Abstract
Developing Alzheimer’s disease (AD) is influenced by multiple genetic
variants that are involved in five major AD-pathways. Per individual,
these pathways may differentially contribute to the modification of the
AD-risk. The pathways involved in the resilience against AD have thus
far been poorly addressed. Here, we investigated to what extent each
molecular mechanism associates with (i) the increased risk of AD and
(ii) the resilience against AD until extreme old age, by comparing
pathway-specific polygenic risk scores (pathway-PRS). We used 29
genetic variants associated with AD to develop pathway-PRS for five
major pathways involved in AD. We developed an integrative framework
that allows multiple genes to associate with a variant, and multiple
pathways to associate with a gene. We studied pathway-PRS in the
Amsterdam Dementia Cohort of well-phenotyped AD patients (N = 1895),
Dutch population controls from the Longitudinal Aging Study Amsterdam
(N = 1654) and our unique 100-plus Study cohort of cognitively healthy
centenarians who avoided AD (N = 293). Last, we estimated the
contribution of each pathway to the genetic risk of AD in the general
population. All pathway-PRS significantly associated with increased
AD-risk and (in the opposite direction) with resilience against AD
(except for angiogenesis, p < 0.05). The pathway that contributed most
to the overall modulation of AD-risk was β-amyloid metabolism (29.6%),
which was driven mainly by APOE-variants. After excluding APOE
variants, all pathway-PRS associated with increased AD-risk (except for
angiogenesis, p < 0.05), while specifically immune response (p = 0.003)
and endocytosis (p = 0.0003) associated with resilience against AD.
Indeed, the variants in these latter two pathways became the main
contributors to the overall modulation of genetic risk of AD (45.5% and
19.2%, respectively). The genetic variants associated with the
resilience against AD indicate which pathways are involved with
maintained cognitive functioning until extreme ages. Our work suggests
that a favorable immune response and a maintained endocytosis pathway
might be involved in general neuro-protection, which highlight the need
to investigate these pathways, next to β-amyloid metabolism.
Subject terms: Clinical genetics, Predictive markers, Diseases
Introduction
Owing to changes in lifestyle and advances in healthcare, life
expectancy has greatly increased during the last century^[44]1. A
consequence of an increased fraction of aged individuals in the
population is the increased prevalence of age-related diseases. A major
contribution to poor health and disability at old age is cognitive
decline due to Alzheimer’s disease (AD)^[45]2. The incidence of AD
increases exponentially with age and reaches ~40% per year at 100
years, making it one of the most prevalent diseases in the
elderly^[46]3. Yet, a small proportion of the population (<0.1%) avoids
the disease, reaching at least 100 years while maintaining a high level
of cognitive health^[47]4.
Both the development and the resilience against AD are determined by a
combination of beneficial and harmful environmental and genetic factors
that is unique for each individual^[48]1,[49]5,[50]6. Thus far, large
collaborative genome-wide association studies (GWAS) have discovered
common genetic variants associated with a small modification of the
risk of AD^[51]7–[52]20. Of these, the alleles that encompass the APOE
gene explain the largest proportion of the risk to develop or the
chance to escape AD. We previously showed that those who avoided
cognitive decline until extreme ages (cognitively healthy centenarians)
were relatively depleted with genetic variants associated with an
increased risk of AD^[53]21. However, the degree of depletion of these
variants in the genomes of cognitively healthy centenarians relative to
the middle-aged healthy individuals was not constant, which might point
towards a differential impact of associated biological pathways on
either avoiding or developing AD. This led us to hypothesize that an
individuals’ chance to develop AD or to being resilient against AD may
be determined by pathway-specific risk.
Previous studies indicated that five specific biological pathways
associate strongly with AD risk: immune response, ß-amyloid metabolism,
cholesterol/lipid dysfunction, endocytosis, and
angiogenesis^[54]22–[55]27. However, the extent to which different
pathways contribute to the polygenic risk of AD is unknown. The degree
to which a pathway contributes to the individual risk can be studied
with pathway-specific polygenic risk scores (PRS)^[56]28,[57]29. In a
typical PRS, the effect-sizes of all genetic variants that
significantly associate with a trait are combined^[58]30. In a
pathway-specific PRS, additional information is necessary: (i) the
association of genetic variants to genes, and (ii) the association of
genes to pathways. Previous studies of pathway-PRS in AD approached
these challenges using the closest gene for variant mapping. For this,
a 1:1 relationship between variants and genes is assumed, however, as
AD-associated variants are mostly intronic or intergenic, the closest
gene is not necessarily the gene affected by the variant. Additionally,
different databases often have different functional annotations of
genes, and this uncertainty was previously not taken into account when
constructing pathway-PRS^[59]28,[60]29.
An accurate mapping of the genetic risk of AD conferred by specific
molecular pathways may lead to a greater comprehension of individual AD
subtypes and might represent a first important step for the development
of targeted intervention strategies and personalized medicine^[61]31.
Here, we propose a novel integrative framework to construct pathway-PRS
for the five major pathways suggested to be involved in AD. We then
tested whether specific pathways differentially contributed to the risk
of AD, as well as to the chance of avoiding AD until extreme old ages.
Finally, we estimated the contribution of each pathway to the polygenic
risk of AD in the general (healthy middle-aged) population.
Methods
Populations
Population subjects are denoted by P: they consist of a representative
Dutch sample of 1779 individuals aged 55–85 years from the Longitudinal
Aging Study Amsterdam (LASA)^[62]32,[63]33. Patients diagnosed with AD
are denoted by A. The patients are either clinically diagnosed probable
AD patients from the Amsterdam Dementia Cohort (N = 1630) or
pathologically confirmed AD patients from the Netherlands Brain Bank
(N = 436)^[64]34–[65]36. Escapers of AD are denoted by C: these are 302
cognitively healthy centenarians from the 100-plus Study cohort. This
study includes individuals who can provide official evidence for being
aged 100 years or older and self-report to be cognitively healthy,
which is confirmed by a proxy^[66]4. All participants and/or their
legal representatives provided written informed consent for
participation in clinical and genetic studies. The Medical Ethics
Committee of the Amsterdam UMC (METC) approved all studies.
Genotyping and imputation
We selected 29 common genetic variants (minor allele frequency >1%) for
which a genome-wide significant association with clinically identified
AD cases was found (Supplementary Table S1)^[67]7–[68]18,[69]37–[70]40.
We genotyped all individuals using Illumina Global Screening Array
(GSAsharedCUSTOM_20018389_A2) and applied established quality control
measures^[71]41. Briefly, we used high-quality genotyping in all
individuals (individual call rate >98%, variant call rate >98%) and
Hardy–Weinberg equilibrium-departure was considered significant at
p < 1 × 10^−6. Genotypes were prepared for imputation using provided
scripts (HRC-1000G-check-bim.pl)^[72]42. This script compares variant
ID, strand and allele frequencies to the haplotype reference panel (HRC
v1.1, April 2016)^[73]35. Finally, all autosomal variants were
submitted to the Michigan imputation server (Error! Hyperlink reference
not valid.). The server uses SHAPEIT2 (v2.r790) to phase data and
imputation to the reference panel (v1.1) was performed with Minimac3.
Variant-genotypes of total of 1779 population subjects, 302
centenarians and 2052 AD cases passed quality control. Prior to
analysis, we excluded individuals of non-European ancestry (N[C] = 2,
N[P] = 63, and N[A] = 94 based on 1000Genomes clustering)^[74]43 and
individuals with a family relation (N[C] = 7, N[P] = 62, and N[A] = 63,
identity-by-descent >0.3), leaving 1654 population subjects, 293
cognitively healthy centenarians, and 1895 AD cases for the analyses.
Polygenic risk score
To calculate the personal PRSs, or the genetic risk of AD that affects
a single individual, the effect-sizes of all genetic variants that
significantly associate with AD are combined. Formally, a PRS is
defined as the sum of trait-associated alleles carried by an individual
across a defined set of genetic loci, weighted by effect-sizes
estimated from a GWAS^[75]30. We constructed a PRS using 29 variants
that were previously associated with AD. As weights for the PRS, we
used the variant effect-sizes (log of odds ratio) as published in large
GWAS of AD (Supplementary Table [76]S1). Given a subject s, the PRS is
defined as:
[MATH: PRSs=
∑KKdosks<
/mi>*βk
, :MATH]
1
where K is the full set of variants,
[MATH: dosks<
/mi> :MATH]
is the allele dosage from the (imputed) genotype of variant k in
subject s and β[k] is the effect-size as determined in the largest
published AD case-control GWAS (Supplementary Table [77]S1).
Mapping variants to pathways
We studied the five pathways implicated in AD: immune response,
ß-amyloid metabolism, cholesterol/lipid dysfunction, endocytosis, and
angiogenesis^[78]22–[79]25,[80]44,[81]45. For these pathways we
developed the variant-pathway mapping
[MATH:
Mpk
:MATH]
, which represents the degree of involvement of a given variant in the
pre-selected pathways. To generate this value, we (i) associated
genetic variants to genes (variant-gene mapping), (ii) associated genes
to pathways (gene-pathway mapping), and (iii) combined these mappings
in the variant-pathway mapping.
Variant-gene mapping: the association of a variant with a specific gene
is not straight-forward as the closest gene is not necessarily the gene
affected by the variant. The two most recent and largest GWAS of AD
addressed the relationship between genetic variants and associated
genes applying two independent methods^[82]19,[83]20. Briefly, one
study used (i) gene-based annotation, (ii) expression-quantitative
trait loci (eQTL) analyses, (iii) gene cluster/pathway analyses, and
(iv) differential gene expression analysis between AD cases and healthy
controls^[84]19. The other study integrated (i) positional mapping,
(ii) eQTL gene-mapping, and (iii) chromatin interaction as implemented
in the tool Functional Mapping and Annotation of Genome-Wide
Association Studies (FUMA)^[85]20,[86]46. The list of genes most likely
affected by each variant was obtained from both studies and used to
derive a weighted mapping for each genetic variant k to one or more
genes g,
[MATH:
mgk
:MATH]
, denoted as the variant-gene-mapping weight. This weight was
calculated by counting the number of times a variant k was associated
with gene g across the two studies and dividing this by the total
number of genes associated with the variant (Supplementary Table
[87]S2). For variants in/near CR1, PILRA, PLCG2, ABCA7 and APOE, we
assumed the culprit gene as known, and we assigned a 1:1 relationship
between the variant and the gene (Supplementary Table [88]S2).
Gene-pathway mapping: each gene from the variant-gene mapping was
classified into the pre-defined set of pathways integrating four
sources of information:
1. Gene-sets from the unsupervised pathway enrichment analysis within
MAGMA statistical framework from Kunkle et al.^[89]19, in which the
authors identified nine significant pathways (coupled with the
genes involved in each pathway), which we mapped to 3 of the 5
pathways of interest (Supplementary Table [90]S3);
2. Associated genes from Gene-ontology (GO, from AmiGO 2 version
2.5.12, released on 2018-04) terms resembling the five pathways of
interest within the biological processes tree (including all
child-terms) (Supplementary Table [91]S4)^[92]47,[93]48;
3. Gene-sets derived from an unsupervised functional clustering
analysis within DAVID (v6.8, released on 2016–10)^[94]49,[95]50:
the gene-set from the variant-gene mapping was used to obtain 12
functional clusters, which were then mapped to the 5 pre-selected
pathways using a set of keywords (Supplementary Tables [96]S5 and
[97]S6);
4. Gene-pathway associations from a recent review concerning the
genetic landscape of AD (Supplementary Table [98]S7)^[99]22;
By counting the number of times each gene was associated to each
pathway according to these sources, and dividing by the total number of
associations per gene, we obtained a weighted mapping of each gene g to
one or more pathways p,
[MATH:
wpg
:MATH]
, denoted as the gene-pathway-mapping weight (Supplementary Tables
[100]S8 and [101]S9). In case the gene-pathway mapping could not be
calculated (i.e., there was no mapping to any of the pathways of
consideration), we excluded the gene from further analyses
(Supplementary Tables [102]S8 and [103]S9).
To associate variants with pathways, we combined the variant-gene
mapping and the gene-pathway mapping. Given a variant k, mapping to a
set of genes G, and a pathway p, we define the weight of the variant to
the pathway (
[MATH:
Mpk
:MATH]
) as:
[MATH:
Mp
k=∑gGmg<
mrow>k*wpg, :MATH]
2
where
[MATH:
mgk
:MATH]
is the variant-gene-mapping weight of variant k to gene g, and
[MATH:
wpg
:MATH]
is the gene-pathway-mapping weight of gene g to pathway p. In this way,
for each variant, we calculated a score indicative of the involvement
of the variant in each of the five pathways (variant-pathway mapping,
Supplementary Table [104]S10). For some variants no
variant-pathway-mapping was possible. We marked these variants as
unmapped (Supplementary Table [105]S10).
Pathway-specific PRS
For the pathway-specific PRS (pPRS), we extended the definition of the
PRS by adding as multiplicative factor the variant-pathway-mapping
weight of each variant. Given a sample s and a pathway p, we defined
the pPRS as:
[MATH: pPRSps
=∑kKdosks<
/mi>*βk
*M
kp, :MATH]
3
where
[MATH:
Mkp
:MATH]
is the variant-pathway mapping of variant k to pathway p.
Association of PRSs in the three cohorts
We calculated the PRS and pathway-PRS (pPRS) for the population
subjects, the AD cases and the cognitively healthy centenarians (P, A,
and C, respectively). Prior to analyses, the PRSs of all three
populations were combined together and were scaled (mean = 0, SD = 1).
We then investigated the influence of APOE, gender and age on the risk
scores: we calculated the PRSs and pPRSs with and without the two APOE
variants and we correlated the resulting (p)PRSs with sex, age (age at
inclusion for controls, age at onset for cases), and population
substructure components. To inspect the differential contributions of
the risk scores to AD development or resilience against AD, we
calculated (i) the association of the risk scores (PRS and pPRS) with
AD status by comparing AD cases and population subjects (A vs. P), and
(ii) the association of the risk scores with resilience against AD by
comparing cognitively healthy centenarians and population subjects (C
vs. P comparison). For the associations, we used logistic regression
models with the PRS and pPRS as predictors, adjusting for population
substructure (principal components 1–5). Resulting effect-sizes (log of
odds ratio) can be interpreted as the odds ratio difference per one
standard deviation (SD) increase in the PRS, with a corresponding
estimated 95% confidence intervals (95% CI). Association analyses of
the (p)PRS in the three population were also stratified by sex. Last,
we verified the classification performances of the single variants, as
well as the (p)PRS by calculating the area under the ROC curve for
classification of AD and resilience against AD.
Comparison of effect-size between resilience against AD and increased AD-risk
To further investigate the relationship between the effect of each
pathway on AD and on resilience against AD, we calculated the change in
effect-size. This corresponds to the ratio between the effect-size of
the association with resilience against AD (log of odds ratios of C vs.
P comparison) and the effect-size of the association with AD (log of
odds ratios of A vs. P comparison). We calculated the change in
effect-size for the pPRS, including and excluding APOE variants. We
estimated 95% confidence intervals for the effect-size ratios by
sampling, and we tested for significant difference between the change
in effect-size, including and excluding APOE variants (respectively,
for each of pPRS) using t-test. A value for the change in effect-size
of 1 indicates a similar effect on increased risk of AD and resilience
against AD. Although a value for the change in effect-size unknown a
priori, since all variants considered are selected to be associated
with AD, a value <1 is expected (i.e., a larger effect on AD than on
resilience against AD).
Contribution of each pathway to polygenic risk of AD
We estimated the contribution of each pathway to the genetic risk of AD
in the general population: this equals to the variance explained by
each of the pre-selected pathways to the genetic risk of AD.
Mathematically, this is the ratio between the variance of each
pathway-PRS and the variance of the combined PRS as calculated in the
individuals in the general population. As such, it is a function of the
variant-pathway mapping, the effect-size (log of odds ratio) of the
variants, and the variant frequencies. Given a variant k and the
relative variant-pathway mapping
[MATH:
Mkp
:MATH]
, we define the percentage of the risk explained by each pathway p as:
[MATH:
Pp=<
/mo>∑kKMk<
mrow>p*βk2*MAFk*
1−MAFk∑kKβk<
mrow>2*MAFk*
1−MAFk, :MATH]
4
where β[k] is the variant effect-size from literature, and
[MATH: MAFk*
1−MAFk :MATH]
is the variance of a Bernoulli random variable that occurs with
probability MAF[k], i.e., the minor allele frequency of each variant k
in our cohort of population subjects. Here,
[MATH:
Mkp
:MATH]
is interpreted as the probability that variant k belongs to pathway p.
Importantly, for each variant,
[MATH:
∑P
PMk
p=1
:MATH]
, so that each variant contributes equally, yet differentially at the
level of each pathway. This means that the variance of a variant is
only counted once, even if the variant contributes to multiple
pathways. When calculating the contributions of each pathway, we also
considered variants with missing variant-pathway mapping. For these
variants, the variant-pathway mapping was set to 1 for an unmapped
pathway. Altogether, the pathway-PRS variances sum to the total PRS
variance.
Implementation
We performed quality control of genotype data, as well as population
stratification analysis and relatedness analysis with PLINK (v2.0). All
subsequent analyses were performed with R (v3.5.2), Bash and Python
(v2.7.14) scripts. We provide a R script to construct pPRS and PRS
using our variant-pathway annotation and user’s genotypes. In addition,
all the scripts we used to perform the analyses can be found at
[106]https://github.com/TesiNicco/pathway-PRS.
Results
After quality control of the genetic data, we included 1654 population
subjects (with mean age at inclusion 62.7 ± 6.4, 53.2% females), 1895
AD cases (with mean age at onset 69.2 ± 9.9, 56.4% females), and 293
cognitively healthy centenarians (with mean age at inclusion
101.4 ± 1.3, 72.6% females) (P, A, and C, respectively).
PRSs associate with AD and escape from AD
To each subject, we assigned a PRS representative of all 29
AD-associated variants, including and excluding APOE variants. We found
that the PRS, when including APOE variants, significantly associated
with an increased risk of AD and, in the opposite direction, with
increased chance of resilience against AD (A vs. P: OR = 2.61, 95%
CI = [2.40–2.83], p = 8.4 × 10^−113 and C vs. P: OR = 0.54, 95%
CI = [0.45–0.65], p = 1.1 × 10^−10) (Fig. [107]1a and Supplementary
Table [108]S11). When excluding APOE variants, the PRS was still
significantly associated with an increased risk of AD and, in the
opposite direction, with increased risk of resilience against AD (A vs.
P: OR = 1.30, 95% CI = [1.22–1.40], p = 3.1 × 10^−14 and C vs. P:
OR = 0.78, 95% CI = [0.69–0.89], p = 2.4 × 10^−4) (Fig. [109]1b and
Supplementary Table [110]S11).
Fig. 1. Boxplots of PRS and pPRS in the different settings.
[111]Fig. 1
[112]Open in a new tab
a (above) shows the PRS including all the 29 known AD-associated
variants, with and without APOE variants. As weight for the PRS, we
used published variant effect-sizes (Supplementary Table [113]S1). b
(central) and c (bottom) show the pPRS for each of the selected
molecular pathways, including and excluding APOE variants,
respectively. For all plots, risk scores were calculated for AD cases,
population subjects, and cognitively healthy centenarians. Then, risk
scores were compared between (i) AD cases and population subjects (A
vs. P comparison) and (ii) cognitively healthy centenarians and
population subjects (C vs. P comparison). For representation, we scaled
all PRS and pathway-PRS to be mean = 0 and SD = 1. For the comparison,
we used logistic regression models with risk scores as predictors.
Annotation: ***p-value of association < 5 × 10^−6; **p-value of
association < 5 × 10^−4; *p-value of association < 5 × 10^−2.
Pathway-specific PRS associate with AD and escape from AD
We annotated the 29 AD-associated genetic variants to 5 selected
pathways (Fig. [114]2). According to our variant-gene mapping, the 29
AD-associated variants mapped to 110 genes (Supplementary Table
[115]S8). The number of genes associated with each variant ranged from
1 (e.g., for variants in/near CR1, PILRA, SORL1, ABCA7, APOE, or
PLCG2), to 30 (a variant in the gene-dense region within the HLA
region) (Fig. [116]2 and Supplementary Table [117]S8). We were able to
calculate the gene-pathway-mapping weight for 69 genes (Supplementary
Table [118]S9). The remaining 41 genes were not mapped to the 5
pathways. In total, we calculated the variant-pathway mapping for 23
loci to at least one of the pre-selected biological pathways (Fig.
[119]2 and Supplementary Table [120]S10).
Fig. 2. Variant-pathways mapping.
[121]Fig. 2
[122]Open in a new tab
Locus: chromosome and position of the AD-associated genetic variants
(coordinates are with respect to GRCh37). N. genes: total number of
genes associated with each variant according to variant-gene mapping.
Variant-gene mapping: Genes: all genes with at least one annotation to
the five selected molecular pathways associated with AD. Weight: the
weight of the variant-gene mapping. Gene-pathway mapping: immune
response, beta-amyloid, endocytosis, cholesterol/lipid, angiogenesis:
the weight of each molecular pathway at the gene level. Variant-pathway
mapping: summarization of each variant’s effect after combining
variant-gene and gene-pathway mappings. Red crosses indicate unmapped
genes.
We then calculated the pPRS for each pathway in population subjects, AD
cases and cognitively healthy centenarians, including and excluding
APOE variants (Fig. [123]1b, c). The number of variants that
contributed to each pPRS was 19 for immune response, 11 for β-amyloid
metabolism, 19 for endocytosis, 8 for cholesterol/lipid dysfunction,
and 4 for angiogenesis pathways (Supplementary Tables [124]S10 and
[125]S11). Overall, the pPRS (including and excluding the APOE
variants) positively and significantly correlated with each other and
with the overall PRS (Supplementary Fig. [126]S1), and did not
correlate with gender and age (Supplementary Fig. [127]S1).
When including APOE variants, the pPRSs of all pathways (except for
angiogenesis) significantly associated with increased risk of AD,
independently from gender (A vs. P, immune response: OR = 2.15, 95%
CI = [1.99–2.32], p = 2.0 × 10^−80; β-amyloid metabolism: OR = 2.52,
95% CI = [2.32–2.73], p = 7.8 × 10^−109; endocytosis: OR = 2.55, 95%
CI = [2.35–2.77], p = 1.7 × 10^−109; cholesterol/lipid dysfunction:
OR = 2.55, 95% CI = [2.35–2.76], p = 2.1 × 10^−110; angiogenesis:
OR = 1.05, 95% CI = [0.98–1.12], p = 0.134) (Fig. [128]1b,
Supplementary Table [129]S11, Supplementary Fig. [130]S2, and
Supplementary Table [131]S12). The association of pPRSs with increased
chance of being resilient against AD was in the opposite direction for
all pathways, and the association was significant for all pathways
except for angiogenesis (C vs. P, immune response: OR = 0.64, 95%
CI = [0.54–0.74], p = 1.4 × 10^−8; β-amyloid metabolism: OR = 0.59, 95%
CI = [0.49–0.71], p = 2.7 × 10^−8; endocytosis: OR = 0.55, 95%
CI = [0.46–0.66], p = 1.3 × 10^−10; cholesterol/lipid dysfunction:
OR = 0.58, 95% CI = [0.48–0.70], p = 1.8 × 10^−8; angiogenesis:
OR = 0.90, 95% CI = [0.79–1.01], p = 0.078) (Fig. [132]1b and
Supplementary Table [133]S11). Directions of effects were consistent in
both males and females, but the significance of associations was
reduced due to stratification (Supplementary Table [134]S12 and
Supplementary Fig. [135]S2).
When excluding APOE variants, the pPRSs of all pathways (except for the
angiogenesis) was still significantly associated with increased risk of
AD without specific gender effects (A vs. P, immune response:
OR = 1.19, 95% CI = [1.11–1.27], p = 5.5 × 10^−7; β-amyloid metabolism:
OR = 1.19, 95% CI = [1.12–1.28], p = 2.0 × 10^−7; endocytosis:
OR = 1.27, 95% CI = [1.19–1.36], p = 2.8 × 10^−12; cholesterol/lipid
dysfunction: OR = 1.18, 95% CI = [1.11–1.27], p = 7.5 × 10^−7;
angiogenesis: OR = 1.05, 95% CI = [0.98–1.12], p = 0.134) (Fig.
[136]1c, Supplementary Table [137]S11, Supplementary Fig. [138]S2, and
Supplementary Table [139]S12). The association of pPRSs with increased
chance of being resilient against AD was in the opposite direction for
all pathways, yet the association was significant only for the immune
response and the endocytosis pPRS (C vs. P, immune response: OR = 0.82,
95% CI = [0.72–0.94], p = 0.003; β-amyloid metabolism: OR = 0.91, 95%
CI = [0.80–1.03], p = 0.131; endocytosis: OR = 0.79, 95%
CI = [0.70–0.90], p = 2.8 × 10^−4; cholesterol/lipid dysfunction:
OR = 0.91, 95% CI = [0.80–1.03], p = 0.145; angiogenesis: OR = 0.90,
95% CI = [0.79–1.01], p = 0.078) (Fig. [140]1c and Supplementary Table
[141]S11).
In the sex-stratified analysis, females reported consistent direction
of effects and significant associations of immune response and
endocytosis pathways, while in males the direction was consistent for
immune response, endocytosis and angiogenesis pathways, and it was
opposite for β-amyloid metabolism and cholesterol/lipid dysfunction
(yet not significant) (Supplementary Fig. [142]S2 and Supplementary
Table [143]S12).
We note that apart from APOE variants (for which we stratified the
analyses for), there was no major driver in the pPRS, as well as the
single-variant associations (Supplementary Figs. [144]S3 and [145]S4).
Comparison of effect on AD and escaping AD
To further evaluate the association of the pPRSs with AD and with
resilience against AD, we compared, for each pPRS, the reciprocal
effect-size associated with resilience against AD with the effect-size
associated with increased risk of AD (change in effect-size, Fig.
[146]3a). When including APOE variants, the change in effect-size was
<1 for all pathways (except for the angiogenesis pathway) (Fig.
[147]3b). This is expected as the effect-size of APOE variants on
causing AD is much larger than its effect on resilience against AD
(Fig. [148]3a). When excluding APOE variants, the change in effect-size
was still <1 for β-amyloid metabolism and cholesterol/lipid metabolism
(respectively, 0.54 and 0.58), but it approximated 1 for endocytosis
(0.96) and it was larger than 1 for the immune response and
angiogenesis (respectively, 1.12 and 2.15) (Fig. [149]3b).
Interestingly, we found that the relative effect-size for immune
response and endocytosis excluding APOE variants was significantly
higher than that including APOE variants (p < 2.1 × 10^−197 and
p < 8.9 × 10^−180, respectively), suggesting a larger effect on
resilience against AD compared to AD-risk for these pathways,
specifically when excluding APOE variants (Fig. [150]3b).
Fig. 3. Change in effect-size between association with escaping AD and
causing AD for the five pPRSs.
[151]Fig. 3
[152]Open in a new tab
a shows the effect-sizes (log of odds ratio) and the relative 95%
confidence intervals of the association of the (p)PRS with both AD-risk
and resilience against AD, grouped by pathway. In b, each bar
represents the ratio between the effect-size of the association with
escaping AD (Resilience effect in a) and with causing AD (Risk effect
in a), respectively, with and without APOE variants. Ratios larger than
1 are then indicative of larger effect-size on resilience against AD
compared to AD-risk. We then compared the change in effect-size for
each pathway when including and excluding APOE variants using t-tests.
Annotation: ***p-value of association < 5 × 10^−6; **p-value of
association < 5 × 10^−4; *p-value of association < 5 × 10^−2.
Contributions of each pathway to the polygenic risk of AD
Finally, we estimated the relative contribution of each pathway to the
polygenic risk of AD in the general population. This is indicative of
the degree of involvement of each pathway to the total polygenic risk
of AD, and as such it is based on our variant-pathway mapping.
Including APOE variants, the contribution of the pathways to the total
polygenic risk of AD was 29.6% for β-amyloid metabolism, 26.6% for
immune response, 21.6% for endocytosis, 19.5% for cholesterol/lipid
dysfunction, 0.3% for angiogenesis, and 2.3% for the unmapped variants
(Fig. [153]4a).
Fig. 4. Explained variance of each pathway-specific PRS to polygenic risk of
AD.
[154]Fig. 4
[155]Open in a new tab
The pie charts represents the explained variance of each
pathway-specific PRS to the polygenic risk of AD, including and
excluding APOE variants. The contributions are calculated according to
(i) our variant-pathway mapping, (ii) the effect-size (log of odds
ratio) of each variant from literature (Supplementary Table [156]S1),
and (iii) variant’s frequency in our cohort of middle-aged healthy
population subjects. We also considered variants with missing
variant-pathway mapping (unmapped pathway).
When we excluded APOE variants, the contribution of the pathways to the
total polygenic risk of AD was 45.5% for immune response, 19.2% for
endocytosis, 13.7% for ß-amyloid metabolism, 8% for cholesterol/lipid
dysfunction, 1.4% for angiogenesis and 12.3% for the unmapped variants
(Fig. [157]4b).
Discussion
In this work, we studied 29 common genetic variants known to associate
with AD using PRSs and pathway-specific PRSs. As expected, we found
that a higher PRS for AD was associated with a higher risk of AD.
Previous studies showed that PRS of AD not only associated with
increased risk of AD, but also with neuropathological hallmarks of AD,
lifetime risk and the age at onset in both APOE ε4 carriers and
non-carriers^[158]28,[159]29,[160]51–[161]55. We now add that, using
our unique cohort of cognitively healthy centenarians, the PRS for AD
also associates with resilience against AD at extremely old ages. This
adds further importance to the potentiality of using PRS and APOE
genotype in a clinical setting^[162]51,[163]52,[164]54,[165]56. In
addition, our analyses suggest that the long-term preservation of
cognitive health is associated with the selective survival of
individuals with the lowest burden of risk-increasing variants or, vice
versa, the highest burden of protective variants.
Using an innovative approach, we studied five pathways previously found
to be involved in AD, as well as the contribution of these pathways to
the polygenic risk of AD. We showed that all pathways-PRS except
angiogenesis associate with increased AD risk, both including and
excluding APOE variants and independently from gender. When we studied
the association of pathways-PRS with resilience against AD until
extreme old ages, we found that, as expected, the enrichment of the
protective APOE ε2 allele and the depletion of the risk-increasing APOE
ε4 allele represented a major factor in avoiding AD. However, when
excluding the two APOE variants, only immune response and endocytosis
significantly associated with an increased chance to be resilient
against AD. Interestingly, both pathways had a larger or similar effect
on resilience against AD-resilience compared to developing AD,
suggesting that these pathways might be involved in general
neuro-protective functions. Based on the variant effect-size, variant
frequency and our variant-pathway mapping, we found that the ß-amyloid
metabolism (29.6%) followed by immune response (26.6%) were the major
contributors to general modification of AD-risk. After excluding APOE
variants, according to our analysis, immune response (45.5%) and
endocytosis (19.2%) contributed most to the modification of AD-risk.
Our approach to map variants to associated genes and to map genes to
pathways resulted in a weighted annotation of variants to pathways that
allowed for uncertainty in gene as well as pathway assignment, which
was not done previously. We note that considering uncertainty in
variant-gene as well as gene-pathway assignments is crucial because
most genetic variants are in non-coding regions, which makes the
closest gene not necessarily the culprit gene, and because different
functional annotation sources often do not overlap. In our
variant-pathway mapping, a larger number of annotations (both
variant-genes and gene-pathways), generally causes a dilution of the
“true” variant effect, reflecting increasing uncertainty in the
annotation sources used. This depends on the specific regions, for
example, the HLA region carries many genes with large linkage signals,
however, all genes in this region are typically annotated with immune
response. We point out that the power of the PRSs does not only reflect
the effect-size of the variants, but also the number and frequency of
the variants that contribute to the PRSs: due to this, a larger number
of very common variants with relatively small effect-size can still
have more power (yet small ORs) than a small number of relatively rare
variants with high effect-size. The pathway-specific PRS that we
proposed in this manuscript can be re-used for the identification of
subtypes of AD patients compromised in a specific AD-associated
pathway. This is of interest for clinical trials, in order to test
responsiveness to compounds in specific subsets of patients. For
example, monoclonal antibody targeting TREM2 receptors could work
better in AD patients who have an impaired immune response pathway.
Recently, several studies attempted to construct pathway-specific PRS
to find heterogeneity in AD patients based on a genetic
basis^[166]28,[167]29. In line with our findings, Ahmad et al.^[168]29
found that genes capturing endocytosis pathway significantly associated
with AD and with the conversion to AD. Other studies used less
variants^[169]28 or less stringent selection for variants, and did not
observe a differential involvement of pathways in AD etiology^[170]57.
The amyloid cascade hypothesis has been dominating AD-related research
in the last two decades. However, treatments targeting amyloid have, so
far, not been able to slow or stop disease progression. This has led to
an increased interest for the other pathways that are important in AD
pathogenesis^[171]22. Part of the current view of the etiology of AD is
that the dysregulation of the immune response is a major causal
pathway, and that AD is not only a consequence of β-amyloid
metabolism^[172]58,[173]59. In addition, previous studies showed that
healthy immune and metabolic systems are associated with longer and
healthier lifespan^[174]1,[175]60. Our results indicate that, excluding
APOE variants, the effect of immune response and endocytosis on
escaping AD is stronger or comparable to the effect on causing AD. This
suggests that these pathways might be involved in the maintenance of
general cognitive health, as the cognitively healthy centenarians
represent the escape of all neurodegenerative diseases until extreme
ages. We recently found evidence for this hypothesis in the protective
low-frequency variant in PLCG2, which is involved in the regulation of
the immune response^[176]53. This variant is enriched in cognitively
healthy centenarians, and protects against AD, as well as
frontotemporal dementia and dementia with Lewy bodies^[177]53. We
included this variant in the total PRS, as well as in the pathway-PRS
for the immune response (variant-pathway mapping was 60%) and
endocytosis (variant-pathway mapping was 40%). Regarding endocytosis,
this pathway is thought to play a role both in neurons, as part of the
β-amyloid metabolism, but also in glia cells, as part of the immune
response. Thus, a dysregulation in the interplay between these pathways
might lead to an imbalance of immune signaling factors, favoring the
engulfment of synapses and AD-associated processes. This, in turn, may
contribute to the accumulation of amyloid and tau
pathologies^[178]61–[179]64.
We assessed the effect of common and low-frequency variants on the
development and the escape of AD. Therefore, the contributions of rare,
causative variants associated with increased AD risk, such as those in
APP, PSEN1, PSEN2, TREM2, and SORL1 were not considered. Despite the
large odds ratios to develop AD associated with carrying such variants,
the frequency of these variants in the population is ultra-low, and
therefore have a minor effect on the total AD risk in the
population^[180]11,[181]12. However, future versions of the PRS will
most likely include the effect of carrying disease-associated rare
variants. This will affect individual PRS scores and the necessity to
accordingly adapt the results generated with current PRSs. Compared to
the sizes of recent GWAS of AD, we included relatively small sample
sizes, particularly with respect to the cognitively healthy
centenarians, a very rare phenotype in the population (<0.1%)^[182]4.
These sample sizes are, however, sufficient to study PRSs. The cohorts
that we used in this study were not used in any GWAS of AD, therefore
we provide independent replication of AD PRS in a homogeneous group of
(Dutch) individuals.
We note that, apart from APOE variants (for which we stratify the
analyses for), none of the other variants have been associated with
longevity or well cognitive functioning in the largest and most recent
GWAS^[183]65,[184]66. We acknowledge that our variant-pathway mapping
reflects the current state of imperfect knowledge at the level of
AD-GWAS findings, variant-gene and gene-mechanism relationships. Thus,
as new variants, pathways or functional relationships will be
identified, the contributions and the pathway-specific PRSs will need
to be recalculated. Of note: the study in which the genome-wide
significant association with AD of the variant in/near KANSL1 was
originally identified, reported a larger effect-size compared to the
effect-size used in our manuscript (β = 0.31 and β = 0.07,
respectively), possibly because the original analysis was stratified by
APOE. We cannot exclude that we underestimated the contribution of
KANSL1 in the analyses. Moreover, since the KANSL1 variant did not map
into one of the analyzed pathways, it was not included in any of the
pathway-specific PRS calculations. A limitation, not exclusive to our
work, is the highly debated role of APOE gene. We mapped the effect of
APOE to four pathways and we are aware this assignment is relatively
arbitrary. We add that APOE has well-studied (cardio)vascular
properties that are included in our cholesterol and lipid metabolism
pathway. The combination of a large effect and unclear pathway
assignment makes that pathway-PRS, including APOE challenging to use.
Lastly, we point out that the variance contributions might change in
different populations, as it depends on variant frequency and
population heterogeneity.
Concluding, with the exclusion of APOE variants and based on our
functional annotation of variants, the aggregate contribution of the
immune response and endocytosis represents >60% of the currently known
polygenic risk of AD. This indicates that an intervention in these
systems may have large potential to prevent AD and potentially other
related diseases and highlights the critical need to study
(neuro)immune response and endocytosis, next to β-amyloid metabolism.
Supplementary information
[185]Supplementary Tables^ (79.9KB, xlsx)
[186]Supplementary Figures^ (2.5MB, pdf)
[187]41398_2020_1018_MOESM3_ESM.xlsx^ (535.5KB, xlsx)
Genotypes of the 29 AD-associated variants
Acknowledgements