Genetic Analysis of Alzheimer\u0026rsquo;s Disease in the Uppsala Longitudinal Study of Adult Men

$Page 1: Genetic Analysis of Alzheimer\u0026rsquo;s Disease in the Uppsala Longitudinal Study of Adult Men$
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Original Research Article

Dement Geriatr Cogn Disord 2009;27:59–68 DOI: 10.1159/000191203

Genetic Analysis of Alzheimer’sDisease in the Uppsala Longitudinal Study of Adult Men

Vilmantas Giedraitis a Lena Kilander a Malin Degerman-Gunnarsson a

Johan Sundelöf a Tomas Axelsson b Ann-Christine Syvänen b Lars Lannfelt a

Anna Glaser a

a Molecular Geriatrics, Department of Public Health and Caring Sciences, and b Molecular Medicine,Department of Medical Sciences, Uppsala University, Uppsala , Sweden

SNP at p ! 0.05 with association to AD. Only 20 genes re-mained significant after a permutation test to correct for multiple comparisons within individual genes. Using pub-licly available data from 2 genome-wide association (GWA) studies and linkage disequilibrium data from HapMap, we attempted to replicate the AD association identified in UL-SAM. In addition to apolipoprotein E, we were able to repli-cate 5 other genes in both GWA studies at p ! 0.05.

Copyright © 2009 S. Karger AG, Basel

Introduction

Genetic factors influencing common complex condi-tions have proven difficult to identify, and data from nu-merous investigations have provided incomplete conclu-sions as to the identity of these genes. So far, only a few genes have been reproducibly associated to common dis-eases. The association of apolipoprotein E (APOE) to Alz-heimer’s disease (AD) has been convincingly and repeat-edly demonstrated in numerous studies [1] . Additional susceptibility genes for AD have been more elusive and studies aimed at identifying such genes have, prior to whole-genome approaches, mainly been focused on genes with a demonstrated or implied function related to de-

Key Words

Dementia � Genetics of complex disorders � Linkage and association analysis � Alzheimer’s disease � Apolipoprotein E � Association study � Gene polymorphism � Longitudinal study

Abstract

Background/Aims: Genetic factors influencing common complex conditions have proven difficult to identify, and data from numerous investigations have provided incom-plete conclusions as to the identity of these genes. Here we aimed to identify susceptibility genes for late-onset Alz-heimer’s disease (AD). Methods: The case-control analysis included samples from 86 AD patients and 404 cognitively healthy controls selected from the Uppsala Longitudinal Study of Adult Men (ULSAM). In the incidence analysis, all 1,088 genotyped ULSAM participants were included. DNA samples from ULSAM participants were analyzed for 2,578 single nucleotide polymorphisms (SNP) within 368 genes. The selection of genes tested for association to AD within this cohort was based on genes previously implicated in conditions with relevance to ULSAM, such as dementia, car-diovascular disease, diabetes and metabolic syndrome, os-teoporosis, and cancer. Results/Conclusion: Association analysis revealed 82 genes containing at least 1 significant

Accepted after revision: October 6, 2008 Published online: January 14, 2009

Vilmantas Giedraitis Public Health and Caring Sciences, Molecular Geriatrics, Uppsala University Dag Hammarskjölds väg 20, SE–751 85 Uppsala (Sweden) Tel. +46 18 471 5038, Fax +46 18 471 4808E-Mail [email protected]

© 2009 S. Karger AG, Basel1420–8008/09/0271–0059$26.00/0

Accessible online at:www.karger.com/dem

Giedraitis et al.

Dement Geriatr Cogn Disord 2009;27:59–68 60

mentia development or genes chromosomally positioned within linkage peaks correlated to AD [2, 3] . However, genes implicated in other complex conditions could also potentially be relevant to dementia. The APOE gene, for example, shows association to both cardiovascular dis-ease and AD [1, 4, 5] . There are also reports suggesting that body mass index (BMI) [6–8] and type 2 diabetes [9, 10] can be risk factors for AD. Although the molecular mechanisms underlying this correlation are largely un-known, one possible pathway could include insulin-de-grading enzyme [11] , which is involved in the degrada-tion of both insulin and amyloid- � (A � ), the latter being a central component in plaques that form in the brains of AD patients [12] . Furthermore, the Wnt signaling path-way, involving genes such as LRP1, 5 and 6, has been im-plicated in osteoporosis, cancer, neurodevelopment and neurodegenerative disorders, suggesting that these genes might be involved in mechanisms common to different complex conditions [13–15] .

The Uppsala Longitudinal Study of Adult Men (UL-SAM) was based on men born between 1920 and 1924 and living in Uppsala, Sweden, at the time of their 50th birthday [16] . These men have been regularly invited for health examinations over the years. The initial focus of ULSAM was on the identification of metabolic risk fac-tors for cardiovascular disease, but examinations have been expanded into other areas of complex traits. This includes significant efforts made to identify individuals diagnosed with different forms of dementia.

In the present study, we investigated the potential association of a large number of genes to AD within ULSAM. The analyzed genes were selected on the basis of being potentially relevant to dementia or to other com-plex conditions investigated within ULSAM. These con-ditions include dementia, diabetes and metabolic syn-drome, cardiovascular disease, osteoporosis, and can-cer.

Methods

Study Population We analyzed samples and data collected from ULSAM. This

study was initiated in 1970–1974, when all 50-year-old men living in Uppsala, Sweden, were invited for a health survey, initially fo-cusing on risk factors for cardiovascular disease. Out of a total of 2,841 men born in 1920–1924, 2,322 (82%) agreed to participate. Since then, the cohort has been re-examined at ages 60, 70, 77 and 82 years. With the aim of identifying men with AD and other cog-nitive disorders at an early point, ULSAM participants have so far been invited for cognitive testing at 72 (n = 999), 77 (n = 804) and 82 (n = 523) years of age. Subjects with low test performances were

encouraged to visit the Geriatric Memory Clinic at the Uppsala University Hospital for a thorough clinical assessment. To iden-tify dementia cases, all available medical records from the Upp-sala University Hospital, the general practitioners in Uppsala,the community nursing homes and dementia group members were reviewed up to December 31, 2005. Most reports on medical care of the ULSAM participants are provided within these set-tings. The records of all possible cases of dementia or cognitive impairment were reviewed and diagnoses were set by 2 experi-enced geriatricians, independently of each other. In case of dis-agreement, a third geriatrician reviewed the case and the diagno-sis was determined by consensus decision. The diagnosis of AD was defined according to the NINCDS-ADRDA criteria [17] and the DSM-IV criteria [18] . In the majority of AD cases, the original diagnostic workup was made after referral to the Geriatric Mem-ory Clinic based on cognitive testing in ULSAM. CT scans were available from 76 AD cases and were consistent with an AD diag-nosis, i.e. were normal or showed atrophy or mild to moderate white matter changes. CT findings of ̂ 2 brain infarctions that had been clinically silent, i.e. onset and progression were slow and gradual, were considered compatible with the diagnosis of AD, and were present in 3 AD cases. In total, 150 dementia cases were identified in ULSAM and 111 of them were diagnosed with AD. DNA samples were available from 86 AD cases. Individuals who at the 82-year follow-up had a Mini Mental State Examination score 6 26 and no dementia diagnoses were considered cogni-tively healthy. In total, 404 individuals met these criteria and had available DNA samples; these were used as a healthy control group. This study was approved by the ethics committee at Upp-sala University and informed consent for all parts of the study was obtained.

DNA Extraction Blood samples for DNA preparation were obtained from 944

men at examination at 70 years of age. In addition, DNA was also extracted from blood samples from examination around 77 years of age and from archived muscle biopsies, yielding a total of 1,088 DNA samples.

Gene Selection and Genotyping In total, 370 candidate genes with demonstrated or implied

involvement in dementia, diabetes, stroke, cancer and other com-mon complex diseases were investigated in this study. Specifical-ly for dementia, 80 genes involved in 3 major pathways known to affect brain function were selected: A � /lipid metabolism, oxida-tive stress and inflammation/apoptosis [2] . In addition, genes po-sitioned within previously identified AD linkage peaks were also selected. For most of the genes, SNP were distributed across the gene with an average distance of 5 kb. For some of the genes, spe-cific SNP associated with disease in previous studies were select-ed. A majority of the SNP were analyzed with the Illumina Gold-enGate � Assay [19] , but other minisequencing-based methods were also used [20, 21] .

Statistical Analysis Allelic frequency and Hardy-Weinberg equilibrium of SNP

were evaluated using the HaploView software [22] . Case-control association analysis was performed using unconditional logistic regression implemented in the Unphased software [23] . The sig-nificance of the results was evaluated by permutation analysis, by

AD Association Analysis in ULSAM Dement Geriatr Cogn Disord 2009;27:59–68 61

running 1,000 permutations per gene. Incidence and survival analysis was performed using Kaplan-Meier curves and extension of Gehan’s generalized Wilcoxon test in the Statistica software.

In silico replication analysis was performed by extracting data from 2 publicly available GWA studies [24, 25] . Using HapMap (www.hapmap.org) data, out of 146 SNP showing association to

AD in ULSAM, we could identify 1,911 SNP within linkage dis-equilibrium (LD; D � = 0.8) in both GWA studies. Reported asso-ciation from the identified SNP within the respective GWA study was examined as unadjusted data and used to confirm or disprove replication. As the association measure from the Li et al. [25] study, we used Fisher exact p values as published by the authors. From Coon et al. [24] , we calculated disease-association p values by the Plink software using original genotyping data available from the authors’ website.

Results

All AD patients and cognitively healthy control (HC) individuals with available DNA samples from ULSAM were included in the present investigation. This resulted in a total of 86 AD and 404 HC samples ( table 1 ). The ini-tial gene selection was based on genes implicated in bio-logical processes with relevance to dementia or genes that have for other reasons been previously investigated in AD association studies. In addition, genes associated with other traits of interest within ULSAM were also included. Altogether, the selected AD and HC groups were geno-typed for 3,118 SNP within 370 genes. The identities and chromosomal localizations of the selected genes are shown in figure 1 . For the majority of genes, SNP were chosen across the entire gene region with an average dis-tance of 5 kb, but, in a few cases, only SNPs of special in-terest based on literature reports were selected (informa-tion on selected SNP is available on request). Prior to the statistical analysis of the data, SNP with a minor allele frequency of ! 1% or with deviations from Hardy-Wein-

berg equilibrium were excluded, which resulted in analy-sis of a total of 2,578 SNP within 368 genes. Association analysis was performed using the Unphased software, which is based on standard unconditional logistic regres-sion. This program also includes a permutation test to establish a gene significance threshold level, taking into account multiple testing and maintaining linkage dis-equilibrium (LD) [23] .

The results of the association analysis revealed a total of 146 SNP within 82 genes with p values ̂ 0.05 ( fig. 1 ). After correcting for multiple testing (1,000 permuta-tions), 29 SNP within 20 genes remained significant atp ̂ 0.05 ( table 2 ). As expected, the APOE (MIM 107741) gene showed the strongest association to AD, with a p value of 10 –5 after permutation analysis (100,000 permu-tations; � 4 allele frequency was 33.1% in the AD group compared to 14.8% in the HC group). Other genes with significant associations to AD both prior and subsequent to permutation analysis included genes with previously described dementia correlations, as well as genes previ-ously associated with other traits. The genes showing the most significant p values both before and after permuta-tion were bone morphogenic protein 4 (BMP4; MIM 112262), bone morphogenic protein 3 (BMP3; MIM 112262) and selectin E (SELE; MIM 131210). In ULSAM, BMP3 and BMP4 were primarily thought of as candidate genes for osteoporosis, and SELE was initially selected as a gene involved in cardiovascular disease and inflamma-tion. The group of 10 genes with the most significant p values both prior and subsequent to permutation includ-ed butyrylcholinesterase (BCHE; MIM 177400) and pre-senilin 1 (PSEN1; MIM 104311), both previously reported to show association to AD [26–28] .

Three genes, BMP4, ESR2 and PSEN1, located in close proximity of each other on chromosome 14q all showed significant association with AD. Therefore, we performed

Table 1. Description of cases (AD) and controls (HC) from ULSAM included in the association study

Group AD HC

Number of individuals 86 404

Average age and range at onset(AD) or examination (HC)

80.2 (72–85) 81.8 (80–84)

Selection criteria AD according to NINCDS-ADRDAand DSM-IV; CT scan

attended examination at 82 years; no dementia diagnosis; MMSE ≥26

MMSE = Mini Mental State Examination.

Giedraitis et al.


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1089

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MTH

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NPP

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1417

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0845

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3164

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8814

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1


haplotype analysis of this region and could demonstrate that the haplotype with the strongest AD association(p = 1.0 ! 10 –5 ) within this region included the most sig-nificantly associated SNP within BMP4 and ESR2 and 1 SNP from PSEN1, which was also significantly associated with AD.

As a supplement to the case-control comparison, we used all 1,088 available samples from ULSAM and per-formed incidence by genotype analysis, using AD diag-nosis as outcome. This analysis included 86 cases (AD) and 404 controls (HC) from the original study and an ad-ditional 598 samples. These 598 samples did not include any patients with AD diagnosis, but did contain samples from ULSAM participants who had been excluded from the control group due to circumstances such as other

forms of dementia or not attending examination around 82 years of age. By this type of incidence analysis, the lon-gitudinal data from ULSAM can be explored in more de-tail than in a case-control study. We performed incidence analysis of all SNP with p ! 0.05 from the initial case-control comparison, and these results were consistent with the results from the case-control comparison ( ta-ble 2 ). Details from 2 SNP with the most significant as-sociation to AD are shown ( fig. 2 ) and the results revealed p values of 5.8 ! 10 –8 and 0.001 for APOE and BMP4, respectively. These results confirmed the initial effect in the case-control comparison and demonstrated the age effect of APOE, as � 4 homozygotes showed an increased AD frequency at an earlier age than other genotypic vari-ants. This effect is not evident for rs762642 from BMP4,

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e

–log

(p v

alue

)–l

og (p

val

ue)

ADIP

OR

2 (1

6704

08)

FGF2

3 (4

3476

54)

VWF

(592

8301

)

CD

9 (6

1791

34)

TNFR

SF1A

(630

8184

)G

APD

H (6

5138

72)

A2M

(910

2453

)

VDR

(465

2158

7)

TEG

T (4

8421

607)

TFC

P2 (4

9774

887)

IGFB

P6 (5

1777

703)

RAR

G (5

1890

621)

MM

P19

(545

1548

1)LR

P1 (5

5808

543)

NR

1H4

(993

9181

0)

IGF1

(101

3138

06)

ACAC

B (1

0803

8783

)

PRK

AB

1 (1

1859

0144

)TC

F1 (1

1990

0932

)

IPF1

(273

9215

7)AL

OX5

AP (3

0207

645)

POST

N (3

7034

722)

TNFS

F11

(420

3487

2)TP

T1 (4

4673

747)

KLF5

(725

3093

1)ED

NR

B (7

7367

617)

IRS2

(109

2041

85)

F7 (1

1280

8106

)F1

0 (1

1282

5114

)LT

B4R

(238

3890

8)PT

GER

2 (5

1850

863)

BMP4

(534

8620

7)

ESR

2 (6

3621

388)

VTI1

B (6

7186

985)

PSEN

1 (7

2672

915)

DLS

T (7

4418

372)

SER

PIN

A3

(941

4846

7)

CYP

46A

1 (9

9220

407)

TRAF

3 (1

0231

3569

)TN

FAIP

2 (1

0266

2417

)AK

T1 (1

0430

6731

)C

YP19

A1

(492

8896

1)

LIPC

(565

1146

7)

APH

1B (6

1356

844)

CSK

(728

6148

9)

IGF1

R (9

7010

288)

CR

EBBP

(371

6568

)C

IITA

(108

7855

8)SO

CS1

(112

5577

5)VK

OR

C1

(310

0967

6)M

MP2

(540

7058

9)

CET

P (5

5553

263)

CX3

CL1

(559

6390

0)

CD

H11

(635

3818

4)

PAR

D6A

(662

5235

2)LC

AT (6

6531

154)

HP

(706

4600

9)M

LYC

D (8

2490

231)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

ALO

X15

(448

0963

)AL

OX1

2 (6

8401

08)

SLC

2A4

(712

5777

)VA

MP2

(800

3189

)EL

AC2

(128

3643

3)

SREB

F1 (1

7655

794)

NO

S2A

(231

0791

9)TN

FAIP

1 (2

3686

913)

TRAF

4 (2

4095

166)

SLC

6A4

(255

4903

2)B

LMH

(255

9934

9)C

CL2

(296

0640

9)PI

P5K2

B (3

4177

324)

THR

A (3

5472

589)

RAR

A (3

5718

972)

IGFB

P4 (3

5853

202)

CN

P (3

7372

285)

MLX

(379

7260

4)IT

GA2

B (3

9805

076)

MA

PT (4

1327

624)

CO

L1A1

(456

1645

6)M

PO (5

3702

201)

AC

E (5

8908

166)

GH

1 (5

9348

294)

GR

B2

(708

2575

2)TI

MP2

(743

6065

4)

NPC

1 (1

9365

461)

TNFR

SF11

A (5

8143

500)

PTBP

1 (7

4841

1)ST

K11

(115

6798

)TB

XA2R

(354

5504

)C

3 (6

6288

78)

ICAM

1 (1

0242

765)

LDLR

(110

6113

2)

CAC

NA1

A (1

3179

088)

PTG

ER1

(144

4427

8)G

DF1

5 (1

8357

968)

CO

MP

(187

5458

3)C

EBPA

(384

8277

6)U

SF2

(404

5172

1)PS

ENEN

(409

2833

4)TG

FB1

(465

2825

4)A

POE

(501

0087

9)PT

GIR

(518

1556

5)N

R1H

2 (5

5571

515)

TNN

I3 (6

0354

950)

IL11

(605

6756

9)TR

IB3

(309

273)

PRN

P (4

6149

96)

SNAP

25 (1

0147

477)

PCSK

2 (1

7154

752)

THBD

(229

7427

0)C

ST3

(235

6229

4)G

HR

H (3

5312

899)

HN

F4A

(424

1785

5)

MM

P9 (4

4070

954)

PTG

IS (4

7553

818)

PTPN

1 (4

8560

294)

BMP7

(551

7721

1)TN

FRSF

6B (6

1759

607)

BA

CE2

(414

6159

8)

CB

S (4

3346

370)

HM

OX1

(341

0705

7)C

YP2D

6 (4

0852

445)

PPAR

A (4

4925

163)

CPT

1B (4

9354

156)

PHEX

(219

6048

0)

TIM

P1 (4

7326

634)

AR (6

6680

599)

TAF1

(705

0283

9)IR

S4 (1

0786

2368

)AG

TR2

(115

2160

03)

CD

40LG

(135

5580

02)

FGF1

3 (1

3754

1401

)SO

X3 (1

3941

2818

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5 5.5 Ch21Ch22 ChX Ch20

Ch12 Ch13 Ch14 Ch15 Ch16

Ch17 Ch18 Ch19

Fig. 1. The complete results from the case-control study showing the distribution of SNP within genes positioned along the chro-mosomes (Ch) and the corresponding p values of AD association. Genes are evenly placed along the chromosomes with no exact chromosomal localization. The gene start positions based on the GeneCards database are shown in parentheses. If the gene posi-

tion is not available, the position of the first SNP in the gene (based on the Single Nucleotide Polymorphism Database) is shown. Horizontal dashed lines show the p value level of 0.05. Vertical dotted lines are separators between genes. Genes primar-ily selected as candidate genes to dementia are shown in bold.

Giedraitis et al.


Table 2. SNP with p < 0.05 after permutation analysis

Chromo-some

Gene SNP withbest p value

Minor allelefrequency

p value p value after 1,000permutations

Incidencep value

1 SELE rs3917410 0.10 0.001 0.009 0.0051 IL10 rs1878672 0.48 0.006 0.03 0.0483 BCHE rs1803274 0.19 0.005 0.02 0.024 PDGFRA rs7686588 0.11 0.01 0.029 0.0254 CCNI rs4241599 0.36 0.009 0.024 0.0474 BMP3 rs10005851 0.17 6 ! 10–4 0.007 0.0076 RXRB rs2269346 0.057 0.011 0.046 0.023

11 INS rs2070762 0.45 0.008 0.042 0.1611 KCNJ11 rs5215 0.40 0.014 0.033 0.04211 GSTP1 rs947894 0.34 0.012 0.027 0.0613 IPF1 rs2297316 0.39 0.007 0.038 0.3213 F10 rs483949 0.16 0.024 0.048 0.2014 BMP4 rs762642 0.42 3 ! 10–4 0.003 0.00114 ESR2 rs1256063 0.10 0.004 0.03 0.02514 PSEN1 rs8006497 0.043 0.003 0.026 1 ! 10–4

15 CSK rs3784789 0.34 0.003 0.026 0.02116 PARD6A rs14920 0.051 0.018 0.017 0.06917 NOS2A rs4796052 0.21 0.012 0.05 0.01719 C3 rs423490 0.28 0.006 0.045 0.1219 APOE rs429358 0.18 3 ! 10–6 10–5 a 6 ! 10–8

a 100,000 permutations.

70

0.7

0.8

Prop

orti

on o

f AD

-fre

e in

div

idua

ls

0.9

1.0

a 72 74 76

APOE ε4 negativeAPOE ε4 heterozygousAPOE ε4 homozygous

78

Age (years)

80 82 84 86 70

0.8

0.9

Prop

orti

on o

f AD

-fre

e in

div

idua

ls

1.0

b 72 74 76

AAACCC

78

Age (years)

80 82 84 86

Fig. 2. Results of incidence analysis revealing the genotypic distribution of AD cases as a function of age: APOE ( a ) and BMP4 ( b ).


which showed a much less age-dependent association to AD ( fig. 2 ).

Because the AD group showed higher age variation than the HC group and also included younger individu-als ( table 1 ), we wanted to exclude the possibility of sur-vival-dependent allelic association, i.e. that a significant p value for a certain SNP is in fact not related to an AD association, but due to increased survival of certain al-lelic carriers. We performed survival analysis including the entire available ULSAM material (1,088 samples) and all SNP showing association with AD. This analysis re-

vealed no significant allele frequency changes with age of survival for the investigated SNP, and we therefore ex-cluded the possibility of allelic association due to surviv-al advantages of individuals with specific alleles (data not shown).

Results from genetic association studies require repli-cation studies to confirm positive findings. For this pur-pose, we used 2 studies of whole-genome association (WGA) of AD with publicly available data [24, 25] . The most straightforward approach would be to compare the SNP from ULSAM with p ! 0.05 with identical SNP from

Table 3. Replication of positive findings from ULSAM in 2 GWA studies [24, 25]

Chromo-some

ULSAM D� Replication

gene SNP p value SNP Li et al. [25]p value

Coon et al. [24]p value

SNP in LD with ULSAM SNP associated with AD in both GWA studies6 FRK rs500972 0.047 1 rs12525087 0.04 0.007

11 APOA5 rs633389a 0.038 1 rs481843 3!10–4 0.02511 APOA5 rs633389a 0.038 1 rs486394 0.024 0.01812 IGF1 rs35767 0.011 1 rs10507144 0.046 0.01513 KLF5 rs4885062 0.04 1 rs9592852 0.024 0.0415 CSK rs1378938 0.02 1 rs7174305 0.017 0.002

Ten SNP with best p values from Coon et al. [24] b

1 ECE1 rs7539287 0.03 1 rs950922 0.35 2!10–5

3 RARB rs957950 0.015 1 rs17526457 0.55 5!10–4

8 MYC rs4645963 0.016 1 rs4733812 0.93 5!10–4

12 RARG rs6580936c 0.018 1 rs11170573 0.82 8!10–4

7 TBXAS1 rs194152 0.015 0.8 rs2267691 0.18 0.0017 STX1A rs941298 0.016 1 rs17146094 0.67 0.0022 IRS1 rs1078533 0.015 1 rs10187726 0.81 0.005

19 CACNA1A rs2419248 0.007 1 rs8113228 0.47 0.0055 PPARGC1B rs1422429 0.032 1 rs4705377 0.49 0.0065 PRKAA1 rs257009 0.033 1 rs11954639 0.34 0.006

Ten SNP with best p values from Li et al. [25] b

3 RARB rs957950 0.015 1 rs6805482 2 ! 10–5 0.9120 TRIB3 rs6115830 0.044 1 rs7268977 0.001 n.a.

6 ESR1 rs7754762 0.048 1 rs9397442 0.003 n.a.4 BMP3 rs10005851 6!10–4 1 rs4270580 0.004 n.a.1 MTR rs2789352 0.035 1 rs4659738 0.004 n.a.6 RXRB rs2269346 0.011 1 rs9276431 0.004 0.626 FRK rs500972 0.047 1 rs11153589 0.005 0.378 MYC rs4645963 0.016 1 rs7004509 0.005 0.324 CCNI rs4241599 0.009 1 rs17002607 0.006 n.a.

14 ESR2 rs1256063 0.004 1 rs17101464 0.006 0.7

The table shows the best AD-associated SNP in LD with ULSAM AD-associated SNP from both GWA studies. n.a. = Not available.a The identical SNP was analyzed in the Li et al. study [25], but the results were insignificant. b SNP in LD are excluded. c The identical SNP was analyzed in both the Coon et al. [24] and Li et al. [25] studies, but the results were insignificant.

Giedraitis et al.


the 2 GWA studies. However, as ULSAM was analyzed with SNP from Illumina and the Coon et al. [24] and Li et al. [25] studies used Affymetrix technology, most SNP are not identical. From 146 SNP within 82 genes associ-ated with AD in ULSAM, only 13 were identical to SNP analyzed in both GWA studies, and an additional 9 were studied in Li et al. [25] . As the number of identical SNP between studies was so limited, we decided not to use identity as a selection criteria for replication, but rather compared SNP from the 2 GWA studies in LD with SNP from ULSAM found to be associated with AD. In both GWA studies, 1 SNP (rs4420638) close to the APOE locus showed a strong association with AD. This SNP is in LD with the 2 SNP (rs429358 and rs7412) identifying the APOE isoforms with D � = 0.86 and 0.90, respectively [24] . Therefore, we chose a threshold level for defining LD at D � = 0.8 for this replication study. Using HapMap data, we found 1,911 SNP from both GWA studies in LD with 114 SNP to be associated in ULSAM. Out of these, 6 SNP within 5 genes were associated with AD in both GWA studies ( table 3 ). An additional 113 SNP from Li et al. [25] and 102 SNP from Coon et al. [24] were found to be as-sociated in the respective study (p ! 0.05). This left 32 SNP from ULSAM that were not available from HapMap or did not show LD to any other SNP, and which could therefore not be assessed.

Discussion

For this association study of AD, we used all available genotypes from cases and controls generated within UL-SAM. In addition to genes with previously reported de-mentia correlations, genes selected for analysis of other complex conditions within ULSAM have also been in-cluded in the investigation. Although not strictly ‘hy-pothesis driven’, this approach could potentially indi-cate genes not previously shown to be associated with de-mentia.

This study did, in addition to the expected association of APOE, also point to several other genes with a poten-tial effect on the risk of developing AD. Amongst these, 2 genes (BMP4 and BMP3) attracted our attention. Al-though not previously reported to be involved in demen-tia development, this group of proteins regulates various biological processes, including cell growth and cell dif-ferentiation, and they play an important role in bone in-duction and fracture repair [29, 30] . Furthermore, BMP signaling and Wnt pathways both contribute to chondro-genesis and osteogenesis, and Wnt pathways have been

proposed to be involved in several neurodegenerative dis-orders, including AD [31] . This might suggest a common molecular mechanism by which the BMPs could also play a role in AD development. Another gene from this study with association to AD is SELE, which belongs to a group of adhesion molecules expressed by endothelial cells and involved in inflammation processes that could be rele-vant to AD pathogenesis. We could also detect associa-tion of 2 genes that in the past have repeatedly been im-plicated in AD association studies, BCHE and PSEN1 [26–28] . The products of these genes are involved in the metabolic pathways of acetylcholine [32] and amyloid precursor protein (APP) [33, 34] , respectively. A reduc-tion in cholinergic neurons is characteristic of AD, and the reduction in cholinergic activity that follows has pre-viously been associated with increased levels of BCHE [35, 36] . PSEN1 plays an important role in the cleavage of A � peptide from its precursor form APP [33, 34] , and A � is a major component in the plaques that form in the brains of AD patients.

Noticeably, on chromosome 14q, haplotype analysis including the genes BMP4, ESR1 and PSEN1, revealed a 3-marker haplotype clearly associated with AD. This haplotype includes 1 SNP from each gene, which are all intronic and not likely to be functional. However, they might be in LD with some functional variation within this region which has still to be identified. Interestingly, 1 SNP (rs165932) within PSEN1, included in this haplo-type, has been analyzed in other association studies with varying results [26, 27] .

Due to the limited number of cases in our study, sepa-rate association analyses for APOE � 4 carriers and non-carriers were not possible.

A problematic aspect of investigating associations be-tween such a large number of genes, as has been done here, is the substantial number of comparisons carried out. We dealt with this problem by permutation tests car-ried out within the Unphased software. This permutation takes into account LD between SNP and multiple com-parisons within individual genes. The method of adjust-ment is therefore fairly strict within genes, but does not take into consideration the number of investigated genes. The haplotype analysis of a 3-marker haplotype on chro-mosome 14q has not been adjusted for multiple compari-sons, but was based on individual SNP that showed sig-nificant AD associations.

When this study was initiated, WGA studies were not practically feasible. There have been rapid developments in WGA examinations in the past few years, and there are now reports on a number of genetic studies of complex


traits employing this technology [37–39] . The advantages are obvious, and there have indeed been several reports of solid reproducible results generated from such investi-gations. Within the field of AD there are, to our knowl-edge, 2 such reports with publicly available data [24, 25] . We used data from these 2 GWA studies in an attempt to replicate association results from ULSAM. Given the large number of observations made within a GWA study, the possibility of finding any chosen SNP with an asso-ciation p value ! 0.05 that is due to pure chance is high. However, the risk of a random finding when investigating the same SNP within 2 independent GWA investigations is substantially decreased. We therefore only considered replication successful if confirmed in both GWA studies. By extracting information from these databases, we were able to replicate only 5 of the associations of AD to spe-cific genes detected within ULSAM. Within the APOA5 gene, an identical SNP (rs633389) that showed associa-tion in ULSAM was studied in Li et al. [25] with no sig-nificant association to AD. However, SNP in LD with rs633389 were associated with AD in the GWA studies by both Li et al. [25] and Coon et al. [24] ( table 3 ). This, of course, might represent just a chance finding, but could also be due to haplotype structure differences between populations. These SNP might be in LD with putative functional SNP, but the level of LD and haplotype struc-ture might differ between populations. Therefore, differ-ent SNP within the same gene show association with dis-ease. The small number of replicable associations is not surprising, given the limited number of AD cases avail-able from ULSAM and the possibility of random associa-

tion effects. Nonetheless, not being able to replicate the best association results from ULSAM is disappointing. However, the in silico replication analysis that was car-ried out here, where different genotyping platforms have been used, can be troublesome. Perhaps the most signifi-cant problem is that very few identical SNP could be iden-tified, and comparisons were based on LD between mark-ers as indicated by HapMap. Sometimes this method may not accurately reflect the real association value, and, more importantly, on a few occasions no SNP in LD with ULSAM-associated SNP could be identified from the GWA studies. In these instances, information on replica-tion is inconclusive.

In summary, we have identified 82 genes that can be associated with AD within ULSAM. Performing an in silico replication analysis using publicly available data from 2 GWA studies confirmed only 5 of these AD-as-sociated genes. However, we cannot exclude the possibil-ity that further attempts at replication using identical SNP from ULSAM and population-matched sample col-lection could support more of the association data sug-gested in this study.

Acknowledgments

This study was supported by Wallenberg Consortium North, Swedish Brain Fund, Alzheimerfonden, Swedish Research Coun-cil (2003-5546), APOPIS (Contract No. LSHM-CT-2003-503330), Stiftelsen Gamla Tjänarinnor, Capios Forskningsstiftelse, Erik, Karin och Gösta Selanders stiftelse and Swedish Lions Research Foundation.

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Genetic Analysis of Alzheimer\u0026rsquo;s Disease in the Uppsala Longitudinal Study of Adult Men

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