Han et al. BMC Medical Genetics 2018, 19(Suppl 1):0
https://doi.org/10.1186/s12881-018-0721-7

RESEARCH

Open Access

Parkinson’s disease and Alzheimer’s disease:a Mendelian randomization study
Zhifa Han1†, Rui Tian1†, Peng Ren1, Wenyang Zhou1, Pingping Wang1, Meng Luo1, Shuilin Jin2* andQinghua Jiang1*
From 29th International Conference on Genome Informatics
Yunnan, China. 3-5 December 2018

Abstract
Background: Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the top two common neurodegenerative
diseases in elderly. Recent studies found the α-synuclein have a key role in AD. Although many clinical andpathological features between AD and PD are shared, the genetic association between them remains unclear,especially whether α-synuclein in PD genetically alters AD risk.
Results: We did not obtain any significant result (OR = 0.918, 95% CI: 0.782–1.076, P = 0.291) in MR analysis
between PD and AD risk. In MR between α-synuclein in PD with AD risk, we only extracted rs356182 as the
IV through a strict screening process. The result indicated a significant association based on IVW method (OR = 0.
638, 95% CI: 0.485–0.838, P = 1.20E-03). In order to examine the robustness of the IVW method, we used other threecomplementary analytical methods and also obtained consistent results.
Conclusion: The overall PD genetic risk factors did not predict AD risk, but the α-synuclein susceptibility genetic
variants in PD reduce the AD risk. We believe that our findings may help to understand the association between
them, which may be useful for future genetic studies for both diseases.
Keywords: Alzheimer’s disease, Parkinson’s disease, α-synuclein, Mendelian randomization, Genetic association

Background
Alzheimer’s disease (AD) and Parkinson’s disease (PD)are the top two common neurodegenerative diseases [1].
In 2015, AD and PD respectively affected about 29.8
million and 6.2 million people worldwide [2]. AD ischaracterized by a progressive decline in memory, language, problem solving and other cognitive functions[3]. The neuropathological changes of AD are mainly
manifested as brain atrophy and the accumulation ofabundant extracellular Aβ plaques and intraneuronal
neurofibrillary tau tangles [4, 5]. The patients sufferingfrom PD show slow movements, tremor, gait andbalance disturbances and other behavioral problems [6].
The main pathological characteristics of PD are the* Correspondence: jinsl@hit.edu.cn; qhjiang@hit.edu.cn†
Zhifa Han and Rui Tian contributed equally to this work.
2
Department of Mathematics, Harbin Institute of Technology, Harbin, China
1
School of Life Science and Technology, Harbin Institute of Technology,
Harbin, China

progressive degeneration of dopaminergic neurons inthe substantia nigra and the presence of Lewy bodies,
which are made of abnormal filaments composed of
α-synuclein [7, 8].
The two neurodegenerative diseases share many clinical and pathological features. Because of the toxicity ofthe α-synuclein or Aβ42 and tau proteins, so many similar cascades of neuronal reactions leading to progressive
neurodegeneration occur in PD and AD patients. The
overlapping pathological changes of AD and PD include
activation of glycogen synthase kinase-3 beta, mitogenactivated protein kinases, cell cycle reentry and oxidative
stress, etc. [9]. As we all know, mild cognitive impairment (MCI) is associated with the risk of progression to
AD. However, MCI was recently also recognized as akind of subtype predicting progression to PD [10, 11].
About 25% of non-demented patients with PD meetneuropsychological test criteria for MCI [12] andmore than 50% of them eventually develop dementia [11].

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
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Han et al. BMC Medical Genetics 2018, 19(Suppl 1):0

All of these suggest that the two neurodegenerative diseases are probably the risk factor of each other.
In addition to the similarities between AD and PD,
some potential mechanisms linking these two neurodegenerative diseases were found recently, especially the
α-synuclein and Lewy pathology in AD. Several studies
found that up to 50% AD patients exhibited extra aggregation of α-synuclein into Lewy bodies [13, 14]. Statistic
test did not identify any significant difference of
α-synuclein levels in cerebrospinal fluid between AD
and PD patients [15]. In transgenic mice, the accumulation of α-synuclein could significantly disrupt cognition[14]. A similar study found that the α-synuclein in thetransgenic mice could also massive increase apolipoprotein E (ApoE) levels and accelerate the accumulation of
insoluble mouse Aβ [16]. Furthermore, the cerebrospinal
fluid total α-synuclein levels contributed to the differential diagnosis of AD versus other dementias [17]. In thefamilial AD patients with presenilin 1 (PSEN1) mutations,which is most commonly associated with familial forms ofAD, the notable interaction between α-synuclein and
PSEN1 was identified in post-mortem brain tissue [9]. Our
previously study conducted an interaction network using
genome-wide association studies (GWASs) data and foundthe significant interaction among AD and PD susceptibility
genes [7]. All of these evidences suggest a further investigation for the potential genetic mechanisms linking PD to
AD, particularly the role that the α-synuclein plays in AD.
In recent years, large-scale GWAS identified somecommon genetic variants and provided insights into thegenetics of AD and PD, including α-synuclein coding
gene Synuclein Alpha (SNCA) 18,19. The large-scale
GWAS datasets provide tremendous support for investigating the potential genetic association between PD and
AD risk by Mendelian randomization analytical method(MR). MR is conceptually similar to randomized controlled trials (RCT) and can be applied to investigate thecausality of biomarkers in disease etiology based on
GWAS summary data20,21. In our study, we conducted a
MR analysis with the lead single nucleotide polymorphisms (lead SNPs, which showed the strongest association in GWAS) from PD susceptibility loci. Here, theselead SNPs defined as instrumental variable (IV). In the
results of sensitivity analysis of the MR between PD and
AD risk, we found that the absence of SNCA lead SNP
would massive decrease the statistical significance. Considering the reported role of α-synuclein in AD pathology, we further performed a MR to investigate thegenetic association of the α-synuclein and AD risk.

Materials and methods
Study design

MR is based on the premise that the human genetic variants are randomly distributed in the population [18, 19].

Page 2 of 31

These genetic variants are largely not associated with
confounders and can be used as IVs to estimate thecausal association of exposures (PD, α-synuclein in PD)
with an outcome (AD) [18, 19]. The MR is based on
three principal assumptions (Fig. 1), which have been
widely described in recent studies [18, 19]. First, thegenetic variants selected to be IVs should be associatedwith the exposures (PD, α-synuclein in PD) (assumption 1
in Fig. 1) 20,22. Second, the genetic variants should not be
associated with confounders (assumption 2 in Fig. 1) [18,19]. Third, genetic variants should affect the risk of theoutcome (AD) only through the exposure (PD or
α-synuclein in PD) (assumption 3 in Fig. 1) 20,22. The
second and third assumptions are collectively known
as independence from pleiotropy [18, 19]. This study
is based on the publicly available, large-scale GWAS
summary datasets, and the genetic variants are SNPs.
All participants gave informed consent in all these
corresponding original studies.
PD GWAS dataset

Here, we first used PD susceptibility SNPs as the IVsthat from the most large-scale PD GWAS dataset [20].
This GWAS included 428,235 individuals (26,035 PD
cases and 403,190 controls) consisting of 416,518 individuals in discovery stage and 21,679 individuals in replication stage [20]. The final joint analysis identified 17
novel loci associated with PD with the genome-wide significance (P < 5.00E-08) [20]. We extracted lead SNPs information of each locus from the joint analysis results
except rs601999, which did not provide sufficient data.
We also obtained 27 genome-wide significant loci fromother more earlier GWASs23,24. But the basic lead SNPs
information of these 27 loci were extracted from the discovery stage of the most large-scale GWAS [19]. All
these 43 lead SNPs were independent [20].
Reported SNCA mutations in PD

We acquired the PD susceptibility SNCA lead SNPs
through a rigorous screening process. We first searched
all previous GWASs which reported SNCA as a PD susceptibility locus. Afterwards, we obtained the lead SNPs
from each GWAS. However, we would exclude the lead
SNP when they were not reported by GWAS of European
ethnicity. When a lead SNP is reported by more than one
GWAS, we would extract the summary statistics from themost large-scale GWAS because of its greater statistic
power. When several lead SNPs were in linkage disequilibrium (LD), we would choose the one from the mostlarge-scale GWAS. We searched the LD information byusing the HaploReg v4.1, which based on LD data from1000 Genomes Project (CEU) [21].
Finally, we only selected one lead SNP from 7 unduplicated lead SNPs. The sources and the detailed information

Han et al. BMC Medical Genetics 2018, 19(Suppl 1):0

Page 3 of 31

Fig. 1 Mendelian randomization assumptions. The Mendelian randomization is based on three principal assumptions, which have been widely
described in recent studies [18, 29]. First, the genetic variants selected to be IVs should be associated with the exposure (PD or α-synuclein in PD)(assumption 1) [18, 29]. Second, the genetic variants should not be associated with confounders (assumption 2) [18, 29]. Third, genetic variants
should affect the risk of the outcome (AD) only through the exposure (PD or α-synuclein in PD) (assumption 3) [18, 29]

about the 7 SNPs are described in Table 1. We excluded
the SNPs rs11931074, rs8180209 and rs6532194 because
they were reported in GWASs of Asian ethnicity. We thenfound that all of the rest 4 SNPs were located in SNCA.
Considering the rs356182 was overwhelmingly significantly associated with PD risk in the most large-scale PD
GWAS [20], we selected this genetic variant as tagged
SNP to perform MR analysis.

International Genomics of Alzheimer’s Project (IGAP)[22]. In stage 1, the IGAP genotyped and imputed
7,055,881 SNPs, and performed a meta-analysis of four
GWAS datasets including 17,008 cases and 37,154 controls of European descent [22]. All patients with AD satisfied the NINCDS-ADRDA criteria or DSM-IV guidelines[22]. Here, we would extract the summary statistics of the
43 lead SNPs from stage 1 results of the AD GWAS.

AD GWAS dataset

Pleiotropy analysis

The AD GWAS dataset was downloaded from thelarge-scale meta-analysis, which was performed by the

In MR study, there is a potential violation of assumption
2 and 3 through pleiotropy that a genetic instrument isTable 1 Characteristics of 7 SNCA genetic variants reported in PD GWASs
SNP

Chr:BPa

rs356182

4: 90626111 2017

Chang D [20]European1Intron variant

GA

0.404 1.33 (1.30–1.36) 5.21E-123

rs356219

4:89716450

2012

Lill CM [47]

Caucasian

0.76

Intron variant

G

A

0.41

rs356220

4:89720189

2014

Hill-Burns EM [48] European

0.51

Intron variant

TC

0.364 1.38 (1.24–1.52) 3.00E-11

rs2736990

4:89757390

2010

Edwards DL [49]0.48Intron variant

G

A

0.52

1.30 (1.18–1.43) 6.74E-8

c

Han Chinese NA

Intron variant

A

G

0.07

0.41 (NAd)crs8180209

4:89723303

Reported Source
year

2017

Foo JN [50]

Reported
ancestry

Caucasian

LD (r2)b Function class

EA NEA EAF

OR (95% CI)

P value

1.29 (1.25–1.33) 6.00E-65

1.02E-32

rs11931074 4:89718364

2009

Satake W [51]JapaneseNA

Intron variant

G

T

0.36

1.37 (1.27–1.48) 7.35E-17

rs6532194

2012

Lill CM [47]

Asian

NAc

Intergenic variant T

C

0.40

1.29 (1.20–1.39) 4.91E-11

a

4:89859751

Chr:BP, Chromosome:Positionbrepresent the r2 value of linkage disequilibrium (LD) between the selected genetic variant and the tagged mutation rs356182. The range of r2 is 0–1, the greater
the value of r2, the stronger the linkage disequilibriumc
The genetic variants were not reported in European (or Caucasian) populationsd
The 95% CI of OR was not available

Han et al. BMC Medical Genetics 2018, 19(Suppl 1):0

associated with a study outcome through biologicalpathways outside the interest exposure. Here, we performed an assessment for pleiotropy to assure that all
the selected genetic variants do not exert effects on AD
risk through biological pathways independent of PD
pathological changes. Five steps were taken to reduce
the risk of pleiotropy. In stage 1, nine potentially modifiable risk factors, which were reported in a major review
about dementia risk factors, were considered [23]. The
nine risk factors consisted of low levels of education,
midlife hearing loss, physical inactivity, high blood pressure (hypertension), type 2 diabetes, obesity, smoking,depression, and social isolation [23]. We excluded obesity because a recent MR study reported that this trait aswell as body mass index (BMI) is not genetically associated with AD risk [24]. In stage 2, we investigatedwhether the dietary factors are the potential confounders[23, 25]. The UK Biobank datasets were available [26].
Some studies reported that alcohol play an importantrole in AD risk, so we considered it as a potential confounder in stage 3 [27]. In stage 4, we evaluated thepotential pleiotropic associations of IVs with AD biomarkers, which contain cerebrospinal fluid tau, phosphorylated tau (ptau) and Aβ42 [28]. In last stage, we
selected MR-Egger intercept test to evaluate the potential pleiotropic associations of these genetic variants withpotential confounders. Here, we provided more detailed
information about the selected exposures and counterpart GWASs are described in Additional file 1.
Mendelian randomization analysis

We conducted two MR analyses to investigate the genetic association between PD and AD risk and the genetic
association between α-synuclein in PD and AD risk.
Until now, four different MR analytical methods havebeen well established including inverse-variance weighted
meta-analysis (IVW), weighted median regression, MREgger regression and maximum-likelihood regression[18, 29]. However, the IVW has more statistical power
than the other three methods [30]. The detailed information of IVW were described in Additional file 2. Particularly,
MR-Egger regression always including a wide confidence intervals even the null hypothesis [30]. Therefore, we selected
the IVW as the main analytical method to perform the MR
analysis, as did in previous MR studies [19, 29].
Besides, in MR analysis of PD and AD risk, we also
selected the weighted median regression, MR-Egger
regression and maximum-likelihood method as thecomplementary analytical methods to examine the robustness of the IVW estimate. MR-Egger could also
provide a statistical test the presence of potential pleiotropy, and account for this potential pleiotropy [31]. If
there was no clear evidence of pleiotropy, we would expect all the four tests to give consistent estimates. We also

Page 4 of 31

performed a sensitivity analysis using the leave-one-out
permutation method. In the MR analysis between
α-synuclein in PD and AD risk, we only selected
maximum-likelihood method as complementary analyticalmethod because of the quantitative limitation of IV.
All analyses were conducted using the R package‘MendelianRandomization’ [32]. We choose P < 0.05 as
discriminant criterion for statistical significant result of
the MR study.

Results
Instrumental variable

In MR analysis between PD and AD risk, we obtained
43 SNPs associated with PD as potential IVs. However,
one of these 43 genetic variants as well as its proxy genetic variants were not available in stage 1 dataset of AD
GWAS. The genetic variant was rs143918452 and we excluded it from the IVs. All the rest SNPs could map togenes implicated in PD pathology pathways, including
autophagy, mitochondria, immune system, lysosome andendocytosis, etc. [20, 33, 34].
In MR analysis between α-synuclein in PD and AD
risk, we only selected rs356182 after a rigorous screening
process. As we all know, α-synuclein plays an importantrole in almost all PD pathology pathways. The genetic
variants at SNCA locus (coding α-synuclein) can alter
the risk of PD and even lead to monogenic and severe
early-onset forms PD [34]. The rs356182 were reported
as lead SNP of SNCA locus in most PD GWASs, which
was also the strongest association signal in the mostlarge-scale PD GWAS [20]. Recently, some studies foundthat rs356182 was associated with a specific SNCA 5′
untranslated region transcript isoform and regular the
SNCA expression in brain [35, 36]. The firm and reliableassociation between rs356182 and α-synuclein make the
SNP perfectly meet the requirements of IV.
Pleiotropy of instrumental variables

We identified that rs11343 and rs17649553 were significantly associated with years of education in stage1 (P =1.20E-4 and P = 2.33E-07 respectively). We also foundthat rs17649553 was associated with Vitamin in stage 2
and continuous alcohol in stage 3. In stage 2, rs9275326
was associated with vitamin C, vitamin B9 and mineral
supplements with P = 6.42E-04, 1.95E-09 and 2.63E-06.
There were no genetic variants associated with AD biomarker. In stage 5, the MR-Egger intercept test showedno significant intercept (MR-Egger intercept β = 0.003;
P = 0.717) in these genetic variants. By iteratively
pruning the corresponding variant list, we also didn’t
find any significant intercept. The detailed information about the pleiotropy of these genetic variants is
described in Table S1-S3 in Additional file 3 andTable S4 in Additional file 4.

Han et al. BMC Medical Genetics 2018, 19(Suppl 1):0

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Table 2 Characteristics of 39 genetic variants in PD and AD GWAS datasets
SNP
rs10797576

Chr
1

Pos
232,664,611

Nearby Genes
SIPA1L2

EA
T

NEA
C

EAFa
0.127

PD

AD GWAS

Betab

SE

P value

Betab

SE

P value

0.113

0.014

8.41E-13

0.006

0.022

0.795

rs10906923

10

15,569,598

FAM171A1

A

C

0.461

0.073

0.014

1.35E-8−0.033

0.017

0.051

rs11060180

12

123,303,586

OGFOD2

A

G

0.748

0.105

0.011

2.05E-20

0.032

0.019

0.081

rs11158026

14

55,348,869

GCH1

C

T

0.490

0.094

0.011

4.30E-16−0.002

0.017

0.894

rs115185635

3

87,520,857

CHMP2B

C

G

0.012

0.191

0.048

1.22E-4−0.022

0.058

0.702

rs11724635

4

15,737,101

FAM200B, CD38

A

C

0.408

0.105

0.011

1.22E-19−0.032

0.016

0.040

rs117896735

10

121,536,327

BAG3

A

G

0.004

0.501

0.057

2.23E-19

0.133

0.086

0.119

rs12456492

18

40,673,380

SYT4

G

A

0.330

0.095

0.012

5.56E-16

0.009

0.017

0.589

rs12497850

3

48,748,989

NCKIPSD, CDC71

T

G

0.731

0.073

0.014

9.16E-9

0.028

0.017

0.088

rs12637471

3

182,762,437

MCCC1

G

A

0.663

0.163

0.015

2.11E-30

0.002

0.019

0.909

rs13294100

917,579,690

SH3GL2

G

T

0.457

0.083

0.014

4.84E-13

0.013

0.017

0.453

rs14235

16

31,121,793

ZNF646, KAT8

A

G

0.359

0.077

0.009

5.44E-12

0.041

0.016

0.011

rs1474055

2

169,110,394

STK39

T

C

0.201

0.186

0.018

5.68E-26

0.004

0.024

0.881

rs1555399

14

67,984,370

TMEM229B

T

A

0.607

0.086

0.012

9.61E-11−0.021

0.016

0.194

rs199347

7

23,293,746

KLHL7, NUPL2, GPNMB

A

G

0.483

0.094

0.011

3.51E-18−0.033

0.016

0.039

rs2280104

822,525,980

SORBS3, PDLIM2,
C8orf58, BIN3

T

C

0.265

0.068

0.012

2.53E-8−0.002

0.016

0.923

rs2414739

15

61,994,134

VPS13C

A

G

0.679

0.094

0.011

3.94E-14−8.00E-04

0.017

0.964

rs2694528

560,273,923

ELOVL7

C

A

0.135

0.140

0.020

4.84E-15−0.028

0.027

0.305

rs2740594

8

11,707,174

CTSB

A

G

0.893

0.086

0.012

5.91E-12

0.002

0.018

0.926

rs329648

11

133,765,367

MIR4697

T

C

0.465

0.086

0.012

1.11E-13−0.007

0.018

0.701

rs34043159

2

102,413,116

IL1R2

C

T

0.332

0.077

0.009

5.48E-11

0.006

0.016

0.695

rs34311866

4951,947

TMEM175,DGKQ

C

T

0.140

0.207

0.014

1.47E-50

0.009

0.021

0.685

rs353116

2

166,133,632

SCN3A

C

T

0.557

0.062

0.011

2.98E-8

0.038

0.016

0.021

rs356182

4

90,626,111

SNCA

G

A

0.404

0.285

0.012

5.21E-123−0.056

0.017

0.001

rs35749011

1

155,135,036

GBA

A

G

0.005

0.545

0.044

2.59E-35

0.188

0.071

0.008

rs3793947

11

83,544,472

DLG2

G

A

0.570

0.073

0.011

3.72E-9−0.010

0.016

0.525

rs4073221

3

18,277,488

SATB1

G

T

0.062

0.095

0.016

1.57E-8

0.007

0.023

0.754

rs4653767

1

226,916,078

ITPKB

T

C

0.726

0.083

0.011

1.63E-11−0.005

0.017

0.756

rs4784227

16

52,599,188

TOX3

T

C

0.200

0.086

0.014

9.75E-11−0.010

0.018

0.580

rs591323

8

16,697,091

MICU3

G

A

0.635

0.094

0.014

2.38E-11−0.002

0.018

0.930

rs62120679

192,363,319

LSM7

T

C

0.426

0.077

0.014

6.64E-7−0.004

0.022

0.843

rs6430538

2

135,539,967

TMEM163,CCNT2

C

T

0.189

0.117

0.011

8.24E-24−0.047

0.017

0.006

rs6812193

4

77,198,986

FAM47E

C

T

0.687

0.083

0.011

1.43E-14−0.017

0.016

0.310

rs76904798

12

40,614,434

LRRK2

T

C

0.132

0.140

0.015

1.21E-19−0.016

0.022

0.483

rs78738012

4

114,360,372

ANK2, CAMK2D

C

T

0.039

0.122

0.018

4.78E-11−0.012

0.029

0.680

rs8005172

14

88,472,612

GALC

T

C

0.434

0.077

0.012

8.77E-11−0.021

0.016

0.171

rs8118008

20

3,168,166

DDRGK1

A

G

0.540

0.068

0.012

1.99E-6

0.014

0.018

0.432

rs823118

1

205,723,572

NUCKS1, SLC41A1

T

C

0.411

0.117

0.011

1.12E-23−0.003

0.016

0.846

rs9468199

6

27,681,215

ZNF184

A

G

0.300

0.104

0.014

1.46E-12

0.044

0.022

0.046

SNP single-nucleotide polymorphism, Chr Chromosome, Pos Position, EA Effect Allele, NEA Non-Effect Allele, EAF Effect Allele Frequency, PD Parkinson’s disease, AD
Alzheimer’s disease, GWAS genome-wide association studies, SE standard erroraFrequency of the effect allele in 1000 Genomes Project (CEU)b
Beta is the regression coefficient based on the effect allele. Beta > 0 and Beta < 0 means that this effect allele regulates increased and reduced PD or AD risk, respectively

Han et al. BMC Medical Genetics 2018, 19(Suppl 1):0

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In summary, our pleiotropy analysis showed that
rs11343, rs17649553 and rs9275326 may have potential
violation of assumption 2 and 3 through pleiotropy. To
meet the MR assumptions (Fig. 1), we excluded thesevariants in following analyses. The rest 39 genetic variants which used in following analyses were detailed described in Table 2.

analysis. The direction and precision of the genetic estimates between PD and the risk of AD remained largely
unchanged using these MR methods except maximumlikelihood method (Table 3). In spite the exclusion of
rs356182 notably increased the significance of the association between PD and AD risk, it is P value still notreach 0.05.

Mendelian randomization between PD and AD risk

Mendelian randomization between α-synuclein in PD and
AD riskUsing the remaining 39 genetic variants, IVW analysis
showed that the genetically predicted PD was not significantly associated with AD risk (OR = 0.918, 95% CI:
0.782–1.076, P = 0.291). Furthermore, all of the weighted
median estimate (OR = 1.069, 95% CI: 0.769–1.140, P =0.509), MR-Egger estimate (OR = 0.863, 95% CI: 0.593–
1.253, P = 0.437) and maximum-likelihood estimate (OR= 0.916, 95% CI: 0.780–1.076, P = 0.292) were consistent
with the IVW estimate in terms of direction and magnitude. Meanwhile, the MR-Egger intercept test showedno significant intercept (β = 0.003, P = 0.717) between
these 39 variants. Figure 2 shows individual genetic estimates from each of the 39 genetic variants using different methods.
Sensitivity analysis between PD and AD risk

To further test the stability of these estimates, we sequentially removed each genetic variant in the MR

Using rs356182 as IV, IVW analysis showed that thegenetically changed PD α-synuclein was significantly associated with a reduced AD risk (OR = 0.638, 95% CI:
0.485–0.838, P = 1.20E-03). The maximum-likelihood estimate (OR = 0.60, 95% CI: 0.34–1.06, P = 0.08) was completely consistent with the IVW estimate.

Discussion
In this study, we firstly selected 39 SNPs as IVs to investigate the association between PD and AD risk by using
MR methods. However, we didn’t identify any significant
results with all P values greater than 0.2. In the sensitivity analysis, we found that in the absence of rs356182,
the P value of the IVW result rose to nearly 1. Considering some PD patients exhibited AD characteristics as
described in the introduction, we secondly selected
rs356182 as IV and conducted another one MR. The

Fig. 2 Individual genetic estimates from each of 39 genetic variants using different methods. This scatter plot show individual causal estimatesfrom each of 6 genetic variants associated with PD on the x-axis and AD risk on the y-axis. The continuous line represents the causal estimate of
PD on AD risk

Han et al. BMC Medical Genetics 2018, 19(Suppl 1):0

Page 7 of 31

Table 3 sensitivity analysis of the association between PD and AD risk
SNPab

rs10797576

SNPa

P valuec

IVW

Wei-Med

MR-Egger

0.281

0.600

0.437

d

rs34043159

P value
IVWb

Wei-Medc

MR-Eggerd

0.277

0.596

0.465

rs10906923

0.365

0.653

0.348

rs34311866

0.245

0.608

0.346

rs11060180

0.200

0.598

0.422

rs353116

0.204

0.593

0.606

rs11158026

0.299

0.660

0.444

rs356182

0.991

0.649

0.313

rs115185635

0.307

0.654

0.454

rs35749011

0.117

0.592

0.075

rs11724635

0.419

0.653

0.441

rs3793947

0.320

0.651

0.418

rs117896735

0.207

0.597

0.240

rs4073221

0.283

0.594

0.446

rs12456492

0.264

0.599

0.450

rs4653767

0.307

0.653

0.438

rs12497850

0.222

0.594

0.527

rs4784227

0.318

0.652

0.433

rs12637471

0.277

0.608

0.411

rs591323

0.297

0.596

0.444

rs13294100

0.260

0.597

0.468

rs62120679

0.302

0.649

0.440

rs14235

0.172

0.593

0.547

rs6430538

0.473

0.648

0.482

rs1474055

0.277

0.605

0.406

rs6812193

0.340

0.653

0.414

rs1555399

0.355

0.652

0.408

rs76904798

0.331

0.656

0.465

rs199347

0.404

0.651

0.405

rs78738012

0.310

0.652

0.448

rs2280104

0.299

0.647

0.445

rs8005172

0.353

0.652

0.385

rs2414739

0.295

0.599

0.445

rs8118008

0.267

0.594

0.490

rs2694528

0.341

0.654

0.474

rs823118

0.301

0.662

0.442

rs2740594

0.290

0.597

0.449

rs9468199

0.201

0.596

0.422

rs329648

0.311

0.654

0.437

a

the SNP that was excluded in sensitivity analysisb
IVW, Inverse-variance weighted meta-analysisc
Wei-Med, Weighted mediandMR-Egger, MR-Egger regression

results of this MR indicated that the α-synuclein in PD
was significantly associated with AD risk. The consistent
results of different MR methods increased the credibilityof our findings.
Besides the similar clinical and neuropathological similarities as we all know, amounts of genetic overlap
between AD and PD were also discovered42,43. The
evaluation of the α-synuclein functional characterizationin AD can be traced back to the last century [37]. Fromthen on, a lot of evidences supported the discovery as
described in introduction. Recently, many studies havefound the potential genetic association between AD and
PD [38–42]. A trans-pQTL study discovered the PD risk
variant rs12456492 affects expression of AD-associatedprotein CD33 in peripheral monocytes [41]. Genetic association study proved rs76904798 of LRRK2 significantly reduce late-onset AD risk in Han Chinese [43].
The AD risk factor SORL1 had been identified to be associated with PD recently [38].
The rs356182 locates in the intron of SNCA, which isthe coding gene of α-synuclein. In addition to the keyrole of α-synuclein in PD, rs356182 can defines PD
endophenotype in different levels [36, 44, 45]. In ourstudy, the association between α-synuclein and AD identified by rs356182 as IV greatly supported and extended
the genetic overlap of AD and PD. The effect size of the
IV was the regression coefficient between SNCA loci(the counts of effect allele) and PD. Therefore our results suggested that the α-synuclein function in PD
probably causes AD pathological changes. This conclusion supported the phenomenon that some PD patients
meet neuropsychological test criteria for MCI [12].
But the overall genetic risk factors of PD were not statistically associated with AD risk in our study. It can’t beignored that PD is a complex disease with so many
pathogenic factors leading to kinds of pathologicalchanges19,41. However, there may be only one kind of
the PD pathological changes is genetically associatedwith AD risk. And including all the PD pathogenic factors will reduce or even eliminate this genetic association statistically. A similar MR study investigated theassociation between type 2 diabetes (T2D) and AD [46].
They chose mechanism-specific genetic variants as IVs
to overcome this problem [46]. And they found only insulin sensitivity genetic variants in T2D are associatedwith AD risk (OR = 1.17; 95% CI: 1.02–1.34) [46].

Han et al. BMC Medical Genetics 2018, 19(Suppl 1):0

However, there are ambiguous boundaries between different pathogenic mechanisms in PD. Furthermore, one
genetic variant is probably involved in many pathologicalpathways. That means the IVs of the mechanism-specificin PD may violate the MR assumptions. So we
adopted the molecular-specific genetic variants as the
IV in our study.
Nevertheless, we still could not exclude all the otherpossible pathways besides the PD pathological pathwaythat α-synuclein play roles in AD. In other words, thepotential pleiotropy of the rs356182 may be the biggest
problem in the study. That’s the insufficient of MR andselecting one genetic variant as IV expands this deficiency. Another limitation of our study is the absence ofa well PD pathogenic mechanism division method. Overcoming these problems will greatly improve our understanding about the effect of PD pathological changes on
AD risk.

Conclusions
As we all know, PD and AD have many clinical and pathological features. However, there is little genetic proof for
the association between these neurodegenerative diseases.
In this MR study, we investigated the genetic roles of PD
and its molecular pathological changes in AD. The results
indicated only the α-synuclein in PD was genetically associated with AD risk. The pleiotropy analysis demonstrated
all of the 39 PD susceptibility SNPs used as IVs were notlinked with any reported AD risk factors. Future studies
could identify the potential biological process that linking
the α-synuclein and AD. We believe this finding will help
to understand the pathology of AD and PD, which will
stimulate subsequent research.
Additional files
Additional file 1: A more detailed description about the selected exposuresand counterpart GWASs. And how we conducted the pleiotropy analysis withthese datasets. (DOCX 95 kb)
Additional file 2: Statistical description of Inverse-variance weighted
method. (DOCX 71 kb)
Additional file 3: The genetic association between PD susceptibility SNPs
with potential AD risk factors. (XLSX 18 kb)
Additional file 4: The results of the MR-Egger intercept test. (XLSX 10 kb)

Abbreviations
AD: Alzheimer’s disease; CI: Confidence interval; GWAS: Genome-wide
association study; IGAP: International Genomics of Alzheimer’s Project;
IV: Instrumental variable; IVW: Inverse-variance weighted meta-analysis;
LD: Linkage disequilibrium; MCI: Mild cognitive impairment; MR: Mendelianrandomization; OR: Odd ratio; PD: Parkinson’s disease; pQTL: Protein quantitative
trait loci; RCT: Randomized controlled trial; SNP: Single nucleotide
polymorphism; T2D: Type 2 diabetes
Acknowledgements
We thank Dr. Guiyou Liu and Dr. Lin Jiao of the School of Life Science andTechnology for their technical and linguistic assistance.

Page 8 of 31

Funding
Publication costs are funded by funding from the National Key R&D Programof China (2016YFC1202302 and 2017YFSF090117), Natural Science Foundationof Heilongjiang Province (F2015006), the National Nature Science Foundation ofChina (Grant No. 61822108 and 61571152), and the Fundamental ResearchFunds for the Central Universities (AUGA5710001716).
Availability of data and materials
The summary statistics used as IVs of the MR study are included within thearticles and its Additional files. The datasets of the AD GWAS are available
from the IGAP on reasonable request.
About this supplement
This article has been published as part of BMC Medical Genetics Volume 19
Supplement 1, 2018: Proceedings of the 29th International Conference on
Genome Informatics (GIW 2018): medical genetics. The full contents of the
supplement are available online at https://bmcmedgenet.biomedcentral.
com/articles/supplements/volume-19-supplement-1.
Authors’ contributions
QHJ and SLJ proposed and designed the research. ZFH extracted andprocessed the GWAS data with assistance from RT, PPW, WYZ, PR, andML. ZFH preformed most of the statistical analyses. ZFH wrote the article
with assistance from RT. QHJ supervised the writing and revised the manuscript.
All authors read and approved the final manuscript.
Consent for publication
All authors agree to publish this article.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Published: 31 December 2018
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