Abstract
Because regulation of gene expression is heritable and
context-dependent, we investigated AD-related gene expression patterns
in cell types in blood and brain. Cis-expression quantitative trait
locus (eQTL) mapping was performed genome-wide in blood from 5257
Framingham Heart Study (FHS) participants and in brain donated by 475
Religious Orders Study/Memory & Aging Project (ROSMAP) participants.
The association of gene expression with genotypes for all cis SNPs
within 1 Mb of genes was evaluated using linear regression models for
unrelated subjects and linear-mixed models for related subjects.
Cell-type-specific eQTL (ct-eQTL) models included an interaction term
for the expression of “proxy” genes that discriminate particular cell
type. Ct-eQTL analysis identified 11,649 and 2533 additional
significant gene-SNP eQTL pairs in brain and blood, respectively, that
were not detected in generic eQTL analysis. Of note, 386 unique target
eGenes of significant eQTLs shared between blood and brain were
enriched in apoptosis and Wnt signaling pathways. Five of these shared
genes are established AD loci. The potential importance and relevance
to AD of significant results in myeloid cell types is supported by the
observation that a large portion of GWS ct-eQTLs map within 1 Mb of
established AD loci and 58% (23/40) of the most significant eGenes in
these eQTLs have previously been implicated in AD. This study
identified cell-type-specific expression patterns for established and
potentially novel AD genes, found additional evidence for the role of
myeloid cells in AD risk, and discovered potential novel blood and
brain AD biomarkers that highlight the importance of cell-type-specific
analysis.
Subject terms: Psychiatric disorders, Genomics
Introduction
Recent expression quantitative trait locus (eQTL) analysis studies
suggest that changes in gene expression have a role in the pathogenesis
of AD^[36]1,[37]2. However, regulation of gene expression, as well as
many biological functions, has been shown to be context-specific (e.g.,
tissue and cell types, developmental time point, sex, disease status,
and response to treatment or stimulus)^[38]3–[39]6. One study of 500
healthy subjects found over-representation of T cell-specific eQTLs in
susceptibility alleles for autoimmune disease and AD risk alleles
polarized for monocyte-specific eQTL effects^[40]7. In addition,
disease and trait-associated cis-eQTLs were more cell-type-specific
than average cis-eQTLs^[41]7. Another study classified 12% of more than
23,000 eQTLs in blood as cell-type-specific^[42]4. Large eQTL studies
across multiple human tissues have been conducted by the GTEx
consortium, with a study on genetic effects on gene expression levels
across 44 human tissues collected from the same donors characterizing
patterns of tissue specificity recently published^[43]8.
Microglia, monocytes, and macrophages share a similar developmental
lineage and are all considered to be myeloid cells^[44]9. It is
believed that a large proportion of AD genetic risk can be explained by
genes expressed in myeloid cells and not other cell types^[45]10.
Several established AD genes are highly expressed in
microglia^[46]9,[47]11, and a variant in the AD-associated gene CELF1
has been associated with lower expression of SPI1 in monocytes and
macrophages^[48]10. AD risk alleles have been shown to be enriched in
myeloid-specific epigenomic annotations and in active enhancers of
monocytes, macrophages, and microglia^[49]12, and to be polarized for
cis-eQTL effects in monocytes^[50]7. These findings suggest that a
cell-type-specific analysis in blood and brain tissue may identify
novel and more precise AD associations that may help elucidate
regulatory networks. In this study, we performed a genome-wide cis
ct-eQTL analysis in blood and brain, respectively, then compared eQTLs
and cell-type-specific eQTLs (ct-eQTLs) between brain and blood with a
focus on genes, loci, and cell types previously implicated in AD risk
by genetic approaches.
Materials, subjects and methods
Study cohorts
Framingham Heart Study (FHS)
The FHS is a multigenerational study of health and disease in a
prospectively followed community-based and primarily non-Hispanic white
sample. Procedures for assessing dementia and determining AD status in
this cohort are described elsewhere^[51]13. Clinical, demographic, and
pedigree information, as well as 1000 Genomes Project Phase 1 imputed
SNP genotypes and Affymetrix Human Exon 1.0 ST array gene expression
data from whole blood, were obtained from dbGaP
([52]https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_
id=phs000007.v31.p12). Requisite information for this study was
available for 5257 participants. Characteristics of these subjects are
provided in Supplementary Table [53]S1.
Religious Orders Study (ROS)/Memory and Aging Project (MAP)
ROS enrolled older nuns and priests from across the US, without known
dementia for longitudinal clinical analysis and brain donation and MAP
enrolled older subjects without dementia from retirement homes who
agreed to brain donation at the time of death^[54]14
([55]http://www.eurekaselect.com/99959/article). RNA-sequencing brain
gene expression and whole-genome sequencing (WGS) genotype data were
obtained from the AMP-AD knowledge portal
([56]https://www.synapse.org/#!Synapse:syn3219045)
([57]https://www.synapse.org/).
Data processing
Generation, initial quality control (QC), and pre-processing procedures
of the FHS GWAS and expression data are described elsewhere^[58]13.
Briefly, the Robust Multichip Average (RMA) method^[59]15,[60]16 was
used for background adjustment and normalization of gene expression
levels and further adjusted for the first principal component of
ancestry. ROSMAP gene expression data were log-normalized and adjusted
for known and hidden variables detected by surrogate variable analysis
(SVA)^[61]17 in order to determine which of these variables should be
included as covariates in analysis models for eQTL discovery.
Additional filtering steps of FHS and ROSMAP GWAS and gene expression
data included eliminating subjects with missing data, restricting gene
expression data to protein-coding genes, and retaining common variants
(MAF ≥ 0.05) with good imputation quality (R^2 ≥ 0.3).
Cis-eQTL mapping
Cis-eQTL mapping was performed using a genome-wide design
(Supplementary Fig. [62]S1). The association of gene expression with
SNP genotypes for all cis SNPs within 1 Mb of protein-coding genes was
evaluated using linear-mixed models adjusting for family structure in
FHS and linear regression models for unrelated individuals in ROSMAP.
In FHS, lmekin function in the R kinship package (version 1.1.3)^[63]18
was applied assuming an additive genetic model with covariates for age
and sex, and family structure modeled as a random-effects term for
kinship—a matrix of kinship coefficients calculated from pedigree
structures. The linear model for analysis of FHS can data be expressed
as follows:
[MATH:
Yi=<
/mo>I+β1Gj
+β2Aij
+β3
Sij+Uij
+εij :MATH]
where Y[i] is the expression value for gene i, G[j] is the genotype
dosage for cis SNP j, Aij and S[ij] are the covariates for age and sex
respectively, U[ij] is the random effect for family structure, and
β[1], β[2], and β[3] are regression coefficients.
ROSMAP data were analyzed using the lm function in the base stats
package in R ([64]http://www.R-project.org/). The regression model,
which included covariates for age, sex, postmortem interval (PMI),
study (ROS or MAP), and a term for a surrogate variable (SV1) derived
from analysis of high dimensional data, can be expressed as:
[MATH:
Yi=<
/mo>I+β1Gj
+β2Aij
+β3
Sij+β3Sij+β4PMij+<
/mo>β5ijS2+β
6ijSV<
mn>1+εij :MATH]
where Y[i] is the expression value for gene i, G[j] is the genotype
dosage for cis SNP j, Aij, S[ij], PM[ij], S2[ij], and SV1[ij] are the
covariates for age, sex, PMI, study, and SV1, respectively, ɛ[ij] is
the residual error, and the βs are regression coefficients.
Cis ct-eQTL mapping
Models testing associations with cell-type-specific eQTLs (ct-eQTLs)
included an interaction term for expression levels of “proxy” genes
that represent cell types. Proxy genes representing ten cell types in
whole blood^[65]4 and five cell types in brain^[66]19–[67]21 were
incorporated in cell-type-specific models (Supplementary Table [68]S2).
These proxy genes for cell types in blood were established previously
using BLUEPRINT expression data to validate cell-type-specific
expression in each cell-type module^[69]4 and the proxy genes for brain
cell types have been incorporated in several studies^[70]19–[71]21.
Cell-type-specific expression analyses in blood of FHS participants
were conducted using the following model:
[MATH:
Yi=<
/mo>I+β1Gj
+β2P+β3(P
*
Gj)+β4<
/msub>Aij+β5Sij
+Uij+εij :MATH]
where in each eQTL[ij] pair, Y[i] is the eQTL expression value for gene
i, G[j] is the genotype dosage for cis SNP j, P is the proxy gene,
P * G[j] is the interaction term representing the effect of genotype in
a particular cell type, Aij and S[ij] are covariates for age and sex,
respectively, U[ij] is the random effect for family structure, and βs
are regression coefficients. Models with significant interaction terms
indicate cell-type-specific eQTLs.
The following model was used to evaluate cell-type-specific expression
in the brain in ROSMAP:
[MATH:
Yi=<
/mo>I+β1Gj
+β2P+β3(P
*
Gj)+β4<
/msub>Aij+β5Sij
+β6PMij<
/msub>+β7i
jS2+β
8ijSV1+εi
j :MATH]
where in each eQTL[ij] pair, variables Y[i], G[j], P, Aij, S[ij],
P[ij], ɛ[ij], and βs are as described above, and PM[ij], S2[ij], and
SV1[ij] are covariates for PMI, study, and SV1, respectively.
A Bonferroni correction was applied to determine the significance
threshold for each analysis (Supplementary Table [72]S3).
We assessed the relevance of the significant findings more directly to
AD in two ways. In one approach, AD status was included as a covariate
in the eQTL and ct-eQTL analysis models. In addition, the significant
eQTLs and ct-eQTLs were evaluated separately in AD cases and controls
separately in the ROSMAP brain expression dataset, but not in the FHS
blood expression dataset due to the paucity of AD cases (2%) in that
sample.
Selection of eQTLs in AD loci and gene-set pathway enrichment analysis
AD loci were determined based on the review of published GWAS and
linkage studies of AD and AD-related traits, and this list was
augmented with genes that are well recognized as functionally related
to AD by experimental approaches (Supplementary Table [73]S4). AD genes
identified by GWAS met genome or study-wide significance thresholds and
some of these were annotated as the closest gene to an intergenic
association signal. eGenes (genes whose expression levels are
associated with variation at a particular eSNP) included 88 genes and
80 eSNPs (no SNPs that significantly influence gene expression) which
include genome-wide significant “peak” SNPs (i.e., top-ranked SNP
within an association signal) for AD. Gene-set enrichment analysis was
performed using the PANTHER (Protein ANalysis THrough Evolutionary
Relationships) software tool^[74]22 to determine if the unique genes in
the significant eQTL/ct-eQTL pairs shared by both brain and blood
datasets are associated with a specific biological process or molecular
function. The significance of the pathways was determined by the
Fisher’s Exact test with false discovery rate (FDR) multiple test
correction.
Colocalization analyses
Assessment of causal variants shared by adjacent GWAS and eQTL signals
was performed using a Bayesian colocalization approach implemented in
the R package coloc^[75]23. This analysis incorporated SNP summary
statistics from a recent large AD GWAS^[76]24 and eQTL analyses
described above. For the purpose of this study, a peak SNP refers to
the most significantly associated AD-SNPs under a particular GWAS
signal and a lead eQTL variant is defined as the eSNP showing the
strongest association with gene expression. Following recommended
guidelines, the variants were deemed to be colocalized by a high
posterior probability that a single shared variant is responsible for
both signals (PP4 > 0.8)^[77]23,[78]25. A lower threshold for
statistical significance with a false discovery rate (FDR) < 0.05 for
eQTL significant results was applied to maximize detection of
colocalized pairs. Regional plots were constructed with
LocusZoom^[79]26.
Differential expression analysis of potential AD biomarker genes
The 386 distinct eGenes in shared eQTL pairs in significant blood and
brain results were further examined for differentially expressed genes
(DEG) between AD cases and controls in the AD enriched ROSMAP RNA-Seq
dataset. After filtering, 308 of the total 386 genes were tested in the
DEG analysis. The differences in expression among the groups were
computed using the log2 transformation of the fold-change (log2FC). The
differential analysis was performed using a linear model to identify DE
genes between AD cases and controls implemented in R package limma
(Linear Model for Microarray Data) version 3.32.7
([80]http://www.R-project.org/). The P values were adjusted for
multiple testing to control the False Discovery Rate (FDR), with the
gene considered DE when the adjusted P value was ≤0.05.
This study was approved by the Boston University Institutional Review
Board.
Results
A total of 173,857 eQTLs and 51,098 ct-eQTLs in the brain, and 847,429
eQTLs and 30,405 ct-eQTLs in blood were significant after Bonferroni
correction (Supplementary Table [81]S3 and [82]Supplemental Resources).
Additional significant gene-SNP eQTLs pairs in the brain (n = 11,649)
and blood (n = 2533) were observed in ct-eQTL analysis that were not
detected in eQTL analysis (Fig. [83]1A).
Fig. 1. Significant gene-SNP eQTLs and ct-eQTLs in blood and brain tissue
genome-wide.
[84]Fig. 1
[85]Open in a new tab
A Venn diagram shows the number of overlapping eQTLs and ct-eQTLs in
blood and brain. Gold color indicates significant eQTLs that are
cell-type-specific. Orange color indicates significant eQTLs that are
shared between blood and brain. B Cell-type distributions of
significant genome-wide ct-eQTL results in blood and brain.
eQTLs and ct-eQTLs common to blood and brain
Of note, 24,028 significant gene-SNP eQTL pairs were shared between
blood and brain. The 386 distinct eGenes among these shared eQTL pairs
(Supplementary Table [86]S5) are most enriched in the apoptosis
signaling (P = 0.023) and Wnt signaling (P = 0.036) pathways
(Supplementary Table [87]S6). Five of these eGenes (HLA-DRB5, HLA-DRB1,
ECHDC3, CR1, and WWOX) were previously associated with
AD^[88]24,[89]27. Three eSNPs in eQTLs involving HLA-DRB1/HLA-DRB5
(rs9271058) and ARL17A/LRRC37A2 (rs2732703 and rs113986870, which are
near KANSL1 and MAPT) were previously associated with AD risk at the
genome-wide significance level^[90]24,[91]28 (Table [92]1).
Table 1.
eQTLs and ct-eQTLs in established AD loci appearing in both blood and
brain.
(A) eQTLs and ct-eQTLS in established AD genes in both blood and brain.
eGene Tissue Cell type Lead eSNP Position MAF Beta Std error P value
Number of total significant eSNPs in gene/cell-type AD GWAS peaks
CR1 Blood NA rs7533408 1:207673631 0.25 0.059 0.006 3.60E-22 169 NA
HLA-DRB5 Blood NA rs9269008 6:32436217 0.17 −2.580 0.057 <1.0E-314 72
NA
HLA-DRB1 Blood NA rs9271058 6:32575406 0.14 −2.950 0.028 <1.0E-314 630
Lead eSNP
ECHDC3 Blood NA rs11257290 10:11780324 0.28 0.041 0.005 2.91E-19 115 NA
WWOX Blood NA rs7202722 16:78282458 0.40 0.023 0.003 2.60E-14 45 NA
HLA-DRB5 Blood Interferon response(+)/antibacterial (−) rs9269047
6:32438783 0.12 −7.120 0.335 3.04E-100 9 [all (−)] NA
HLA-DRB5 Blood Monocytes/macrophages rs9269047 6:32438783 0.12 −11.600
1.030 2.02E-29 1 NA
HLA-DRB5 Blood NK cells/CD8 + T cells rs9269047 6:32438783 0.12 −7.660
0.994 1.30E-14 1 NA
HLA-DRB1 Blood NK cells/CD8 + T cells rs9270928 6:32572461 0.15 −4.070
0.377 3.60E-27 287 rs9271058
HLA-DRB1 Blood Eosinophils rs9270994 6:32574250 0.14 −2.700 0.415
7.72E-11 42 NA
HLA-DRB1 Blood Interferon response (+)/antibacterial (−) rs9271147
6:32577385 0.14 −5.510 0.250 1.19E-107 346 [260 (−)/86 (+)] rs9271058
HLA-DRB1 Blood Monocytes/macrophages rs9271148 6:32577442 0.13 −6.110
0.709 6.83E-18 222 rs9271058
CR1 Brain NA rs12037841 1:207684192 0.17 −0.096 0.007 9.25E-44 64
rs6656401
HLA-DRB5 Brain NA rs3117116 6:32367017 0.12 −2.780 0.070 <1.0E-314
10537 rs9271058, rs9271192
HLA-DRB1 Brain NA rs73399473 6:32538959 0.26 −2.050 0.058 8.78E-272
10792 rs9271058, rs9271192
ECHDC3 Brain NA rs866770710 10:11784320 0.0002 −0.252 0.018 4.61E-44 45
NA
WWOX Brain NA rs12933282 16:78124987 0.45 −0.133 0.017 1.13E-15 75 NA
HLA-DRB5 Brain Microglia rs67987819 6:32497655 0.14 −1.900 0.137
9.82E-44 754 NA
HLA-DRB5 Brain Endothelial cells rs67987819 6:32497655 0.14 −2.410
0.220 6.32E-28 343 NA
HLA-DRB1 Brain Microglia rs72847627 6:32538512 0.28 −2.130 0.125
4.15E-65 2305 rs9271058, rs9271192
HLA-DRB1 Brain Neurons rs115480576 6:32538570 0.26 −2.210 0.153
2.72E-47 3263 rs9271058, rs9271192
HLA-DRB1 Brain Endothelial cells rs9269492 6:32542924 0.30 −2.250 0.243
2.06E-20 351 rs9271192
HLA-DRB5 Brain Neurons rs9270035 6:32553446 0.14 −2.520 0.137 1.46E-75
2540 rs9271058, rs9271192
ECHDC3 Brain Neurons rs866770710 10:11784320 0.0002 0.328 0.045
3.13E-13 2 NA
(B) eQTLs and ct-eQTLs involving AD GWAS association peak SNPs in both
brain and blood.
eGene Tissue Cell type eSNP + GWAS SNP Position^a MAF Beta Std error P
value
HLA-DRB1 Blood NA rs9271058 6:32575406 0.27 −2.950 0.028 <1.0E-314
ARL17A Blood NA rs2732703 17:44353222 0.21 0.147 0.023 5.95E-11
ARL17A Blood NA rs113986870 17:44355683 0.09 0.166 0.025 2.30E-11
HLA-DRB1 Blood Interferon response (+)/antibacterial (−) rs9271058
6:32575406 0.27 −3.010 0.159 6.36E-80
HLA-DRB1 Blood NK cells/CD8 + T cells rs9271058 6:32575406 0.27 −4.090
0.464 1.20E-18
HLA-DRB1 Blood Monocytes/macrophages rs9271058 6:32575406 0.27 −3.540
0.497 1.06E-12
HLA-DRB1 Brain NA rs9271058 6:32575406 0.27 −1.690 0.054 1.94E-213
HLA-DRB5 Brain NA rs9271058 6:32575406 0.27 −1.770 0.081 2.28E-106
LRRC37A2 Brain NA rs2732703 17:44353222 0.21 1.370 0.053 4.13E-150
LRRC37A2 Brain NA rs113986870 17:44355683 0.09 1.260 0.068 1.98E-76
ARL17A Brain NA rs113986870 17:44355683 0.09 −0.326 0.047 4.96E-12
HLA-DRB1 Brain Microglia rs9271058 6:32575406 0.27 −1.400 0.111
1.80E-36
HLA-DRB1 Brain Neurons rs9271058 6:32575406 0.27 −1.650 0.135 2.37E-34
HLA-DRB5 Brain Neurons rs9271058 6:32575406 0.27 −1.550 0.201 1.24E-14
LRRC37A2 Brain Neurons rs2732703 17:44353222 0.21 1.520 0.140 1.84E-27
LRRC37A2 Brain Microglia rs2732703 17:44353222 0.21 1.480 0.147
7.65E-24
LRRC37A2 Brain Endothelial cells rs2732703 17:44353222 0.21 1.750 0.233
5.88E-14
LRRC37A2 Brain Microglia rs113986870 17:44355683 0.09 1.530 0.195
4.29E-15
LRRC37A2 Brain Neurons rs113986870 17:44355683 0.09 1.400 0.184
2.77E-14
[93]Open in a new tab
^aPosition according to GRCh37 assembly.
MAF = minor allele frequency of variant in 1000 Genomes Combined
European Population; cell-type-specific result rows are in bold.
eQTLs involving CR1, ECHDC3, and WWOX were much more significant in the
brain than blood, whereas HLA-DRB5 and HLA-DRB1 were more significant
in blood when comparing the effect sizes. ECHDC3 was a significant
eGene in blood and brain eQTLs (specifically in neurons). HLA-DRB5 and
HLA-DRB1 were the only eGenes ascribed to significant ct-eQTLs in both
blood and brain noting that of the ten distinct lead eSNPs, five are
unique to each tissue (Table [94]1). Although the eQTLs involving these
genes with the largest effect were observed in blood across multiple
cell types, the total number of significant eSNP-eGene combinations was
far greater in brain (particularly in microglia and neurons). The only
instance in which the lead eSNP is also associated with AD risk at the
GWS level was observed in the blood eQTL pair of HLA-DRB1 with eSNP
rs9271058 (Table [95]1). Among the AD-associated SNPs at the GWS level,
rs9271058 is a significant eSNP for HLA-DRB1 in both blood and brain
cell types (the most significant association by P value was observed in
antibacterial cells and microglia) and rs9271192 is a significant
ct-eQTL for the gene in multiple brain cell types (Table [96]1). Both
of these SNPs are also eSNPs for HLA-DB5 in the brain in neurons only.
There were 657 gene–SNP eQTL pairs comprising 16 unique eGenes that
were significant in blood and brain overall as well as in specific cell
types in both blood and brain (Supplementary Table [97]S7). None of
these eGenes were observed in significant pathways enriched for AD
genes, however, they included AD-associated genes HLA-DRB1 and
HLA-DRB5.
eQTLs and ct-eQTLs among previously established AD loci
Slightly more than half (42/80 = 52.5%) of the established AD
associations (Supplementary Table [98]S3) are eGene targets for
significant eQTLs in blood (Supplementary Table [99]S8). By comparison,
only seven established AD loci were eGene targets for significant eQTLs
in the brain, among which OARD1 was significant in endothelial cells
only (Supplementary Table [100]S8). Many GWS SNPs for AD risk are eSNPs
affecting the expression of the nearest gene, which is usually
recognized as the causative gene, but several GWS SNPs target other
genes (Supplementary Table [101]S9). For example, AD-associated eSNPs
rs113986870 and rs2732703 in the MAPT/KANSL1 region target ARL17A in
blood, but are paired in seven of eight eQTLs and ct-eQTLs with
LRRC37A2 in the brain (Supplementary Table [102]S9). HLA-DRB1 is the
only AD gene with a significant ct-eQTL in blood, whereas many AD genes
have significant blood eQTLs. In the brain, only four AD loci (CR1,
HLA-DRB1/DRB5, IQCK, and MAPT/KANSL1) have significant brain eQTLs of
which HLA-DRB1/DRB5 and MAPT/KANSL1 are the only brain ct-eQTLs, noting
that all are significant in microglia, neurons, and endothelial cells.
Next, we evaluated whether the most significant eSNPs and SNPs
genome-wide significantly associated with AD status (i.e., AD-SNPs)
co-localize and thus to identify a single shared variant responsible
for both signals (posterior probability of shared signals (PP4) > 0.8).
This analysis revealed eight eQTL/ct-eQTL signals that colocalized with
seven AD GWAS signals and half of the colocalized signals involved a
ct-eQTL (Table [103]2 and Supplementary Fig. [104]S2). Two different
eSNPs for CD2AP, rs4711880 (eQTL P = 1.4 × 10^−104) and rs13201473
(NK/CD8 + T cell ct-eQTL P = 1.47 × 10^−9), flank CD2AP GWAS SNP
rs10948363 which is also the second most significant eQTL
(P = 2.32 × 10^−104) and the second most significant ct-eQTL in NK
cells/CD8 + T cells (P = 2.66 × 10^−9). These three SNPs span a 9.0-kb
region in intron 2 and are in complete linkage disequilibrium (LD,
r^2 = 1.0), indicating that any one or more of them could affect the
function of target gene CD2AP. Rs6557994 is the most significant eSNP
for and located in PTK2B (blood interferon ct-eQTL P = 2.58 × 10^−9)
and is moderately correlated with the PTK2B GWAS SNP (rs28834970,
r^2 = 0.78, P = 1.58 × 10^−9). Thus, it is not surprising that
rs6557994 is also significantly associated with AD risk
(P = 8.19 × 10^−7). Rs6557994 is also correlated with a GWAS SNP in
CLU, located approximately 150 kb from PTK2B, that is not significantly
associated with the expression of any gene. Because PTK2B and CLU are
independent AD risk loci^[105]27, it is possible that this eSNP has an
effect on AD pathogenesis through independent pathways (Supplementary
Fig. [106]S2). The most significant eSNP in MADD (rs35233100,
P = 2.88 × 10^−10) was predicted to have functional consequences
because it is a stop-gained mutation. This brain eQTL is colocalized
(PP4 = 0.95) and weakly correlated with a GWAS SNP (P = 1.91 × 10^−5)
in CELF1 rs10838725 (r^2 = 0.12).
Table 2.
Colocalized AD GWAS/lead eQTL SNP pairs.
Region^a AD GWAS Variant Lead eQTL variant eQTL type PP4 r^2
rsID Nearest gene MAF P value eQTL P value eGene Cell type rsID MAF
eGene eQTL P value Cell type GWAS P value
6:46487762–48487762 rs10948363 CD2AP 0.72 1.77E-07 2.32E-104 CD2AP NA
rs4711880 0.23 CD2AP 1.36E-104 NA 2.57E-07 Blood eQTL 0.909 1.00
6:46487762–48487762 rs10948363 CD2AP 0.72 1.77E-07 2.66E-09 CD2AP NK
cells/CD8 + T cells rs13201473 0.27 CD2AP 1.47E-09 NK cells/CD8 + T
cells 2.74E-07 Blood ct-eQTL 0.917 1.00
8:26195121–28195121 rs28834970 PTK2B 0.63 1.58E-09 9.15E-09 PTK2B
Interferon response/antibacterial cells rs6557994 0.41 PTK2B 2.58E-09
Interferon response/antibacterial cells 8.19E-07 Blood ct-eQTL 0.990
0.78
8:26467686–28467686 rs9331896 CLU 0.61 3.62E-16 Not an eSNP rs6557994
0.45 PTK2B 2.58E-09 Interferon response/antibacterial cells 8.19E-07
Blood ct-eQTL 0.990 0.00
1:206692049–208692049 rs6656401 CR1 0.19 2.17E-15 1.05E-43 CR1 NA
rs12037841 0.19 CR1 9.25E-44 NA 1.77E-15 Brain eQTL 0.993 1.00
11:46557871–48557871 rs10838725 CELF1 0.68 1.91E-05 Not an eSNP
rs35233100 0.068 MADD 2.88E-10 NA 1.25E-03 Brain eQTL 0.954 0.12
11:58923508–60923508 rs983392 MS4A6A 0.59 4.76E-15 Not an eSNP
rs11230563 0.35 CD6 2.31E-113 NA 0.48 Brain eQTL 0.854 0.00
19:44411941–46411941 rs429358 APOE 0.78 < 1.0E-300 Not an eSNP
rs74253343 0.47 RELB 1.9E-14 Oligodendroglia 0.23 Brain ct-eQTL 0.971
0.00
[107]Open in a new tab
^aMap position within 1 Mb of AD GWAS SNP according to GRCh37 assembly.
MAF minor allele frequency, NA not available, PP4 posterior probability
of colocalization, r^2 correlation of AD and eQTL variants.
ct-eQTLs genome-wide
Examination of the distribution of the significant ct-eQTL results
genome-wide showed that nearly two-thirds of the ct-eQTLs in blood
occurred in interferon response/antibacterial cells which are defined
as type I interferon viral response cells in upregulated genes and type
II interferon antibacterial inflammatory response cells in
downregulated genes^[108]4, whereas brain ct-eQTLs are highly
represented in endothelial cells, neurons, and microglia (Fig. [109]1B
and Supplementary Table [110]S10). Further examination of significant
results within myeloid cell lineages (i.e., microglia and
monocytes/macrophages) which account for a large proportion of the
genetic risk for late-onset AD^[111]10 revealed that 3234 or 10.6% of
all significant ct-eQTLs in blood were in monocytes/macrophages. This
subset includes 128 unique eGenes which are significantly enriched in
the AD amyloid secretase pathway (FDR P = 0.013, Supplementary Table
[112]S11). A total of 974 or 30.1% of ct-eQTLs including 4 of the 20
most significant eGenes in monocytes/macrophages are located within
1 Mb of established AD loci. One of the eGenes in this top-ranked group
(HLA-DRB5) is an established AD gene, and three others that are near
established AD loci (DLG2 near PICALM^[113]29, C4BPA near CR1^[114]30,
and MYO1E near ADAM10 ^[115]31) are reasonable AD gene candidates based
on evidence using non-genetic approaches (Table [116]3). Microglia
accounted for 15,560 (30.5%) of significant ct-eQTLs in the brain
(Supplementary Table [117]S10) which involved 304 unique eGenes.
Approximately 52% of significant ct-eQTLs in microglia are located in
AD regions including five of the 20 most significant ct-eQTLs in this
group (Table [118]3). One of these five eGenes is an established AD
gene (HLA-DRB1) and two others (ALCC^[119]32 and WNT3^[120]33) have
been linked to AD in previous studies.
Table 3.
Top-ranked ct-eQTLs in myeloid cell types.
(A) Monocytes/macrophages
eGene Lead eSNP Position^a MAF Beta Std error P value Number of
significant eSNPs in gene/cell type
SLC12A1 rs8037626 15:48606346 0.17 −3.340 0.219 1.62E-52 126
DLG2 rs75798025 11:84018349 0.01 5.350 0.364 6.66E-49 597
ABCA9 rs4147976 17:66925923 0.44 0.872 0.068 1.97E-37 48
PTPRG rs116497321 3:62245373 0.01 2.650 0.221 3.96E-33 10
CLNK rs5028371 4:10452986 0.50 1.060 0.092 7.66E-31 272
NFXL1 rs10938499 4:47848377 0.33 −1.270 0.112 8.38E-30 73
FCRL5 rs12760587 1:157526021 0.23 2.140 0.19 1.99E-29 93
HLA-DRB5 rs9269047 6:32438783 0.12 −11.600 1.03 2.02E-29 1
FMOD NA 1:203263699 NA 2.110 0.2 5.08E-26 42
ABCA6 rs144031521 17:67162715 0.01 8.620 0.833 4.27E-25 9
INPP5F rs181735165 10:121555618 0.02 7.150 0.701 1.99E-24 11
RBMS3 rs192885607 3:29612955 0.00 2.570 0.257 1.52E-23 34
ARHGAP44 NA 17:12750576 NA 1.760 0.177 2.69E-23 54
C4BPA rs74148971 1:207275799 0.07 −2.300 0.234 8.44E-23 24
DCLK2 rs114930380 4:150954757 0.03 1.630 0.169 5.16E-22 39
PAM NA 5:102153433 NA −0.691 0.073 2.00E-21 47
MYO1E rs146483144 15:59422810 0.03 4.300 0.453 2.26E-21 17
DSP rs4960328 6:7495948 0.42 0.554 0.061 9.30E-20 6
ROR1 rs1557596882 1:64453767 0.01 3.570 0.393 1.05E-19 31
CACNB2 rs117299889 10:18404550 0.06 1.510 0.168 2.52E-19 61
(B) Microglia
eGene Lead eSNP Position^a MAF Beta Std error P value Number of
significant eSNPs in gene/cell type
[121]AC142381.1 rs199931530 16:33047273 0.45 −0.401 0.012 3.89E-233 43
MLANA rs201480524 9:68457329 0.50 −0.176 0.007 4.82E-124 18
[122]AC015688.3 rs62058902 17:25303954 0.50 −0.213 0.009 1.94E-113 11
HNRNPCL1 rs75627772 1:13182567 0.00 0.186 0.008 4.31E-113 8
[123]AL050302.1 rs3875276 21:14472722 0.50 −0.890 0.040 2.62E-111 142
ALLC rs9808287 2:3624799 0.11 0.833 0.044 1.41E-79 12
FAM21B NA 10:47917284 NA −2.210 0.118 2.88E-78 22
WNT3 rs9904865 17:44908263 0.37 −1.570 0.084 3.90E-78 1
RPL9 rs1458255 4:39446549 0.28 −2.370 0.137 4.76E-67 37
HLA-DRB1 rs72847627 6:32538512 0.32 −2.130 0.125 4.15E-65 2305
XRCC2 rs80034602 7:152104360 0.50 2.120 0.128 1.30E-61 5
WI2-3308P17.2 rs4067785 1:120576209 0.50 −0.184 0.011 1.33E-58 9
DEFB121 rs117541536 20:29422202 0.49 −7.420 0.460 1.56E-58 1
GINS1 rs75374582 20:26109209 0.50 −33.200 2.060 1.95E-58 5
EXOSC10 rs2580511 1:121113600 0.50 −4.390 0.276 5.78E-57 5
TRIM49B rs202086299 11:48363026 0.50 0.205 0.013 2.16E-54 5
TMPRSS9 rs7248384 19:23936403 0.48 −1.410 0.093 1.83E-52 1
LDHC NA 11:18432033 NA 0.428 0.030 1.07E-47 73
HLA-DOB rs201194354 6:32796857 NA 1.190 0.084 4.39E-46 70
DEFB119 rs78099404 20:29617870 0.50 −0.377 3.51E-15 <1.0E-314 142
[124]Open in a new tab
^aMap position according to GRCh37 assembly.
MAF minor allele frequency, NA not available.
Overlap of eQTLs and ct-eQTLs among myeloid cell types
Considering significant eGene–eSNP pairs in myeloid cell types, 251
pairs including five distinct eGenes (BTNL3, FAM118A, HLA-DOB,
HLA-DRB1, and HLA-DRB5) are shared between microglia and
monocytes/macrophages (Table [125]4A and Fig. [126]2A). Three of these
pairs involving eSNPs rs3763355, rs3763354, and rs1183595100 have the
same target gene HLA-DOB and occur only in microglia and
monocytes/macrophages (Table [127]4B). Among the significant ct-eQTLs
in the brain, the cell types with the largest proportion that were also
significant in monocytes/macrophages were microglia (1.6%) and neurons
(1.3%) (Table [128]4). Conversely, among the significant ct-eQTLs in
blood, the cell types with the largest proportion that were also
significant in microglia were NK/CD + T cells (12.9%) and
monocytes/macrophages (7.8%). Among ct-eQTLs which are significant only
for one cell-type each in blood and one in the brain,
monocytes/macrophages shared three ct-eQTLs with microglia but with no
other brain cell types (Fig. [129]2B and Table [130]4C). By comparison,
microglia shared 63 ct-eQTLs with interferons/antibacterial cells, but
with no other blood cell types. The proportions of overlap of ct-eQTLs
between blood and brain across ten paired cell types are significantly
different (Fisher’s Exact test
[MATH:
χ92
:MATH]
= 789.8, P = 2.2 × 10^−16). The much larger number of ct-eQTLs in
microglia that were common with interferons/bacterial cells than
monocytes/macrophages may reflect the substantially greater proportion
of significant eQTLs in blood involving interferons/antibacterial cells
(64%) than monocytes/macrophages (10.6%) (Supplementary Table
[131]S10). The only other ct-eQTLs that were unique to a pair of cell
types in brain and blood cell type involved neurons paired with
neutrophils (n = 3) and with interferons/antibacterial cells (n = 65)
(Fig. [132]2B).
Table 4.
Overlap of ct-eQTLs in myeloid cell types in brain and blood.
(A) Unique eGenes shared in significantly associated ct-eQTLs in
monocytes/macrophages and microglia. Number below each gene represents
significant eGene-eSNP eQTL pairs in each gene.
BTNL3 FAM118A HLA-DOB HLA-DRB1 HLA-DRB5
1 43 6 200 1
(B) eSNP-eGene pairs among ct-eQTLs significant in both
monocytes/macrophages and microglia.
eGene eSNP Position MAF Monocytes/macrophages Microglia AD GWAS P
value^[133]23
Beta P value Beta P value
HLA-DOB rs3763355 6:32786882 0.06 −2.02 9.98E-15 0.938 3.89E-14 0.001
HLA-DOB rs3763354 6:32786917 0.15 −1.11 1.40E-10 −0.642 2.80E-13 0.652
HLA-DOB rs1183595100 6:32768232 NA −1.13 8.34E-11 −0.605 1.98E-11 NA
(C) Overlap of significant eQTLs in brain and blood with ct-eQTLs in
myeloid cell types.
Cell types Monocytes/macrophages Microglia
Blood # ct-eQTLs common to cell-type pair # ct-eQTLs unique to
cell-type pair # ct-eQTLs common to cell-type pair # ct-eQTLs unique to
cell-type pair
Neutrophils1 3 (0.3%)^a 0
CD4 + T cells 3 (0.5%) 0
NK/CD8 + T cells 337 (12.9%) 0
Erythrocytes 119 (5.6%) 0
Monocytes/macrophages 251 (7.8%) 3
Unknown 0 0
Interferon/antibacterial 628 (3.3%) 63
Neutrophils2 0 0
B cells 0 0
Eosinophils 38 (5.2%) 0
Brain
Endothelial cells 55 (0.5%) 0
Neurons 250 (1.3%) 0
Microglia 251 (1.6%) 3
Astrocytes 0 0
Oligodendroglia 0 0
[134]Open in a new tab
^aNumber in parentheses represent the proportion of ct-eQTLs for each
cell type on the left that were also observed in either microglia or
monocytes/macrophages.
Fig. 2. Intersection of significant gene-SNP eQTL pairs between cell types in
blood and brain tissue.
[135]Fig. 2
[136]Open in a new tab
A Venn diagram showing overlap of ct-eQTL pairs in myeloid cell types
(microglia and monocytes/macrophages). B Number of significant eQTLs
unique to and that overlap cell types in blood and brain. The bar chart
on the left side indicates the number of significant eQTLs involving
each cell type and the bar chart above the matrix indicates the number
of significant eQTLs that are unique to each cell type and set of cell
types. Pink colored bar indicates the number of eQTLs pairs that are
unique to microglia and monocytes/macrophages.
Effect of AD status on significant eQTLs and ct-eQTLs
None of the significant eQTLs and ct-eQTLs observed in the brain (Table
[137]1) were influenced by the inclusion of AD status in the analysis
models. Stratified analyses revealed that the top findings involving
eSNPs that were previously associated with AD at the genome-wide
significant level were evident in both AD cases and controls
(Supplementary Table [138]12A). Although most of the findings were more
significant in AD cases than controls (noting that the ROSMAP brain
sample of AD cases was 44% larger than the control sample), the effect
size for most eSNP–eGene pairs was similar. However, patterns among AD
cases and controls differed when focusing on the most significant eQTLs
and ct-eQTLs in established AD genes. For example, eQTLs observed in
undifferentiated brain cells involving CR1 paired with rs6656401
(P = 7.85 × 10^−22), in endothelial cells involving HLA-DRB1 paired
with rs73399473 (P = 2.5 × 10^−10) and HLA-DRB5 paired with rs1064697
(P = 2.18 × 10^−14), in microglia involving HLA-DRB1 paired with
rs72847627 (P = 4.43 × 10^−51), and in neurons involving ECHDC3 paired
with rs866770710 (P = 5.79 × 10^−13) were significant only in AD cases
(Supplementary Table [139]12B). Other eQTLs observed in multiple cell
types involving these same genes (HLA-DRB1: rs111976080,
P = 1.68 × 10^−25; HLA-DRB5: rs2395517, P = 8.64 × 10^−12, rs9271184,
P = 5.42 × 10^−41, and rs80141235, P = 3.94 × 10^−9) were significant
only in controls. Several other eQTLs and ct-eQTLs in CR1, HLA-DRB1,
and HLA-DRB5 were highly significant in one group but showing a much
less significant effect in the opposite direction in the other group.
Among the 386 eGenes that were significant in both blood and brain
(Supplementary Table [140]S5), 87 were differentially expressed between
AD cases and controls (Supplementary Table [141]S13). This includes
WWOX (P[adj] = 1.02 × 10^−4) and LRRC2 (P[adj] = 2.38 × 10^−3) which
have been associated with AD risk by GWAS^[142]24,[143]34.
Discussion
We identified several novel AD-related eQTLs that highlight the
importance of cell-type-dependent context. It is noteworthy that there
were more significant ct-eQTLs in the brain (n = 51,098) than blood
(n = 30,405) even though the dataset containing expression data from
blood (FHS) is several times larger than the brain expression dataset
(ROSMAP). This could be due to greater cell-type heterogeneity in the
brain, the enrichment of AD cases in the ROSMAP dataset who may show
different patterns of gene expression compared to persons without AD,
or highly variable gene expression across cell types in the nervous
system^[144]35. Because expression studies in the brain are often
constrained by the small number of specimens compared to studies in
other tissues, postmortem changes that may affect gene expression in
the brain^[145]36, and the growing recognition that AD is a systemic
disease^[146]37–[147]39, incorporating expression data from multiple
tissues can enhance discovery of additional genetic influences on AD
risk and pathogenesis.
Although most significant findings were tissue-specific, the 386
distinct eGenes among more than 24,000 significant gene-SNP eQTL pairs
that were shared between blood and brain were enriched in the apoptosis
signaling pathway which has been suggested to contribute to the
underlying pathology associated with AD^[148]40,[149]41. Six
established AD genes (CR1, ECHDC3, HLA-DRB1, HLA-DRB5, LRRC2, and
WWOX^[150]24,[151]27,[152]34) were shared eGenes in the brain and
blood. They were also involved in eQTLs and ct-eQTLs that showed
different patterns of association in cases versus controls (i.e., CR1,
HLA-DRB1, HLA-DRB5, and ECHD3) or differentially expressed in AD cases
versus controls (i.e., WWOX and LRRC2).
The complement receptor 1 (CR1) gene encodes a transmembrane
glycoprotein functioning in the innate immune system by promoting
phagocytosis of immune complexes, cellular debris, and Aβ^[153]42. CR1
is an eGene for several eSNPs, including AD GWAS peak SNP rs6656401
located within the gene, in brain and blood eQTLs and the effects on
CR1 expression are opposite in blood and brain. There are multiple
possible explanations for the effect direction differences across
tissues. The effect of eSNP rs6656401 on CR1 expression may be
developmental, noting that the average age of the FHS subjects (a group
with expression data in blood) is more than 30 years younger than the
ROSMAP subjects (group with expression data in the brain). The
difference between brain and blood may also reflect postmortem changes
in the brain that are not indicative of expression in vivo.
Alternatively, these effects may be related to AD because few FHS
subjects were AD cases at the time of blood draw, whereas 60% of
subjects in the ROSMAP sample are AD cases. This idea is supported by
the observation of a larger and positive effect of rs6656401 on CR1
expression in AD (β = 0.020) compared to control brains (β = −0.0086).
Opposite effect directions of expression in brain and blood from AD
patients compared to controls have been observed for several ribosomal
genes^[154]43. GWS variants located in the region spanning ECHDC3 and
USP6NL have previously been associated with AD^[155]44. Altered ECHDC3
expression in AD brains^[156]45 supports the idea that this gene has a
role in AD. Knockout of WWOX in mice leads to aggregation of amyloid-β
(Aβ) and Tau, and subsequent cell death^[157]46,[158]47. LRRC2 is
located in a region including GWS variants that modify the inverse
relationship between education attainment and AD^[159]34. A recent
study showed that the expression of a LRRC2 long noncoding RNA
(LCCR2-AS1) is significantly increased in children with autism spectrum
disorder compared to children with normal development^[160]48.
The human leukocyte antigen (HLA) region is the key susceptibility gene
in many immunological diseases and many associations have been reported
between neurodegenerative diseases and HLA haplotypes^[161]49. In
addition, the most widely used marker to examine activated microglia in
normal and diseased human brains is HLA-DR and microglia activation
increases with the progression of AD^[162]50,[163]51. HLA-DRB5 and
HLA-DRB1 have been implicated in numerous GWAS studies as significantly
associated with AD risk^[164]24,[165]27 and appeared frequently among
significant results in blood and brain in this study. Rs9271058, which
is located approximately 17.8 kb upstream of HLA-DRB1, is significantly
associated with AD risk (P = 5.1 × 10^−8)^[166]24. and when paired with
HLA-DRB1 is a significant eQTL and ct-eQTL in multiple cell types in
blood and brain including myeloid lineage cells (i.e.,
monocytes/macrophages and microglia). This eSNP is also a significant
eQTL in the brain and specifically in neurons when paired with
HLA-DRB5. Rs9271192, which is adjacent to rs9271058 and also
significantly associated with AD risk (P = 2.9 × 10^−12)^[167]27, is a
significant eQTL and ct-QTL with multiple cell types in brain but not
blood when paired with HLA-DRB5 and HLA-DRB1.
Significant associations for AD have been reported with variants
spanning a large portion of the major histocompatibility (MHC) region
in HLA class I, II, and III loci^[168]49,[169]52,[170]53. While the
strongest statistical evidence for association in this region is with
variants in HLA-DRB1^[171]24, fine mapping in this region suggests that
a class I haplotype (spanning the HLA-A and HLA-B loci) and a class II
haplotype (including variants in HLA-DRB1, HLA-DQA1, and HLA-DQB1) are
more precise markers of AD risk. Given the complexity of the MHC region
and extensive LD, further work is needed to confirm whether this is a
true eQTL or a signal generated from a specific HLA allele or
haplotype. Although functional studies may be required to discern which
HLA variants have AD relevant consequences and HLA polymorphisms
methods would be required to detect differential gene expression
between the HLA alleles, our findings support a role for the immune
system in AD^[172]37,[173]54 and the hypothesis that a large proportion
of AD risk can be explained by genes expressed in myeloid
cells^[174]10.
The potential importance and relevance to AD of eQTLs and ct-eQTLs in
myeloid cell types are supported by the observation that a large
portion of GWS ct-eQTLs we identified map within 1 Mb of established AD
loci, and 58% (12/20 in monocytes/macrophages and 11/20 in microglia)
of the most significant eGenes have been previously implicated in AD.
DLG2 encodes a synaptic protein whose expression was previously
reported as downregulated in an AD proteome and transcriptome
network^[175]55 and inversely associated with AD Braak stage^[176]29.
Genome-wide significant associations of AD risk with PTPRG was observed
in a family-based GWAS^[177]56 and with CLNK in a recent large GWAS for
which the evidence was derived almost entirely with a proxy AD
phenotype in the UK Biobank^[178]57. NFXL1 is a novel putative
substrate for BACE1, an important AD therapeutic target^[179]58. FCRL5
may interact with the APOE*E2 allele and also modifies AD age of
onset^[180]59. C4BPA was shown to be consistently downregulated in MCI
and AD patients, and the protein encoded by this gene accumulates in Aβ
plaques in AD brains^[181]30,[182]60. Lower levels of the PAM have been
observed in the brains and CSF of AD patients compared to healthy
controls^[183]61 and MYO1E is expressed by anti-inflammatory
disease-associated microglia^[184]31. As a calcium channel protein,
CACNB2 may affect AD risk by altering calcium levels which could cause
mitochondrial damage and then induce apoptosis^[185]62,[186]63.
Likewise, several eGenes of top-ranked ct-eQTLs in microglia that are
not established AD loci may have a role in the disease. It was shown
that copy number variants (CNVs) near HNRNPCL1 overlapped the coding
portion of the gene in AD cases but not controls^[187]64. A region of
epigenetic variation in ALLC was associated with AD
neuropathology^[188]32. FAM21B, a retromer gene in the
endosome-to-Golgi retrieval pathway, was associated with AD in a
candidate gene study^[189]65. Vacuolar sorting proteins genes in this
pathway including SORL1 have been functionally linked to AD through the
trafficking of Aβ^[190]66. One study demonstrated that WNT3 expression
in the hippocampus was increased by exercise and alleviated
AD-associated memory loss by increasing neurogenesis^[191]33.
Expression of RPL9 is downregulated in severe AD^[192]67 and
significantly differs by sex among persons with the APOE ɛ4
allele^[193]68. Significant evidence of association with a TRIM49B SNP
was found in a genome-wide pleiotropy GWAS of AD and major depressive
disorder (MDD)^[194]69.
HLA-DOB, which is one of the five distinct eGenes (BTNL3, FAM118A,
HLA-DOB, HLA-DRB1, and HLA-DRB5) for significant ct-eQTLs shared
between microglia and monocytes/macrophages, and is the target gene for
three eSNPs (rs3763355, rs3763354, and rs1183595100) that were evident
only in these myeloid cell types. These eSNPs have similar eQTL
p-values in both cell types, but have slightly larger effect sizes in
monocytes (Fig. [195]2). The effect of rs3763355 on expression is in
opposite directions in monocytes and microglia which suggests HLA-DOB
may be acting in different immune capacitates in AD in blood and brain.
Though the functions of the genes BTNL3 and FAM118A are unknown, a
BTNL8-BTNL3 deletion has been correlated with TNF and ERK1/AKT
pathways, which have an important role in immune regulation inducing
inflammation, apoptosis, and proliferation, suggesting the deletion
could be correlated to inflammatory disease^[196]70. This suggests that
the majority of the shared myeloid cell-type genes—the HLA genes and
possibly BTNL3—are all immune-related. Ct-eQTLs involving microglia and
monocytes/macrophages had a larger proportion of total intersection, an
isolated set interaction and a statistically significant overlap
(P < 1.0 × 10^−314), demonstrating a stronger connection than other
brain/blood cell types in this study and thus providing further
evidence for the importance of the immune system in AD.
The proportions of significant ct-eQTLs in NK cells/CD8 + T cells,
monocytes/macrophages, and eosinophils are comparable to those observed
in reference blood tissue
([197]https://www.miltenyibiotec.com/US-en/resources/macs-handbook/huma
n-cells-and-organs/human-cell-sources/blood-human.html#gref),
([198]https://www.stemcell.com/media/files/wallchart/WA10006-Frequencie
s_Cell_Types_Human_Peripheral_Blood.pdf). Similarly, significant eQTL
distributions in endothelial cells, neurons, and glia are consistent
with reference brain tissue^[199]71. The majority of significant blood
eQTLs were type I interferon response cells which cross-regulate with
pro-inflammatory cytokines that drive the pathogenesis of autoimmune
diseases including AD and certain heart diseases^[200]72–[201]74 and
the enrichment of interferon ct-eQTLs in this study could possibly be
due to the high proportion of subjects these diseases in the FHS
dataset. In contrast, the proportion of significant ct-eQTLs among
glial cells is much lower in astrocytes and oligodendrocytes and
three-fold higher in microglia than in reference brain tissue
([202]https://www.stemcell.com/media/files/wallchart/WA10006-Frequencie
s_Cell_Types_Human_Peripheral_Blood.pdf). Because many AD risk genes
are expressed in myeloid cells including microglia^[203]10, the large
number of microglia ct-eQTLs is consistent with the high proportion of
AD subjects in the ROSMAP dataset.
Several SNPs previously reported to be associated with AD at the GWS
level were associated with eGenes that differ from genes ascribed to AD
loci and thus may have a role in AD. Karch et al. observed that the
expression of PILRB, which is involved in immune response and is the
activator receptor to its inhibitory counterpart PILRA, an established
AD gene^[204]75,[205]76, was highest in microglia^[206]11. CNN2, the
eGene for eSNP rs4147929 located near the end of ABCA7, significantly
alters extracellular Aβ levels in human induced pluripotent stem
cell-derived neurons and astrocytes^[207]77. Rs4147929 also targeted
HMHA1 which plays several roles in the immune system in an
HLA-dependent manner^[208]78. The eSNP/GWAS SNP rs3740688 located in
SPI1 also affects expression of MYBPC3 and MADD. MYBPC3 was recently
identified as a target gene for eSNPs located in CELF1 and MS64A6A in a
study of eQTLs in blood for GWS AD loci^[209]79. MADD is expressed in
neurons^[210]11, is involved in neuronal cell death in the
hippocampus^[211]80, and was shown to be a tau toxicity
modulator^[212]81. Although eSNP rs113986870 in KANSL1 when paired with
the nearby eGene LRRC37A2 was a significant brain eQTL and ct-eQTL,
LRRC37A2 encodes a leucine-rich repeat protein that is expressed
primarily in testis and has no apparent connection to AD. However,
rs113986870 also significantly influenced the expression of another
gene in this region, ARL17A, that was previously linked to progressive
supranuclear palsy by analysis of gene expression and
methylation^[213]82.
The aim of this study was to identify context-dependent (i.e.,
cell-type-specific) eQTLs in blood and brain among older individuals
including AD cases using a genome-wide approach. Previous studies have
evaluated ct-eQTLs using purified cells, but they focused on only one
or two cell types^[214]7,[215]10. Other studies examined multiple cell
types but using expression data generated from individuals who were on
average much younger than the FHS and ROSMAP
participants^[216]4,[217]83,[218]84. With the exception of a recent
report by Patrick et al. who applied a deconvolution approach to
estimate cell-type proportions from in cortical tissue obtained from
ROSMAP participants but did not examine ct-eQTLs^[219]85, our study is
one of the first to study eQTLs and ct-eQTLs in a sample enriched for
AD cases and link these findings to established AD genes and AD risk.
Our study has several noteworthy limitations. The proxy genes
individually or collectively may not accurately depict
cell-type-specific context. In addition, the comparisons of gene
expression in blood and brain may yield false results because they are
based on independent groups ascertained from a community-based
longitudinal study of health (FHS—blood) and multiple sources for
studies of AD (ROSMAP—brain) which were not matched for age, sex,
ethnicity and other factors which may affect gene expression. Moreover,
the FHS cohort contains many elderly but relatively few AD cases,
whereas ~60% of the ROSMAP participants in this autopsy sample are AD
cases. Although the dataset for eQTL analysis in blood was much larger
than the dataset derived from the brain, the effect sizes associated
with many of the eQTLs common to both tissues were similar. Also,
findings in the brain may reflect postmortem changes unrelated to
disease or cell-type different expression^[220]36. Another limitation
of our findings is that some cell types are vastly underrepresented
compared to others. Because myeloid cell types are represented in only
a small proportion of the total cell populations in the brain and blood
(generally ~10%), it is difficult to identify myeloid-specific
signals^[221]12. Despite this limitation, many of the most significant
and noteworthy results of this study involved monocytes/macrophages and
microglia.
Conclusion
Our observation of cell-type-specific expression patterns for
established and potentially novel AD genes, finding of additional
evidence for the role of myeloid cells in AD risk, and discovery of
potential novel blood and brain AD biomarkers highlight the importance
of cell-type-specific analysis. Future studies that use more robust
computational approaches such as deconvolution to reliably estimate
cell type proportions^[222]83–[223]85, compare cell-type-specific
differential gene expression among AD cases and controls using
single-cell RNA-sequencing or cell count data and conduct functional
experiments are needed to validate and extend our findings.
Supplementary information
[224]Supplementary Tables and Figures^ (6.1MB, docx)
[225]Supplemental Resources^ (4.8MB, pdf)
Acknowledgements