Abstract
Background
Alzheimer’s disease (AD) and age-related macular degeneration (AMD)
share similar pathological features, suggesting common genetic
aetiologies between the two. Investigating gene associations between AD
and AMD may provide useful insights into the underlying pathogenesis
and inform integrated prevention and treatment for both diseases.
Methods
A stratified quantile–quantile (QQ) plot was constructed to detect the
pleiotropy among AD and AMD based on genome-wide association studies
data from 17 008 patients with AD and 30 178 patients with AMD. A
Bayesian conditional false discovery rate-based (cFDR) method was used
to identify pleiotropic genes. UK Biobank was used to verify the
pleiotropy analysis. Biological network and enrichment analysis were
conducted to explain the biological reason for pleiotropy phenomena. A
diagnostic test based on gene expression data was used to predict
biomarkers for AD and AMD based on pleiotropic genes and their
regulators.
Results
Significant pleiotropy was found between AD and AMD (significant
leftward shift on QQ plots). APOC1 and APOE were identified as
pleiotropic genes for AD–AMD (cFDR <0.01). Network analysis revealed
that APOC1 and APOE occupied borderline positions on the gene
co-expression networks. Both APOC1 and APOE genes were enriched on the
herpes simplex virus 1 infection pathway. Further, machine
learning-based diagnostic tests identified that APOC1, APOE (areas
under the curve (AUCs) >0.65) and their upstream regulators, especially
ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and
AMD (AUCs >0.8).
Conclusion
In this study, we confirmed the genetic pleiotropy between AD and AMD
and identified APOC1 and APOE as pleiotropic genes. Further, the
integration of multiomics data identified ZNF131, ADNP2 and HINFP as
novel diagnostic biomarkers for AD and AMD.
Keywords: ALZHEIMER'S DISEASE, OPHTHALMOLOGY, GENETICS
__________________________________________________________________
WHAT IS ALREADY KNOWN ON THIS TOPIC.
* Alzheimer’s disease (AD) and age-related macular degeneration (AMD)
exhibit overlapping pathological characteristics and are recognised
as comorbid conditions in clinical practice.
WHAT THIS STUDY ADDS
* This study confirmed the genetic pleiotropy between AD and AMD.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
* The findings from this study may promote the co-diagnosis/treatment
for AD and AMD.
Introduction
Alzheimer’s disease (AD) and age-related macular degeneration (AMD) are
both common progressive neurodegenerative diseases associated with
significant comorbidity. Both AD and AMD are common comorbidities in
chronic diseases and represent major global public health
challenges.[54]^1 2 The clinical practice of AD has established several
biomarkers, including MRI, Fluorodeoxyglucose-Positron Emission
Tomography (FDG-PET), tau PET, cerebrospinal fluid measures of amyloid
and tau, and plasma biomarkers, which are currently undergoing approval
processes.[55]^3 Some AMD biomarkers have been reported, such as
complement factor H, age-related maculopathy susceptibility 2,
high-density lipoprotein cholesterol and vascular endothelial growth
factor.[56]^4
Past epidemiological studies have shown a substantial association
between AD and AMD at the phenotype level.[57]^5 In 1999, a study of
1438 patients diagnosed with both AD and AMD demonstrated that these
two diseases may have common pathogenesis.[58]^5 A meta-analysis
consisting of 11 840 patients found that AD and AMD had a significant
association.[59]^6 Consistently with this, our recent study, based on
12 364 patients with eye and dementia tests from the UK Biobank,
demonstrated that patients with existing AMD have an increased risk of
dementia.[60]^7
Accumulating evidence shows that AD and AMD share similar pathological
mechanisms.[61]^8 For instance, ageing is a key risk factor for AD and
AMD, and hypercholesterolaemia, hypertension, obesity, arteriosclerosis
and smoking are common risk factors for them; deposition of amyloid
beta plaques, other extracellular deposition, increased oxidative
stress, and apolipoprotein and complement activation pathways have also
all been implicated in the pathogenesis of both.[62]^8–11 However, the
nature of the relationship linking AD and AMD remains
contentious.[63]^5 12 13 Investigating gene associations between AD and
AMD could provide mechanistic insights into the pathogenesis underlying
these two diseases and their potential shared pathogenesis, promoting
integrated prevention and treatment for AD and AMD. Due to the
complexity of accurately diagnosing AD, the diagnosis of AMD could
potentially enhance AD diagnosis, provided their pleiotropy is
confirmed. Additionally, the identification of shared biomarkers for
both AD and AMD would hold significant importance. Since 2005,
genome-wide association studies (GWAS) have been widely used in the
biomedical sphere to identify relationships between genetic
variations—usually single nucleotide polymorphisms (SNPs). Logue et al
[64]^6 and Tan et al [65]^14 independently explored the shared genetic
aetiology between AD and AMD, and both teams suggested the genetic
associations between these two diseases were significant. However, in
their studies, further characterisation of the shared genetic
mechanisms has been restricted by the relatively limited genetic
information used for AMD and the lack of other validation data to
independently verify their results.
To date, despite the substantial amount of data pointing towards the
shared genetic aetiology of these two diseases, no previous studies
have specifically analysed the pleiotropic genes implicated in AMD and
AD on a biological network level. The proliferation of omics and
complex network theory may herald a novel approach to investigating
these associations. In the present study, we aimed to investigate the
shared genetic aetiology between AD and AMD across genetic network,
pathway and clinical levels. We used multiomics data to investigate the
pleiotropy between AD and AMD. We further explored the pleiotropic
genes on the genetic networks, pathways and tissues to identify
specific common topological and biological features and potential
diagnostic biomarkers for both diseases.
Methods
Data collection
We downloaded genetic data from GWAS summary statistics, RNA sequencing
(RNA-seq) and microarray to conduct pleiotropy analysis and biomarker
identification (download date: January 2022). A detailed description of
included datasets was presented in [66]table 1.
Table 1.
Descriptive characteristics of datasets included in this study
Disease Sample size (cases/controls) Mean age (years) Gender (% female)
Source Phase Reference
AD 17 008/37 154 63.6–82.1 57.6–74.5 GWAS Discovery Lambert et al
[67]^16
AMD 16 144/17 832 73.33 58 GWAS Discovery Fritsche et al [68]^17
AMD 14 034/91 214 47.5–77.2 NA GWAS Replication Winkler et al [69]^18
AD 2943/502 854 56.5 54.4 GWAS Verification UK Biobank[70]^19
AMD 7308/174 957 57.2 54.4 GWAS Verification UK Biobank[71]^19
AD–AMD 86/174 957 57.2 54.4 GWAS Verification UK Biobank[72]^19
Healthy 838 NA 32.9 RNA-seq Verification GTEx[73]^25
AD 26/62 93.1 56.3 Microarray Discovery Hokama et al [74]^21
AD 97/98 85.02 48 Microarray Replication Piras et al [75]^22
AMD 8/3 76.6 37.5 Microarray Discovery Strunnikova et al [76]^23
AMD 5/5 84 80 RNA-seq Replication Wang et al [77]^24
[78]Open in a new tab
AD, Alzheimer's disease; AMD, age-related macular degeneration; GTEx,
Genotype-Tissue Expression; GWAS, genome-wide association studies; NA,
not available; RNA-seq, RNA sequencing.
GWAS data
The AD dataset, downloaded from the GRASP database,[79]^15 consisted of
17 008 AD cases with approximately 7 million SNPs. This dataset was
derived from a meta-analysis by Lambert et al. [80]^16
We downloaded two AMD datasets from two separate meta-analyses: (1) a
discovery dataset containing 16 144 patients of GWAS data integrated
from 26 studies[81]^17; (2) a replication AMD dataset including 14 034
patients from 11 studies for verification.[82]^18
The association of pleiotropic genes derived from the GWAS with
phenotypes was validated in the UK Biobank.[83]^19 We included 2943
patients with AD and 7308 patients with AMD (86 with both AD and AMD)
in the analysis.
The quality control and overall genetic statistics of these datasets
have been described in detail elsewhere.[84]^16–19 In order to expand
the collection of AD and AMD-related SNPs, a nominal p value threshold
of 1×10^–05 was considered to be of statistical significance for
relationships between SNPs and diseases.
Gene expression data
Gene expression datasets for the biomarker selection from the
pleiotropic genes were downloaded from the GEO database.[85]^20 We
included discovery datasets and replication datasets for AD and AMD
separately. The AD discovery dataset ([86]GSE36980) comprised
expression data from the frontal cortex, temporal cortex and
hippocampus from 26 patients with AD and 62 healthy controls.[87]^21
The AD replication dataset ([88]GSE132903) included expression data of
the middle temporal gyrus from 97 patients with AD and 98 healthy
controls.[89]^22
The AMD discovery dataset ([90]GSE1719) consisted of 36 samples
(comprising 18 patients and 18 controls) from 8 patients with AMD and 3
controls,[91]^23 and the replication dataset ([92]GSE99287) included 26
early/late AMD samples and 19 normal samples of the retina and retinal
pigment epithelium from 5 patients with AMD and 5 controls.[93]^24
The Genotype-Tissue Expression database provided the gene expression
data from RNA-seq for 54 non-diseased tissue sites of 838
volunteers,[94]^25 to observe the expression situation of pleiotropic
genes in different tissues and cells.
Pleiotropy analysis
A stratified quantile–quantile (QQ) plot with −log[10](p
value-exposure) as the x-axis and −log[10](p value-outcome) as the
y-axis was constructed to detect the pleiotropy between AD|AMD (AD to
AMD) and AMD|AD (AMD to AD). Different p value cut-off thresholds for
exposure were set to delineate individual curves. The assessment of
pleiotropy was based on the level of the leftward shift from the null
lines expected.
In 2015, a Bayesian conditional false discovery rate-based (cFDR)
method was created to detect the pleiotropy between two
diseases,[95]^26–28 which has since been applied successfully in the
discovery of a series of pleiotropic SNPs.[96]^29–32 The false
discovery rate (FDR) is a statistical approach for the correction in
multiple hypothesis testing.[97]^33 34 In pleiotropy analysis, FDR is
used to reflect the possibility of non-pleiotropy for an SNP.
[MATH: FDR(pi)=<
mi>Pr(Ho
(i)|Pi≤pi) :MATH]
Where the P[i] is the random variable of p value for a trait i among
all SNPs, and p[i] is the instance of P[i] to a specific SNP. H[0] ^(i)
represents the null hypothesis that the specific SNP is not associated
with trait i. Detection of pleiotropy between two diseases can be
enhanced with cFDR, an extension of FDR.
[MATH: cFDR(pi|pj)=Pr(<
/mo>Ho(i)|Pi≤pi,
Pj≤pj)
:MATH]
Where p[i] is the association of a specific SNP with the principal
disease, and p[j] is with the conditional disease.
To find the pleiotropic SNPs both significant in AD|AMD and AMD|AD, a
conjunction-cFDR (ccFDR)[98]^31 was developed. In this study, we
calculate cFDR from AD to AMD and AMD to AD separately, then select the
larger as ccFDR.
[MATH: ccFDRi&j=m
ax(cFDRi|j,cFDRj|i) :MATH]
where
[MATH: ccFDRi&j
:MATH]
is the max value of cFDR (AD|AMD) and cFDR (AMD|AD). The threshold of
<0.01 for ccFDR was designated as the threshold for significance for
pleiotropic SNP.
We selected the bigger ccFDR value among the discovery and replication
GWAS datasets and defined it as MccFDR (max ccFDR).
The KehaoWu/GWAScFDR package in R (V.4.1.0) was used to conduct the
cFDR analysis. The pleiotropic SNPs were mapped to the corresponding
genes from the information in the AD dataset.[99]^16
Linkage disequilibrium analysis
Linkage disequilibrium (LD) analysis was conducted using LDlink and R^2
was used to measure the LD level between SNPs. R^2 ranked among 0–1,
and 1 implies the SNPs provide exactly the same information.
Epidemiological verification for pleiotropic genes in AD and AMD
The logistic regression model evaluated ORs and 95% CIs for pleiotropic
SNPs with AD and AMD. We used two logistical models in this analysis:
model 1 was adjusted for age and gender, and model 2 was adjusted for
model 1 plus ethnicity, education and income.
Identification of upstream regulators
The TF2DNA database[100]^35 and the Kyoto Encyclopaedia of Genes and
Genomes (KEGG)[101]^36 database were used to search the upstream
regulators for AD–AMD pleiotropy genes.
Biological network analysis
The CEMiTool database[102]^37 was used to conduct the gene
co-expression network (GCN) analysis for AD ([103]GSE36980,
[104]GSE132903) and AMD ([105]GSE1719, [106]GSE99287) gene expression
data.
AD–AMD pleiotropic genes along with their co-expressed genes were
mapped on the human protein–protein interaction (PPI) network from the
String database.[107]^38 Cytoscape V.3.7.2 was used to calculate the
network topology features of these genes. In this study, we used two
popular network indicators to describe the positions and features of
pleiotropic genes. ‘Degree’ indicated the number of neighbours that
directly connect to the specified node. ‘Average shortest path length’
was the average shortest distance (number of nodes on the way) of a
random node connecting to other nodes on the network.
[MATH: C(v)=∑w
d(v,w)n−1 :MATH]
Where d (v, w) is the distance between nodes v and w, and n is the
number of nodes on the network. We also calculated the average values
and SD in whole network models of degree and average shortest path
length.
Functional analysis
The KEGG pathway enrichment analysis and Gene Ontology annotation were
conducted for the functional analysis of these pleiotropic genes, which
was performed by the String database.[108]^38
Discovery of novel biomarkers
Diagnostic logistic regression tests were used to discover new
biomarkers from pleiotropic genes and their regulators for AD and AMD.
The gene expression was used as the predictor for the diagnostic test.
The receiver operating characteristic curve was used to evaluate
biomarker performance, and a value of greater than 0.6 for the area
under the curve (AUC) was designated as the cut-off for an adequate
biomarker. Python toolbox sklearn.metrics was used to perform this
test. The DeLong test was used to statistically compare the AUCs for
different tests. P<0.05 was considered as with significant differences
among diagnostic tests.
Results
Genetic overlap of AD and AMD
Conditional QQ plots were plotted to identify the pleiotropy between AD
and AMD ([109]figure 1). Both discovery datasets showed significant
leftward shift, indicating high pleiotropy among AD and AMD.
Figure 1.
[110]Figure 1
[111]Open in a new tab
(A) Conditional QQ plot for AD|AMD discovery. The x-axis is −log[10] (p
value of AD SNP), and the y-axis is −log[10] (p value of AMD SNP).
Different curves represent different cut-offs for the AMD p value. A
significant left deviation was found among all the curves, indicating
obvious pleiotropy for AD|AMD. (B) Conditional QQ plot for AMD
discovery|AD. (C) Conditional QQ plot for AD|AMD replication. (D)
Conditional QQ plot for AMD replication|AD. AD, Alzheimer's disease;
AMD, age-related macular degeneration; QQ, quantile–quantile; SNP,
single nucleotide polymorphism.
The combined AD–AMD Manhattan plot of discovery and replication GWAS
datasets were presented in [112]figure 2A, where the MccFDR and
chromosomal for SNPs were presented. 62 significant pleiotropic SNPs
were found in the discovery dataset, of which 5 (rs429358, rs12721051,
rs10414043, rs12721046 and rs7412) were verified on the replication
data ([113]figure 2A and [114]online supplemental tables 1–3). The
significant SNPs were all mapped on chromosome 19. In particular, SNPs
rs12721051, rs12721046 and rs10414043 were mapped to the APOC1 gene,
and rs429358 and rs7412 were mapped to APOE.
Figure 2.
[115]Figure 2
[116]Open in a new tab
(A) AD–AMD combined Manhattan plot. MccFDR was set as y-axis, which
indicated the max ccFDR value among discovery and replication datasets.
Five SNPs (rs429358, rs12721051, rs10414043, rs12721046 and rs7412)
were identified as shared SNPs for AD and AMD, and they are all located
on chromosome 19. Their corresponding genes were APOC1 and APOE. (B) LD
analysis results; rs12721051 was removed because of significant LD
level. (C) RNA-seq expression heatmap for APOC1 and APOE. Both APOE and
APOC1 expressed significantly high in the liver, adrenal gland and
brain. AD, Alzheimer's disease; AMD, age-related macular degeneration;
ccFDR, conjunction-conditional false discovery rate; EBV, Epstein-Barr
virus; LD, linkage disequilibrium; SNPs, single nucleotide
polymorphisms.
Supplementary data
[117]bmjno-2023-000570supp001.pdf^ (6.6MB, pdf)
We have checked the LD states among the five pleiotropic SNPs
([118]figure 2B), and the three SNPs within APOC1 showed moderate LD
(R^2=0.213 for rs12721051 and rs10414043; R^2=0.668 for rs12721051 and
rs12721046; R^2=0.340 for rs10414043 and rs12721046). rs7412 is
independent from SNPs within APOE, with the highest R^2=0.014; rs429358
demonstrates moderate LD with rs12721051, with an R^2=0.4. Thus,
rs12721051 was removed, in order to keep the remaining SNPs more
independent.
Phenotypical verification of the association between APOC1/APOE to AD and AMD
Using the UK Biobank dataset, we examined the associations between the
pleiotropic genes (APOC1 and APOE) with AD and AMD phenotypes
([119]online supplemental table 4). APOC1 and APOE showed significant
associations with AD, AMD and their comorbidities (p<0.05).
Genomic verification for APOC1 and APOE in healthy controls
Gene expression analysis in 54 tissues of 838 healthy controls for
these pleiotropic genes was expressed on the heatmap in [120]figure 2C
and [121]online supplemental figure 1. The expression of APOE and APOC1
was significantly higher in the liver, adrenal gland and brain. APOE
was also highly expressed in the skin.
Biological network analysis for pleiotropic genes
Gene expression datasets were used to construct the GCNs ([122]figure 3
and [123]online supplemental figure 2). We observed that APOC1 and APOE
expression was assessed on three datasets. The GCNs for APOE and APOC1
were presented in [124]figure 3A–C and [125]figure 3D–F, separately.
Overall, APOC1-related network models were larger in size and exhibited
stronger compactness. However, no significant differences were found
between patients and controls ([126]online supplemental figure 1).
Further, APOC1 and APOE were not identified as hubs on the GCNs, with
degrees less than 10 in all models. The relatively medium average
shortest path lengths (~3 in most models) indicated that the
connections of APOC1 and APOE with other genes on the GCNs were not
discrete. These features suggested that APOC1 and APOE were situated at
peripheral positions on the GCNs but maintained moderate interaction
with other genes.
Figure 3.
[127]Figure 3
[128]Open in a new tab
GCNs for APOC1 and APOE in different datasets. The degree and average
shortest path length for APOC1 and APOE and average in whole network
models were presented. (A) AD discovery-frontal cortex-APOC1 model. (B)
AD discovery-temporal cortex-APOC1 model. (C) AD replication-APOC1
model. (D) AD discovery-frontal cortex-APOE model. (E) AD
discovery-temporal cortex-APOE model. (F) AD replication-APOE model.
AD, Alzheimer's disease; GCNs, gene co-expression networks.
Regulators of APOC1 and APOE
16 and 14 regulators for APOC1 and APOE were identified, respectively.
Five were replications (ZBTB48, MZF1, ZNF131, ZNF319 and ZNF273) for
both APOC1 and APOE ([129]figure 4A). The locations and binding
strengths for these regulators were presented in [130]online
supplemental figure 3A,B. Most of APOC1, APOE and their regulators had
no interaction with each other on the PPI network ([131]online
supplemental figure 4). The reason for no interaction may be the
discovery of PPI is not complete.
Figure 4.
[132]Figure 4
[133]Open in a new tab
(A) Regulators for APOC1 and APOE. Five shared regulators were found
for APOC1 and APOE. (B) Heatmap of diagnostic performance (y-axis: AUC)
for APOC1, APOE and their regulators in different expression datasets
(x-axis). Average AUCs were calculated and displayed in the last row
(average). APOC1 demonstrated good performance in six datasets (AUC:
0.71, 0.74, 0.72, 0.84, 0.66, 0.69) except on two datasets (AD
discovery-hippocampus and AMD discovery). APOE only performed well on
two datasets (AMD replication-retinal pigment epithelium: AUC=0.90; and
AD discovery-hippocampus: AUC=0.65). 23 of 25 regulators showed good
performance in the diagnostic test (average AUCs in all datasets >0.6).
Among the five shared regulators for APOC1 and APOE, ZNF131 showed the
best performance in all datasets (all AUCs >0.6, AUC=0.8 in AD
discovery-frontal cortex; 0.88 in AD discovery-hippocampus (AUC: 0.88),
AMD replication-retinal pigment epithelium (AUC:0.90) and AMD
replication-retina (AUC: 0.84)). ADNP2 was the best-performed regulator
for APOC1, which showed good diagnostic values in AD
discovery-hippocampus (AUC: 0.85), AMD replication-retinal pigment
epithelium (AUC:0.98) and AMD replication-retina (AUC: 0.92). Among the
regulators of APOE, HINFP performed best (average AUC=0.72). We have
combined APOC1 with APOE, and got AUCs of 0.71, 0.72, 0.57, 0.61, 0.54,
0.90, 0.62 and 0.67 in different groups, which showed significant
improvements compared with APOC1 or APOE separately. Meanwhile, we also
combined APOC1, APOE and their regulators, and did not find significant
improvements. AD, Alzheimer's disease; AMD, age-related macular
degeneration; AUC, area under the curve.
Biological function analysis results for pleiotropic genes and their
regulators
Regulation of the cellular biosynthetic process, binding and
intracellular membrane-bounded organelle was annotated by Gene Ontology
analysis for APOC1, APOE and their regulators ([134]online supplemental
tables 5–7). In KEGG pathway analysis, six genes were mapped on the
herpes simplex virus 1 (HSV1) infection ([135]online supplemental
figure 5 and online supplemental table 8).
Notably, we found several APOE–APOC1-specific enriched pathways,
including very low-density lipoprotein particle clearance, chylomicron
remnant clearance and positive regulation of cholesterol esterification
on the biological process level, phosphatidylcholine-sterol
o-acyltransferase activator activity on the molecular function level
and chylomicron on the cellular component level ([136]online
supplemental tables 6–8). We also found that APOE was mapped on the AD
pathway, regulated by amyloid beta, which was further regulated by the
APP gene through C99 ([137]online supplemental figure 6).
Novel biomarker discovery for AD and AMD
Diagnostic test results for APOC1, APOE and their regulators have been
presented in [138]figure 4B. APOC1 demonstrated good performance in six
datasets (AUC: 0.71, 0.74, 0.72, 0.84, 0.66, 0.69). APOE only performed
well on two datasets (AMD replication-retinal pigment epithelium:
AUC=0.90; AD discovery-hippocampus: AUC=0.65). 23 of 25 regulators
showed good performance in the diagnostic test (average AUCs in all
datasets >0.6). Among the five shared regulators for APOC1 and APOE,
ZNF131 showed the best performance in all datasets (all AUCs >0.6, four
AUCs >0.8). ADNP2 was the best-performing regulator for APOC1, which
showed good diagnostic values in three datasets (AUCs >0.85). Among the
regulators of APOE, HINFP performed best (average AUC=0.72).
The DeLong test was used to statistically compare the AUCs for the
diagnostic tests among APOC1 and APOE ([139]online supplemental table
9). Except in AD replication group (p=0.0000009173), all the AUCs
showed no differences (p>0.05).
In order to detect the performance of combined biomarkers, we combined
APOC1 and APOE together and got AUCs of 0.71, 0.72, 0.57, 0.61, 0.54,
0.90, 0.62 and 0.67 in different groups, which showed significant
improvements compared with APOC1 or APOE separately. Meanwhile, we also
combined APOC1, APOE and their regulators, and did not find significant
improvements ([140]figure 4B).
Discussion
This study demonstrated that AD and AMD shared a common genetic
aetiology, consistent with previous findings of Logue et al [141]^6 and
Tan et al.[142]^14 The identification of pleiotropy between AD and AMD
holds significant clinical significance. It is widely recognised that
the diagnosis and prevention of AD pose global challenges. Confirming
the shared genetic aetiology between AD and AMD could enhance AD
prevention and diagnosis through accurate and timely detection of AMD.
Advancing from these studies, our study investigates the overall
association between AD and AMD GWAS data using cFDR, a robust Bayesian
algorithm that greatly facilitated pleiotropic gene identification.
Further, for both diseases, we included both discovery and replication
datasets and identified the identical SNPs, lending further credence to
the generalisability of our analysis. In addition, the application of
network topology analysis enabled the observation of the position of
pleiotropic genes on the AD–AMD GCNs. We found that although APOC1 and
APOE were not hubs on the GCNs, they present an ‘average shortest path
lengths’ of around three, indicating that pleiotropic genes may have a
moderate ability to interact with other genes on GCN ([143]figure 3).
This finding may prompt the identification and verification of further
pleiotropic genes on networks.
Our study verified that both APOC1 and APOE were pleiotropic genes for
both AD and AMD. The protein encoded by APOE serves as a major lipid
carrier and has been confirmed as a key risk factor for AD and
AMD.[144]^34 39 APOC1, as a close neighbour of APOE ([145]online
supplemental figure 4), also encodes a protein in the apolipoprotein C
family that plays an essential role in lipoprotein metabolism. APOC1
has been considered to be a risk factor for the development of
AD.[146]^40 However, no previous studies reported the association
between APOC1 and AMD. Our study, verified with the UK Biobank dataset,
confirms that both APOE and APOC1 significantly contribute to the
development of AD and AMD, particularly the comorbidity of AD and AMD
([147]online supplemental table 4). We also conducted a genomic
analysis to understand the pleiotropy of AD and AMD, by located gene
expression level of their pleiotropic genes on tissues, to further
understand the pleiotropy aetiology in phenotype level. Since both are
neurodegenerative diseases that are closely related to neuronal tissue,
their pleiotropy exhibits a substantially high expression in the brain
([148]figure 2B).
Our study identified several upstream regulators for both APOC1 and
APOE and a subsequent biological function analysis to detect the
pleiotropic genes. Regulators are genes with the ability to control the
expression of other genes and are instrumental to the maintenance of
healthy biological processes. The investigation of upstream regulators
for pleiotropic genes is critical to the understanding of the
pleiotropy of AD–AMD. We found significant pathways ([149]online
supplemental figure 5 and online supplemental tables 5–8) that may be
essential for AD–AMD pathogenesis and may guide future pleiotropic gene
discovery. We found that most of the enriched pathways are key pathways
for the basic biological process like metabolic and binding-related
pathways ([150]online supplemental tables 5–8). Interestingly, as these
genes are enriched on the HSV1 infection pathway ([151]online
supplemental figure 5), HSV1 infection is a risk factor for AD,[152]^41
but its association with AMD remains unknown. Our study may inspire
future research to investigate the association between HSV1 with AMD or
AD–AMD comorbidity. According to our pathway enrichment finding, we
postulate that the pleiotropy of AD–AMD comorbidity may be mediated by
basic biological processes and may occur even at a molecular level.
Our study used pleiotropic genes and their regulators to identify
multifunctional biomarkers for AD, AMD and AD–AMD comorbidity. Our
investigation, using gene expression data for multifunctional AD–AMD
biomarkers detection among pleiotropic genes, suggests that APOC1 has
good diagnostic potential for both AD and AMD while APOE might more
focus on the AMD diagnosis from the retinal pigment epithelium.
Further, most of the regulators of APOC1 and APOE showed good
diagnostic values, especially ZNF131, ADNP2 and HINFP. ZNF131 is a
protein-coding gene, which conducts function in the adult central
nervous system.[153]^42 ADNP2 is also a protein-coding gene, which has
been reportedly related to autosomal dominant non-syndromic
intellectual disability.[154]^43 HINFP is the final link in the
cyclinE/CDK2/p220NPAT/HINFP pathway, playing a key role in the G1/S
phase transition, which has been identified as a biomarker for
colorectal cancer[155]^44 and type 2 diabetes.[156]^45
Our study has several limitations. First, we did not analyse the
subtypes of AD and AMD as the GWAS datasets did not specify this
information. The subtypes of these diseases may be important
information for clinical diagnosis and treatment. Second, the sample
sizes in microarray datasets for diagnosis tests were relatively small,
limiting our potential further identification of multifunctional AD–AMD
biomarkers among pleiotropic genes. Third, we were able to examine the
diagnostic value of the identified biomarkers in the patients with
either AD or AMD but not in patients with both AD and AMD due to data
availability. Fourth, we did not take into consideration the onset time
and duration of diseases of AD and AMD in our analyses. Fifth, our
consideration of only two network features in our network analysis
leaves room for further analysis of additional network features in
future studies.
Conclusions
In this study, we constructed the genetic pleiotropy between AD and AMD
and identified APOC1 and APOE to be pleiotropic genes for both
diseases. These results support existing clinical and biochemical
evidence that demonstrates common features in the pathophysiological
pathways leading to both AD and AMD. Further, the biological network
and pathway analysis in our study characterise the network topology
features and pathways for pleiotropic genes for AD–AMD. The integration
of multiomics data identified ZNF131, ADNP2 and HINFP as novel
diagnostic biomarkers for AD and AMD.
Acknowledgments