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Association analysis of 528 intra-genic SNPs in a region of chromosome 10 linked to late onset Alzheimer's disease.

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American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 147B:727 –731 (2008)
Association Analysis of 528 Intra-Genic SNPs in a Region of
Chromosome 10 Linked to Late Onset Alzheimer’s Disease
A.R. Morgan,1* G. Hamilton,2 D. Turic,1 L. Jehu,1 D. Harold,1 R. Abraham,1 P. Hollingworth,1 V. Moskvina,3
C. Brayne,4 D.C. Rubinsztein,4 A. Lynch,5 B. Lawlor,5 M. Gill,5 M. O’Donovan,1 J. Powell,2
S. Lovestone,2 J. Williams,1,3 and M.J. Owen1
1
Department of Psychological Medicine, School of Medicine, Cardiff University, Cardiff, UK
Department of Neuroscience, Institute of Psychiatry, Kings College, London, UK
3
Biostatistics and Bioinformatics Unit, Cardiff University, Cardiff, UK
4
Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
5
Mercer’s Institute for Research on Aging, St James Hospital and Trinity College, Dublin, Ireland
2
Late-onset Alzheimer’s disease (LOAD) is a genetically complex neurodegenerative disorder. Currently, only the e4 allele of the Apolipoprotein
E gene has been identified unequivocally as a
genetic susceptibility factor for LOAD. Others
remain to be found. In 2002 we observed genomewide significant evidence of linkage to a region on
chromosome 10q11.23–q21.3 [Myers et al. (2002)
Am J Med Genet 114:235–244]. Our objective in
this study was to test every gene within the
maximum LOD-1 linkage region, for association
with LOAD. We obtained results for 528 SNPs
from 67 genes, with an average density of 1 SNP
every 10 kb within the genes. We demonstrated
nominally significant association with LOAD for
4 SNPs: rs1881747 near DKK1 (P ¼ 0.011, OR ¼ 1.24),
rs2279420 in ANK3 (P ¼ 0.022, OR ¼ 0.79), rs2306402
in CTNNA3 (P ¼ 0.024, OR ¼ 1.18), and rs5030882
in CXXC6 (P ¼ 0.046, OR ¼ 1.29) in 1,160 cases
and 1,389 controls. These results would not
survive correction for multiple testing but warrant attempts at confirmation in independent
samples.
ß 2007 Wiley-Liss, Inc.
KEY WORDS:
late-onset Alzheimer’s disease;
chromosome 10; SNP; association
Please cite this article as follows: Morgan AR, Hamilton
G, Turic D, Jehu L, Harold D, Abraham R, Hollingworth
P, Moskvina V, Brayne C, Rubinsztein DC, Lynch A,
Lawlor B, Gill M, O’Donovan M, Powell J, Lovestone S,
Williams J, Owen MJ. 2008. Association Analysis of
528 Intra-Genic SNPs in a Region of Chromosome
10 Linked to Late Onset Alzheimer’s Disease. Am
J Med Genet Part B 147B:727–731.
This article contains supplementary material, which may be
viewed at the American Journal of Medical Genetics website
at http://www.interscience.wiley.com/jpages/1552-4841/suppmat/
index.html.
Grant sponsor: Medical Research Council, UK; Grant sponsor:
Alzheimer’s Research Trust, UK; Grant sponsor: Welsh Assembly
Government, UK.
*Correspondence to: Dr. A.R. Morgan, Department of Psychological Medicine, Cardiff University, Heath Park, Cardiff CF14
4XN, UK. E-mail: morganar1@cardiff.ac.uk
Received 10 July 2007; Accepted 18 October 2007
DOI 10.1002/ajmg.b.30670
ß 2007 Wiley-Liss, Inc.
INTRODUCTION
Alzheimer’s disease (AD) is a common disease affecting 1% of
people up to the age of 60 and as much as 40% over the age of
90 [Bachman et al., 1992]. Rare autosomal dominant forms of
familial, early-onset AD can be caused by mutations in betaamyloid precursor protein (APP) [Goate et al., 1991], presenilin
1 [Campion et al., 1995] and presenilin 2 [Sherrington et al.,
1996]. However, the more common form of the disease, lateonset AD (LOAD), accounts for the majority of cases (95%)
and has a complex pattern of inheritance. The e4 allele of
apolipoprotein E (APOE) [Corder et al., 1993] is the only wellestablished genetic risk factor widely accepted to increase the
likelihood of developing LOAD [Farrer et al., 1997]. Although
present in a large proportion of LOAD cases possession of an
APOE e4 allele is neither necessary nor sufficient for development of the disease, supporting the hypothesis for the existence
of other genetic risk factors.
Previously, we performed a two-stage genome-wide screen
for linkage with LOAD in Caucasian affected sibling pairs
(ASPs), which provided evidence for susceptibility loci on
chromosomes 9 (MLS ¼ 1.8), 10 (MLS ¼ 3.9), 12 (MLS ¼ 1.3),
and 19 (MLS ¼ 1.3) [Myers et al., 2000, 2002]. The locus on
chromosome 10, 10q11.23–q21.3, was the only one to achieve
genome-wide significance, and other studies have also shown
evidence of linkage to chromosome 10 for AD and associated
phenotypes [Bertram et al., 2000; Ertekin-Taner et al., 2000;
Blacker et al., 2003; Farrer et al., 2003]. Thus, it is reasonable
to hypothesize that a genetic risk factor (or factors) for LOAD
may lie on chromosome 10.
Our linkage peak on chromosome 10 covers a large (44 cM)
region and was defined by microsatellite markers D10S1426
(59 cM) and D10S2327 (103 cM). The LOD-1 linkage region is
commonly taken as a crude estimate of the 95% confidence
interval for the location of a disease causing gene, assuming
that the linkage reflects the presence of a single susceptibility
locus [Ott, 1991]. In this study we set out to test every gene
within the LOD-1 linkage region (defined as the area encompassed by all marker’s producing LOD scores between 2.83 and
3.83, flanked by markers D10S1220 (70 cM/50 Mb) and
D10S1670 (87 cM/70 Mb)) for association with LOAD. We
investigated SNPs located within these genes at an average
density of one SNP every 10 kb. SNPs were first tested for
association in pooled DNA samples, and SNPs showing
evidence of association in these pools were then individually
genotyped.
METHODS
SNP Selection
All known and predicted genes in the LOD-1 region (between
D10S1220 (70 cM/50 Mb) and D10S1670 (87 cM/70 Mb)) were
728
Morgan et al.
downloaded from the April 2003 and July 2004 freeze of the
Ensembl database using the ENSMART data-mining tool,
resulting in 67 positional candidates (see supplementary
Table S1). SNPs were identified within the genes using
dbSNP (http://www.ncbi.nih.gov) and SNPper (http://snpper.
chip.org/bio/). Validated SNPs were chosen preferentially and
SNPs with a frequency of less than 5% were discarded. We
initially chose SNPs to cover all the genes plus 1,000 bp of
50 flanking sequence with a SNP density of 1 SNP/8 kb.
Towards the end of the study we re-assessed our coverage
taking into account PCR failures and some chosen markers
proving non-polymorphic. We genotyped alternative SNPs
where possible and obtained genotypes for 528 SNPs at a
final average density of 1 SNP/10 kb in each of the 67
candidates (with the exception of the larger genes CTNNA3
and ANK3 (as these are very large genes we did not cover
large introns. However, exons of the genes were covered with
1 SNP/10 kb)).
Samples
DNA samples, and clinical data were collected from
1,160 individuals (70.1% 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 Medical Research Council genetic resource for
LOAD. For further information and full descriptive data on the
sample see Morgan et al. [2007].
DNA Pooling
The concentrations of all DNA samples used for pool
construction were determined by the PicoGreen dsDNA
Quantitation method (Molecular Probes, Eugene, OR) in a
Labsystems Ascent Fluoroskan (Life Sciences International,
Basingstoke, UK). Equimolar amounts of DNA were taken
from each sample and used to construct DNA pools aiming
for a final pool concentration of 5 ng/ml. The DNA pools were
validated by genotyping six SNPs in the pools and in
the individual samples comprising the pools. Estimation of
differences between case and control pools was found to be
extremely accurate, with a mean error of 0.007.
In the first stage SNPs were typed in DNA pools that
were constructed using 366 AD patients (75.9% female,
mean AAO ¼ 75.5 years (SD ¼ 6.29)) and 366 age and sexmatched control subjects (mean age ¼ 76.3 years (SD ¼ 6.41),
76.1% female). SNPs showing evidence of association in these
pools were then typed in a larger pooled sample consisting
of 1,001 AD cases (70.7% female, mean AAO ¼ 75.9 years
(SD ¼ 6.43)) and 1,001 age and sex-matched controls (70.7%
female, mean age ¼ 76.3 years (SD ¼ 6.17)), and comprising the
366 cases and 366 controls from the first stage. Some SNPs
bypassed the first stage and underwent pooled genotyping only
in the stage 2 pools. This reflected sample availability as the
study progressed and we were able to exploit the growing
sample collection.
Pooled Genotyping
A highly accurate protocol for SNP allele frequency estimation in DNA pools based upon the SNaPshot (Applied Bio-
systems, Warrington, UK) chemistry, which is an adaptation
of primer extension methods, was applied [Norton et al.,
2002]. Forward and Reverse primers were designed using
primer 3 software (http://frodo.wi.mit.edu/cgi-bin/primer3/
primer 3_www.cgi) [Rozen and Skaletsky, 2000]. Extension
primers were designed using FP PRIMER 1.0.1b software
(http://m034.pc.uwcm.ac.uk/fp_primer.html). PCR was performed under standard conditions for SNaPshot, using 15 ng
pooled genomic DNA and HotStar Taq DNA polymerase
(Qiagen, Crawley, UK). Primer extension products were run
on a 3100 DNA sequencer (Applied Biosystems) and the data
were processed by using GeneScan Analysis 3.7 (Applied
Biosystems). SNP allele frequencies were estimated from
peak heights obtained by using Genotyper 2.5 (PE Biosystems,
Cheshire, UK).
Since small measurement errors in allele frequencies
determined from DNA pools can result in false negative as
well as false positive findings, we adopted a relaxed significance threshold (P < 0.1) to select markers for further
genotyping.
Individual Genotyping
Polymorphisms showing any evidence of a difference
(P < 0.1) in allele distribution between pooled cases and
controls, in our enlarged pooled screening sample consisting
of 1,001 AD cases and 1,001 age and sex-matched controls,
were individually genotyped by either ampliflour or Sequenom
technologies.
The ampliflour genotyping assay, based on amplification in
the presence of tailed allele-specific primers, a common reverse
primer and universal fluorescence labeled ampliflour primers
(Serologicals: www.serologicals.com), was used for some of
the individual genotyping. Primers were designed by ampliflour assay architect software (www.assayarchitect.com), and
standard ampliflour PCR conditions were used as described
in Myakishev et al. [2001]. The amplification signals were
analyzed by endpoint measurement using the ABI system
(Applied Biosystems).
Some SNPs were genotyped using the Sequenom (www.
sequenom.de) genotyping platform, which uses the MALDITOF primer extension assay [Jurinke et al., 2002; Storm et al.,
2003]. The Iplex genotyping assay was employed, which has
increased plexing efficiency and flexibility for the MassARRAY
system through single base primer extension with massmodified terminators. Standard conditions were followed as
described in Oeth et al. [2005].
For both genotyping methods samples were aliquoted and
PCR performed in 384 sample plates containing both cases
and controls on each plate. Quality control measures 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,
and a greater than 90% genotyping success.
Statistical Analysis
For the pooled genotyping, each pool was run at
least three times and average allele frequencies calculated
for each pool and then for cases and controls. A correction
factor (k) was estimated using either a known heterozygote
CEPH individual or the mean peak height ratio for that
SNP base change [Moskvina et al., 2005] to allow for the
fact that systematic unequal representation of primer extension products may occur [Norton et al., 2002]. Estimated
allele frequencies were converted to allele counts and were
tested for approximate statistical significance by chi-square
analysis.
Association Analysis of 528 Intra-Genic SNPs
Genotype and allele frequencies from individual genotyping
for each SNP were analyzed by 2 3 and 2 2 chi-square
respectively, to determine if there were differences between
cases and controls.
Polymorphisms that underwent individual genotyping were
tested for deviation from Hardy–Weinberg equilibrium (HWE)
in cases and controls.
RESULTS
Markers were initially tested for association with LOAD
using allele counts estimated from pooled genotyping. DNA
pooling is a simple yet efficient method that allows a large
number of markers to be quickly screened and those that are
most likely to show association identified [Bansal et al., 2002;
Sham et al., 2002]. Individual genotyping was used to confirm
the results of pooled genotyping.
Our first sets of markers underwent pooled genotyping
in 366 cases and 366 controls. SNPs showing evidence of
association in these pools were then typed in our second larger
pooled sample consisting of 1,001 AD cases and 1,001 controls.
Some SNPs bypassed the first stage and underwent pooled
genotyping only in the stage 2 pools (this reflected sample
availability as the study progressed and we were able to exploit
the growing sample collection). From the pooled genotyping we
obtained results on 528 SNPs (for list of the 528 SNPs see
supplementary Table S2). Eighteen SNPs were associated with
LOAD in the stage 2 pools (see results in Table I). These
underwent individual genotyping in our complete dataset
consisting of 1,160 LOAD patients and 1,389 control subjects
(see results in Table I). All individually genotyped markers
were in HWE in both cases and controls.
Examining the individual genotyping results, it was
observed that for 14 of the markers allele frequencies were
not significantly different between cases and controls. However, 4 markers continued to show some evidence of association: rs1881747 (allelic P ¼ 0.0107 OR ¼ 1.24, genotypic
P ¼ 0.038), rs2279420 (allelic P ¼ 0.022 OR ¼ 0.79, genotypic P ¼ 0.028), rs2306402 (allelic P ¼ 0.024 OR ¼ 1.18, genotypic P ¼ 0.05), and rs5030882 (allelic P ¼ 0.0455 OR ¼ 1.29,
genotypic P ¼ 0.07). These levels of significance do not survive
adjustment for multiple testing.
Two SNPs showed evidence of association with LOAD
in our stage 2 pools, but failed to be successfully genotyped
individually: rs524256 P ¼ 0.05494 in
rs10509119 P ¼ 0.05799 in stage 2 pools.
pools,
DISCUSSION
Pooled genotyping 2
rs10508926
rs1881747
rs4245599
rs919827
rs12354621
rs7906222
rs10509109
rs2279420
rs1050745
rs10994167
rs2393612
rs7072073
rs2248570
rs3793862
rs1629654
rs2306402
rs7091896
rs5030882
2
We tested every known and predicted gene within our LOD-1
linkage region on chromosome 10 for association with LOAD.
We covered the genes at an average density of 1 SNP every
10 kb (larger genes in the region: CTNNA3 and ANK3 have not
been so extensively covered. The large introns of these genes
were excluded for practical reasons). Our strategy for this
study was to test for associations using pooled genotyping, with
progression to individual genotyping for SNPs that showed
evidence of association from pooling.
From the 528 SNPs investigated we have observed evidence
for association with LOAD for 4 SNPs on Chr 10—rs2279420,
rs5030882, rs2306402, and rs1881747.
rs2279420 is located at Chr 10: 61,451,953 within the 30 UTR
of ANK3. ANK3 (ankyrin 3) (MIM: 600465) is a membranecytoskeleton linker. It participates in the maintenance/targeting of ion channels and cell adhesion molecules at the nodes of
Ranvier and axonal initial segments.
rs5030882 is located at chr10: 70,122,477 and is a synonymous SNP in an exon of CXXC6 (CXXC finger 6) (MIM:
607790). There is not much known about the protein encoded
by this gene, except that CXXC6 is fused to MLL in a case
of pediatric acute myeloid leukemia containing the t(10;11)
(q22;q23) [Lorsbach et al., 2003].
rs2306402 is located at chr10: 68,605,491 and is an intronic
SNP in CTNNA3 (catenin, alpha 3) (MIM: 607667). CTNNA3 is
predominantly expressed in heart and testis and is expressed
at lower levels in brain. CTNNA3 is a strong biological
candidate for AD as it inhibits WNT signaling, a pathway
implicated in AD not least as it negatively regulates the
tau-kinase glycogen synthase-3 [Busby et al., 2004]. Also,
Alpha-T-catenin is a binding partner of beta-catenin and in
turn, beta-catenin interacts with PSEN1. CTNNA3 has had
both positive and negative associations reported with AD
previously [Blomqvist et al., 2004; Busby et al., 2004; Cellini
et al., 2005; Martin et al., 2005; Bertram et al., 2007].
rs1881747 is located at chr10: 54,003,581 between the genes
DKK1 (256,159 bp away) and MBL2 (292,566 bp away). DKK1
(dickkopf homolog 1 precursor) (MIM: 605189) encodes a
TABLE I. Results—Significant SNPs From Stage 2 Pooled Genotyping, and Individual Genotyping
Results
SNP
stage
729
Individual genotyping
SNP position
Cases
Controls
P
Cases
Controls
P
OR
51,790,795
54,003,581
60,035,761
60,555,306
60,979,709
61,000,491
61,028,388
61,451,953
61,457,255
61,480,366
61,551,943
61,565,377
61,571,799
61,615,065
67,341,116
68,605,491
69,343,251
70,122,477
0.84
0.84
0.54
0.86
0.81
0.87
0.67
0.89
0.75
0.79
0.80
0.51
0.86
0.59
0.80
0.84
0.74
0.92
0.86
0.81
0.57
0.84
0.78
0.84
0.70
0.91
0.80
0.81
0.84
0.54
0.90
0.67
0.78
0.80
0.72
0.90
0.13
0.01935
0.033
0.06
0.014
0.00967
0.07
0.00636
0.0035
0.08
0.00034
0.046
0.00028
0.00000148
0.11
0.012
0.0557
0.02597
0.86
0.85
0.55
0.87
0.80
0.88
0.71
0.88
0.77
0.98
0.59
0.46
0.94
0.93
0.77
0.82
0.68
0.95
0.88
0.83
0.55
0.85
0.80
0.88
0.71
0.90
0.77
0.98
0.61
0.46
0.95
0.92
0.75
0.79
0.67
0.94
0.0782
0.0107
0.76
0.24
1
0.96
0.86
0.022
0.75
0.29
0.25
0.88
0.17
0.37
0.19
0.024
0.39
0.0455
1.19
1.24
1.02
0.90
0.99
0.99
0.99
0.79
1.02
0.79
0.89
1.01
1.19
0.91
1.11
1.18
0.93
1.29
730
Morgan et al.
protein that is a member of the dickkopf family. It is a secreted
protein with two cysteine rich regions and is involved in
embryonic development through its inhibition of the WNT
signaling pathway. MBL2 (soluble mannose-binding lectin
precursor) (MIM: 154545) encodes the soluble mannosebinding protein found in serum. MBL2 recognizes mannose
and N-acetylglucosamine on bacterial pathogens, and is
capable of activating the classical complement pathway.
rs1881747 does not appear to be in LD with SNPs in DKK1 or
MBL2 using the results of the HapMap project.
In conclusion, we have observed association with SNPs in
ANK3, CXXC6, CTNNA3 and in the region near DKK1.
However, we recognize that the strength of the effects appears
small and these results will not survive adjustment for
multiple testing and could thus be false positives. However, it
is possible that we may be observing small genetic effects.
Alternatively, the associations could reflect underlying
LD with other variants in this region on Chromosome 10.
As no convincing gene association was identified in our
analysis of the LOD-1 region of our chromosome 10 linkage
peak it is possible that the gene(s) responsible for the linkage
may lie outside this region, and within the wider area of
our linkage peak. However, despite our lack of highly
significant findings, this study cannot fully exclude any of
the genes within the LOD-1 region of our linkage peak on
Chromosome 10 as potential contributors to the overall
risk for the disease. It is possible that the linkage peak may
be explained by many weak variants in different genes.
Although we have covered the genes with SNPs every 10 kb,
current methods employing the results of the HapMap
project and high-density SNP genotyping incorporating
an LD-based approach to SNP selection in the case of the
common disease–common variant hypothesis and sequencing
of genes to exclude rare pathogenic variants would sample
more of the variance in the genes in the LOD-1 region of our
linkage peak. Also, chromosome 10 has become more fully
sequenced/annotated during recent years compared to when
this study began. We have checked the most recent genome
assemblies and can confirm that there are no ‘new’ genes in
this region that we have not covered. However, it is possible
that the locations of some of the SNPs (and genes in which
the SNPs are located) may have changed since the original
design of the project and so some of the genes within our LOD-1
region may not have been so extensively covered as originally
planned.
Recently, we have undertaken a further linkage analysis
in which we amalgamated 3 large samples to give a total of
723 affected relative pairs [Hamshere et al., 2007]. Our linkage
peak on chromosome 10 remains. However it has shifted
slightly to the right, with a LOD-1 region spanning 17 cM
from 68 to 85 cM. Our LOD-1 region from Myers et al. [2002],
and on which the study presented here is based, is 70.2–
86.2 cM. Thus we now have approximately 2 cM that we have
not covered.
Further analysis in our linked region is required. It is likely
that within the next year detailed genotyping of genes in this
region will be undertaken as part of genome wide association
studies and we anticipate that such studies will provide further
information that may help to identify the elusive chromosome
10 gene/genes for LOAD.
ACKNOWLEDGMENTS
We would like to thank the many individuals with AD and
their families who participated in this study. Our research is
supported by grants from the Medical Research Council and
the Alzheimer’s Research Trust. The BBU is supported by the
Welsh Assembly Government.
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