Abstract
Alzheimer’s disease (AD) is the main cause of dementia worldwide, and
the genetic mechanism of which is not yet fully understood. Much
evidence has accumulated over the past decade to suggest that after the
first large-scale genome-wide association studies (GWAS) were
conducted, the problem of “missing heritability” in AD is still a great
challenge. Epistasis has been considered as one of the main causes of
“missing heritability” in AD, which has been largely ignored in human
genetics. The focus of current genome-wide epistasis studies is usually
on single nucleotide polymorphisms (SNPs) that have significant
individual effects, and the amount of heritability explained by which
was very low. Moreover, AD is characterized by progressive cognitive
decline and neuronal damage, and some studies have suggested that
hyperphosphorylated tau (P-tau) mediates neuronal death by inducing
necroptosis and inflammation in AD. Therefore, this study focused on
identifying epistasis between two-marker interactions at marginal main
effects across the whole genome using cerebrospinal fluid (CSF) P-tau
as quantitative trait (QT). We sought to detect interactions between
SNPs in a multi-GPU based linear regression method by using age,
gender, and clinical diagnostic status (cds) as covariates. We then
used the STRING online tool to perform the PPI network and identify
two-marker epistasis at the level of gene–gene interaction. A total of
758 SNP pairs were found to be statistically significant. Particularly,
between the marginal main effect SNP pairs, highly significant SNP–SNP
interactions were identified, which explained a relatively high
variance at the P-tau level. In addition, 331 AD-related genes were
identified, 10 gene–gene interaction pairs were replicated in the PPI
network. The identified gene-gene interactions and genes showed
associations with AD in terms of neuroinflammation and
neurodegeneration, neuronal cells activation and brain development,
thereby leading to cognitive decline in AD, which is indirectly
associated with the P-tau pathological feature of AD and in turn
supports the results of this study. Thus, the results of our study
might be beneficial for explaining part of the “missing heritability”
of AD.
Keywords: Alzheimer’s disease, epistasis, hyperphosphorylated tau
(P-tau), PPI, ADNI
1. Introduction
Alzheimer’s disease (AD) is an insidious neurodegenerative disorder.
The currently available therapies do not slow disease progression,
provides short-term symptomatic relief only [[42]1,[43]2]. Genome-wide
association studies (GWAS) and related techniques are gradually
discovering variants of causal gene variants that contribute to complex
human diseases. However, after years of GWAS efforts by countless
researchers, these findings can explain only a small fraction of the
heritability and the genetic factors of many human diseases and traits
failed to be discovered, the so-called “missing heritability”
[[44]3,[45]4]. Many research suggest that “missing heritability” in AD
remains extensive with an estimated 25% of phenotypic variance
unexplained by known variants, which may be explained by epistasis
[[46]5,[47]6,[48]7]. A single nucleotide polymorphism (SNP) is defined
as single nucleotide alteration in a DNA sequence among individuals
[[49]8,[50]9]. As a major drawback, in common GWAS, the focus is
usually on SNPs that have significant individual effects
[[51]10,[52]11]. Epistasis is the phenomenon about the interaction
alleles of different loci when expressing a certain phenotype, and it
cannot be attributed to the additive combination of effects
corresponding to the individual loci. If the effect of one variant
affecting a complex trait depends on the genotype of a second variant
affecting that trait, epistasis will occurs [[53]12]. To be specfic,
epistasis leads to complex phenotypic effects, in which the effect of
one locus is masked by the effects on another locus or the joint
effects of two SNPs may be significant whereas they are ineffective
separately [[54]13,[55]14]. Therefore, epistasis detection is expected
to explain the “missing heritability” of many complex diseases such as
AD, diabetes, and hypertension [[56]15,[57]16,[58]17].
As the number of interactions grows exponentially with the number of
variants, computational limitation is a bottleneck [[59]18]. Most
methods of epistasis detection choose to refrain from the brute force
search in the SNP–SNP interaction space and try to reduce computational
burden using dimensionality reduction screening and priori knowledge
[[60]19,[61]20]. However, using more subjective priori knowledge or
random factors for dimension reduction search will lead to signal loss
because the risk of epistatic interaction is unknown [[62]21].
Moreover, most methods of epistasis detection have been designed for
case-control tasks over the past decade, with few on quantitative
traits (QT) [[63]19]. Compared to case–control status, QT has increased
statistical significance and could better track AD progression
[[64]22]. Therefore, mining more potential loci by QT, which are
implicated in AD without dropping signals across the whole genome, is
urgently needed.
Emerging data have suggested that the prevailing amyloid cascade
hypothesis is insufficient to explain many aspects of AD pathogenesis,
neuroinflammation also plays an important role in the pathogenesis of
AD [[65]2,[66]23]. AD is characterized by extracellular amyloid-
[MATH: β :MATH]
(A
[MATH: β :MATH]
) peptides in cortical A
[MATH: β :MATH]
plaques, intracellular phosphorylated tau protein as neurofibrillary
tangles, and neuronal as well as axonal degeneration. These key
hallmarks of AD can be measured in vivo with positron emission
tomography (PET) imaging and biofluid markers including plasma and CSF
assays [[67]24]. CSF tau, P-tau, and A
[MATH: β42 :MATH]
are established biomarkers for AD and have been widely used as QT for
genetic analyse [[68]25]. Furthermore, accumulated P-tau may be the
primary contributor to neurodegeneration during AD, and
neuroinflammation is a central mechanism involved in neurodegeneration
as observed in AD and might play a critical role in inducing
neurodegeneration [[69]26,[70]27,[71]28]. Moreover, P-tau, one of the
three candidate CSF QT, has been widely studied in the Alzheimer’s
Disease Neuroimaging Initiative (ADNI) cohort. For example,
heterogeneity in p-tau species carries predictive power in the
identification of disease severity in incipient AD [[72]29]. P-tau
might start neurodegenerative processes and are necessary for cognitive
decline [[73]30]. Tau pathology is an initiating factor in sporadic AD
[[74]31]. Increase of p181tau levels might predict preclinical AD in
cognitively normal elderly [[75]32].
Therefore, CSF P-tau was used as a QT to advance statistical power and
biological interpretation in this study. Then, we performed genome-wide
epistasis detection in ADNI cohort based on a multi-GPU method, which
has a better detection power outperform other competitive approaches.
2. Materials and Methods
2.1. Genotyping Data and Subjects Processing
Data used in this study were obtained from the ADNI database. The ADNI
is a longitudinal multi-center study designed to be used for the early
detection and tracking of AD, which was founded in 2004 under the
leadership of Dr. Michael W. Weine and supported by the Foundation for
the National Institutes of Health ($27 million) and the National
Institute on Aging ($40 million). The primary goal of ADNI was
developing biomarkers as outcome measures for clinical trials,
examining biomarkers in earlier stages of the disease, and developing
biomarkers as predictors of cognitive decline, etc. The SNP data were
collected from the Illumina 2.5M array and the Illumina OmniQuad array
including ADNI-1, ADNI-GO, and ADNI-2 cohorts, which can be downloaded
from the LONI website ([76]https://adni.loni.usc.edu, accessed on 19
June 2023). A total of 687,414 SNPs were involved in the study. To get
pure SNP data, genetic analysis tool PLINK v1.90 was used to filter the
SNPs according to the following quality control (QC) criteria: (1) SNPs
on chromosome 1–22; (2) minimum call rate for SNPs and subjects ≥ 95%;
(3) minimum allele frequencies (MAF) ≥ 5%; (4) Hardy-Weinberg
equilibrium (HWE) test p ≥ 10
[MATH:
−6 :MATH]
. After the QC, a total of 563,980 SNPs participated in this study.
Subjects were checked by the following QC flow: (1) call rate per
subjects ≥ 90%; (2) gender check; (3) identity check. Then, EIGENSTRAT
was used to perform the population stratification analysis [[77]33].
The population stratification analysis yielded 89 subjects who were
non-Hispanic Caucasians. These 89 participants were excluded from the
analysis. Finally, 1079 subjects passed the QC.
CSF P-tau phenotype was used as QT in this study. The QC criteria of
phenotype was based on two principles: baseline consistency principle
and normal distribution principle. Out of the 1079 subjects retained
after the QC, 860 subjects had both genotype data and phenotype (CSF
P-tau). These subjects (N = 860) including 201 cognitive normal
cognition (CN), 84 significant memory impairment (SMC), 251 early mild
cognitive impairment (EMCI), 209 late mild cognitive impairment (LMCI),
and 115 AD subjects.
Overall, 860 valid P-tau of CSF subjects and 563,980 remained for the
subsequent genome-wide SNP-SNP interaction analysis.
2.2. Genome-Wide SNP-SNP Interaction Analysis
In this study, including as covariates in the liner regression analysis
such as age, gender and clinical diagnostic status (cds), we consider
the linear regression model of additive main effect of two SNPs,
[MATH:
L1,2
=α0+α
1×SNP1+α2×
SNP2+age+gender+cds+εi :MATH]
(1)
where
[MATH:
α0,α
1 :MATH]
and
[MATH: α2 :MATH]
are regression coefficients;
[MATH: εi :MATH]
is a residual that follows a normal distribution with mean zero and
variance
[MATH: σ2 :MATH]
. Therefore, the sum of both additive main effect and SNP
[MATH: 1 :MATH]
-SNP
[MATH: 2 :MATH]
interaction is then given by
[MATH:
L1,2
S=α0+α1×SNP1+α
2×SNP2+α1,2
×SNP1<
/mn>×SNP2+age+gender+cds+εi
:MATH]
(2)
where
[MATH: α0 :MATH]
,
[MATH: α1 :MATH]
,
[MATH: α2 :MATH]
and
[MATH:
α1,2 :MATH]
are regression coefficients. We set Y = Y
[MATH: 1 :MATH]
Y
[MATH: 2 :MATH]
… Y
[MATH: n :MATH]
, where n is the number of subjects in the sample, then the signal of
SNPs is set in the form of S
[MATH: i :MATH]
= S
[MATH:
1j :MATH]
S
[MATH:
2j :MATH]
… S
[MATH:
nj :MATH]
, where j = 1 or 2; S
[MATH:
ij :MATH]
is the genotype of the allele on the SNP
[MATH: j :MATH]
of the ith subject; S
[MATH:
ij :MATH]
= 0, 1 or 2. For each SNP-SNP interaction pair, the interaction effect
was evaluated by two linear regression models according to the CSF
P-tau quantitative trait. In practice, we test the significance of the
interaction terms using an F test, then the p-value would be
calculated.
2.3. Bioinformatics Analyses
To further explain the biological functions of SNP-SNP interaction
pairs with significant interactions, all SNPs were mapped to the
corresponding genes according to position based on the Homo sapiens
genome assembly GRCh37 (hg19), and SNPs not located within the gene
region were mapped to nearby genes by position offset of 100 kb.
Moreover, the gene databases National Center for Biotechnology
Information Phenotype-Genotype Integrator (NCBI PheGenI) was used to
analyze the association of the candidate genes with phenotype trait,
and Reactome 2022 was used to conduct pathway analysis for discover
associated biological processes. For the gene-gene interaction pairs
after mapping from the SNP level, functional enrichment processes were
performed by PPI network enrichment analysis through the STRING
database.
3. Results
3.1. SNP-SNP Interaction Results
In this study, a genome-wide SNP-SNP interaction detection using CSF
P-tau as the intermediate quantitative phenotype was implemented. With
the assistance of the GEEpiQt tool [[78]34], we completely detected all
SNP-SNP interaction pairs with significant interaction across the whole
genome. According to the set p-value criteria, 758 SNP interaction
pairs passed the significance requirement. After the GWAS analysis was
conducted on the SNPs of 758 interaction pairs, all the results
indicated that the statistical significance of the interaction effect
was much higher than the main effect. The interaction effects and main
effects of 758 significant SNP-SNP interaction pairs are shown in
[79]Figure 1.
Figure 1.
[80]Figure 1
[81]Open in a new tab
(a) The 3D waterfall plot reveals interaction effects and main effects
of each SNP with −log
[MATH: 10 :MATH]
(p-values). The number of significant SNP-SNP pairs was 758. For each
interaction pair, the blue waterfall represents main effects of SNP
[MATH: 1 :MATH]
; the green waterfall represents main effects of SNP
[MATH: 2 :MATH]
; and the orange waterfall represents the interaction effects of SNP
[MATH: 1 :MATH]
–SNP
[MATH: 2 :MATH]
pairs. (b) The violin plot shows the distribution state and probability
density of SNP-SNP interaction effect, main effect of SNP
[MATH: 1 :MATH]
and SNP
[MATH: 2 :MATH]
.
To further confirm the association of SNP interactions with
quantitative phenotypes, the explained variance of genetic epistasis of
CSF P-tau was calculated by IBM SPSS 24.0. Two general linear models
were used, with age, sex and disease diagnosis status added as
covariates, and interaction terms were added to one model for
calculating the main effect and interaction effect on phenotypic
explanatory rate separately. The R square of the interaction terms and
the additive terms are shown in [82]Figure 2. The top 10 R-square of
SNP-SNP interaction pairs and results of post hoc analysis on P-tau
level are seen in [83]Table 1. Age, gender, and cds accounted for 9.3%
of variance on the P-tau level. Moreover, [84]Table 1 gives the
proportion of additional variance in P-tau level explained by the
combined main effect and interaction effect of SNP
[MATH: 1 :MATH]
and SNP
[MATH: 2 :MATH]
after accounting for age, gender, cds, SNP
[MATH: 1 :MATH]
and SNP
[MATH: 2 :MATH]
. The percentages of each interaction pair are as follows. For
rs2291948 (APOOP5)—rs2619171, the interaction term accounted for 5.6%
of variance, and the main effects accounted for 0.1% of variance (5.7%
combined). For rs17069204 (SEC63)—rs4983187 (LINC02588), the
interaction term accounted for 5.5% of variance, and the main effects
accounted for 0.8% of variance (6.3% combined). For
rs6882813—rs17416058, the interaction term accounted for 5.5% of
variance, and the main effects accounted for 0.7% of variance (6.2%
combined). For rs129600 (PPARA)—rs6602151 (RSU1), the interaction term
accounted for 5.4% of variance, and the main effects accounted for 0.5%
of variance (5.9% combined). For rs6796502 (PRSS42P)—rs6999890
(SLC45A4), the interaction term accounted for 5.3% of variance, and the
main effects accounted for 0.4% of variance (5.7% combined). For
rs1412839 (PDPN)—rs2397718, the interaction term accounted for 5.3% of
variance, and the main effects accounted for 0.5% of variance (5.8%
combined). For rs2219872 (GRIP1)—rs2647911 (C12orf66), the interaction
term accounted for 5.2% of variance, and the main effects accounted for
1.3% of variance (6.5% combined). For rs9320250 (OSTM1)—rs4983187
(LINC02588), the interaction term accounted for 5.2% of variance, and
the main effects accounted for 1.1% of variance (6.3% combined). For
rs10802434 (SCCPDH)—rs12470444 (NRP2), the interaction term accounted
for 5.2% of variance, and the main effects accounted for 0.6% of
variance (5.8% combined). For rs2487643 (PDPN)—rs2397718, the
interaction term accounted for 5.2% of variance, and the main effects
accounted for 0.6% of variance (5.8% combined).
Figure 2.
[85]Figure 2
[86]Open in a new tab
(a) The 3D waterfall plot reveals interaction and additive terms with R
square in the linear regression model. The orange area represents the
variance explained by interaction term on P-tau. The green area
represents the variance explained by additive term on P-tau. (b) The
violin plot shows the distribution state and probability density of
interaction R square, additive terms of R square the two SNPs.
Table 1.
Top10 R Square Of Snp-Snp Interaction Pairs.
NO SNP
[MATH: 1×
:MATH]
SNP
[MATH: 2
:MATH]
GENE CHR p-Value Explained Variance (R Square)
GWAS Interaction Age + Gender + cdsr
[MATH: 1
:MATH]
SNP
[MATH: 1
:MATH]
+ SNP
[MATH: 22 :MATH]
SNP
[MATH: 1
:MATH]
× SNP
[MATH: 23 :MATH]
1 rs2291948 APOOP5 16 0.963536 1.70 × 10
[MATH:
−9 :MATH]
0.093 0.001 0.056
rs2619171 - 15 0.911948
2 rs17069204 SEC63 6 0.0222884 6.73 × 10
[MATH:
−11 :MATH]
0.093 0.008 0.055
rs4983187 LINC02588 14 0.592583
3 rs6882813 - 5 0.420613 2.86 × 10
[MATH:
−10 :MATH]
0.093 0.007 0.055
rs17416058 - 11 0.212032
4 rs129600 PPARA 22 0.635138 4.92 × 10
[MATH:
−10 :MATH]
0.093 0.005 0.054
rs6602151 RSU1 10 0.870792
5 rs6796502 PRSS42P 3 0.676528 4.80 × 10
[MATH:
−11 :MATH]
0.093 0.004 0.053
rs6999890 SLC45A4 8 0.454329
6 rs1412839 PDPN 1 0.251693 6.00 × 10
[MATH:
−10 :MATH]
0.093 0.005 0.053
rs2397718 - 5 0.785923
7 rs2219872 GRIP1 12 0.016483 3.52 × 10
[MATH:
−12 :MATH]
0.093 0.013 0.052
rs2647911 C12orf66 12 0.292968
8 rs9320250 OSTM1 6 0.0269661 4.37 × 10
[MATH:
−10 :MATH]
0.093 0.011 0.052
rs4983187 LINC02588 14 0.592583
9 rs10802434 SCCPDH 1 0.873933 8.49 × 10
[MATH:
−10 :MATH]
0.093 0.006 0.052
rs12470444 NRP2 2 0.291239
10 rs2487643 PDPN 1 0.231468 1.31 × 10
[MATH:
−9 :MATH]
0.093 0.006 0.052
rs2397718 - 5 0.785923
[87]Open in a new tab
[MATH: 1 :MATH]
Age + gender + cds: percent of variance in P-tau level explained by
age, gender and cds.
[MATH: 2 :MATH]
SNP
[MATH: 1 :MATH]
+ SNP
[MATH: 2 :MATH]
: percent of additional variance in P-tau level explained by the
combined main effect of SNP
[MATH: 1 :MATH]
and SNP
[MATH: 2 :MATH]
after accounting for age, gender and diagnosis.
[MATH: 3 :MATH]
SNP
[MATH: 1 :MATH]
× SNP
[MATH: 2 :MATH]
: percent of additional variance in P-tau level explained by the
interaction effect of SNP
[MATH: 1 :MATH]
and SNP
[MATH: 2 :MATH]
after accounting for age, gender, diagnosis, SNP
[MATH: 1 :MATH]
and SNP
[MATH: 2 :MATH]
. The genes with bold italics in the table are AD-related genes.
3.2. Functional Annotations for Significant Interaction Pairs
A total of 1161 SNPs were mapped onto 578 genes. Then, gene set
enrichment analysis was performed based on HDSigDB Human 2021 in
Enrichr. The results of enrichment analysis indicate that 331 genes
have been shown associated with AD, and the number of unconfirmed
AD-related genes is 247. In order to make more sensible explanations at
the gene level in subsequent study, these gene pairs were categorized
according to the relationship of genes with AD on both sides. The
interactions were classified into three categories: both genes in each
pair are AD-related, only one gene in each pair is AD-related, and none
gene in each pair is associated with AD, as shown in [88]Figure 3.
Figure 3.
[89]Figure 3
[90]Open in a new tab
Three categories of different relationships with AD correlation in a
gene-gene interaction pair. (a) The 1st relationship, include 95 gene
pairs, both genes in each pair are AD-related. (b) The 2nd
relationship, includes 79 gene pairs, only one gene in each pair is
AD-related. (c) The 3rd relationship, includes 25 gene pairs, none gene
in each pair is associated with AD. The red spots represent AD-related
genes. The blue spots represent AD-unrelated genes. Multiple lines
between two points represent multiple pairs of SNP mapped ont to the
same pair of genes.
Moreover, the gene databases National Center for Biotechnology
Information Phenotype-Genotype Integrator (NCBI PhenGenI) and Reactome
2022 were used to analyze the association of the detected genes with
CSF P-tau quantitative traits. Pathway enrichment analysis of genes
mapped by the identified SNPs based on their enrichment adjusted
p-values is presented in [91]Figure 4. The Reactome enrichment analysis
showed that three of the top ten pathways were significant, Cell-Cell
Communication, Cell Junction Organization and Neuronal System pathways.
Among them, there is evidence that “Cell-Cell Communication” is
associated with the abnormal accumulation of phosphorylated tau protein
in Alzheimer’s disease. In the Neuronal System pathway, tau protein is
normally involved in stabilizing microtubules in neurons, but in
Alzheimer’s disease, it can become hyperphosphorylated and form
aggregates called neurofibrillary tangles. These tangles can disrupt
the transport of nutrients and other substances within neurons, which
can further damage the pathways of the neuronal system. The results of
the PhenGenI Association enrichment analysis were found significant in
several diseases, such as Platelet Function Tests, Alzheimer Disease,
and Stroke.
Figure 4.
[92]Figure 4
[93]Open in a new tab
Significantly enriched pathways of studied genes. x-axis indicates the
number of overlapped genes of related pathway, y-axis indicates
significant pathways. The gradient of the color represents the level of
significance. The red bars represent high significance. (a) Top 20
pathways identified by PhenGenI Association 2021 enrichment, −log10
adjusted p-value. (b) Top 10 pathways identified by Reactome 2022,
adjusted p-value.
3.3. Potential Interactions via Protein-Protein Interaction Analysis
To investigate and validate the potential interactions additionally, we
submitted 174 gene-gene interaction pairs to the STRING database for
PPI enrichment analysis. All the 174 gene-gene interaction pairs were
selected from the 1st relationship shows in [94]Figure 3a and the 2nd
relationship as shows in [95]Figure 3b. Notably, 10 of the gene
interaction pairs overlapped with PPI networks in the database, as
shown in [96]Figure 5, including: SPSB1-EPHB1, HNRNPU-NEDD4L,
MYT1L-NYAP2, GGCX-F13A1, LRP1B-PDE4D, RARB-NR3C1, CCL2-SEMA6D,
ROBO1-TLE1, CSNK1A1-PTK7, MYO5B-PCDH15.
Figure 5.
[97]Figure 5
[98]Open in a new tab
The PPI subnetwork of studied genes. The nodes and edges represent the
proteins (genes) and their interactions, respectively. The PPI
subnetwork contained 20 nodes and 33 edges. The light pink connections
represent ten overlapped gene-gene interaction pairs with the PPI
network.
4. Discussion
In this study, detection of genome-wide SNP-SNP interaction based on
multi-GPU were performed. To our knowledge, this study is a highly
comprehensive epistatic study of QT at the P-tau level. A total of 758
SNP-SNP pairs were found to be statistically significant, and highly
significant SNP–SNP interactions were detected between the marginal
main effect SNPs. In particular, the interaction effects were much
higher than the main effects ([99]Figure 1). As we expected, all
identified interaction pairs explained a relatively high-level variance
at the P-tau level ([100]Figure 2 and [101]Table 1), which could be
helpful for explaining some part of the “missing heritability” of AD.
To identify potential genetic epistasis implicated in AD and obtain
biologically meaningful explanations at gene-gene interaction level,
174 gene-gene interaction pairs, which from 1st relationship
([102]Figure 3a) and 2nd relationship ([103]Figure 3b) were submitted
to the STRING database to perform the PPI enrichment analysis. As shown
in [104]Figure 5, the PPI sub-network containing 20 genes and 33
gene-gene interactions were identified. As a result, ten gene-gene
interaction pairs overlapped with the PPI network need further
discussion.
CSNK1A1 is a casein kinase which is involved in the phosphorylation
state of tau [[105]35]. Protein tyrosine kinase 7(PTK7) is a regulator
of Wnt signaling pathways [[106]36]. Wnt signaling is deregulated in
AD, which could contribute to synapse degeneration and cognitive
decline. This deficiency in Wnt signaling may further exacerbate tau
hyperphosphorylation [[107]37]. Therefore, the CSNK1A1 and PTK7
interaction shows strong associations with tau phosphorylation.
The SPRY domain-containing SOCS box protein 1 (SPSB1) is involved in
the development of AD through nitric oxide (NO) pathways. To be
specific, NO pathways contribute to pathogenesis of neurodegeneration
in AD and other neurodegenerative dementias by involving in
neuroinflammation, while SPSB1 negatively control NO production and
limit cellular toxicity [[108]38]. Ephrin type-B receptor 1 (EphB1) is
upregulated in injured motor neurons, and then activated astrocytes
[[109]39]. Furthermore, activated astrocytes mediate neuroinflammation
and neurodegeneration. Neuroinflammation induces neurodegeneration and
the processes involved in neurodegeneration augments neuroinflammation
[[110]26].
Neurodegeneration is mediated by inflammatory and neurotoxic mediators
such as chemokine (C-C motif) ligand 2 (CCL2), CCL5, tumor necrosis
factor-alpha (TNF-
[MATH: α :MATH]
) and interleukin-6 (IL-6) etc. The increased level of these mediators
including CCL2 lead to neurodegeneration and neuronal death in
neurodegenerative diseases [[111]26,[112]40]. These inflammatory and
neurotoxic mediators directly or indirectly through glial cells and
inflammatory cells affect neuronal survival and induce
neurodegeneration [[113]26]. Moreover, CCL2 is implicated in the
pathways recruiting microglia and the development of P-tau pathology,
and might be related to reducing neuroinflammation [[114]41]. SEMA6D is
a regulator of microglial phagocytosis and inflammatory cytokine (TNF
[MATH: α :MATH]
, IL-6 etc.) release in a TREM2-dependent manner [[115]42,[116]43].
Moreover, microglial phagocytosis is a disease-associated process
emerging from AD genetics [[117]44]. Excessive microglial phagocytosis
of synapses can be observed in AD, leading to significant synapse loss
and memory impairment [[118]45]. And lack of microglial phagocytosis
can exacerbate pathology of AD and induce memory impairment [[119]46].
Microglia are also major players in neuroinflammation [[120]23].
RP1B belonged to the low-density lipoprotein (LDL) receptor family, and
several members of the LDL family have been implicated in cellular
processes relevant to neurodegeneration, including tau uptake et al.
Enhanced LRP1B activity can protect against the pathogenesis of AD and
cognitive decline in old age. Inhibition of phosphodiesterase 4D
(PDE4D) activity can enhance phosphorylation of tau.
MYT1L is a critical mediator of directly converting human brain
vascular pericytes (HBVPs) into cholinergic neuronal cells, and the
cholinergic deficit is thought to underlie progressed cognitive decline
in AD. Neuronal tyrosine-phosphorylated phosphoinositide-3-kinase
adapter 2 (NYAP2) is involved in remodeling of actin cytoskeleton
[[121]47]. Actin cytoskeleton has been described as an underlying
factor of synaptic failure in AD, which could contribute to AD
pathology [[122]48].
Regulation of HNRNPU expression ameliorates impairments of learning and
memory abilities in an AD rat model. NEDD4L is identified as potential
nuclear enriched abundant transcript 1 (NEAT1) interaction proteins.
upregulated NEAT1 can give rise to the amyloid accumulation and
cognitive decline in AD.
In summary, CSNK1A1-PTK7 interaction and PDE4D gene shows strong
associations with P-tau, which is directly associated with pathogenesis
of AD. Two pairs of gene-gene interactions show strong associations
with AD in terms of neuroinflammation and neurodegeneration:
SPSB1-EPHB1, CCL2-SEMA6D. MYT1L gene, LRP1B gene and NEDD4L gene show
strong associations with AD, and leading to cognitive decline in AD.
LRP1B gene is also associated with pathogenesis of AD. HNRNPU gene
exerts its effects on learning and memory abilities in AD. NYAP2 gene
is involved in remodeling of actin cytoskeleton, which is indirectly
associated with AD pathology. Two pairs of gene-gene interactions can
affect the activation of neuronal cells and contribute to brain
development: RARB-NR3C1, ROBO1-TLE1 [[123]49,[124]50]. MYO5B gene and
GGCX gene show strong associations with schizophrenia [[125]51].
Neuroinflammation is well established in a subset of schizophrenia
patients [[126]52]. In addition, two genes have not yet been associated
with AD pathology: F13A1, PCDH15, which warrant further investigation.
To our knowledge, neurodegeneration appears to be the biological
mechanism most proximate to cognitive decline in AD [[127]53]. A
[MATH: β :MATH]
and tau pathologies interact synergistically in the preclinical stages
of AD, which contributing to faster neurodegeneration and cognitive
decline [[128]54,[129]55]. Therefore, the identified gene-gene
interactions and genes in the PPI network might be related to
neuroinflammation and neurodegeneration, thereby leading to cognitive
decline in AD, which is indirectly proves that accumulated P-tau may be
the primary contributor to neurodegeneration during AD [[130]27], and
in turn supports the results of this study.
5. Conclusions
Aimed at performing genome-wide epistasis detection in the ADNI cohort,
we used CSF P-tau as a QT. 331 AD-related gene were replicated, which
are previously confirmed AD risk genes. We also replicated 10 findings
in the PPI network, which are SPSB1-EPHB1, HNRNPU-NEDD4L, MYT1L-NYAP2,
GGCX-F13A1, LRP1B-PDE4D, RARB-NR3C1, CCL2-SEMA6D, ROBO1-TLE1, CSNK1A1-
PTK7, MYO5B-PCDH15. Moreover, 3 gene-gene pairs of interaction showed
strong association with AD: CSNK1A1-PTK7, SPSB1-EPHB1, CCL2-SEMA6D.
Interactions between RARB and NR3C1, between ROBO1 and TLE1 can affect
the activation of neuronal cells and contribute to brain development.
Our study also revealed two genes have not yet been associated with AD
pathology: F13A1, PCDH15, which warrant further investigation. In
summary, our results can provide useful clues to the aspect of inducing
neuroinflammation and neurodegeneration, and show strong association
with AD in terms of cognitive decline. Therefore, this study might open
new avenues to complement common GWAS. Furthermore, our results may be
replicated by considering other quantitative traits using different
databases and methods to complement the PPI network. Biological
interpretation is also a direction of future research.
Acknowledgments