) clinical diagnostic criteria for AD [McKhann et al.,
1984]. Controls were matched for age (mean ¼ 76.26 years,
SD ¼ 6.23), gender and ethnicity. The sample consisted of individuals ascertained from both community and hospital settings in
the UK collected as part of the National Alzheimer’s Research
Initiative, funded by the Medical Research Council. AD cases and
controls described here were ascertained by four collaborating
centers: Department of Psychological Medicine, Cardiff University,
Cardiff (coordinating center); Institute of Psychiatry, London;
Cambridge University, Cambridge; and Mercer’s Institute for
Research on Aging, St. James Hospital and Trinity College,
Dublin [see Morgan et al., 2007 for full descriptive data for whole
sample]. Ethical permission was obtained from the Multi-centre
Research Ethics Committee (MREC), relevant local Ethics Committees and NHS trusts.
Genotyping
The Sequenom (www.sequenom.de) genotyping platform, which
uses the MALDI-TOF primer extension assay [Jurinke et al., 2002;
Storm et al., 2003], was used to genotype the six SNPs.
Quality control measures, for all genotyping results, included
independent double genotyping, genotyping of duplicate samples
on our sample plates and genotyping of CEPH samples and
subsequent comparison with genotypes in the hapmap where
available. We also ensured that all results were in HWE.
Statistical Analysis
SNPs were tested for deviation from the Hardy–Weinberg
equilibrium (HWE) in both cases and controls.
Genotype and allele frequencies for each SNP were analyzed by
2 3 and 2 2 Chi-square respectively, to determine if there were
significant differences between cases and controls.
Differences in allele and genotype frequencies between cases and
controls stratified by apoe e4 carrier status were also tested using
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
Chi-square analysis (e4 negative controls vs. e4 negative cases and
e4 positive controls vs. e4 positive cases).
Calculations of statistical power were completed using PS 2.1.31
[Dupont and Plummer, 1998].
RESULTS
All six SNPs were in HWE in both cases and controls.
See Table I for genotype and allele counts and frequencies
for each of the six SNPs in our dataset. There were no significant differences between cases and controls for any of the six
SNPs.
When the sample was stratified by apoe e4 carrier status there
were no significant differences in allele or genotype frequencies
between e4 positive cases and e4 positive controls for any of the six
SNPs (Supplementary Table SI). When comparing e4 negative cases
and e4 negative controls five SNPs did not demonstrate evidence of
association. However, one SNP—rs11190302 was associated with
LOAD (genotypic P ¼ 0.027, allelic P ¼ 0.066; see Table II for
results for rs11190302 and Supplementary Table SII for results of
all six SNPs).
We also investigated other sub-phenotypes of AD for association
(family history, gender, age of onset, diabetes, depression) but
found no other associations for any of the six SNPs (results available
upon request).
DISCUSSION
With our sample we had 94.2% power to detect the reported
odds ratio of 1.22 in the total sample and 95.3% power to detect
the reported odds ratio of 1.38 in individuals lacking the e4
allele.
We did not observe a statistically significant association between
the associated SNPs from Kuwano et al., with the risk of AD in the
whole sample set and only one SNP was marginally statistically
significant when studying individuals lacking the e4 allele. And this
was in the opposite direction (in our sample the C allele was overrepresented in the cases, whereas Kuwano et al. found the T allele
to be associated). This SNP (rs11190302) is intergenic and lies
approximately 4 kb from DNMBP.
The difference may be due to the different ethnic backgrounds of
the two populations (our allele frequencies differ from those
reported in the Japanese population by Kuwano et al. but are more
similar to those reported in the Caucasian American sample
reported by Minster et al.). The different populations are likely to
have differences in their genetic background which includes LD and
this could account for the different associations. Another possibility
is that there is a multilocus effect at this chromosomal region and
there may be differences between the two populations in this SNPs
correlation with other, possibly causal, variants. Or it is possible
that we may be seeing a false positive result in our sample for
rs11190302. Our result is only marginally statistically significant,
and will not survive multiple testing. However, it is not uncommon
for different studies to report disease-marker association but with
opposite alleles associated. For example, in LOAD two independent
studies have reported opposite alleles of the same SNP in the gene
GSTO1 as being associated with age at onset in LOAD [Li et al.,
MORGAN ET AL.
63
TABLE I. Genotype and Allele Counts (Frequencies) for the Six SNPs in the Whole Sample (Unstratified by APOE e4 Status)
Number of subjects (frequency)
Number of alleles (frequency)
SNP ID
rs911541
Genotype
GG
GA
AA
LOAD
11 (0.01)
236 (0.23)
765 (0.76)
Control
15 (0.01)
260 (0.22)
890 (0.76)
P ¼ 0.79
Allele
G
A
LOAD
258 (0.13)
1,766 (0.87)
Control
290 (0.12)
2,040 (0.88)
P ¼ 0.77
OR ¼ 1.03 (0.86–1.23)
rs3740066
TT
TC
CC
120 (0.14)
411 (0.47)
342 (0.39)
149 (0.15)
462 (0.48)
360 (0.37)
P ¼ 0.51
T
C
651 (0.37)
1,095 (0.63)
769 (0.39)
1,182 (0.61)
P ¼ 0.25
OR ¼ 0.91 (0.80–1.04)
rs11190302
TT
TC
CC
197 (0.19)
489 (0.48)
332 (0.33)
240 (0.21)
587 (0.50)
342 (0.29)
P ¼ 0.24
T
C
883 (0.43)
1,153 (0.57)
1,067 (0.46)
1,271 (0.54)
P ¼ 0.13
OR ¼ 0.91 (0.81–1.03)
rs11190305
GG
GT
TT
150 (0.15)
481 (0.48)
380 (0.38)
178 (0.15)
583 (0.50)
401 (0.35)
P ¼ 0.32
G
T
781 (0.39)
1,241 (0.61)
939 (0.40)
1,385 (0.60)
P ¼ 0.23
OR ¼ 0.93 (0.82–1.05)
rs35715207
CC
CG
GG
154 (0.15)
490 (0.48)
380 (0.37)
180 (0.15)
591 (0.50)
414 (0.35)
P ¼ 0.55
C
G
798 (0.39)
1,250 (0.61)
951 (0.40)
1,419 (0.60)
P ¼ 0.43
OR ¼ 0.95 (0.84–1.08)
rs3740058
AA
AG
GG
159 (0.16)
471 (0.47)
369 (0.37)
190 (0.16)
561 (0.48)
414 (0.36)
P ¼ 0.80
A
G
789 (0.39)
1,209 (0.61)
941 (0.40)
1,389 (0.60)
P ¼ 0.55
OR ¼ 0.96 (0.85–1.09)
2003; Kolsch et al., 2004]. For further discussion concerning
association of opposite alleles at the same SNP with the same
disease see Lin et al. [2007].
Although DNMBP is an excellent candidate gene because of its
role in the APP recycling pathways and being located within a
chromosome 10 linkage region, we are unable to confirm that
DNMBP is associated with LOAD and recommend that further
replication studies are undertaken to determine if this is a true
association.
Minster et al. [2007] also failed to replicate the findings by
Kuwano et al. [2006]. rs3740058 [the most significant SNP from
Kuwano et al., 2006] was genotyped in 1,030 Caucasian Americans
with LOAD and 910 healthy Caucasian Americans. The other 5
SNPs were genotyped in a smaller sample set of 298 LOAD cases and
311 controls. For all six SNPs differences between cases and controls
in genotype and allele frequencies were not statistically significant,
and allele frequencies in cases and controls stratified by APOE e4
carrier status were also not statistically significant.
In our sample none of the six SNPs were associated with APOE e4
positive LOAD. This was also true for Kuwano et al. We also did
not find evidence for an association for any SNP with any other
phenotype (e.g., AAO, family history, gender). It is not
clear whether Kuwano et al. [2006] or Minster et al. [2007]
investigated other phenotypes as these are not reported in their
publications.
Further replication in other large sample sets, and particularly
replication in another Japanese sample, will be required to assess the
true effects of DNMBP variants in LOAD.
TABLE II. Genotype and Allele Counts (Frequencies) for rs11190302 in Individuals Lacking the e4 Allele
Number of subjects (frequency)
SNP ID
rs11190302
Genotype
TT
TC
CC
LOAD
75 (0.20)
167 (0.44)
140 (0.37)
Control
154 (0.20)
387 (0.51)
222 (0.29)
P ¼ 0.027
Number of alleles (frequency)
Allele
T
C
LOAD
317 (0.42)
447 (0.58)
Control
695 (0.46)
831 (0.54)
P ¼ 0.066
OR ¼ 0.85 (0.71–1.01)
64
ACKNOWLEDGMENTS
We would like to thank the many patients with AD and their
families who participated in this study.
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
Kolsch H, Linnebank M, Lutjohann D, et al. 2004. Polymorphisms in
glutathione S-transferase omega-1 and AD, vascular dementia, and
stroke. Neurology 63:2255–2260.
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