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Summary The most important genetic causes of Alzheimer's disease in a super scientific way
New insights into the genetic etiology of Alzheimer’s disease and related dementias
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New insights into the genetic etiology ofAlzheimer’s disease and related dementias
Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique
opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage
genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk
loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau
pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive
of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain
assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from
mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the
lowest to the highest decile, in addition to effects of age and the APOE ε4 allele.
A
D is the most common form of dementia. The heritability P ≤ 5 × 10−8 in the stage I and stage II meta-analysis. Furthermore,
is high, estimated to be between 60% and 80%1. This strong we applied a PLINK clumping procedure4 to define potential inde-
genetic component provides an opportunity to determine pendent hits within the stage I results (Methods). After validation
the pathophysiological processes in AD and to identify new bio- by conditional analyses (Supplementary Note and Supplementary
logical features, new prognostic/diagnostic markers and new thera- Tables 3 and 4), this approach enabled us to define 39 signals in
peutic targets through translational genomics. Characterizing the 33 loci already known to be associated with the risk of developing
genetic risk factors in AD is therefore a major objective; with the ADD3,5–10 and identify 42 loci defined as new at the time of analysis
advent of high-throughput genomic techniques, a large number of (Tables 1 and 2, Supplementary Table 5 and Supplementary Figs.
putative AD-associated loci/genes have been reported2. However, 2–29). Of the 42 new loci, 17 had P ≤ 5 × 10−8 in stage I and 25 were
much of the underlying heritability remains unexplained. Hence, associated with P ≤ 5 × 10−8 after follow-up (stage I and stage II
increasing the sample size of genome-wide association studies meta-analysis, including the ADGC, CHARGE and FinnGen data).
(GWASs) is an obvious solution that has already been used to char- We also identified 6 loci with P ≤ 5 × 10−8 in the stage I and stage
acterize new genetic risk factors in other common, complex dis- II analysis but with P > 0.05 in stage II (Supplementary Table 6). It
eases (e.g., diabetes). is noteworthy that the magnitude of the associations in stage I did
not change substantially if we restricted the analysis to clinically
GWAS meta-analysis diagnosed AD cases (Supplementary Table 7 and Supplementary
The European Alzheimer & Dementia Biobank (EADB) consor- Fig. 30). Similarly, none of the signals observed appeared to be
tium brings together the various European GWAS consortia already especially driven by the UKBB data (Supplementary Table 7 and
working on AD. A new dataset of 20,464 clinically diagnosed AD Supplementary Figs. 2–29). Nine of these loci (APP, CCDC6, GRN,
cases and 22,244 controls has been collated from 15 European coun- LILRB2, NCK2, TNIP1, TMEM106B, TSPAN14 and SHARPIN)
tries. The EADB GWAS results were meta-analyzed with a proxy-AD were recently reported in three articles using part of the GWAS
GWASs of the UK Biobank (UKBB) dataset. The UKBB’s proxy-AD data included in our study11–13. We also generated a detailed anal-
designation is based on questionnaire data in which individuals are ysis of the human leukocyte antigen (HLA) locus on the basis of
asked whether their parents had dementia. This method has been the clinically diagnosed AD cases (Supplementary Tables 8 and 9,
used successfully in the past3 but is less specific than a clinical or Supplementary Figs. 31 and 32 and Supplementary Note).
pathological diagnosis of AD; hence, we will refer to these cases as
proxy AD and related dementia (proxy-ADD). EADB stage I (GWAS Genetic overlap with other neurodegenerative diseases
meta-analysis) was based on 39,106 clinically diagnosed AD cases, We tested the association of the lead variants within our new loci
46,828 proxy-ADD cases (as defined in the Supplementary Note), with the risk of developing other neurodegenerative diseases or
401,577 controls (Supplementary Tables 1 and 2) and 21,101,114 AD-related disorders (Supplementary Fig. 33 and Supplementary
variants that passed our quality control (Fig. 1; see Supplementary Tables 10–12). We also performed more precise colocalization
Fig. 1 for the quantile–quantile plot and genomic inflation factors). analyses (using Coloc R package, https://cran.r-project.org/web/
We selected all variants with a P value below 1 × 10−5 in stage I. We packages/coloc/index.html) for five loci known to be associated
defined nonoverlapping regions around these variants, excluded the with Parkinson’s disease (IDUA and CTSB), types of frontotem-
region corresponding to APOE and examined the remaining vari- poral dementia (TMEM106B and GRN) and amyotrophic lateral
ants in a large follow-up sample that included AD cases and controls sclerosis (TNIP1) (Supplementary Tables 13 and 14). The IDUA sig-
from the ADGC, FinnGen and CHARGE consortia (stage II; 25,392 nal for Parkinson’s disease was independent of the signal in ADD
AD cases and 276,086 controls). A signal was considered as signifi- (coloc posterior probability (PP)3 = 99.9%), but we were not able to
cant on the genome-wide level if it (1) was nominally associated determine whether the CTSB signals colocalized. The TMEM106B
(P ≤ 0.05) in stage II, (2) had the same direction of association in the and GRN signals in frontotemporal lobar degeneration with TAR
stage I and II analyses and (3) was associated with the ADD risk with DNA-binding protein (TDP-43) inclusions (frontotemporal lobar
A full list of author and affiliations appears at the end of the paper.
412 NAture GeNeticS | VOL 54 | ApriL 2022 | 412–436 | www.nature.com/naturegenetics
,NATure GeneTiCs Articles
APOE
PICALM
BIN1
36
CLU
CR1
32
ABCA7
TREM2
28
MS4A
24
APH1B
ZCWPW1/NYAP1
SCIMP/RABEP1
CASS4
20
−log10(P )
SLC24A4/RIN3
INPP5D
HLA
ABI3
PLCG2
EPHA1
ACE
CLNK/HS3ST1
TSPAN14
PTK2B
TMEM106B
SLC2A4RG
SORL1
16
COX7C
GRN
CELF1/SPI1
RASGEF1C
CD2AP
SHARPIN
APP
PRDM7
ECHDC3
IGH gene cluster
DOC2A
ADAM10
MYO15A WDR81
JAZF1 NME8
12
NCK2
ICA1
LILRB2
FERMT2
ADAMTS1
IDUA
MAF FOXF1
SEC61G
CTSH KAT8
MAPT
KLF16
CTSB
SIGLEC11
ABCA1
TSPOAP1
ANK3
PLEKHA1
HS3ST5
ADAM17
MME
SPPL2A
WDR12
UMAD1
SORT1
PRKD3
TPCN1
RBCK1
RHOH
TNIP1
ANKH
BLNK
SNX1
8
IL34
4
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Chromosome
Fig. 1 | Manhattan plot of the stage I results. P values are two-sided raw P values derived from a fixed-effect meta-analysis. Variants with a P value
below 1 × 10−36 are not shown. Loci with a genome-wide significant signal are annotated (known loci in black and new loci in red). Variants in new loci are
highlighted in red. The red dotted line represents the genome-wide significance level (P = 5 × 10−8), and the black dotted line represents the suggestive
significance level (P = 1 × 10−5).
degeneration TDP) probably share causal variants with ADD (coloc was observed using a mouse single-cell dataset14 (Supplementary
PP4 = 99.8% and coloc PP4 = 80.1%, respectively). Lastly, we were Table 17 and Supplementary Note).
not able to determine whether the TNIP1 signals colocalized for Lastly, we looked at whether the relationship between an elevated
ADD and amyotrophic lateral sclerosis. microglia Av. Exp. and a genetic association with the ADD risk was
specific to particular biological processes (Supplementary Table 18)
Pathway analyses by analyzing the interaction between microglia Av. Exp. and pathway
Next, we sought to perform a pathway enrichment analysis on the membership in MAGMA15. Of the five most significant interaction
stage I association results to gain better biological understanding signals (q ≤ 10−3), two were directly associated with endocytosis pro-
of this newly expanded genetic landscape for ADD. Ninety-three cesses (GO:0006898 and GO:0031623); this suggested a functional
gene sets were still statistically significant after correction for mul- relationship between microglia and endocytosis, which is known to
tiple testing (q ≤ 0.05; Methods and Supplementary Table 15). As be involved in phagocytosis (Supplementary Table 18). It is notewor-
described previously, the most significant gene sets are related to thy that we also detected an interaction between GO:1902991 (regu-
amyloid and tau5; other significant gene sets are related to lipids, lation of amyloid precursor protein (APP) catabolic process) and
endocytosis and immunity (including macrophage and microglial the gene expression level in microglia (q = 1.4 × 10−3; Supplementary
cell activation). When restricting this analysis to the meta-analysis Table 18). Even though these data suggest a functional relationship
based on the clinically diagnosed AD cases, 54 gene sets were sig- between microglia and APP/amyloid beta (Aβ) peptide pathways,
nificant (q ≤ 0.05). Of these 54 gene sets, 33 reached q ≤ 0.05 in this observation reinforces the likely involvement of microglial endo-
the stage I analysis and all reached P ≤ 0.05. This indicates that cytosis in AD, a mechanism that is also strongly involved in APP
the inclusion of proxy-ADD cases does not cause disease-relevant metabolism16. Of note, there are overall similarities in the interaction
biological information to be missed and underlines the additional effects of human and mouse microglia expression with genes in bio-
power of this type of analysis. logical pathways of relevance to the AD genetic risk (Supplementary
We next performed a single-cell expression enrichment analysis Table 18 and Supplementary Note).
by using the average gene expression per nucleus (Av. Exp.) data in
the human Allen Brain Atlas (49,495 nuclei from 8 human brains). Gene prioritization
Only the microglial expression reached a high level of significance We next attempted to identify the genes most likely to be respon-
(P = 1.7 × 10−8; Supplementary Table 16); greater expression corre- sible for the association signal with ADD at each new locus. To
sponded to a more significant association with ADD. After adjust- this end, we studied the downstream effects of ADD-associated
ing for microglial Av. Exp., the remaining associations became variants on molecular phenotypes (i.e., expression, splicing, pro-
nonsignificant; this indicates that microglial Av. Exp. drives all the tein expression, methylation and histone acetylation) in various
other cell-type associations. These results were observed whatever cis-quantitative trait locus (cis-QTL) catalogues from AD-relevant
the brain region studied (Supplementary Table 16). A similar result tissues, cell types and brain regions. We investigated the genetic
Nature Genetics | VOL 54 | April 2022 | 412–436 | www.nature.com/naturegenetics 413
,Articles NATure GeneTiCs
Table 1 | Summary of association results in the stage I and stage II analysis for known loci with a genome-wide significant signal
Varianta Chromosome Positionb Genec Known locus Minor/major allele MAFd ORe 95% CI P value
rs679515 1 207577223 CR1 CR1 T/C 0.188 1.13 1.11–1.15 7.2 × 10−46
rs6733839 2 127135234 BIN1 BIN1 T/C 0.389 1.17 1.16–1.19 6.1 × 10−118
rs10933431 2 233117202 INPP5D INPP5D G/C 0.234 0.93 0.92–0.95 3.6 × 10−18
rs6846529 4 11023507 CLNK CLNK/HS3ST1 C/T 0.283 1.07 1.05–1.08 2.2 × 10−17
rs6605556 6 32615322 HLA-DQA1 HLA G/A 0.161 0.91 0.90–0.93 7.1 × 10−20
rs10947943 6 41036354 UNC5CL TREM2 A/G 0.142 0.94 0.93–0.96 1.1 × 10−9
rs143332484 6 41161469 TREM2 TREM2 T/C 0.013 1.41 1.32–1.50 2.8 × 10−25
rs75932628 6 41161514 TREM2 TREM2 T/C 0.003 2.39 2.09–2.73 2.5 × 10−37
rs60755019 6 41181270 TREML2 TREM2 G/A 0.004 1.55 1.33–1.80 2.1 × 10−8
rs7767350 6 47517390 CD2AP CD2AP T/C 0.271 1.08 1.06–1.09 7.9 × 10−22
rs6966331 7 37844191 EPDR1 NME8 T/C 0.349 0.96 0.94–0.97 4.6 × 10−10
rs7384878 7 100334426 SPDYE3 ZCWPW1/NYAP1 C/T 0.31 0.92 0.91–0.94 1.1 × 10−26
rs11771145 7 143413669 EPHA1 EPHA1 A/G 0.348 0.95 0.93–0.96 3.3 × 10−14
rs73223431 8 27362470 PTK2B PTK2B T/C 0.369 1.07 1.06–1.08 4.0 × 10−22
rs11787077 8 27607795 CLU CLU T/C 0.392 0.91 0.90–0.92 1.7 × 10−44
rs7912495 10 11676714 USP6NL ECHDC3 G/A 0.462 1.06 1.05–1.08 9.7 × 10−19
rs10437655 11 47370397 SPI1 CELF1/SPI1 A/G 0.399 1.06 1.04–1.07 5.3 × 10−14
rs1582763 11 60254475 MS4A4A MS4A A/G 0.371 0.91 0.90–0.92 3.7 × 10−42
rs3851179 11 86157598 EED PICALM T/C 0.358 0.9 0.89–0.92 3.0 × 10−48
rs74685827 11 121482368 SORL1 SORL1 G/T 0.019 1.19 1.13–1.25 2.8 × 10−11
rs11218343 11 121564878 SORL1 SORL1 C/T 0.039 0.84 0.81–0.87 1.4 × 10−21
rs17125924 14 52924962 FERMT2 FERMT2 G/A 0.089 1.1 1.07–1.12 8.3 × 10−16
rs7401792 14 92464917 SLC24A4 SLC24A4/RIN3 G/A 0.371 1.04 1.02–1.05 4.8 × 10−8
rs12590654 14 92472511 SLC24A4 SLC24A4/RIN3 A/G 0.328 0.93 0.92–0.95 4.2 × 10−21
rs8025980 15 50701814 SPPL2A SPPL2A G/A 0.345 0.96 0.94–0.97 1.3 × 10−8
rs602602 15 58764824 MINDY2 ADAM10 A/T 0.28 0.94 0.93–0.96 2.1 × 10−15
rs117618017 15 63277703 APH1B APH1B T/C 0.144 1.11 1.09–1.13 2.2 × 10−25
rs889555 16 31111250 BCKDK KAT8 T/C 0.281 0.95 0.94–0.97 2.0 × 10−11
rs4985556 16 70660097 IL34 IL34 A/C 0.115 1.07 1.05–1.09 6.0 × 10−10
rs12446759 16 81739398 PLCG2 PLCG2 G/A 0.403 0.95 0.94–0.96 1.2 × 10−13
rs72824905 16 81908423 PLCG2 PLCG2 G/C 0.008 0.74 0.68–0.81 8.5 × 10−12
rs7225151 17 5233752 SCIMP SCIMP/RABEP1 A/G 0.124 1.08 1.05–1.10 4.1 × 10−13
rs199515 17 46779275 WNT3 MAPT G/C 0.219 0.94 0.93–0.96 9.3 × 10−13
rs616338 17 49219935 ABI3 ABI3 T/C 0.012 1.32 1.23–1.42 2.8 × 10−14
rs2526377 17 58332680 TSPOAP1 TSPOAP1 G/A 0.445 0.95 0.94–0.97 1.6 × 10−12
rs4277405 17 63471557 ACE ACE C/T 0.384 0.94 0.93–0.95 8.8 × 10−20
rs12151021 19 1050875 ABCA7 ABCA7 A/G 0.336 1.1 1.09–1.12 1.6 × 10−37
rs6014724 20 56423488 CASS4 CASS4 G/A 0.09 0.89 0.87–0.91 4.1 × 10−21
rs2830489 21 26775872 ADAMTS1 ADAMTS1 T/C 0.281 0.95 0.94–0.97 1.7 × 10−10
P values are two-sided raw P values derived from a fixed-effect meta-analysis.CI, confidence interval; OR, odds ratio; MAF, minor allele frequency. Reference single-nucleotide polymorphism (SNP)
a
(rs) number, according to dbSNP build 153. bGRCh38 assembly. cNearest protein-coding gene according to GENCODE release 33. dWeighted average MAF across all discovery studies. eApproximate OR
calculated with respect to the minor allele.
colocalization between association signals for the ADD risk and that yielded a total weighted score of between 0 and 100 for each
those for the molecular phenotypes and the association between gene (Supplementary Fig. 34 and Supplementary Note). This score
the ADD risk and these phenotypes by integrating cis-QTL infor- was used to compare and prioritize genes in the new loci within
mation into our ADD GWAS. Moreover, we considered the lead 1 Mb upstream and 1 Mb downstream of the lead variants. Genes
variant annotation (the allele frequency, protein-altering effects either were ranked as tier 1 (greater likelihood of being the causal
and nearest protein-coding gene) and a genome-wide, high-content risk gene responsible for the ADD signal) or tier 2 (lower likeli-
short interfering RNA screen for APP metabolism17. Based on this hood and the absence of a minimum level of evidence as a causal
evidence, we developed a systematic gene prioritization strategy risk gene) or were not ranked.
414 Nature Genetics | VOL 54 | April 2022 | 412–436 | www.nature.com/naturegenetics
, NATure GeneTiCs Articles
Table 2 | Summary of association results in the stage I and stage II analysis for new loci at the time of analysis with a genome-wide
significant signal
Locus Varianta Chromosome Positionb Genec Minor/major MAFd ORe 95% CI P value
number allele
1 rs141749679 1 109345810 SORT1 C/T 0.004 1.38 1.24–1.54 7.5 × 10−9
2 rs72777026 2 9558882 ADAM17 G/A 0.144 1.06 1.04–1.08 2.7 × 10−8
3 rs17020490 2 37304796 PRKD3 C/T 0.145 1.06 1.04–1.08 3.3 × 10−9
4 rs143080277 2 105749599 NCK2 C/T 0.005 1.47 1.33–1.63 2.1 × 10−13
5 rs139643391 2 202878716 WDR12 T/TC 0.131 0.94 0.92–0.96 1.1 × 10−8
6 rs16824536 3 155069722 MME A/G 0.054 0.92 0.89–0.95 3.6 × 10−8
6 rs61762319 3 155084189 MME G/A 0.026 1.16 1.11–1.21 2.2 × 10−11
7 rs3822030 4 993555 IDUA G/T 0.429 0.95 0.94–0.96 8.3 × 10−12
8 rs2245466 4 40197226 RHOH G/C 0.343 1.05 1.03–1.06 1.2 × 10−9
9 rs112403360 5 14724304 ANKH A/T 0.073 1.09 1.06–1.12 2.3 × 10−9
10 rs62374257 5 86927378 COX7C C/T 0.23 1.07 1.05–1.09 1.4 × 10−15
11 rs871269 5 151052827 TNIP1 T/C 0.326 0.96 0.95–0.97 8.7 × 10−9
12 rs113706587 5 180201150 RASGEF1C A/G 0.11 1.09 1.07–1.12 2.2 × 10−16
13 rs785129 6 114291731 HS3ST5 T/C 0.35 1.04 1.03–1.06 2.4 × 10−9
14 rs6943429 7 7817263 UMAD1 T/C 0.42 1.05 1.03–1.06 1.0 × 10−10
15 rs10952097 7 8204382 ICA1 T/C 0.114 1.07 1.05–1.10 6.8 × 10−9
16 rs13237518 7 12229967 TMEM106B A/C 0.411 0.96 0.94–0.97 4.9 × 10−11
17 rs1160871 7 28129126 JAZF1 G/GTCTT 0.222 0.95 0.93–0.97 9.8 × 10−9
18 rs76928645 7 54873635 SEC61G T/C 0.103 0.93 0.91–0.95 1.6 × 10−10
19 rs1065712 8 11844613 CTSB C/G 0.053 1.09 1.06–1.12 1.9 × 10−9
20 rs34173062 8 144103704 SHARPIN A/G 0.081 1.13 1.09–1.16 1.7 × 10−16
21 rs1800978 9 104903697 ABCA1 G/C 0.13 1.06 1.04–1.08 1.6 × 10−9
22 rs7068231 10 60025170 ANK3 T/G 0.403 0.95 0.94–0.96 3.3 × 10−13
23 rs6586028 10 80494228 TSPAN14 C/T 0.196 0.93 0.91–0.94 2.0 × 10−19
24 rs6584063 10 96266650 BLNK G/A 0.043 0.89 0.86–0.92 6.7 × 10−11
25 rs7908662 10 122413396 PLEKHA1 G/A 0.467 0.96 0.95–0.97 2.6 × 10−9
26 rs6489896 12 113281983 TPCN1 C/T 0.076 1.08 1.05–1.10 1.8 × 10−9
27 rs7157106 14 105761758 IGH gene cluster A/G 0.36 1.05 1.03–1.07 2.0 × 10−8
27 rs10131280 14 106665591 IGH gene cluster A/G 0.133 0.94 0.92–0.96 4.3 × 10−10
28 rs3848143 15 64131307 SNX1 G/A 0.22 1.05 1.04–1.07 8.4 × 10−11
29 rs12592898 15 78936857 CTSH A/G 0.133 0.94 0.92–0.96 4.2 × 10−9
30 rs1140239 16 30010081 DOC2A T/C 0.379 0.94 0.93–0.96 2.6 × 10−13
31 rs450674 16 79574511 MAF C/T 0.373 0.96 0.95–0.98 3.2 × 10−8
32 rs16941239 16 86420604 FOXF1 A/T 0.029 1.13 1.08–1.17 1.3 × 10−8
33 rs56407236 16 90103687 PRDM7 A/G 0.069 1.11 1.08–1.14 6.5 × 10−15
34 rs35048651 17 1728046 WDR81 T/TGAG 0.214 1.06 1.04–1.08 7.7 × 10−11
35 rs2242595 17 18156140 MYO15A A/G 0.112 0.94 0.92–0.96 1.1 × 10−9
36 rs5848 17 44352876 GRN T/C 0.289 1.07 1.06–1.09 2.4 × 10−20
37 rs149080927 19 1854254 KLF16 G/GC 0.48 1.05 1.04–1.07 5.1 × 10−10
38 rs9304690 19 49950060 SIGLEC11 T/C 0.24 1.05 1.03–1.07 4.7 × 10−9
39 rs587709 19 54267597 LILRB2 C/T 0.325 1.05 1.04–1.07 3.6 × 10−11
40 rs1358782 20 413334 RBCK1 A/G 0.246 0.95 0.94–0.97 1.6 × 10−8
41 rs6742 20 63743088 SLC2A4RG T/C 0.221 0.95 0.93–0.97 2.6 × 10−9
42 rs2154481 21 26101558 APP C/T 0.476 0.95 0.94–0.97 1.0 × 10−12
P values are two-sided raw P values derived from a fixed-effect meta-analysis. rs number, according to dbSNP build 153. GRCh38 assembly. Nearest protein-coding gene according to GENCODE release
a b c
33. dWeighted average MAF across all discovery studies. eApproximate OR calculated with respect to the minor allele.
Nature Genetics | VOL 54 | April 2022 | 412–436 | www.nature.com/naturegenetics 415
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