Epistasis analysis links immune cascades and cerebral amyloidosis
Benedet et al. Journal of Neuroinflammation
Epistasis analysis links immune cascades and cerebral amyloidosis
Andréa L. Benedet 1 3
Aurélie Labbe 2 7 8
Philippe Lemay 6
Eduardo R. Zimmer 1 5 10
Tharick A. Pascoal 1
Antoine Leuzy 0 1 4 9
Sulantha Mathotaarachchi 1
Sara Mohades 1
Monica Shin 1
Alexandre Dionne-Laporte 11 12
Thomas Beaudry 1
Cynthia Picard 2
Serge Gauthier 11
Judes Poirier 0 2 4 11
Guy Rouleau 11 12
Pedro Rosa-Neto 0 1 4 11 12
for the Alzheimer's Disease Neuroimaging Initiative
0 Alzheimer's Disease Research Unit, McGill University Research Centre for Studies in Aging, McGill University , Montreal , Canada
1 Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging , 6825 LaSalle Blvd, H4H 1R3 Montreal, QC , Canada
2 Douglas Hospital Research Centre, McGill University , Montreal , Canada
3 CAPES Foundation, Ministry of Education of Brazil , Brasília , Brazil
4 Alzheimer's Disease Research Unit, McGill University Research Centre for Studies in Aging, McGill University , Montreal , Canada
5 Department of Biochemistry, Federal University of Rio Grande do Sul , Porto Alegre , Brazil
6 Department of Biochemistry, Université de Montréal , Montréal , Canada
7 Department of Psychiatry, McGill University , Montreal , Canada
8 Department of Epidemiology, Biostatistics & Occupational Health, McGill University , Montreal , Canada
9 Department of NVS, Center for Alzheimer Research, Translational Alzheimer Neurobiology, Karolinska Institutet , Stockholm , Sweden
10 Brain Institute of Rio Grande do Sul (BraIns), Pontifical Catholic University of Rio Grande do Sul (PUCRS) , Porto Alegre , Brazil
11 Department of Neurology and Neurosurgery, McGill University , Montreal , Canada
12 Montreal Neurological Institute , Montreal , Canada
Background: Several lines of evidence suggest the involvement of neuroinflammatory changes in Alzheimer's disease (AD) pathophysiology such as amyloidosis and neurodegeneration. In fact, genome-wide association studies (GWAS) have shown a link between genes involved in neuroinflammation and AD. In order to further investigate whether interactions between candidate genetic variances coding for neuroinflammatory molecules are associated with brain amyloid β (Aβ) fibrillary accumulation, we conducted an epistasis analysis on a pool of genes associated with molecular mediators of inflammation. Methods: [18F]Florbetapir positron emission tomography (PET) imaging was employed to assess brain Aβ levels in 417 participants from ADNI-GO/2 and posteriorly 174 from ADNI-1. IL-1β, IL4, IL6, IL6r, IL10, IL12, IL18, C5, and C9 genes were chosen based on previous studies conducted in AD patients. Using the [18F]florbetapir standardized uptake value ratio (SUVR) as a quantitative measure of fibrillary Aβ, epistasis analyses were performed between two sets of markers of immune-related genes using gender, diagnosis, and apolipoprotein E (APOE) as covariates. Voxel-based analyses were also conducted. The results were corrected for multiple comparison tests. Cerebrospinal fluid (CSF) Aβ1-42/phosphorylated tau (p-tau) ratio concentrations were used to confirm such associations. Results: Epistasis analysis unveiled two significant single nucleotide polymorphism (SNP)-SNP interactions (false discovery rate (FDR) threshold 0.1), both interactions between C9 gene (rs261752) and IL6r gene (rs4240872, rs7514452). In a combined sample, the interactions were confirmed (p ≤ 10-5) and associated with amyloid accumulation within cognitively normal and AD spectrum groups. Voxel-based analysis corroborated initial findings. CSF biomarker (Aβ1-42/p-tau) confirmed the genetic interaction. Additionally, rs4240872 and rs7514452 SNPs were shown to be associated with CSF and plasma concentrations of IL6r protein. Conclusions: Certain allele combinations involving IL6r and C9 genes are associated with Aβ burden in the brain. Hypothesis-driven search for epistasis is a valuable strategy for investigating imaging endophenotypes in complex neurodegenerative diseases.
Alzheimer’s disease (AD) is the most common form of
dementia worldwide and has been recently
reconceptualized as a dynamic and progressive process in which
pathological changes start decades prior to the onset of
clinical symptoms [
]. According to the amyloid
cascade hypothesis , the accumulation of brain amyloid β
(Aβ) sets a cascade of progressive neurodegenerative
changes—including the formation of intracellular
inclusion of neurofibrillary tangles (NFTs)—resulting in
cognitive impairment and, ultimately, dementia. Imaging
and cerebrospinal fluid (CSF) biomarkers have
successfully advanced our knowledge in terms of the evolution
of AD [
]. However, the most recent hypothetical model
of AD biomarkers [
] has not explored the role of
neuroinflammation, a phenomenon implicated in the
pathogenesis of AD by several lines of evidence [
It is becoming a common theme the high likelihood
that neuroinflammation in AD is dependent on several
genetic factors and is affected by environmental
interactions that happen during an individual’s lifetime (for
review, see [
]). Previous studies have shown important
interactions between immune responses and brain
], with both in vitro and in vivo studies
demonstrating altered cytokine expression in AD. In addition,
neuroinflammation secondary to systemic infections,
traumatic brain injuries, or other neurologic
conditions has been shown to increase the risk of sporadic
Currently, it is widely accepted that Aβ is associated
with innate immunity pathways—as well as molecular
mediators such as cytokines, chemokines, and
complement molecules—leading to neuroinflammation and
disturbance in brain homeostasis. However, findings linking
immune-related genes with AD have raised the
possibility that inflammation is the cause of brain amyloid load.
In fact, the activation of the immune response by
damage-associated factors is able to increase Aβ
production (for review, see [
]). Thus, it has been
hypothesized that impaired immune response either fails to clear
Aβ from the brain or drives an overreaction against this
protein, resulting in chronic inflammation, which effects
could be either harmful or protective in nature.
Endophenotypes associated with variations in
immunerelated genes, particularly related to AD neuropathological
features, remain elusive. Genome-wide association studies
(GWAS) and meta-analysis have found immunogenetic
variants associated with AD, namely CR1, CLU, TREM2,
PICALM, CD33, and MEF2C, reasserting the role of the
immune system in AD pathophysiology [
recent investigations did not reveal a link between brain
amyloidosis and immunologic genetic variants [
suggesting that some endophenotypes might be affected
by gene-to-gene interactions or epistasis.
In multifactorial diseases such as AD, the power to
detect isolated genetic variants can be reduced due to
epistatic effects, which occur when one locus masks or
alters the effect of another [
]. In this respect,
approaches moving beyond single-marker outcomes may
better capture heritability links [
In this study, we aimed to investigate the interactions
between immune-related genes—primarily molecular
mediators of inflammation—and the accumulation of Aβ
in vivo, as quantitated by amyloid imaging with positron
emission tomography (PET). We hypothesize that
differential amyloid burden is associated with the deregulation
of innate immunity response, which could be evidenced
by epistasis analysis of genes that encode for immune
proteins reported to be related to AD.
Data used in the preparation of this article were
obtained from the Alzheimer’s Disease Neuroimaging
Initiative (ADNI) database (adni.loni.usc.edu). The
ADNI was launched in 2003 as a public-private
partnership, led by Principal Investigator Michael W.
Weiner, MD. The primary goal of ADNI has been to
test whether serial magnetic resonance imaging
(MRI), PET, other biological markers, and clinical
and neuropsychological assessment can be combined
to measure the progression of mild cognitive impairment
and early Alzheimer’s disease. To date, over 1500 adults
with ages ranging from 55 to 90 years old participate in
the research, consisting of cognitively normal (CN) older
individuals, subjects with amnestic mild cognitive
impairment (MCI), and individuals that met the NINCDS/
ADRDA criteria for probable AD. Further details
about inclusion and exclusion criteria can be found at
the ADNI website (http://www.adni-info.org/Scientists/
All subjects included in the ADNI project provided
written informed consent, according to Helsinki Declaration,
at the time of enrolment for imaging and genetic
sample collection and completed clinical symptom
assessments approved by each participating sites’
Institutional Review Board. Following ADNI’s policies,
the principal investigator of the present study has
accepted ADNI Data Use Agreement and is
authorized to use ADNI data.
This report is based on data acquired from 417
participants from ADNI-GO/2 and 174 from ADNI-1, from
whom both genetic and PET data were available.
Demographic data is summarized in Table 1.
Amyloid load was estimated using the [18F]florbetapir
PET standardized uptake value ratio (SUVR). A detailed
The baseline considered here is the date of the first [18F]florbetapir PET acquisition
SD standard deviation, CDR-SOB Clinical Dementia Rating Scale Sum of Boxes, MMSE Mini-Mental State Examination, SUVR standard uptake value ratio
aStatistically different between all groups from the same sample
bStatistically different from the other groups from the same sample
description of the [18F]florbetapir imaging acquisition
protocol can be found online at the ADNI website. PET
image processing and estimation of global SUVRs have
been described previously [
]. All image processing,
including generation of regions of interest, is summarized
in Additional file 1: Figure S1.
We have chosen to verify possible interactions between
the main interleukins (IL) reported to be associated with
AD pathology and proteins of the membrane attack
complex (MAC). Selected MAC key proteins include
complement 5 (C5) and complement 9 (C9). They are
respectively the first and last proteins to be activated in
the MAC cascade, and both are also found to be
associated with amyloid plaques in AD brain [
interleukins selected were the most frequently reported
to be related to AD [
]. IL1β, IL6 (and its receptor
IL6r), IL12, and IL18 have shown to be differentially
expressed in AD brain when compared to controls.
These pro-inflammatory cytokines display increased
expression in AD brains and/or are associated with
amyloid plaques [
]. They also seem to reduce
AD-like phenotypes when inhibited in animal models
]. IL4 and IL10 have anti-inflammatory
properties, and they all have been found to be associated
with AD, either by in vitro studies, genetic studies, or
biochemical analysis of plasma, CSF, and/or AD
The ADNI-GO/2 subjects were genotyped using the
Illumina HumanOmniExpress BeadChip (Illumina, Inc.,
San Diego, CA) array [
], while for ADNI-1 subjects,
correspondent genotypes were obtained from
HumanOmni2.5 BeadChip (Illumina, Inc., San Diego, CA).
Quality control was performed using PLINK software
(version 1.07) [
] excluding single nucleotide
polymorphisms (SNPs) with a genotyping efficiency <95 %, a
minor allele frequency of <5 %, or deviation from
Hardy-Weinberg equilibrium <1 × 10−6. Subjects were
excluded if they had a call rate <95 % and if genetic
relatedness was detected (PI_HAT > 0.5). Population
stratification was accounted for by subtracting the
effect of the first two principal components using the
Eigenstrat routine [
] in the Eigensoft V5.0 package
] and posterior visualization of the Q-Q plots.
For the epistasis analysis, we selected 10 genes related
to molecular mediators of inflammation as mentioned
above. These genes were grouped into two sets
according to their protein function, set 1 being composed of
interleukins and one interleukin receptor (IL-1β, IL4,
IL6, IL6r, IL10, IL12, IL18) and set 2 of proteins of the
MAC (C5, C9). Using the UCSC Genome Browser
(http://genome.ucsc.edu), we annotated the start and
end points of each selected gene and all SNPs present
within this region were obtained with PLINK. SNAP
Proxy Search (version 2.2) [
] was used to verify and
then remove markers in high linkage disequilibrium
(r2 > 0.8). The remaining SNPs were grouped together
within the respective set (31 SNPs in set 1 and 21 in
set 2) as summarized in Additional file 2: Table S1.
Epistasis analysis was performed using R [
Cerebrospinal fluid concentrations of phosphorylated tau
Baseline CSF amyloid-β1-42 (Aβ1-42) and phosphorylated
tau (p-tau) levels were measured using the multiplex
xMAP Luminex platform (Luminex Corp, Austin, TX)
and Innogenetics/Fujirebio AlzBio3 immunoassay kits.
The methodology applied for aliquot collection, peptide
quantification, as well as quality control and data
normalization are described in previous reports [
The normalized data was used to calculate the
Aβ1-42/ptau ratio, which is the phenotype used to confirm
findings obtained with [18F]florbetapir.
Cerebrospinal fluid and plasmatic protein levels
The Biomarkers Consortium CSF and Plasma Proteomics
Project multiplex data are available for ADNI-1
subjects from whom protein levels were measured at the
baseline for both biospecimens and 12-month
followup visit only in the plasma. The description of the
methodology regarding the sample acquisition, sample processing
and analysis, as well as quality control procedures is
available at the ADNI website (http://adni.loni.usc.edu/
methods/biomarker-analysis/proteomic-analysis and http://
The epistasis analysis was performed with R, in which a
linear model was used to test the interaction between
two SNPs in a given pair. Each SNP pair was composed
of one SNP from each set, resulting in 651 pairs tested.
Subjects’ genotypes were acquired using PLINK and
categorized based on minor allele counts (additive model).
The quantitative trait analyzed was the [18F]florbetapir
SUVR. In the model, diagnostic status (AD, MCI, or
CN), gender, and number of apolipoprotein E allele 4
(ApoE ε4) were added as covariates. False discovery rate
(FDR) was used to correct for multiple comparisons.
The significant interactions (after FDR correction at 0.1
level) found with the ADNI-GO/2 sample were tested
for replication using both the ADNI-1 sample and the
combined dataset (ADNI-1 and ADNI-GO/2). The
comparison of the [18F]florbetapir SUVR means between
genotype groups was performed using the combined
dataset. Tukey’s honest significant difference (HSD) test
was used in the post hoc analysis. The significant
interactions were also tested within the two groups obtained
from the combined dataset: CN and AD spectrums
(MCI and Alzheimer dementia patients). In these late
comparisons, the model applied followed the same
criteria described for the analysis with the whole sample.
Voxel-based analysis was carried out to confirm
volume of interest analysis. Parametric images were
obtained using the methodology summarized in Additional
file 1: Figure S1. First, the model was tested for the most
significant interaction. Then, we compared the groups
participating in the two most significant contrasts found
in Tukey’s HSD test applied in the global SUVR analysis.
Voxel-based statistical differences were obtained by
contrasting the [18F]florbetapir SUVR between genotype
groups, adjusting for gender, diagnostic status, and ApoE
ε4 using the RMINC imaging tool. RMINC is an
imaging package that allows images files in the Medical
Imaging NetCDF (MINC) to be analyzed with the
powerful statistical environment R. After random field
theory (RFT) [
] correction for multiple comparisons,
the T value threshold of significance is ≥3.0 (p ≤ 0.05)
for the interaction model and ≥3.2 (p ≤ 0.05) for the
To confirm the association found with [18F]florbetapir
phenotype, the most significant interacting pair of SNPs
was tested using the baseline Aβ1-42/p-tau ratio as the
dependent variable, which was available for a subsample
of 208 subjects. The applied model had diagnosis,
gender, and number of ApoE ε4 as covariates.
The effect of the polymorphisms on their respective
protein levels in the plasma was tested in ADNI-1
subjects with proteomic data available (n = 114). A linear
mixed-effects model—adjusted for diagnosis and
gender—was applied to analyze the effect of the genotype
in both baseline and 12-month follow-up data. The
genotypes were categorized according to the presence
or absence of the minor allele. An additional linear
model tested for associations between the
polymorphisms and the CSF concentrations of their respective
proteins (n = 81).
Epistasis analysis indicates that the interaction between
C9 and IL6R genes is associated with brain amyloid deposition
After applying the quality control steps as previously
described, one pair of subjects was found to be genetically
related. One of the subjects was thus randomly selected
and excluded from the study. No difference was seen in
the Q-Q plots for SNPs as main effects, before and after
adjustment for the first two principal components (data
not shown). This suggests that our sample is genetically
homogeneous. Epistasis analysis unveiled two significant
SNP-SNP interactions after FDR correction (FDR
threshold p = 0.1; see Table 2 and Additional file 2: Table S2).
The most significant interaction was between the SNP
rs261752 of the complement 9 (C9) gene and the SNP
rs7514452 annotated to the interleukin 6 receptor (IL6r)
gene (t = 3.92, unadjusted p = 1.0 × 10−4). This interaction
showed a trend level association in the ADNI-1 group
(t = 1.82, unadjusted p = 0.06) and a very significant
association in the combined dataset (t = 4.42,
unadjusted p = 1.1 × 10−5). A second interaction was
noted between this C9 SNP and another SNP in IL6r
(rs4240872) (t = 3.73, unadjusted p = 2.1 × 10−4).
Similar to the first interaction, this association was found
to be significant only in the combined sample (t = 4.15,
unadjusted p = 3.7 × 10−5; ADNI-1, t = 1.85, unadjusted
p = 0.06).
Group comparisons between genotypes showed
similar results for the two interactions reported.
Interestingly, the IL6r and C9 SNP interaction showed
that, despite being not AD and having only one ApoE
ε4-positive subject, the combination of both minor
alleles (CC(C9)*CC(IL6r)) was associated with higher
mean SUVR values when compared to almost all
other genotype combinations (see Fig. 1. For post hoc
results, see Additional file 2: Table S3).
In order to assess whether the interaction was specific
to individuals in the AD spectrum, we stratified the
individuals in the CN and AD spectrums (MCI and
dementia phase). Both groups presented similar results than
those obtained using the entire sample (see Table 3).
Voxel-based analysis revealed that the epistasis is related to amyloid deposition in AD-related brain regions
The voxel-based analysis showed that the interaction
between C9 and IL6r SNPs is associated with amyloid
load in the anterior and posterior cingulate, temporal,
and inferior parietal cortices bilaterally (see Fig. 2).
Additionally, voxel-wise comparisons revealed that
homozygous subjects for both minor alleles, when compared to
either carriers of the genotype CC(C9)*TT(IL6r) or the
genotype TT(C9)*TC(IL6r), have more amyloid load in
the brain regions mentioned above. These differences
are corrected for multiple comparisons (at 0.05 level).
CSF biomarkers of AD neurodegeneration replicated the
results obtained using the [18F]florbetapir SUVR
A subsample of 208 subjects (85 CN, 113 MCI, and
10 AD) who had baseline CSF measures was used to
confirm the interaction model. The Aβ1-42/p-tau ratio
was used as a dependent variable (CN average = 7.73,
MCI average = 6.05, AD average = 2.76; difference
between all groups statistically significant p = 5.3 × 10−5).
The interaction analysis was replicated in the tested
pair of SNPs (rs261752*rs7514452 t = −2.82, p = 0.005)
1.1 × 10−5
3.7 × 10−5
(Additional file 2: Table S4). Similar observations to
the findings using [18F]florbetapir were found;
homozygous subjects for both minor alleles tend to have
the lowest Aβ1-42/p-tau ratio (Fig. 3). However, due to
sample size restrictions, group comparisons could not
Plasma and CSF levels of IL6R protein were associated with the genetic polymorphisms
The IL6r genotypes of both SNPs were associated with
plasmatic levels of IL6r protein (rs7514452 t = −2.42,
unadjusted p = 0.01; rs4240872 t = −2.94, unadjusted
p = 0.003), which shows carriers of the minor alleles
having a lower level of the protein compared to
non-carriers. Due to the sample size, it was not
possible to verify if the same effect is present within
diagnostic groups; therefore, the diagnosis was used
as a covariate in the analysis. Similarly, CSF levels of
IL6r protein were associated with rs4240872 (t = −3.17,
unadjusted p = 0.002) but not with rs7514452 (t = −1.52,
unadjusted p = 0.12). Unfortunately, there is no data
available to date reflecting plasmatic levels of C9 protein that
would permit us to do the correspondent analysis with the
In the present study, two interactions between two
immune-related genes, C9 and IL6r, were found to be
associated with [18F]florbetapir SUVRs. This result
suggests that Aβ burden in the brain may be
differentially affected depending on the allelic combination of
the cited variants.
The SNP rs261752 is an intronic variation of the C9
gene, with no previously reported association to any
phenotypic feature or neurodegenerative endophenotype.
However, it has been associated with age-related macular
degeneration, a highly frequent disorder among AD
]. Moreover, several studies have described
increased immunoreactivity of classical complement
molecules, including C9, in the vicinity of brain Aβ
25, 48, 49
]. C9 protein is also a component of
the MAC, which is responsible for disrupting cellular
homeostasis, causing cell death following activation of
the complement pathway . Indeed, it is well known
that extracellular Aβ triggers the complement cascade,
leading to MAC formation [
26, 48, 51
]. Since MAC
requires a lipid bilayer structure to act upon, it binds to
the surrounding neurites [
], leading to
neurodegeneration and cell death. Furthermore, the protein
clusterin, encoded by the AD-related CLU gene, has been
shown to play an important role in reducing
inappropriate MAC activity tied to physical interaction with the C9
The two SNPs from the IL6r gene are more than
1800 bp apart from each other (r2 = 0.69) and, despite
not being in high linkage disequilibrium, might reflect
the same signal. The SNP rs4240872 is an intronic
variant of the IL6r gene while the variant rs7514452 is
located in the 3’-untranslated region (3’-UTR), an
important sequence at the end of the messenger RNA
(mRNA) known to affect post-translational regulation
and subsequent protein expression [
]. A previous
study suggested a possible association between 3’-UTR
markers and diabetes mellitus type 2 [
], an association
of possible relevance owing to evidence showing that
insulin signaling is down-regulated in AD (for review, see
]). Additionally, Walston et al. [
] reported that
some IL6r SNPs are associated with plasmatic levels of
interleukin 6 (IL6), a cytokine that plays an important
role in the regulation of neuroimmune responses,
promoting both pro-inflammatory and anti-inflammatory
]. Similar results were reported here
showing that CSF levels of IL6r are associated with one IL6r
SNP while plasmatic levels are associated with both
SNPs (rs7514452 and rs4240872) in ADNI-1 subsample,
reflecting a genotype-phenotype effect. The IL6r protein is
either a part of the ligand-binding receptor of IL6 or a
soluble form (s-IL6r), which binds to IL6 to enhance its
]. Deregulation of immune response signaling
in AD is evidenced by altered protein expression in the
]. Differences in CSF and serum levels of both
IL6 and s-IL6r are also evident when comparing AD
patients to CN [
Voxel-based findings revealed by this study further
corroborate global increases of amyloid load in regions
typically affected by AD pathophysiology. Homozygous
subjects for minor alleles of both IL6r and C9 genes
show higher levels of amyloid in brain areas that
correspond to regions impaired in AD [
amyloid plaques depicted by amyloid imaging agents are
typically surrounded by neuroinflammatory changes
such as astrocytosis and microglial activation (for review,
]), reinforcing a link between amyloidosis and
immune response. Additionally, one could claim that a
reduction in the IL6r levels causes decreases in the IL6
activity, contributing to Aβ accumulation through
different possible mechanisms.
In agreement with [18F]florbetapir findings, the
interactions between C9 and IL6r genes were also associated
with the CSF Aβ1-42/p-tau ratio. This finding based on
an independent measurement of brain amyloidosis
provides additional evidence that C9 and IL6r interactions
affect brain accumulation of neuritic plaques in a
disease-specific manner [
]. However, it is important to
take the reduced sample size present in the CSF
population into consideration.
Based on our results, it seems plausible that a
combination of gene polymorphisms in complement factors and
interleukins plays a synergic role in determining amyloid
burden in the brain. Specifically, a particular
combination of genotypes that up-regulate both C9 and IL6r
may exert an additive effect via neuroinflammatory
processes. Besides the supposition of how these SNPs may
jointly affect amyloid accumulation in the brain, no
relationship between these two genes or proteins has been
reported to date with respect to amyloid metabolism.
However, it has been shown that the protein IL6 is able
to stimulate C9 mRNA expression in post-mortem
human astrocytes and neuroblastoma cells [
showing a metabolic link between the two proteins in
the cells of the nervous system.
In order to overcome the well-known limitations of
association studies, several assumptions need to be addressed.
For example, although all the cited proteins are related to
the immune system, their roles in Aβ accumulation remain
unclear. Presently, the functions of the reported SNPs
remain elusive due to the lack of relevant literature.
Regarding the association found between IL6r levels and IL6r
SNPs, linking the genotype with the phenotype, it is
important to mention that [
] (1) protein levels were
measured on average 55 months prior to [18F]florbetapir image
acquisition; (2) there was no association between the use of
anti-inflammatory drugs and IL6r levels in this sample; and
(3) beyond the effect that IL6r SNPs can have at the protein
level, it is very important to know the effect of the C9
genotypes on C9 protein to better understand how they jointly
impact the immune response.
Among the limitations of the study, the ADNI is a
cohort mostly composed of non-Hispanic Caucasians,
limiting the extrapolation of the present findings to other
population groups. A wider range of subjects varying in
terms of ethnicity, family history, and disease progression
should be considered for future replication of this study. It
is also important to acknowledge that, despite postulated
that amnestic MCI have high probability to convert
to dementia due to AD, a considerable proportion of
these individuals remain stable or convert to non-AD
], being a methodological limitation to
the study of the AD spectrum. Moreover, currently, it
is thought that the Aβ oligomers (soluble forms) are
the most synaptotoxic (for review, see [
]) and the
most chased by the immune system; however, it is
not possible to detect these forms in vivo using brain
imaging; [18F]florbetapir is only able to bind to
amyloid plaques. Recently, MRI probes for targeting Aβ
oligomers have been developed and will likely provide
further information regarding the association between
Aβ oligomers and the immune system [
]. In fact,
more studies are needed to address the biological
mechanisms in which gene interactions may affect the
phenotype, using both amyloid plaque and Aβ
It is also important to mention that statistical analyses
between genetic factors do not define their biological
interactions or interferences [
], necessitating more
investigation. It should be noted that age showed
negligible or no effect in our analyses and does not alter
the conclusions if added in the model. Though
ADNI1 data was used to confirm significant associations, the
reduced sample size could have been a limiting factor
with respect to the achievement of statistical
significance. Based on the effect size of the interactions
found in the first analysis with ADNI-GO/2 data (data
not shown), the sample size required to reach 95 % of
power and a type I error of 0.05 is 497 subjects. For
this reason, a less strict FDR correction was adopted in
the first step of the analysis. Sample size requirements
might also explain why it was not possible to fully
replicate the results using data from ADNI-1, while results
were replicated in the combined sample—the p values
obtained for the interactions in the combined sample
would still be significant at 0.05 level if FDR
correction had to be applied. Additionally, the highly significant
p values obtained with the combined sample indicate a
high likelihood that the initial results obtained using
ADNI 2 data were not a consequence of a type II error.
In conclusion, using a clinically well-characterized and
genetically homogenous sample, as well as a
confirmatory imaging analysis, our hypothesis-driven analyses
identified several epistatic links between IL6r and C9
genes, suggesting genetic components linking the
immune system and brain amyloidosis. Though further
studies are required, these results suggest that these
interacting genotypes may represent potential biomarkers
for differential treatment of AD.
Additional file 1: Figure S1. [18F]Florbetapir SUVR analytical method.
Flowchart showing acquisition methods (purple), image processing
(blue), and outcomes (green). PET positron emission tomography, MRI
magnetic resonance imaging, GM gray matter, WM white matter, CSF
cerebrospinal fluid, FWHM first width half maximum, ROI region of
interest. (TIFF 2485 kb)
Additional file 2: Tables S1–S4. Table S1. List of SNPs within each set.
Table S2. R epistasis results (first 15 lines). Table S3. Tukey’s HSD test
results. Table S4. Interaction tested using Aβ1-42/p-tau ratio as phenotype.
(DOCX 66 kb)
AD: Alzheimer’s disease; ADNI: Alzheimer’s Disease Neuroimaging Initiative;
APOE: apolipoprotein E; Aβ: amyloid β; C: complement factor;
CN: cognitively normal; CSF: cerebrospinal fluid; FDR: false discovery rate;
GWAS: genome-wide association studies; HSD: honest significant
difference; IL: interleukin; MAC: membrane attack complex; MCI: mild
cognitive impairment; MINC: Medical Imaging NetCDF; MRI: magnetic
resonance imaging; NFT: neurofibrillary tangle; PET: positron emission
tomography; p-tau: phosphorylated tau; RFT: random field theory;
SNP: single nucleotide polymorphism; SUVR: standardized uptake
The authors declare no competing financial interests.
AB, ALa, JP, and PR participated in the design of this study. ALa and PR
supervised the study. AB and ALa carried out the statistical analysis. PL, AD,
and CP provided support in the genetic analysis. AB, SMa, SMo, MS, and TB
performed the imaging processing and quality control. SMa and TP provided
support in the imaging analysis. AB wrote the paper. ERZ, ALe, SG, and GR
contributed to the revision of the paper. All authors read and approved the
final version of the manuscript.
Data collection and sharing for this project was funded by the Alzheimer’s
Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant
U01 AG024904) and DOD ADNI (Department of Defense award number
W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the
National Institute of Biomedical Imaging and Bioengineering, and through
generous contributions from the following: Alzheimer’s Association; Alzheimer’s
Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.;
Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and
Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company
Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer
Immunotherapy Research & Development, LLC.; Johnson & Johnson
Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck &
Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack
Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal
Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The
Canadian Institutes of Health Research is providing funds to support
ADNI clinical sites in Canada. Private sector contributions are facilitated
by the Foundation for the National Institutes of Health (www.fnih.org).
The grantee organization is the Northern California Institute for Research
and Education, and the study is coordinated by the Alzheimer’s Disease
Cooperative Study at the University of California, San Diego. ADNI data
are disseminated by the Laboratory for Neuro Imaging at the University
of Southern California.
This work was also supported by the Canadian Institutes of Health Research
(CIHR) (MOP-11-51-31), the Alan Tiffin Foundation, the Alzheimer’s
Association (NIRG-08-92090), the Fonds de la recherche en santé du Québec
(chercheur boursier,PRN), and by the CAPES Foundation [0327/13-1]. SG and
PR are members of the CIHR Canadian Consortium of Neurodegeneration
Data used in preparation of this article were obtained from the Alzheimer’s
Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such,
the investigators within the ADNI contributed to the design and
implementation of ADNI and/or provided data but did not participate in the
analysis or writing of this report. A complete listing of ADNI investigators can
be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/
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