# Genome-wide meta-analysis for Alzheimer's disease cerebrospinal fluid biomarkers

Iris E. Jansen, Sven J van der Lee, Duber Gomez-Fonseca, Itziar de Rojas, Maria Carolina Dalmasso, Benjamin Grenier-Boley, Anna Zettergren, Aniket Mishra, Muhammad Ali, Victor Andrade, et al.

► To cite this version:

Iris E. Jansen, Sven J van der Lee, Duber Gomez-Fonseca, Itziar de Rojas, Maria Carolina Dalmasso, et al.. Genome-wide meta-analysis for Alzheimer's disease cerebrospinal fluid biomarkers. Acta Neuropathologica, 2022, 144 (5), pp.821-842. 10.1007/s00401-022-02454-z. hal-03902957

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<figure><img src="image_1.png" /></figure>
Genome-wide meta-analysis for Alzheimer's disease cerebrospinal fluid biomarkers

Iris E. Jansen<sup>1,2,3</sup> · Sven J. van der Lee<sup>1,2,4</sup> · Duber Gomez-Fonseca<sup>5,6,7</sup> · Itziar de Rojas<sup>8,9</sup>.

Maria Carolina Dalmasso<sup>10,11</sup> · Benjamin Grenier-Boley<sup>12</sup> · Anna Zettergren<sup>13</sup> · Aniket Mishra<sup>14</sup>.

Muhammad Ali<sup>5,6,7</sup> · Victor Andrade<sup>10,15</sup> · Céline Bellenguez<sup>12</sup> · Luca Kleineidam<sup>10,15,16</sup> · Fahri Küçükali<sup>17,18</sup>.

Yun Ju Sung<sup>5,6,7</sup> · Niccolo Tesí<sup>1,2,4</sup> · Ellen M. Vromen<sup>1,2</sup> · Douglas P. Wightman<sup>3</sup> · Daniel Alcolea<sup>9,19</sup>.

Montserrat Alegret<sup>8,9</sup> · Ignacio Alvarez<sup>20,21</sup> · Philippe Amouyel<sup>12</sup> · Lavinia Athanasiu<sup>22</sup> · Shahram Bahrami<sup>22</sup>.

Henri Bailly<sup>23</sup> · Olivia Belbin<sup>9,19</sup> · Sverre Bergh<sup>24,25</sup> · Lars Bertram<sup>26</sup> · Geert Jan Biessels<sup>27</sup> · Kaj Blennow<sup>28,29</sup>.

Rafael Blesa<sup>9,19</sup> · Mercè Boada<sup>8,9</sup> · Anne Boland<sup>30</sup> · Katharina Buerger<sup>31,32</sup> · Ángel Carracedo<sup>33,34</sup>.

Laura Cervera-Carles<sup>9,19</sup> · Geneviève Chene<sup>14,35</sup> · Jurgen A. H. R. Claassen<sup>36,37</sup> · Stephanie Debette<sup>14,35,38</sup>.

Jean-Francois Deleuze<sup>30</sup> · Peter Paul de Deyn<sup>39</sup> · Janine Diehl-Schmid<sup>40,41</sup> · Srdjan Djurovic<sup>42,43</sup>.

Oriol Dols-Icardo<sup>9,19</sup> · Carole Dufouil<sup>14,44</sup> · Emmanuelle Duron<sup>45</sup> · Emrah Düzel<sup>46,47</sup> · EADB consortium.

Tormod Fladby<sup>48,49</sup> · Juan Fortea<sup>9,19</sup> · Lutz Frölich<sup>50</sup> · Pablo García-González<sup>8,9</sup> · Maria Garcia-Martinez<sup>51</sup>.

Ina Giegling<sup>52</sup> · Oliver Goldhardt<sup>40</sup> · Johan Gobom<sup>29</sup> · Timo Grimmer<sup>40</sup> · Annakaisa Haapasalo<sup>53</sup>.

Harald Hampel<sup>54,55</sup> · Olivier Hanon<sup>23,56</sup> · Lucrezia Hausner<sup>50</sup> · Stefanie Heilmann-Heimbach<sup>57</sup> · Seppo Helisalmi<sup>58</sup>.

Michael T. Heneka<sup>15,16</sup> · Isabel Hernández<sup>8,9</sup> · Sanna-Kaisa Herukka<sup>59</sup> · Henne Holstege<sup>1,2,4</sup> · Jonas Jarholm<sup>60</sup>.

Silke Kern<sup>13,61</sup> · Anne-Brita Knapskog<sup>62</sup> · Anne M. Koivisto<sup>59,63,64</sup> · Johannes Kornhuber<sup>65</sup> · Teemu Kuulasmaa<sup>66</sup>.

Carmen Lage<sup>51,67</sup> · Christoph Laske<sup>68,69</sup> · Ville Leinonen<sup>70,71</sup> · Piotr Lewczuk<sup>65,72</sup> · Alberto Lleó<sup>9,19</sup>.

Adolfo López de Munain<sup>9,73,74,75</sup> · Sara Lopez-García<sup>51</sup> · Wolfgang Maier<sup>15</sup> · Marta Marquié<sup>8,9</sup> · Merel O. Mol<sup>78</sup>.

Laura Montrreal<sup>8</sup> · Fermin Moreno<sup>9,73,74</sup> · Sonia Moreno-Grau<sup>8,9</sup> · Gael Nicolas<sup>77</sup> · Markus M. Nöthen<sup>57</sup>.

Adelina Orellana<sup>8,9</sup> · Lene Pålhaugen<sup>48,49</sup> · Janne M. Papma<sup>78</sup> · Florence Pasquier<sup>12</sup> · Robert Perneczky<sup>31,79,80,81</sup>.

Oliver Peters<sup>46,82</sup> · Yolande A. L. Pijnenburg<sup>1,2</sup> · Julius Popp<sup>83,84</sup> · Danielle Posthuma<sup>3</sup> · Ana Pozueta<sup>51</sup>.

Josef Priller<sup>82,85,86</sup> · Raquel Puerta<sup>8</sup> · Inés Quintela<sup>33</sup> · Inez Ramakers<sup>87</sup> · Eloy Rodriguez-Rodriguez<sup>51</sup>.

Dan Rujescu<sup>52</sup> · Ingvild Saltvedt<sup>88,89</sup> · Pascual Sanchez-Juan<sup>90</sup> · Philip Scheltens<sup>1,2</sup> · Norbert Scherbaum<sup>91</sup>.

Matthias Schmid<sup>92</sup> · Anja Schneider<sup>15,16</sup> · Geir Selbæk<sup>25,48,62</sup> · Per Selnes<sup>49</sup> · Alexey Shadrin<sup>22</sup> · Ingmar Skoog<sup>13,61</sup>.

Hilkka Soininen<sup>59</sup> · Lluís Tárraga<sup>8,9</sup> · Stefan Teipel<sup>93,94</sup> · The GR@ACE study group · Betty Tijms<sup>1,2</sup> · Magda Tsolaki<sup>95</sup>.

Christine Van Broeckhoven<sup>18,96</sup> · Jasper Van Dongen<sup>17,18</sup> · John C. van Swieten<sup>78</sup> · Rik Vandenberghe<sup>97,98</sup>.

Jean-Sébastien Vidal<sup>23</sup> · Pieter J. Visser<sup>1,2,99,100</sup> · Jonathan Vogelgsang<sup>101,102</sup> · Margda Waern<sup>13,103</sup>.

Michael Wagner<sup>15,16</sup> · Jens Wiltfang<sup>101,104,105</sup> · Mandy M. J. Wittens<sup>18,106</sup> · Henrik Zetterberg<sup>28,29,107,108,109</sup>.

Miren Zulaica<sup>9,73,74</sup> · Cornelia M. van Duijn<sup>76,110</sup> · Maria Bjerke<sup>18,106,111</sup> · Sebastiaan Engelborghs<sup>18,106,111,112</sup>.

Frank Jessen<sup>16,113,114</sup> · Charlotte E. Teunissen<sup>2,115</sup> · Pau Pastor<sup>116</sup> · Mikko Hiltunen<sup>117</sup> · Martin Ingelsson<sup>118,119,120</sup>.

Ole A. Andreassen<sup>22,121</sup> · Jordi Clarimón<sup>9,19</sup> · Kristel Sleegers<sup>17,18</sup> · Agustín Ruiz<sup>8,9</sup>.

Alfredo Ramirez<sup>10,15,16,114,122</sup> · Carlos Cruchaga<sup>5,6,7</sup> · Jean-Charles Lambert<sup>12</sup> · Wiesje van der Flier<sup>1,2</sup>.

Received: 16 February 2022 / Revised: 18 May 2022 / Accepted: 7 June 2022 / Published online: 6 September 2022
© The Author(s) 2022

Iris E. Jansen, Sven J. van der Lee, Duber Gomez-Fonseca, Itziar de Rojas, Maria Carolina Dalmasso and Benjamin Grenier-Boley are co-first authors.

Agustín Ruiz, Alfredo Ramirez, Carlos Cruchaga, Jean-Charles Lambert and Wiesje van der Flier are co-last authors.

Members of “EADB consortium” and “The GR@ACE study group” are provided in Supplementary File 2.

Extended author information available on the last page of the article
**Abstract**

Amyloid-beta 42 (Aβ42) and phosphorylated tau (pTau) levels in cerebrospinal fluid (CSF) reflect core features of the pathogenesis of Alzheimer's disease (AD) more directly than clinical diagnosis. Initiated by the European Alzheimer & Dementia Biobank (EADB), the largest collaborative effort on genetics underlying CSF biomarkers was established, including 31 cohorts with a total of 13,116 individuals (discovery *n* = 8074; replication *n* = 5042 individuals). Besides the *APOE* locus, novel associations with two other well-established AD risk loci were observed; *CR1* was shown a locus for Aβ42 and *BIN1* for pTau. *GMNC* and *C16orf95* were further identified as loci for pTau, of which the latter is novel. Clustering methods exploring the influence of all known AD risk loci on the CSF protein levels, revealed 4 biological categories suggesting multiple Aβ42 and pTau related biological pathways involved in the etiology of AD. In functional follow-up analyses, *GMNC* and *C16orf95* both associated with lateral ventricular volume, implying an overlap in genetic etiology for tau levels and brain ventricular volume.

**Keywords** GWAS · Alzheimer's disease · Cerebrospinal fluid · Amyloid-beta · Tau

**Introduction**

Resolving the genetic background of Alzheimer's disease (AD) has proven to contribute greatly to our understanding of underlying disease processes, for instance with the discovery of APP [25], PSEN1 [53], and PSEN2 [52] in family-based studies, leading to the amyloid cascade theory [38]. In addition, genome-wide association studies (GWAS) in AD have convincingly highlighted the importance of microglia [33, 56], a finding previously supported by research from other scientific fields [18, 72, 75], and now also widely accepted as a genetic cause rather than a result of AD pathogenesis. Further exploration of genetic risk factors contributing to AD development and pathogenesis might reveal more biological insights, an important step in the quest for AD treatment that will slow down or even halt disease progression.

GWAS of clinically diagnosed AD patients have been successful, and current efforts largely focus on increasing sample size to improve the statistical power to detect genetic variants [6, 70]. An alternative approach is to study effects of genetic variants on pathophysiological features of AD. The strength of such studies is based on the assumption that more objective measurable biological properties are more strongly associated with the underlying AD pathology than the clinical diagnostic classifications (e.g., misclassifications or symptoms not manifested yet), thereby allowing to detect larger effects by reducing heterogeneity [26]. The use of biomarkers further enables to identify genetic effects specific for certain AD-related biological mechanisms. This is an advantage over the conventional GWAS approach for clinical AD diagnosis, where it generally remains unclear through what causal gene or cellular process a locus is associated to AD.

It is possible to measure levels of amyloid-beta-42 (Aβ42) and (phosphorylated) tau (pTau and Tau) in cerebrospinal

fluid (CSF), the two major proteins implicated in the AD pathological process. Aβ42 pathology in the brain is negatively correlated with CSF Aβ42 levels, where a decrease in CSF Aβ42 is indicative of AD [49, 58]. CSF (p)Tau is positively correlated with (p)Tau pathology in the brain, and therefore higher CSF (p)Tau levels are observed in patients with AD. CSF pTau is presumed to reflect AD-type tangles more specifically than total tau [49, 58]. Previous studies on CSF amyloid-beta and (p)Tau have identified genetic risk loci, the most recent one including 3,146 individuals [17]. Some of the 8 discovered loci had not been previously associated with AD, emphasizing the potential of endophenotypes to reveal novel genetic risk factors. Our current study aimed to further define the genetic background of AD by studying the genetic effects on CSF Aβ42 and pTau levels in a total of 13,116 individuals.

**Materials and methods**

**Participants**

We combined data from 16 European cohorts, encompassing a total of 8074 individuals (Table 1; Online Resource 1—Table 1; Online Resource 2—Fig. 1) with both genotype data and CSF measurements. The majority of these cohorts (82%) are part of the EADB consortium [6], and included the full spectrum of clinical severity potentially leading to AD, from subjective cognitive decline, mild cognitive impairment, to dementia. Written informed consent was obtained from study participants or, for those with substantial cognitive impairment, from a caregiver, legal guardian, or other proxy. Study protocols for all cohorts were reviewed and approved by the appropriate institutional review boards.

For replication, 15 cohorts totaling 5042 individuals (Online Resource 1—Table 2) were available to attempt

<figure><img src="image_2.png" /></figure>
<table><caption>**Table 1** Demographic information on cohorts of stage 1 discovery analysis</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Cohort</th><th>Age</th><th>Gender</th><th colspan="4">Diagnoses</th><th colspan="2">APOE4 carrier</th><th colspan="2">pTau</th></tr><tr><th>Mean years (sd)</th><th>% male</th><th>AD (%)</th><th>MCI (%)</th><th>Control (%)</th><th>Other dem (%)</th><th>% APOE4 +</th><th>Mean levels (sd)</th><th>n</th><th>Mean levels (sd)</th><th>n</th></tr></thead><tbody><tr><td>Belgium</td><td>DEM</td><td>78.3 (7.3)</td><td>40</td><td>72</td><td>27</td><td>1</td><td>0</td><td>48</td><td>562.7 (250.8)</td><td>587</td><td>71.6 (35.4)</td><td>585</td></tr><tr><td>Finland</td><td>ADGEN</td><td>70.2 (8.0)</td><td>34</td><td>89</td><td>1</td><td>10</td><td>0</td><td>73</td><td>477.1 (183.2)</td><td>226</td><td>80.4 (34.8)</td><td>155</td></tr><tr><td>France1</td><td>BALTAZAR</td><td>77.0 (6.7)</td><td>45</td><td>43</td><td>57</td><td>0</td><td>0</td><td>43</td><td>825.7 (358.9)</td><td>420</td><td>73.0 (34.0)</td><td>419</td></tr><tr><td>France2</td><td>MEMENTO</td><td>69.2 (8.9)</td><td>47</td><td>0</td><td>100</td><td>0</td><td>0</td><td>30</td><td>1078.6 (406.7)</td><td>389</td><td>62.6 (29.8)</td><td>386</td></tr><tr><td>France3</td><td>CNRMAI-Lille</td><td>66.0 (8.7)</td><td>47</td><td>100</td><td>0</td><td>0</td><td>0</td><td>56</td><td>664.8 (205.1)</td><td>127</td><td>101.8 (53.4)</td><td>125</td></tr><tr><td>Germany1</td><td>Delcode</td><td>71.7 (5.9)</td><td>52</td><td>13</td><td>23</td><td>64</td><td>0</td><td>39</td><td>965.3 (338.2)</td><td>465</td><td>63.2 (35.1)</td><td>462</td></tr><tr><td>Germany2</td><td>KND</td><td>67.3 (8.7)</td><td>57</td><td>18</td><td>82</td><td>0</td><td>0</td><td>49</td><td>724.0 (347.9)</td><td>309</td><td>69.4 (36.8)</td><td>305</td></tr><tr><td>Germany3</td><td>TUM</td><td>70.2 (9.2)</td><td>48</td><td>98</td><td>1</td><td>0</td><td>1</td><td>58</td><td>536.8 (240.0)</td><td>151</td><td>88.8 (48.7)</td><td>152</td></tr><tr><td>Germany4</td><td>PAGES</td><td>73.4 (7.7)</td><td>40</td><td>70</td><td>30</td><td>0</td><td>0</td><td>59</td><td>528.2 (245.5)</td><td>136</td><td>90.8 (60.0)</td><td>137</td></tr><tr><td>Germany5</td><td>UHB</td><td>70.3 (7.2)</td><td>42</td><td>69</td><td>30</td><td>1</td><td>0</td><td>68</td><td>545.7 (327.8)</td><td>111</td><td>82.0 (44.2)</td><td>111</td></tr><tr><td>Netherlands</td><td>ADC &amp; Pearl ND</td><td>64.1 (8.9)</td><td>59</td><td>42</td><td>10</td><td>24</td><td>23</td><td>50</td><td>842.8 (288.9)</td><td>2936</td><td>65.9 (35.9)</td><td>2931</td></tr><tr><td>Spain1</td><td>ACE</td><td>72.7 (8.2)</td><td>43</td><td>27</td><td>59</td><td>8</td><td>6</td><td>36</td><td>779.2 (314.3)</td><td>609</td><td>68.2 (35.5)</td><td>609</td></tr><tr><td>Spain2</td><td>SIGNAL &amp; SPIN</td><td>70.6 (8.0)</td><td>43</td><td>34</td><td>45</td><td>19</td><td>2</td><td>39</td><td>707.2 (368.3)</td><td>394</td><td>71.1 (41.9)</td><td>370</td></tr><tr><td>Spain3</td><td>Valdecilla</td><td>67.0 (9.0)</td><td>39</td><td>10</td><td>37</td><td>45</td><td>8</td><td>29</td><td>887.5 (360.5)</td><td>98</td><td>56.8 (27.9)</td><td>99</td></tr><tr><td>Sweden1</td><td>Birth cohort &amp; Clin. AD</td><td>75.0 (9.4)</td><td>45</td><td>51</td><td>0</td><td>49</td><td>0</td><td>52</td><td>533.5 (271.9)</td><td>856</td><td>68.9 (35.5)</td><td>694</td></tr><tr><td>Sweden2</td><td>Uppsala university</td><td>71.0 (6.3)</td><td>46</td><td>58</td><td>37</td><td>0</td><td>6</td><td>64</td><td>489.0 (238.1)</td><td>260</td><td>77.6 (37.2)</td><td>259</td></tr></tbody></table>

For most cohorts, one of the two CSF levels is missing for a small number of samples. The demographics for age, gender diagnoses and APOE4 carriers status are then displayed for the largest group of samples with at least one CSF measurement. AB42 levels are corrected according to known drift over time for the Dutch ADC and Pearl ND cohorts. All, except the Swedisch Birth cohort and clinical AD samples, are part of EADB.

Aβ42 amyloid-beta 42, pTau phosphorylated tau, AD Alzheimer's disease, MCI mild cognitive impairment, other dem other dementia, n sample size, DEM Antwerp prospective dementia cohort
replication of the association signals to Aβ42 and pTau, for
the variants with *P* value < 1e-5 in the discovery analysis.
Data from all cohorts, except one (NorCog from Univer-
sity of Oslo, Norway), were obtained through collaboration
with the previous largest GWAS on CSF Aβ42 and pTau,
mostly including cohorts originating from the United States
[17]. Basic demographics are described in Online Resource
1—Table 2, more detailed cohort information is described
elsewhere [17].

CSF measurements

Due to the multi-center approach, CSF protein levels were
measured with various CSF protein assays (Online Resource
1—Table 2). Aβ42 was measured with ELISA, Lumipulse
or V-PLEX, and pTau with ELISA or Lumipulse. For details
on specific lab procedures, see the original studies [2–4, 17,
21, 23, 27, 34, 36, 42, 47, 50, 57, 62, 78]. Protein levels
were log10 transformed and normalized within cohorts and
CSF assay type (if multiple assays were used within a single
cohort) to approximate a normal distribution to correct for
the application of various CSF assays across different stud-
ies. Then, the normalized protein levels were used as con-
tinuous phenotypes in the association analyses. The distribu-
tion of raw and normalized CSF protein levels are displayed
in Online Resource 2—Figs. 2–13, for the cohorts for which
we have individual-level data available (indicated in Online
Resource 1—Table 1; column D). For the stratified analyses,
two subgroups of individuals, amyloid normal and amyloid
abnormal, were defined based on their Aβ42 status. Indi-
viduals with an untransformed Aβ42 level below a threshold
were assigned to the abnormal amyloid level group. The
thresholds were defined by the individual research groups
as it depends on technical circumstances, and are displayed
in Online Resource 1—Table 2.

Genotyping, quality control and imputation

The genetic data for the EADB cohorts have been processed
in a homogeneous approach (Online Resource 1—Table 1),
in which the Illumina Infinium Global Screening Array
(GSA, GSAsharedCUSTOM_24+v1.0) was predominantly
used for data generation. Additional arrays included the
Axiom 815 K Spanish biobank array (Thermo Fisher) for
ACE (Barcelona, Spain) and Valdecilla (Santander, Spain)
cohorts, and the Illumina Neurochip array (Gothenburg,
Sweden). Standard quality control (QC) procedures were
performed to exclude individuals and variants with low qual-
ity, in general followed by imputation with the Trans-Omics
for Precision Medicine (TOPMed) reference panel [13, 59].
For the EADB cohorts for which GSA genotype level data

were available, the details on QC steps and imputation with
the TOPMed reference panel were previously described [6].

For the Spanish ACE and Valdecilla cohorts, QC pro-
cedures are described in another study [16], followed by
imputation with the TOPMed reference panel. For the
Gothenburg H70 Birth Cohort studies and clinical AD
samples from Sweden, the QC and imputation procedures
were described elsewhere [48]. Post-imputation QC only
included variants with a high imputation quality (RSQ
[imputation quality] > 0.8). The UCSC LiftOver program
(https://genome-store.ucsc.edu/) and Plink v2.0 (www.cog-
genomics.org/plink/2.0/) [10] were used to lift the GRCh37
genomic positions to GRCh38, the genomic build for all
other datasets. All genotypes were hard called using the
default Plink v2.0 (http://www.cog-genomics.org/plink/2.
0/) settings.

Heritability and genetic correlation

For the estimation of the SNP-heritability, two distinct tools
were used. With LD score regression (LDSC), it was pos-
sible to perform the calculations with the full number of
samples as the input for this analysis is the summary sta-
tistics. Besides heritability estimates, genetic correlations
were also calculated for Aβ42, pTau, tau (to test the simi-
larity in genetic background to pTau), and two previously
published AD summary statistics [33, 43]. Precalculated
LD scores from the 1,000 Genomes European reference
population were obtained online. All estimates were based
on HapMap3 SNPs only to ensure high-quality LD score
calculations (https://alkesgroup.broadinstitute.org/LDSCO
RE/). As a rule of thumb, LD Score regression tends to yield
very noisy results when applied to datasets with fewer than
5000 individuals (https://github.com/bulik/ldsc/wiki/FAQ)
[9]. The summary statistics for the stratified analyses were,
therefore, not considered.

For comparison to SNP-heritability estimates of previous
studies for Aβ42 and pTau, GCTA v1.9 [74] was applied
to the individual-level genotype data of the largest dataset
(Netherlands). Other datasets were not considered as the
sample size was too low for small standard errors, thereby
impossible to draw any meaningful conclusions from the
estimates. The restricted maximum likelihood (REML)
analysis was performed for the log10-transformed normal-
ized CSF Aβ42 and pTau adjusted for gender, age, and the
first 10 principal components. Variance explained could
not be calculated for significant loci only as p-values from
GWAS results of a large independent sample are unavailable,
and calculation in the Dutch sample would be hampered by
winners-curse, causing inflation.
## Single-marker association

Genome-wide association analysis for each cohort was performed in PLINK v2.0 [10], using linear regression for the continuous phenotypes Aβ42, tau and pTau. Association tests were adjusted for gender, age, assay type (if applicable), and ten ancestry principal components. Only variants with a minor allele frequency threshold above 0.01 were tested. For smaller cohorts (n < 250 individuals) this threshold was set to 0.05 to avoid false positive findings.

Association analyses were repeated for subgroups, stratified according to APOE4 status (based on the high-quality ($R^2$>0.8) imputed variants rs429358 and rs7412) or dichotomous Aβ42 status, resulting in the following groups: (1) APOE4 (hetero- and homozygous) carriers; (2) APOE4 non-carriers; (3) individuals with abnormal Aβ42 levels; and (4) individuals with normal Aβ42 levels. After stratification, cohorts with a minimal sample size of 100 individuals were included. Covariates were those described for the main analyses above.

## Independent replication

A total of 5042 samples from 15 cohorts were included for the replication analysis. The genetic data for NorCog were generated with 2 different genotyping assays. Extensive QC procedures which are detailed elsewhere [6], allowed for joined genetic analyses of these sub-datasets. Variant association testing was performed according to the association analysis section above. For all other replication cohorts, QC, imputation and association testing procedures are described elsewhere [17]. In short, individual and variant QC standards were met, and imputation was performed using the 1000 Genomes Project Phase 3 reference panel. Each dataset was QCed and imputed independently. The additive linear regression model in PLINK v1.9 [10] was used for single-variant analyses.

## Meta-analyses

METAL [71] was used for meta-analyses in stages 1–3 of the per-cohort association results, applying the default approach that utilizes *P* value and direction of effect, while weighted according to sample size. For stage 1, we used the genome-wide threshold for significance of $P < 5 \times 10^{-8}$, and a suggestive threshold of $P < 1 \times 10^{-5}$ to select variants to study in Stage 2. Stage 2 variants were considered a replication with $P < 0.05$ and same direction of effect in comparison

to stage 1. The genome-wide threshold for significance of $P < 5 \times 10^{-8}$ was used to defined GWAS hits in stage 3.

## Colocalization

All variants within 1.5 megabases (Mb) of the lead variant of each genomic risk loci were used in the colocalization analysis. The stage 1 GWAS summary statistics were used for the CSF loci aiming consist sample sizes across the variants. Colocalization comparisons were performed to eQTL data for brain and immune-related tissues and cell types, which were obtained from the eQTL catalog [39] release 5. The microglia data were obtained from Young et al. [77]. GWAS summary statistics for loci comparison to other GWAS studies were obtained from Kunkle et al. [43] for AD, and from Vojinovic et al. [65] for brain ventricular volume. The GWAS data and eQTL data were trimmed so that all variants overlap. Colocalization was performed with the Coloc R package [24], using the coloc.abf function for the approach assuming a single causal variant, and the runsusie and coloc.susie functions to test for colocalization relaxing this assumption to multiple variants. The latter approach was performed for the comparisons to AD and brain volume loci, and LD matrices were calculated with our own individual-level data for the significant CSF loci using LDstore R package [7]. Default priors were used for prior probability of association with the GWAS data and eQTL data. The prior probability of colocalization was set as $5 \times 10^{-6}$ as recommended [67]. Nominal *P*, sample size and MAF were used when beta and variance of beta were not available for the GWAS data or eQTL data. Colocalizations with a posterior probability > 0.8 were considered successful colocalizations. Comparisons were visualized with the R package LocusCompareR (https://github.com/boxiangliu/locuscomparer).

## Gene-based analysis

Gene-based and gene-set association tests were performed using MAGMA v1.08 [14], which was implemented by FUMA [69]. The per variant association summary statistics for the main results served as the input, where variants were selected if mapped within 18,870 protein-coding genes (with unique ensembl ID). The mean SNP-wise model was implemented. The Bonferroni-corrected significance threshold was set to $P < 2.65 \times 10^{-6}$, based on the number of tested genes.
## Gene mapping

The genome-wide significant loci of the main results were further explored for promising causal AD genes using FUMA [69], after lifting over the results with genomic build GRCh38 to GRCh37 with the UCSC LiftOver Program (https://genome-store.ucsc.edu/). Two gene mapping strategies were used:

- Positional mapping maps SNPs to genes based on physical distance (within a 10-kb window) from known protein-coding genes in the human reference assembly (GRCh37/hg19).

- eQTL mapping maps SNPs to genes with which they show a significant eQTL association (that is, allelic variation at the SNP is associated with the expression level of that gene). eQTL mapping uses information from 85 brain- and immune-related tissue types in 11 data repositories (BIOSQTL, BloodeQTL, BRAINEAC, CMC, DICE, eQTLcatalogue, eQTLGen, GTEx, PsychENCODE, scRNA_eQTLs, xQTLServer), and is based on cis-eQTLs which can map SNPs to genes up to 1 Mb apart. We used a false discovery rate of 0.05 to define significant eQTL associations.

## Phenome-wide association studies (PheWAS)

We conducted phenome-wide association studies (PheWAS) on the top SNPs, rs4844610, rs429358, rs744373, rs9877502, rs4843559. A PheWAS starts out with a single to a few variants of interest that are systematically being tested for association to many phenotypes. We used the ‘phewas’ function of the R-package ‘ieugwasr’ [19, 31]. Using this function, we searched traits that associate with the list of SNPs with $P < 1 \times 10^{-7}$ in all GWAS harmonized summary statistics in the MRC IEU OpenGWAS data infrastructure [31]. In short, this enables us to screen for other traits to which these SNPs are associated. The database (May 2021) includes the GWAS summary statistics of 19,649 traits.

## Association with CSF proteomics

We associated the lead variants near *GMNC* (rs9877502) and in *C16orf95* (rs4843559) with CSF proteomics data of two different sources (EMIF-AD MBD and Knight-ADRC). For the EMIF-AD MBD data, a total of 2,136 proteins were quantified centrally using 11-plex tandem mass tag spectrometry in 366 individuals from the EMIF-AD MBD study [61] (subset of Amsterdam Dementia Cohort within EADB). We selected proteins with a maximum of 50% missing values. For related proteins that had identical values due to fragment aspecificity, we randomly selected one protein

for analysis (52 proteins were excluded). Out of the 2136 proteins quantified, 1282 (55.4%) proteins respected these criteria and were included in the study.

For the Knight-ADRC data, levels of 1305 proteins were quantified using the SOMAscan assay, a multiplexed, aptamer-based platform CSF ($n=717$) [73]. Quality control was performed at the sample and aptamer levels using control aptamers (positive and negative controls) and calibrator samples. As described in detail [73], additional quality control was performed that included limit of detection cut-off, scale factor, coefficient of variation, and outlier variation. Only proteins with a call rate higher than 85% call rate were included. A total of 713 proteins passed quality control. pQTL analyses was performed and reported in previous studies [73].

g: Profiler and Enrichment map [44], a Cytoscape App, were used to perform pathway enrichment analyses on proteins with a certain level of association (EMIF-AD MBD: $P < 0.05$; Knight-ADRC: $P < 0.004$, corresponding to proteins with a similar effect size as in EMIF-AD MBD). The results are shown as functionally grouped networks. We used GO biological processes and Reactome as ontology sources. For this explorative analysis, only pathways with $P < 0.05$ (corrected for multiple testing) are shown.

## Effects of AD-associated variants on Aβ42 and pTau

We assessed the most recent GWAS [6] for AD and extracted the top loci of 83 variants (excluding *APOE ε4* and *APOE ε2*) that showed genome-wide significant association with AD [6]. We extracted Z-scores and *P* values and plotted them in a heatmap. Rows and columns were clustered using Euclidean distances and average hierarchical clustering. We performed a gene-set enrichment analysis to find molecular pathways enriched within each cluster. The SNP-gene assignment corresponds to the one described in the recent main EADB GWAS [6], including several annotation strategies. When multiple genes were reported to associate with the same SNP (rs12590654 near *SLC24A4/RIN3*, rs7225151 near *SCIMP/RABEP1*, rs6846529 near *CLNK/HS3ST1*, rs7384878 near *ZCWPW1/NYAP1* and rs10437655 near *CELF1/SPI1*), we considered both genes for the gene-set enrichment analysis. In addition, for SNP rs6605556, located in the complex HLA region, we considered HLA-DRB1 gene (eQTL in blood with rs6605556), and for SNPs rs7157106 and rs10131280, both located in the gene-dense IGH region, we considered IGHG2 and IGHV2-70 (eQTLs in blood with rs7157106 and rs10131280, respectively). The gene-set enrichment analysis was performed specifying Biological Processes from Gene Ontology [1, 5] as gene-set and correcting *P* values with Bonferroni. Biological pathways were considered significant at corrected $P < 0.01$. To help

<figure><img src="image_3.png" /></figure>
with the interpretation of each cluster's function, we plot the most recurring words of the significant terms underlying each cluster using wordclouds. The following R packages were used for these analysis: gprofiler2 [41] and wordcloud2 (https://github.com/lchiffon/wordcloud2).

## Results

The overview of the study design is illustrated in Online Resource 2—Fig. 1. The GWA results from 16 studies were combined in stage 1. Variants that reached a suggestive level of significance ($P < 1 \times 10^{-5}$) were subsequently evaluated in an independent sample from 15 studies in stage 2. Finally, the results of stage 1 and stage 2 analyses were combined in stage 3. Detailed information on study participants, CSF acquisition and genotyping is provided in Table 1 and Online Resource 1—Tables 1 and 2. The results for tau and pTau are strongly correlated ($r_g = 0.94; P = 1.86 \times 10^{-118}$), and therefore only pTau findings are reported.

## Genetic architecture

The fraction of variance in Aβ42 and pTau protein levels that could be explained by the additive effect of the genetic variants tested, was estimated on 0.13 (SE = 0.06) and 0.21 (SE = 0.07) by LDSC, respectively. These SNP-heritabilities are substantially higher than the 0.07 previously estimated with LDSC for the diagnosis AD [80], or similarly reported for AD by this study using the same LDSC method on more recent public GWAS summary statistics of AD [33, 43] (Online Resource 1—Table 3). GCTA estimated the SNP-heritability to be higher for both Aβ42 and pTau, namely 0.27 (SE = 0.13) and 0.34 (SE = 0.12), respectively. Both methods are reporting a higher SNP-heritability for pTau than for Aβ42. Genetic correlation estimates with AD GWAS summary statistics are described in Online Resource 1—Results and Online Resource 1—Table 4.

## GWAS variants associated with CSF Aβ42 and pTau

The stage 1 meta-analyses (QQ plots and lambda shown in Online Resource 2—Fig. 14) identified 4 independent significant variant associations, 1 for Aβ42 and 3 for pTau (Table 2). The strongest associations were observed for both Aβ42 and pTau in the APOE locus (Table 2). The variant that determines the APOE ε4 allele (rs429358-C) decreased Aβ42 ($Z = -36.29; P = 2.0 \times 10^{-288}$) and increased pTau ($Z = 18.31; P = 6.87 \times 10^{-75}$) in CSF. In contrast, the variant determining the APOE ε2 haplotype (rs7412-T) increased Aβ42 ($Z = 11.97; P = 5.09 \times 10^{-33}$) and decreased pTau ($Z = -6.59; P = 4.49 \times 10^{-11}$). The APOE ε2 association

<table><caption>**Table 2** Meta-analysis association results for the 3 stages</caption><thead><tr><th rowspan="2">Protein</th><th rowspan="2">Locus</th><th rowspan="2">rsID</th><th rowspan="2">Chr</th><th rowspan="2">Pos (hg38)</th><th rowspan="2">A1</th><th rowspan="2">A2</th><th colspan="3">Stage 1</th><th colspan="3">Stage 2</th><th colspan="3">Stage 3</th></tr><tr><th>Freq A1</th><th>n</th><th>Z</th><th>P</th><th>n</th><th>Z</th><th>P</th><th>n</th><th>Z</th><th>P</th></tr></thead><tbody><tr><td rowspan="2">AB42</td><td>1q32.2 (CRI)</td><td>rs4844610</td><td>1</td><td>207,629,207</td><td>A</td><td>C</td><td>0.20</td><td>8074</td><td>-4.87</td><td>1.13E-06</td><td>5015</td><td>-3.54</td><td>4.08E-04</td><td>13,089</td><td>-6.01</td><td>1.84E-09</td></tr><tr><td>APOE</td><td>rs429358</td><td>19</td><td>44,908,684</td><td>C</td><td>T</td><td>0.29</td><td>8074</td><td>-36.29</td><td>2.00E-288</td><td>4488</td><td>-25.51</td><td>1.57E-143</td><td>12,562</td><td>-41.68</td><td>1.00E-379</td></tr><tr><td rowspan="4">pTau</td><td>2q14.3 (BIN1)</td><td>rs744373</td><td>2</td><td>127,137,039</td><td>G</td><td>A</td><td>0.30</td><td>7798</td><td>5.39</td><td>6.99E-08</td><td>3948</td><td>3.03</td><td>2.48E-03</td><td>11,746</td><td>6.15</td><td>7.88E-10</td></tr><tr><td>3q28 (GMNC)</td><td>rs9877502</td><td>3</td><td>190,951,729</td><td>A</td><td>G</td><td>0.39</td><td>7798</td><td>9.06</td><td>1.28E-19</td><td>4742</td><td>8.97</td><td>3.00E-19</td><td>12,540</td><td>12.66</td><td>9.65E-37</td></tr><tr><td>16q24.2 (C16orf95)</td><td>rs4843559</td><td>16</td><td>87,191,825</td><td>G</td><td>A</td><td>0.38</td><td>7798</td><td>6.41</td><td>1.49E-10</td><td>3985</td><td>3.88</td><td>1.03E-04</td><td>11,783</td><td>7.47</td><td>8.03E-14</td></tr><tr><td>APOE</td><td>rs429358</td><td>19</td><td>44,908,684</td><td>C</td><td>T</td><td>0.28</td><td>7798</td><td>18.31</td><td>6.87E-75</td><td>4741</td><td>13.83</td><td>1.66E-43</td><td>12,539</td><td>20.65</td><td>9.59E-95</td></tr></tbody></table>

Z-statistic summarizes the magnitude and the direction of effect relative to the effect allele. For APOE stage 2 and stage 3, the statistics are reported for the most significant stage 3 variants rs4420638 (G allele) for AB42 and rs769449 (A allele) for pTau, as rs429358 was not genotyped or imputed in the replication datasets

rsID ID number in SNP database, Chr chromosome, Pos genomic base pair position, A1 effect allele, A2 other allele, Freq A1 allele frequency effect allele, n sample size, P p value

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 1** Manhattan plots of the stage 3 GWAS results. a Results visualized for CSF Aβ42; b Results visualized for CSF pTau. The y-axes are limited to visualize the non-APOE loci. The lowest *P* values for APOE are 4.07×10⁻³⁵⁵ and 3.74×10⁻⁹⁴ for Aβ42 and pTau, respectively</figcaption></figure>

was replicated in stage 2 (Aβ42: Z=7.27; P=3.73×10⁻¹³, and pTau: Z=−6.43; P=1.26×10⁻¹⁰), and APOE ε4 with rs4420638-G for Aβ42 (Z=−25.51; P=1.57×10⁻¹⁴³), and with rs769449-A for pTau (Z=13.83; P=1.66×10⁻⁴³), both variants in high linkage disequilibrium with rs429358, as the original APOE ε4 variant was not genotyped or imputed in the replication datasets.

In stage 1, no other significant loci were observed for Aβ42. For pTau, we further identified significant associations mapping to two chromosomal regions at 3q28 and 16q24.2 (Table 2). The 3q28-locus (Z=9.06; P=1.28×10⁻¹⁹) also known as the *GMNC* locus, was reported previously for its association with pTau [17]. The cohorts from this previous study are the replication cohorts of the current study, thereby logically *GMNC* was replicated (Z=8.97; P=3.00×10⁻¹⁹). The 16q24.2 locus (Z=6.41; P=1.49×10⁻¹⁰), which is novel for pTau, was replicated in the stage 2 meta-analysis (Z=3.88; P=1.03×10⁻⁰⁴).

Subsequently, the results from all individual studies were combined in the stage 3 meta-analysis (N=13,116). In stage 3, two well-known AD loci showed additional genome-wide

significant associations (Fig. 1, Table 2) with Aβ42 in chromosomal region 1q32.2 (Z=−6.01; P=1.84×10⁻⁹, CR1; Fig. 1a), and with pTau for the region 2q14.3 (Z=6.15; P=7.88×10⁻¹⁰, BIN1; Fig. 1b). The per-cohort and zoomed in genomic location details of all significantly associated loci of stage 3 are visualized in Online Resource 2—Figs. 15–20. Colocalization analyses (results detailed in Online Resource 1—Table 5; Online Resource 2—Fig. 21 and 22) showed colocalization of the *CR1* (posterior probability=0.97) and *BIN1* (posterior probability=0.82) loci to these loci in a recent AD GWAS [43]. For the *BIN1* locus of the AD GWAS two independent causal signals were observed, of which our *BIN1* locus colocalized (posterior probability=0.84) with the first signal that was tagged by rs6733839, which is the most significant variant of the *BIN1* locus of the AD GWAS.

Explorative meta-analyses were repeated stratified for APOE (APOE ε4 carriers (n=3240) vs. APOE ε4 non-carriers (n=3201)) and amyloid status (Amyloid normal levels (n=3182) vs. amyloid abnormal levels (n=3775)) for stage 1 (QQ plots and lambda shown in Online Resource 2—Figs. 23 and 24), of which the results are visualized in

<figure><img src="image_6.png" /></figure>
Online Resource 2—Figs. 25 and 26, and detailed in Online Resource 2—Results and Online Resource 1—Table 6. Besides the *APOE* and *GMNC* loci, two novel loci are observed that have previously not been linked to any AD phenotype.

## Functional interpretation

To interpret the functional effects of the identified variants beyond AD, we performed gene prioritization (based on positional mapping, gene-based association results, and brain and immune eQTL annotations) using FUMA [69], colocalization analyses and PheWAS. The results of

the FUMA annotation are detailed in the Online Resource 2—Results and Online Resource 1—Table 7. Ten of our CSF Aβ42 and pTau loci colocalized with one of the brain or immune eQTLs from the 63 tested datasets, which are reported in Online Resource 1—Table 8. The *APOE* locus for both Aβ42 and pTau colocalized with an eQTL for *NKPDI* in a specific immune helper T cell. The *CR1* locus for AB42 colocalized with an eQTL for the *CR1* gene in 6 brain tissues (hippocampus, caudate, putamen, dorsolateral prefrontal cortex, frontal cortex, cortex). Additionally for the dorsolateral prefrontal cortex, the CR1 locus also colocalized with an eQTL for the *AL137789.1* gene. The *BIN1*

<figure><img src="image_7.png" /><figcaption>**Fig. 2** LocusZoom plots showing variant association results for a *GMNC* and b *C16orf95* loci. In black, the pTau association signals of this study; and in orange, the lateral ventricular volume (LVV) association signals observed in other studies</figcaption></figure>
locus colocalized with an eQTL for *BIN1* in lymphoblastoid cell lines.

For the PheWAS, using data from publicly available genome-wide association studies (N=19,649) of the five top variants yielded 529 associations at $P < 1 \times 10^{-7}$ (Online Resource 1—Table 9). The majority is the known wide range of 490 traits associations with the APOE ε4 allele. For the other variants 39 associations were reported for 27 unique traits. These traits can be categorized in three groups: traits related to brain ventricular volumes in particular the lateral-ventricle (GMNC and C16orf95), Alzheimer's disease diagnosis (BIN1 and CR1), and measures of blood cell/lymphocyte counts (CR1). The regional pTau associations of GMNC and C16orf95 overlapped with ventricular volume (Fig. 2). Colocalization results (Online Resource 1—Table 5; Online Resource 2—Figs. 27 and 28) imply the same causal variant for C16orf95 (posterior probability = 0.93). The GMNC locus cannot be convincingly explained by the same causal variant (posterior probability = 0.63). The coloc method combined with Susie reports only 1 causal signal for both GWAS. Of note, the colocalization probability improves to 0.74 when first reducing the GMNC-locus variants to a credible set of variants. Although these results are subthreshold to the predefined posterior probability limit of 0.8, they do imply that the same causal variant is likely underlying the GMNC association signals for pTau and AD.

Because of the overlap in effect of *GMNC* and *C16orf95*, we hypothesized that these two loci affect the same biological pathways. We explored this hypothesis using CSF proteomics datasets of the EMIF consortium with 1284 quantified proteins and of Knight-ADRC with 696 quantified proteins (of which 42% overlap with the EMIF-AD MBD proteins). For *GMNC* there were 279 (22%) proteins associated in the EMIF-AD MBD data (Online Resource 1—Table 10) and 255 (36%) proteins in the Knight-ADRC data (Online Resource 1—Table 11). *C16orf95* could only be tested in the EMIF-AD MBD data in which 73 (6%) proteins were associated (Online Resource 1—Table 10). Only 2 proteins (CDH9 and DPP6) overlapped between the 2 loci. We studied the overlap in affected pathways between the associated protein lists. For *GMNC*, consistent functional group networks between the 2 tested datasets were axon guidance and ephrin signaling (Online Resource 2—Fig. 29, Online Resource 1—Tables 12 and 13), while for *C16orf95* (only based on the EMIF-AD MBD data) glycosaminoglycan metabolism and ECM organization were overrepresented functional groups (Online Resource 2—Fig. 30, Online Resource 1—Table 14). There was little overlap between the loci in the pathways that emerged from the protein lists.

## Relation to AD-associated genetic variants

Because of the evident overlap in etiology with clinical AD dementia, we examined the association of all known AD loci (excluding the APOE locus) with CSF Aβ42 and pTau. This analysis has an explorative character as the small contribution of each individual AD loci to CSF amyloid and pTau is in general reflected in moderate effect sizes and association signals. However, patterns of reasonable signal could facilitate the generation of biological hypotheses to test in future experiments. The results are shown in the heatmap of Fig. 3 and Online Resource 1—Tables 15 and 16. The variants could be clustered in 4 groups of AD-associated genes based on their associations with Aβ42 and pTau. The first cluster of 14 variants showed strong association with both decreased levels of Aβ42 and increased levels of pTau in CSF. A pathway enrichment analysis of the genes associated with the variants showed 29 GO terms enriched and ‘amyloid’ is the common denominator in the names of these terms, of which the signal is mostly driven by *BIN1*, *PICALM*, *ABCA7* and *CLU*. The second cluster contained 21 variants and included genes that have also been related to other dementia types (e.g., *GRN*, *TMEM107B*, *SNX1*, *MAPT*, *CTSB* and *CTSH*). This cluster was associated with decreased pTau levels, an no general effect on Aβ42 levels. Pathway analysis of the genes suggests an enrichment for 8 GO terms of which the names have ‘immune’ as a common denominator that is mostly driven by 12 genes of which *TREM2* and *GRN* are the most frequent contributors. The third cluster consisted of 22 variants, which were related to decreased levels of Aβ42 but not increased levels of pTau. Nine GO terms were enriched and ‘migration’ and ‘tyrosine’ are the words that occur most often in these terms, though each word only based on 2 GO terms each. The last cluster of 20 variants group because they increased pTau, but did not decrease Aβ42 levels. No GO terms are significantly enriched in this gene cluster.

## Discussion

We identified 2 loci (*CR1* and *APOE*) for Aβ42, and 4 loci (*BIN1*, *GMNC*, *C16orf95* and *APOE*) for pTau in a total of 13,116 individuals (discovery *n* = 8074; replication *n* = 5402 individuals). In concordance with previous GWAS studies [12, 17], both proteins showed the strongest association for *APOE*, where *APOE* ε4 decreased amyloid-beta levels and increased pTau levels, while *APOE* ε2 had the opposite effect. We confirmed *GMNC* as a risk factor for CSF pTau levels. We identified *CR1* as a novel locus for CSF Aβ42 levels, and we observed 2 novel loci (*BIN1* and *C16orf95*)
<figure><img src="image_8.png" /><figcaption>**Fig. 3** The effects of all AD-associated loci. The names of the loci are named according to their linked gene names in Bellinguez et al. (2021). Hierarchical clustering was performed on the rows and columns using Euclidian distances and the method 'average' for clustering Pathway enrichment analyses was performed on the four first clusters. The enrichment analyses are in Online Resource 1—Table 9. The upper bar shows the odds ratio for AD, where alleles for variants with protective effects have been flipped to show AD-risk increasing effects for all variants. The increases in Ap42 and pTau (positive Z scores) are shown in red and decreases in AB42 and PTau (negative Z scores) are shown in blue. \* = *P* value < 0.05, X = 0.05 < *P* value < 0.001, \# = 0.001 < *P* value < 5 × 10⁻⁸, \$ = *P* value < 5 × 10⁻⁸</figcaption></figure>
for CSF pTau levels. So other than *APOE*, no risk loci overlap is observed for Aβ42 and pTau, implying at least partly separate genetic backgrounds for both pathological hallmarks. Amyloid-beta appears to be dominated by the effect of *APOE*, while pTau is influenced by multiple genetic components. Such a divergence in genetic influences is not in concordance with a genetic etiology where accumulation of Tau tangles is a direct downstream effect of amyloid plaque formation, as proposed by the amyloid cascade theory [29, 30]. Rather, it seems that these pathologies have an independent component in its origin, as already extensively reviewed [64], thereby implying a dual-cascade hypothesis in which sporadic AD pathogenesis is caused by defects in correlated but independent cellular processes. This is highly relevant biological knowledge for the development of potential AD treatments. The limited clinical efficacy of agents that aim to reduce beta-amyloid plaques might potentially be due to this [76].

In line with this implication is the observed difference in genetic subgroups based on CSF protein patterns as proposed by the explorative cluster analysis, suggesting multiple Aβ42 and pTau related biological pathways to be involved in the etiology AD. The first genetic subgroup is defined by the word ‘amyloid’, where *BIN1*, *PICALM*, *ABCA7* and *CLU* contribute most to this signal. The involvement of these genes in amyloid-beta pathology is supported by previous studies [15, 46, 63, 79]. ‘Immune’ is labeling the second subgroup, thereby implying that the genes included in this cluster are influencing AD by altering immune responses. The largest genetic drivers of this signal are *TREM2* and *GRN* which have been previously described for their functional role in AD via the immune system [45, 54]. A substantial involvement of the immune system in AD is a generally accepted concept, supported by clinical, functional and genetic research [8]. As mentioned in the Results section, this second cluster includes genes that have also been related to other dementia types (e.g., *GRN*, *TMEM107B*, *SNX1*, *MAPT*, *CTSB* and *CTSH*). This is in line with more recent insights where dysfunction of the immune response is seen as a common cause for multiple neurodegenerative diseases [28]. The third and fourth clusters are less straightforward to interpret as many terms are similarly enriched, or no significant enrichment is observed. The definition of the first 3 subclasses in its current state is the best approximate within reach, though presumably not a perfect reflection of the genuine genetic etiology of AD. Subsequent genetic studies with larger sample sizes are anticipated to facilitate an improved understanding of the biological implications of the underlying genetic subclasses.

The variety in subclasses of genetic contributors for AD etiology could mean that different patient groups might benefit from distinct AD treatment depending on the biological pathway that is affected. Although, our genetic results in its

current state are not applicable for clinical trials, we believe by improving the definition of the amyloid-tau clusters (e.g., by increasing sample sizes), it will be possible to assign individuals to one of the groups based on the distribution of their AD risk variants. For example, individuals with relatively more AD risk variants in the first cluster should preferably be included in a clinical trial addressing amyloid formation, while individuals with higher sub PRS scores for the second immune cluster, would have a higher probability benefitting from a clinical trial targeting the immune system. Alternatively, when sample sizes are increasing for future CSF amyloid and tau GWAS, an amyloid or tau PRS could be calculated to identify individuals at high genetic risk for amyloid and tau, thereby defining the individuals that should be included in trials aiming to reduce amyloid plagues or tau neurofibrillary tangles, respectively.

As mentioned above, *APOE* is the strongest locus for both CSF Aβ42 and pTau. The functional implications of the relation between *APOE* and the 2 pathological hallmarks has been excessively studied and reviewed in the scientific field of Alzheimer’s disease [20, 32, 60, 64]. In short, studies in human and mice models have shown higher Aβ42 levels and plaque load for APOE4 carriers. Whether this is a result of gain of toxic effect (e.g., enhanced ability of the APOE4 isoform to bind to Aβ42 [40]), or loss of defensive mechanism (e.g., less effective microglial response[20]), or a combination of both, remains to be elucidated. For Tau, studies have shown hyperphosphorylation and faster accumulation for APOE4 carriers, both in mice and human models [32, 64]. Again, the functional route through which APOE is affecting the Tau aggregates is unknown, and multiple cellular processes are being proposed as mediators, including neuronal endocytosis, lipid metabolism and glial function. An important functional observation though is the influence of APOE4 on tau accumulation in absence of Aβ42 pathology [55, 68], which is in agreement with the genetic implications of this current study. An unexpected observation for the APOE locus in this study is the successful colocalization with an eQTL for the *NKPD1* in a specific type of T cells. However, due to the convincing functional involvement of *APOE* in AD pathogenesis via the presence of cysteine or arginine at APOE amino acid residuals 112 and 158, that we anticipate this successful *NKPD1* eQTL colocalization result to be a false positive.

The *CR1*-locus findings are in agreement with the well-known observed association for AD risk, where rs6656401 (*R*²=0.88 with lead SNP of current study) carriers are more susceptible for AD. The similarity in these association signals is strengthened by the convincing colocalization of the *CR1* locus between our CSF pTau observation and the *CR1* locus of Kunkle et al. [43]. We furthermore observe a successful colocalization with an eQTL for the CR1 gene in multiple brain regions. In concordance with this observation

<figure><img src="image_9.png" /></figure>
is functional work on the effect of CR1 further which sug-
gests that CR1 is involved in AD pathogenesis by regulating
Aβ42 clearance in the brain itself, but also peripherally in
blood cells [81]. More recent research in red blood cells
of AD patients showed deficient CR1 immunoreactivity,
including CR1-mediated capture of circulating amyloid-
beta [35]. They observed decreased CR1 protein levels in
red blood cells for CR1 SNPs that associate with higher
AD risk. The second novel locus in this study is BIN1 for
CSF pTau, for which we observe a successful colocaliza-
tion for an eTQL for the BIN1 gene in lymphoblastoid cell
lines, implying BIN1 to be the causal gene for this locus.
The BIN1-locus furthermore localizes with the same locus
in the AD GWAS, implying that a causal variant of the
BIN1-locus for AD contributes to AD pathogenesis via tau
pathology. BIN1 has already been linked to Tau pathology
in several functional studies, first shown in fruit flies where
a decrease in the BIN1 ortholog gene expression suppressed
Tau-mediated neurotoxicity [11]. More recently, research in
mice showed physical protein interaction between BIN1 and
Tau [51], and BIN1 involvement in Tau-dependent hyper-
excitability in AD [66]. In human subjects, BIN1-carriers
were associated with lower memory performance, medi-
ated by higher tau-PET levels [22]. Our observed BIN1-Tau
association contributes valuable knowledge on this topic, by
observing the same trend in a substantial larger study (89 vs
13,118 individuals) using different techniques to measure
Tau pathology (PET vs. CSF). We provide in vivo confirma-
tion that CR1 is associated with AD via Aβ42, while BIN1
relates to Tau pathology.

The third novel locus in this study is the region on
genomic location 16q24.2 for pTau, of which the strongest
associated variants are located within intronic regions of
*C16orf95*. This locus has not been linked to (p)Tau pathol-
ogy or AD in previous research, though it associated to lat-
eral ventricular volume in the CHARGE study, including
23.5 k healthy individuals [65]. Similarly, the 3q28-locus
for pTau from our findings colocalizes inconclusively (pos-
terior probability of 0.73) to lateral ventricular volume by
the CHARGE study, implying that the same genetic risk
factors contribute to both phenotypes, strengthening the
notion that neurodegeneration and (p)Tau pathology are
highly correlated. 3q28 has been linked to (p)Tau by pre-
vious CSF studies in dementia cohorts and was identified
as the *GMNC* locus [12, 17]. In comparison to the latest
GWAS of Deming et al. [17], increasing the sample size with
a small 10 k individuals in this study strengthens the associa-
tion of this locus from 3.07×10⁻¹¹ to 1.19×10⁻³⁶, thereby
turning it into a well-established locus for pTau pathology
(similar for Tau: Deming *P* = 3.07×10⁻¹¹ (*n* = 3146); current
*P* = 9.65×10⁻³⁷ (*n* = 12,540). The *C16orf95*-locus convinc-
ingly colocalized with the same locus in ventricular volume.
The formerly reported directions of effect of *GMNC* and

*C16orf95* for ventricular volume are counterintuitive. For both loci the allele that associated with an increase in pTau pathology in our dementia cohorts associated with a smaller ventricular volume, implying less neurodegeneration. We explored if these loci work through the same biological pathways using CSF proteomics data. The consistently highlighted functional groups for the *GMNC* locus (axon guidance and ephrin signaling) were different than for the *C16orf95*-locus (extracellular matrix components), thereby implying at least partly distinct functional routes via which they influence pTau protein levels in CSF. The power of the proteomics dataset is rather limited. We anticipate that these analyses would benefit and potentially find overlapping biological pathways, when AD proteomics datasets at hand will increase in sample size, or apply the same proteomics assay (rather than using different ones with little overlap in proteins).

We furthermore identified two novel loci in the strati-
fied analyses for Aβ42 levels: 7q11.22 for APOE ε4 non-
carriers and 12q13.3 in individuals with abnormal amyloid
levels. We were unable to test for replication of these loci
in independent replication datasets, as such analyses were
not performed. Neither loci have been previously linked to
AD, or any other trait. The lead SNP for the locus on chro-
mosome 7 is a common intronic variant for the lncRNA
LOC105375341, which according to GTEx is only expressed
in testis and prostate, and thereby not a promising causal
gene for AD or AD-related phenotypes. The locus on chro-
mosome 12 consists of just one rare intronic variant that
was only detected in the Dutch cohort with the largest sam-
ple size (n=498). Future studies including more cohorts of
large sample sizes are required to study this rare variant in
more detail.

Besides *APOE* and *GMNC*, no other loci from the lat-
est GWAS of CSF amyloid and tau [17] were replicated
by this study. For Aβ42, it was not possible to test the
*GLIS1*-locus as this variant was not observed in our data,
which is in concordance with the gnomAD browser [37]
reporting extremely low coverage and a MAF of 0.007 for
rs185031519, the strongest *GLIS1* SNP in Deming et al. For
the other loci that we were unable to replicate (*SERPINB1*
for Aβ42; and *GLIS3*, *PCDH8*, *CTDP1* for pTau), there
were no significant associations (*P* > 0.05) despite substan-
tial sample sizes (*n* > 7000). These differences in findings
might be due to differences in study design, for example
inclusion of cohorts with other diagnoses and/or differences
in analysis strategies.

The GCTA-based SNP-heritability estimates of 27% and
34% for Aβ42 and pTau, respectively, are in a similar range
to the estimates calculated by Deming et al. [17] (36% for
Aβ42 and 25% for pTau). Notable is that the previous study
estimated Aβ42 to be most heritable, while we observed
the highest estimate for pTau, though the standard errors
were about 10% for both studies. Our LDSC-based SNP-
heritabilities show a similar trend with higher estimates for
pTau. Furthermore, these LDSC-based 13% for Aβ42 and
21% for pTau protein levels are considerably higher than the
7% previously observed for the diagnosis AD. The higher
heritability for the tested CSF protein levels strengthens the
assumption that more objective measurable biological prop-
erties are more strongly associated with AD pathogenesis
than the diagnostic classifications.

Due to this assumption, the number of risk loci identified
in this study might be lower than anticipated. However, our
study design is suboptimal for obtaining the most powerful
model for genetic association identification. Meta-analyzing
31 heterogeneous cohorts (differences in genotyping array,
imputation references, CSF protein assays, and patient/
control ratios) is more challenging rather than analyzing
a homogeneous dataset of similar sample size. Alterna-
tively, increasing the sample size by adding more cohorts
to increase power is presumably a more feasible approach
for the future.

In conclusion, the current findings clearly show that
studying the genetic effects of AD-related endophenotypes
has the potential to reveal novel associations, and highlight
important biological insights. The clear distinction in genetic
findings for amyloid-beta and tau emphasizes the (partly)
genetic independence of these two biological mechanisms
in AD pathogenesis. Moreover, the identification of *CR1*
and *BIN1*, which are the second and third strongest asso-
ciated AD locus after *APOE*, furthermore implies that by
increasing sample size of genetic analysis in CSF biomark-
ers it will become more apparent through which biolog-
cal mechanisms certain AD loci have their effect on AD
pathogenesis. Even larger collaborative efforts with more
homogeneous sample definitions are, therefore, encouraged
to be undertaken to enhance our genetic understanding of
AD, ultimately leading to improved biological knowledge
for the development of drug treatment.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s00401-022-02454-z.

**Acknowledgements** This work was supported by a grant (European Alzheimer DNA BioBank, EADB) from the EU Joint Programme, Neurodegenerative Disease Research (JPND). Amsterdam dementia Cohort (ADC): Research of the Alzheimer center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. The Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUMc fonds. The clinical database structure was developed with funding from Stichting Dioraphte. Genotyping of the Dutch case-control samples was performed in the context of EADB (European Alzheimer DNA biobank) funded by the JPco-fuND FP-829-029 (ZonMW projectnumber 733051061). Part of the work described in this study was carried out in the context of the Parelsnoer Institute (PSI). PSI was part of and funded by the Dutch Federation of University Medical Centers and has received initial funding from the Dutch Government (from 2007-2011). Since 2020, this

work was carried out in the context of Parelsnoer clinical biobanks
at Health-RI (https://www.health-ri.nl/initiatives/parelsnoer). Part of
the genotyping included in this work was funded by the JPND EADB
grant (German Federal Ministry of Education and Research (BMBF)
grant: 01ED1619A). Alfredo Ramirez is also supported by the German
Research Foundation (DFG) grants Nr: RA 1971/6-1, RA1971/7-1,
and RA 1971/8-1. We would like to thank patients and controls who
participated in this project. The Genome Research @ Fundació ACE
project (GR@ACE) is supported by Grifols SA, Fundación bancaria
'La Caixa', Fundació ACE, and CIBERNED. A.R. and M.B. receive
support from the European Union/EFPIA Innovative Medicines Ini-
tiative Joint undertaking ADAPTED and MOPEAD projects (grant
numbers 115975 and 115985, respectively). M.B. and A.R. are also
supported by national grants PI13/02434, PI16/01861, PI17/01474,
PI19/01240 and PI19/01301. Acción Estratégica en Salud is integrated
into the Spanish National R + D + I Plan and funded by ISCIII (Insti-
tuto de Salud Carlos III)-Subdirección General de Evaluación and the
Fondo Europeo de Desarrollo Regional (FEDER-‘Una manera de hacer
Europa’). The position held by I.d.R. is funded by grant. FI20/00215.
PFIS Contratos Predoctorales de Formación en Investigación en Salud.
We would like to thank UCL Genomics, London, UK, for performing
the genotyping analyses of the samples within the Gothenburg H70
Birth Cohort Studies and Clinical AD Sweden. The recruitment and
clinical characterization of research participants at Washington Uni-
versity were supported by NIH P30AG066444, and P01AG003991.
This work was supported by access to equipment made possible by
the Hope Center for Neurological Disorders, the Neurogenomics and
Informatics Center (NGI: https://neurogenomics.wustl.edu/)and the
Departments of Neurology and Psychiatry at Washington University
School of Medicine. Research at the Belgian EADB site is funded in
part by the Alzheimer Research Foundation (SAO-FRA), The Research
Foundation Flanders (FWO), and the University of Antwerp Research
Fund. FK is supported by a BOF DOCPRO fellowship of the University
of Antwerp Research Fund. The work of Valdecilla was supported by
grants from the Instituto de Salud Carlos III (Fondo de Investigación
Sanitario, PI08/0139, PI12/02288, PI16/01652, and PI20/01011), the
JPND (DEMTEST PI11/03028), the CIBERNED, and the Siemens
Healthineers. We thank the Valdecilla Biobank (PT17/0015/0019),
integrated into the Spanish Biobank Network, for their support and col-
laboration in sample collection and management. Our heartfelt thanks
to the participants of the Valdecilla Cohort for their generosity. The
work of ADGEN was supported by EU Joint Programme—Neurode-
generative Disease Research (301220) and the Academy of Finland
(338182). The DELCODE study (Study-ID:BN012 ) was supported
and conducted by the German Center for Neurodegenerative Diseases
(DZNE). The data samples were provided by the DELCODE study
group. Details and participating sites can be found at www.dzne.de/
en/research/studies/clinical-studies/delcode. The German Dementia
Competence Network (KND) is funded by the German Federal Min-
istry of Education and Research (BMBF) grants Number: 01G10102,
01G10420, 01G10422, 01G10423, 01G10429, 01G10431, 01G10433,
04G10434, 01G10711. WF, SvdL, HHolstege, CT and PhS are recipi-
ents of ABOARD, which is a public-private partnership receiving
funding from ZonMW (#73305095007) and Health~Holland, Topsec-
tor Life Sciences & Health (PPP-allowance; #LSHM20106). More
than 30 partners participate in ABOARD (www.aboard-project.nl).
ABOARD also receives funding from de Hersenstichting, Edwin Bouw
Fonds and Gieskes-Strijbisfonds. IEJ was partially supported by NWO
Gravitation program BRAINSCAPES: A Roadmap from Neurogenet-
ics to Neurobiology (NWO: 024.004.012). AZ was supported by the
Swedish Alzheimer Foundation (AF-939988, AF-930582, AF-646061,
AF-741361), and the Dementia Foundation (2020-04-13, 2021-04-
17). ISk was supported by the Swedish state under the agreement
between the Swedish government and the county councils, the ALF-
agreement (ALF 716681), the Swedish Research Council (no 11267,
825-2012-5041, 2013-8717, 2015-02830, 2017-00639, 2019-01096), Swedish Research Council for Health, Working Life and Welfare (no 2001-2646, 2001-2835, 2001-2849, 2003-0234, 2004-0150, 2005-0762, 2006-0020, 2008-1229, 2008-1210, 2012-1138, 2004-0145, 2006-0596, 2008-1111, 2010-0870, 2013-1202, 2013-2300, 2013-2496), Swedish Brain Power, Hjärnfonden, Sweden (FO2016-0214, FO2018-0214, FO2019-0163), the Alzheimer's Association Zenith Award (ZEN-01-3151), the Alzheimer's Association Stephanie B. Overstreet Scholars (IIRG-00-2159), the Alzheimer's Association (IIRG-03-6168, IIRG-09-131338) and the Bank of Sweden Tercentenary Foundation. SK was supported by the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (ALFGBG-81392, ALF GBG-771071), the Swedish Alzheimer Foundation (AF-842471, AF-737641, AF-939825), and the Swedish Research Council (2019-02075). MW was supported by the Swedish Research Council 2016-01590. DP was supported by BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology (grant no. 024.004.012), and a European Research Council advanced grant (Grant No, ERC-2018-AdG GWAS2FUNC 834057. HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (2018-02532), the European Research Council (681712), Swedish State Support for Clinical Research (ALFGBG-720931), the Alzheimer Drug Discovery Foundation (ADDF), USA (201809-2016862), the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), and the UK Dementia Research Institute at UCL. KB was supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017-0243), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986), the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236), the National Institute of Health (NIH), USA, (grant #1R01AG068398-01), and the Alzheimer's Association 2021 Zenith Award (ZEN-21-848495). CC receives support from the National Institutes of Health (R01AG044546, R01AG064877, RF1AG053303, R01AG058501, U01AG058922, RF1AG058501, R01AG064614), and the Chuck Zuckerberg Initiative (CZI).

**Data availability statement** Genome-wide summary statistics have been deposited to the European Bioinformatics Institute GWAS Catalog (https://www.ebi.ac.uk/gwas/) under accession IDs GCST90129599 and GCST90129600.

## Declarations

**Conflict of interest** Research programs of Wiesje van der Flier have been funded by ZonMW, NWO, EU-FP7, EU-JPND, Alzheimer Nederland, CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences & Health, stichting Dioraphte, Gieskes~Strijbis fonds, stichting Equilibrio, Pasman stichting, stichting Alzheimer & Neuropsychiatrie Foundation, Biogen MA Inc, Boehringer Ingelheim, Life-MI, AVID, Roche BV, Fujifilm, Combinostics. WF holds the Pasman chair. WF is recipient of ABOARD, which is a public-private partnership receiving funding from ZonMW (#73305095007) and Health~Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). WF has performed contract research for Biogen MA Inc, and Boehringer Ingelheim. WF has been an invited speaker at Boehringer Ingelheim, Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), Springer Healthcare. WF is consultant to Oxford Health Policy Forum CIC, Roche, and Biogen MA Inc. WF participated in advisory boards of Biogen MA Inc and Roche. All funding is paid to her institution. WF was associate editor of Alzheimer, Research & Therapy in 2020/2021. WF is associate editor at Brain. CC

receives research support from: Biogen, EISAI, Alector and Parabon. The funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. CC is a member of the advisory board of Vivid genetics, Halia Therapeutics and ADx Healthcare. HZ has served at scientific advisory boards for Denali, Roche Diagnostics, Wave, Samumed, Siemens Healthineers, Pinteon Therapeutics and CogRx, has given lectures in symposia sponsored by Fujirebio, Alzecure and Biogen, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KBlennow has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Roche Diagnostics, and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. OAA is a consultant to HealthLytix, and received speaker's honorarium from Lundbeck and Sunovion. SE has served at scientific advisory boards for Biogen, Danone, Eisai, icometrix, Pfizer, Novartis, Nutricia, Roche and has received unrestricted research grants from ADx Neurosciences and Janssen Pharmaceutica. CC receives research support from: Biogen, EISAI, Alector, GSK and Parabon. The funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. CC is a member of the advisory board of Vivid genetics, Halia Therapeutics and ADx Healthcare. HHampel is an employee of Eisai Inc. and serves as Senior Associate Editor for the Journal Alzheimer's & Dementia; during the past three years he had received lecture fees from Servier, Biogen and Roche, research grants from Pfizer, Avid, and MSD Avenir (paid to the institution), travel funding from Eisai, Functional Neuromodulation, Axovant, Eli Lilly and company, Takeda and Zinfandel, GE-Healthcare and Oryzon Genomics, consultancy fees from Qynapse, Jung Diagnostics, Cytox Ltd., Axovant, Anavex, Takeda and Zinfandel, GE Healthcare, Oryzon Genomics, and Functional Neuromodulation, and participated in scientific advisory boards of Functional Neuromodulation, Axovant, Eisai, Eli Lilly and company, Cytox Ltd., GE Healthcare, Takeda and Zinfandel, Oryzon Genomics and Roche Diagnostics. DA participated in advisory boards from Fujirebio-Europe and Roche Diagnostics and received speaker honoraria from Fujirebio-Europe, Roche Diagnostics, Nutricia, Krka Farmacéutica SL, Biogen and Zambon. PSJ has received personal fees for advisory board from Roche Diagnostics and Zambon. GS participated in one advisory board meeting from Biogen. MI is a paid consultant to BioArctic AB. KS is editor at Acta Neuropathologica and associate editor at Alzheimer's Research & Therapy. HZ has served at scientific advisory boards for Denali, Roche Diagnostics, Wave, Samumed, Siemens Healthineers, Pinteon Therapeutics and CogRx, has given lectures in symposia sponsored by Fujirebio, Alzecure and Biogen, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). All other authors declare no financial interests or potential conflicts of interest.

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## References

1. (2021) The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res 49:D325–D334. https://doi.org/10.1093/nar/gkaa1113

2. Alcolea D, Martínez-Lage P, Sánchez-Juan P, Olazárán J, Antúnez C, Izagirre A et al (2015) Amyloid precursor protein metabolism and inflammation markers in preclinical Alzheimer disease. Neurology 85:626–633. https://doi.org/10.1212/wnl.000000000001859

3. Alcolea D, Pegueroles J, Muñoz L, Camacho V, López-Mora D, Fernández-León A et al (2019) Agreement of amyloid PET and CSF biomarkers for Alzheimer's disease on Lumipulse. Ann Clin Transl Neurol 6:1815–1824. https://doi.org/10.1002/acn3.50873

4. Almdahl IS, Lauridsen C, Selnes P, Kalheim LF, Coello C, Gajdzik B et al (2017) Cerebrospinal fluid levels of amyloid beta 1–43 mirror 1–42 in relation to imaging biomarkers of Alzheimer's disease. Front Aging Neurosci 9:9. https://doi.org/10.3389/fnagi.2017.00009

5. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29. https://doi.org/10.1038/75556

6. Bellenguez C, Küçükali F, Jansen IE, Kleineidam L, Moreno-Grau S, Amin N et al (2022) New insights into the genetic etiology of Alzheimer's disease and related dementias. Nat Genet 54:412–436. https://doi.org/10.1038/s41588-022-01024-z

7. Benner C, Havulinna AS, Järvelin MR, Salomaa V, Ripatti S, Pirinen M (2017) Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am J Hum Genet 101:539–551. https://doi.org/10.1016/j.ajhg.2017.08.012

8. Bettcher BM, Tansey MG, Dorothée G, Heneka MT (2021) Peripheral and central immune system crosstalk in Alzheimer disease—a research prospectus. Nat Rev Neurol 17:689–701. https://doi.org/10.1038/s41582-021-00549-x

9. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Patterson N et al (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47:291–295. https://doi.org/10.1038/ng.3211

10. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. https://doi.org/10.1186/s13742-015-0047-8

11. Chapuis J, Hansmann F, Gastelinck M, Mounier A, Van Cauwenberghe C, Kolen KV et al (2013) Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology. Mol Psychiatry 18:1225–1234. https://doi.org/10.1038/mp.2013.1

12. Cruchaga C, Kauwe JS, Harari O, Jin SC, Cai Y, Karch CM et al (2013) GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease. Neuron 78:256–268. https://doi.org/10.1016/j.neuron.2013.02.026

13. Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A et al (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284–1287. https://doi.org/10.1038/ng.3656

14. de Leeuw CA, Mooij JM, Heskes T, Posthuma D (2015) MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 11:e1004219. https://doi.org/10.1371/journal.pcbi.1004219

15. De Roeck A, Van Broeckhoven C, Sleegers K (2019) The role of ABCA7 in Alzheimer's disease: evidence from genomics, transcriptomics and methylomics. Acta Neuropathol 138:201–220. https://doi.org/10.1007/s00401-019-01994-1

16. de Rojas I, Moreno-Grau S, Tesi N, Grenier-Boley B, Andrade V, Jansen IE et al (2021) Common variants in Alzheimer's disease and risk stratification by polygenic risk scores. Nat Commun 12:3417. https://doi.org/10.1038/s41467-021-22491-8

17. Deming Y, Li Z, Kapoor M, Harari O, Del-Aguila JL, Black K et al (2017) Genome-wide association study identifies four novel loci associated with Alzheimer's endophenotypes and disease modifiers. Acta Neuropathol 133:839–856. https://doi.org/10.1007/s00401-017-1685-y

18. El Khoury J, Toft M, Hickman SE, Means TK, Terada K, Geula C et al (2007) Ccr2 deficiency impairs microglial accumulation and accelerates progression of Alzheimer-like disease. Nat Med 13:432–438. https://doi.org/10.1038/nm1555

19. Elsworth B, Lyon M, Alexander T, Liu Y, Matthews P, Hallett J et al (2020) The MRC IEU OpenGWAS data infrastructure. bioRxiv. https://doi.org/10.1101/2020.08.10.244293

20. Fitz NF, Nam KN, Wolfe CM, Letronne F, Playso BE, Iordanova BE et al (2021) Phospholipids of APOE lipoproteins activate microglia in an isoform-specific manner in preclinical models of Alzheimer's disease. Nat Commun 12:3416. https://doi.org/10.1038/s41467-021-23762-0

21. Fladby T, Pålhaugen L, Selnes P, Waterloo K, Bråthen G, Hesen E et al (2017) Detecting at-risk Alzheimer's disease cases. J Alzheimers Dis 60:97–105. https://doi.org/10.3233/jad-170231

22. Franzmeier N, Rubinski A, Neitzel J, Ewers M, Weiner MW, Aisen P et al (2019) The BIN1 rs744373 SNP is associated with increased tau-PET levels and impaired memory. Nat Commun 10:1766. https://doi.org/10.1038/s41467-019-09564-5

23. Geijselaers SLC, Aalten P, Ramakers I, De Deyn PP, Heijboer AC, Koek HL et al (2018) Association of cerebrospinal fluid (CSF) insulin with cognitive performance and CSF biomarkers of Alzheimer's disease. J Alzheimers Dis 61:309–320. https://doi.org/10.3233/jad-170522

24. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C et al (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10:e1004383. https://doi.org/10.1371/journal.pgen.1004383

25. Goate A, Chartier-Harlin MC, Mullan M, Brown J, Crawford F, Fidani L et al (1991) Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer's disease. Nature 349:704–706. https://doi.org/10.1038/349704a0

26. Gottesman II, Gould TD (2003) The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 160:636–645. https://doi.org/10.1176/appi.ajp.160.4.636

27. Grimmer T, Riemenschneider M, Förstl H, Henriksen G, Klunk WE, Mathis CA et al (2009) Beta amyloid in Alzheimer's disease: increased deposition in brain is reflected in reduced concentration in cerebrospinal fluid. Biol Psychiatry 65:927–934. https://doi.org/10.1016/j.biopsych.2009.01.027

28. Hammond TR, Marsh SE, Stevens B (2019) Immune Signaling in Neurodegeneration. Immunity 50:955–974. https://doi.org/10.1016/j.immuni.2019.03.016

29. Hardy JA, Higgins GA (1992) Alzheimer's disease: the amyloid cascade hypothesis. Science (New York, NY) 256:184–185. https://doi.org/10.1126/science.1566067
30. He Z, Guo JL, McBride JD, Narasimhan S, Kim H, Changolkar L et al (2018) Amyloid-β plaques enhance Alzheimer's brain tau-seeded pathologies by facilitating neuritic plaque tau aggregation. Nat Med 24:29–38. https://doi.org/10.1038/nm.4443

31. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife. https://doi.org/10.7554/eLife.34408

32. Husain MA, Laurent B, Plourde M (2021) APOE and Alzheimer's disease: from lipid transport to physiopathology and therapeutics. Front Neurosci. https://doi.org/10.3389/fnins.2021.630502

33. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S et al (2019) Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk. Nat Genet 51:404–413. https://doi.org/10.1038/s41588-018-0311-9

34. Jessen F, Spottke A, Boecker H, Brosseron F, Buerger K, Catak C et al (2018) Design and first baseline data of the DZNE multicenter observational study on predementia Alzheimer's disease (DELCODE). Alzheimer's Res Ther 10:15. https://doi.org/10.1186/s13195-017-0314-2

35. Johansson JU, Brubaker WD, Javitz H, Bergen AW, Nishita D, Trigunaite A et al (2018) Peripheral complement interactions with amyloid β peptide in Alzheimer's disease: polymorphisms, structure, and function of complement receptor 1. Alzheimer's Dementia 14:1438–1449. https://doi.org/10.1016/j.jalz.2018.04.003

36. Kaipainen A, Jääskeläinen O, Liu Y, Haapalinna F, Nykänen N, Vanninen R et al (2020) Cerebrospinal fluid and MRI biomarkers in neurodegenerative diseases: a retrospective memory clinic-based study. J Alzheimers Dis 75:751–765. https://doi.org/10.3233/jad-200175

37. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q et al (2020) The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581:434–443. https://doi.org/10.1038/s41586-020-2308-7

38. Karran E, Mercken M, De Strooper B (2011) The amyloid cascade hypothesis for Alzheimer's disease: an appraisal for the development of therapeutics. Nat Rev Drug Discov 10:698–712. https://doi.org/10.1038/nrd3505

39. Kerimov N, Hayhurst JD, Peikova K, Manning JR, Walter P, Kolberg L et al (2021) A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat Genet 53:1290–1299. https://doi.org/10.1038/s41588-021-00924-w

40. Kloske CM, Wilcock DM (2020) The important interface between apolipoprotein E and neuroinflammation in Alzheimer's disease. Front Immunol. https://doi.org/10.3389/fimmu.2020.00754

41. Kolberg L, Raudvere U, Kuzmin I, Vilo J, Peterson H (2020) gprofiler2—an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Res. https://doi.org/10.12688/f1000research.24956.2

42. Kornhuber J, Schmidtke K, Frolich L, Perneczky R, Wolf S, Hampel H et al (2009) Early and differential diagnosis of dementia and mild cognitive impairment: design and cohort baseline characteristics of the German Dementia Competence Network. Dement Geriatr Cogn Disord 27:404–417. https://doi.org/10.1159/000210388

43. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC et al (2019) Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet 51:414–430. https://doi.org/10.1038/s41588-019-0358-2

44. Merico D, Isserlin R, Stueker O, Emili A, Bader GD (2010) Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5:e13984. https://doi.org/10.1371/journal.pone.0013984

45. Minami SS, Min S-W, Krabbe G, Wang C, Zhou Y, Asgarov R et al (2014) Progranulin protects against amyloid β deposition and toxicity in Alzheimer's disease mouse models. Nat Med 20:1157–1164. https://doi.org/10.1038/nm.3672

46. Miyagawa T, Ebinuma I, Morohashi Y, Hori Y, Young Chang M, Hattori H et al (2016) BIN1 regulates BACE1 intracellular trafficking and amyloid-β production. Hum Mol Genet 25:2948–2958. https://doi.org/10.1093/hmg/ddw146

47. Mulder C, Verwey NA, van der Flier WM, Bouwman FH, Kok A, van Elk EJ (2010) Amyloid-beta(1–42), total tau, and phosphorylated tau as cerebrospinal fluid biomarkers for the diagnosis of Alzheimer disease. Clin Chem 56:248–253. https://doi.org/10.1373/clinchem.2009.130518

48. Najar J, van der Lee SJ, Joas E, Wetterberg H, Hardy J, Guerreiro R et al (2021) Polygenic risk scores for Alzheimer's disease are related to dementia risk in APOE ε4 negatives. Alzheimer's Dementia: Diagn Assess Dis Monitor 13:e12142. https://doi.org/10.1002/dad2.12142

49. Olsson B, Lautner R, Andreasson U, Öhrfelt A, Portelius E, Bjerke M et al (2016) CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. Lancet Neurol 15:673–684. https://doi.org/10.1016/S1474-4422(16)00070-3

50. Ramirez A, van der Flier WM, Herold C, Ramonet D, Heilmann S, Lewczuk P et al (2014) SUCLG2 identified as both a determinator of CSF Aβ1-42 levels and an attenuator of cognitive decline in Alzheimer's disease. Hum Mol Genet 23:6644–6658. https://doi.org/10.1093/hmg/du372

51. Sartori M, Mendes T, Desai S, Lasorsa A, Herledan A, Malmanche N et al (2019) BIN1 recovers tauopathy-induced long-term memory deficits in mice and interacts with Tau through Thr(348) phosphorylation. Acta Neuropathol 138:631–652. https://doi.org/10.1007/s00401-019-02017-9

52. Sherrington R, Froelich S, Sorbi S, Campion D, Chi H, Rogaeva EA et al (1996) Alzheimer's disease associated with mutations in presenilin 2 is rare and variably penetrant. Hum Mol Genet 5:985–988. https://doi.org/10.1093/hmg/5.7.985

53. Sherrington R, Rogaev EI, Liang Y, Rogaeva EA, Levesque G, Ikeda M et al (1995) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer's disease. Nature 375:754–760. https://doi.org/10.1038/375754a0

54. Shi Y, Holtzman DM (2018) Interplay between innate immunity and Alzheimer disease: APOE and TREM2 in the spotlight. Nat Rev Immunol 18:759–772. https://doi.org/10.1038/s41577-018-0051-1

55. Shi Y, Yamada K, Liddelow SA, Smith ST, Zhao L, Luo W et al (2017) ApoE4 markedly exacerbates tau-mediated neurodegeneration in a mouse model of tauopathy. Nature 549:523–527. https://doi.org/10.1038/nature24016

56. Sims R, van der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J et al (2017) Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease. Nat Genet 49:1373–1384. https://doi.org/10.1038/ng.3916

57. Somers C, Struyfs H, Goossens J, Niemantsverdriet E, Luyckx J, De Roeck N et al (2016) A decade of cerebrospinal fluid biomarkers for Alzheimer's disease in Belgium. J Alzheimers Dis 54:383–395. https://doi.org/10.3233/jad-151097

58. Sunderland T, Linker G, Mirza N, Putnam KT, Friedman DL, Kimmel LH et al (2003) Decreased beta-amyloid1-42 and increased tau levels in cerebrospinal fluid of patients with Alzheimer disease. JAMA 289:2094–2103. https://doi.org/10.1001/jama.289.16.2094

59. Taliun D, Harris D, Kessler M, Carlson J, Szpiech Z, Torres R et al (2019) Sequencing of 53,831 diverse genomes from the
NHLBI TOPMed Program. bioRxiv. https://doi.org/10.1101/563866

60. Therriault J, Benedet AL, Pascoal TA, Mathotaarachchi S, Cham-oun M, Savard M et al (2020) Association of apolipoprotein E ε4 with medial temporal tau independent of amyloid-β. JAMA Neurol 77:470–479. https://doi.org/10.1001/jamaneurol.2019.4421

61. Tijms BM, Gobom J, Reus L, Jansen I, Hong S, Dobricic V et al (2020) Pathophysiological subtypes of Alzheimer's disease based on cerebrospinal fluid proteomics. Brain 143:3776–3792. https://doi.org/10.1093/brain/awaa325

62. Tijms BM, Willemse EAJ, Zwan MD, Mulder SD, Visser PJ, van Berckel BNM et al (2018) Unbiased approach to counter-act upward drift in cerebrospinal fluid amyloid-β 1–42 analysis results. Clin Chem 64:576–585. https://doi.org/10.1373/clinchem.2017.281055

63. Uddin MS, Kabir MT, Begum MM, Islam MS, Behl T, Ashraf GM (2021) Exploring the role of CLU in the pathogenesis of Alzheimer's disease. Neurotox Res 39:2108–2119. https://doi.org/10.1007/s12640-020-00271-4

64. van der Kant R, Goldstein LSB, Ossenkoppele R (2020) Amyloid-β-independent regulators of tau pathology in Alzhei-mer disease. Nat Rev Neurosci 21:21–35. https://doi.org/10.1038/s41583-019-0240-3

65. Vojinovic D, Adams HH, Jian X, Yang Q, Smith AV, Bis JC et al (2018) Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume. Nat Commun 9:3945. https://doi.org/10.1038/s41467-018-06234-w

66. Voskobiynyk Y, Roth JR, Cochran JN, Rush T, Carullo NV, Mes-ina JS et al (2020) Alzheimer's disease risk gene BIN1 induces Tau-dependent network hyperexcitability. Elife. https://doi.org/10.7554/eLife.57354

67. Wallace C (2020) Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet 16:e1008720. https://doi.org/10.1371/journal.pgen.1008720

68. Wang C, Najm R, Xu Q, Jeong DE, Walker D, Balestra ME et al (2018) Gain of toxic apolipoprotein E4 effects in human iPSC-derived neurons is ameliorated by a small-molecule struc-ture corrector. Nat Med 24:647–657. https://doi.org/10.1038/s41591-018-0004-z

69. Watanabe K, Taskesen E, van Bochoven A, Posthuma D (2017) Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8:1826. https://doi.org/10.1038/s41467-017-01261-5

70. Wightman DP, Jansen IE, Savage JE, Shadrin AA, Bahrami S, Rongve A et al (2020) Largest GWAS (N=1,126,563) of Alzhei-mer's disease implicates microglia and immune cells. medRxiv. https://doi.org/10.1101/2020.11.20.20235275

71. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics

(Oxford, England) 26:2190–2191. https://doi.org/10.1093/bioinformatics/btq340

72. Wyss-Coray T, Yan F, Lin AH, Lambris JD, Alexander JJ, Quigg RJ et al (2002) Prominent neurodegeneration and increased plaque formation in complement-inhibited Alzheimer's mice. Proc Natl Acad Sci USA 99:10837–10842. https://doi.org/10.1073/pnas.1623501199

73. Yang C, Farias FHG, Ibanez L, Suhy A, Sadler B, Fernandez MV et al (2021) Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci 24:1302–1312. https://doi.org/10.1038/s41593-021-00886-6

74. Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:76–82. https://doi.org/10.1016/j.ajhg.2010.11.011

75. Yeh FL, Wang Y, Tom I, Gonzalez LC, Sheng M (2016) TREM2 binds to apolipoproteins, including APOE and CLU/APOJ, and thereby facilitates uptake of amyloid-beta by microglia. Neuron 91:328–340. https://doi.org/10.1016/j.neuron.2016.06.015

76. Yiannopoulou KG, Anastasiou AI, Zachariou V, Pelidou SH (2019) Reasons for failed trials of disease-modifying treatments for Alzheimer disease and their contribution in recent research. Biomedicines. https://doi.org/10.3390/biomedicines7040097

77. Young AMH, Kumasaka N, Calvert F, Hammond TR, Knights A, Panousis N et al (2021) A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat Genet 53:861–868. https://doi.org/10.1038/s41588-021-00875-2

78. Zettergren A, Höglund K, Kern S, Thorvaldsson V, Johan Skoog M, Hansson O et al (2019) Association of IL1RAP-related genetic variation with cerebrospinal fluid concentration of Alzheimer-associated tau protein. Sci Rep 9:2460. https://doi.org/10.1038/s41598-018-36650-3

79. Zhao Z, Sagare AP, Ma Q, Halliday MR, Kong P, Kisler K et al (2015) Central role for PICALM in amyloid-β blood-brain barrier transcytosis and clearance. Nat Neurosci 18:978–987. https://doi.org/10.1038/nn.4025

80. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC et al (2017) LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics (Oxford, England) 33:272–279. https://doi.org/10.1093/bioinformatics/btw613

81. Zhu X-C, Yu J-T, Jiang T, Wang P, Cao L, Tan L (2015) CR1 in Alzheimer's disease. Mol Neurobiol 51:753–765. https://doi.org/10.1007/s12035-014-8723-8

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## Authors and Affiliations

Iris E. Jansen¹,²,³ Ⓓ Sven J. van der Lee¹,²,⁴ Ⓓ Duber Gomez-Fonseca⁵,⁶,⁷ Ⓓ Itziar de Rojas⁸,⁹ Ⓓ

Maria Carolina Dalmasso¹⁰,¹¹ Ⓓ Benjamin Grenier-Boley¹² Ⓓ Anna Zettergren¹³ Ⓓ Aniket Mishra¹⁴.

Muhammad Ali⁵,⁶,⁷ Ⓓ Victor Andrade¹⁰,¹⁵ Ⓓ Céline Bellenguez¹² Ⓓ Luca Kleineidam¹⁰,¹⁵,¹⁶ Ⓓ Fahri Küçükali¹⁷,¹⁸ Ⓓ

Yun Ju Sung⁵,⁶,⁷ Ⓓ Niccolo Tesí¹,²,⁴ Ⓓ Ellen M. Vromen¹,² Ⓓ Douglas P. Wightman³ Ⓓ Daniel Alcolea⁹,¹⁹ Ⓓ

Montserrat Alegret⁸,⁹ Ⓓ Ignacio Alvarez²⁰,²¹ Ⓓ Philippe Amouyel¹² Ⓓ Lavinia Athanasiu²² Ⓓ Shahram Bahrami²².

Henri Bailly²³ Ⓓ Olivia Belbin⁹,¹⁹ Ⓓ Sverre Bergh²⁴,²⁵ Ⓓ Lars Bertram²⁶ Ⓓ Geert Jan Biessels²⁷ Ⓓ Kaj Blennow²⁸,²⁹.

Rafael Blesa⁹,¹⁹ Ⓓ Mercè Boada⁸,⁹ Ⓓ Anne Boland³⁰ Ⓓ Katharina Buerger³¹,³² Ⓓ Ángel Carracedo³³,³⁴.

Laura Cervera-Carles⁹,¹⁹ Ⓓ Geneviève Chene¹⁴,³⁵ Ⓓ Jurgen A. H. R. Claassen³⁶,³⁷ Ⓓ Stephanie Debette¹⁴,³⁵,³⁸.

Jean-Francois Deleuze³⁰ Ⓓ Peter Paul de Deyn³⁹ Ⓓ Janine Diehl-Schmid⁴⁰,⁴¹ Ⓓ Srdjan Djurovic⁴²,⁴³ Ⓓ

<figure><img src="image_10.png" /></figure>
Oriol Dols-Icardo⁹,¹⁹ · Carole Dufouil¹⁴,⁴⁴ · Emmanuelle Duron⁴⁵ · Emrah Düzel⁴⁶,⁴⁷ · EADB consortium ·

Tormod Fladby⁴⁸,⁴⁹ · Juan Fortea⁹,¹⁹ · Lutz Frölich⁵⁰ · Pablo García-González⁸,⁹ ID · Maria Garcia-Martinez⁵¹ ·

Ina Giegling⁵² · Oliver Goldhardt⁴⁰ · Johan Gobom²⁹ · Timo Grimmer⁴⁰ · Annakaisa Haapasalo⁵³ ·

Harald Hampel⁵⁴,⁵⁵ · Olivier Hanon²³,⁵⁶ · Lucrezia Hausner⁵⁰ · Stefanie Heilmann-Heimbach⁵⁷ · Seppo Helisalmi⁵⁸ ·

Michael T. Heneka¹⁵,¹⁶ ID · Isabel Hernández⁸,⁹ · Sanna-Kaisa Herukka⁵⁹ · Henne Holstege¹,²,⁴ · Jonas Jarholm⁶⁰ ·

Silke Kern¹³,⁶¹ · Anne-Brita Knapskog⁶² · Anne M. Koivisto⁵⁹,⁶³,⁶⁴ · Johannes Kornhuber⁶⁵ ID · Teemu Kuulasmaa⁶⁶ ·

Carmen Lage⁵¹,⁶⁷ · Christoph Laske⁶⁸,⁶⁹ · Ville Leinonen⁷⁰,⁷¹ ID · Piotr Lewczuk⁶⁵,⁷² ID · Alberto Lleó⁹,¹⁹ ·

Adolfo López de Munain⁹,⁷³,⁷⁴,⁷⁵ · Sara Lopez-García⁵¹ · Wolfgang Maier¹⁵ · Marta Marquié⁸,⁹ ID · Merel O. Mol⁷⁸ ID ·

Laura Montrreal⁸ · Fermin Moreno⁹,⁷³,⁷⁴ · Sonia Moreno-Grau⁸,⁹ · Gael Nicolas⁷⁷ · Markus M. Nöthen⁵⁷ ·

Adelina Orellana⁸,⁹ · Lene Pålhaugen⁴⁸,⁴⁹ · Janne M. Papma⁷⁸ · Florence Pasquier¹² ID · Robert Perneczky³¹,⁷⁹,⁸⁰,⁸¹ ·

Oliver Peters⁴⁶,⁸² · Yolande A. L. Pijnenburg¹,² · Julius Popp⁸³,⁸⁴ ID · Danielle Posthuma³ · Ana Pozueta⁵¹ ·

Josef Priller⁸²,⁸⁵,⁸⁶ · Raquel Puerta⁸ · Inés Quintela³³ ID · Inez Ramakers⁸⁷ · Eloy Rodriguez-Rodriguez⁵¹ ID ·

Dan Rujescu⁵² · Ingvild Saltvedt⁸⁸,⁸⁹ · Pascual Sanchez-Juan⁹⁰ ID · Philip Scheltens¹,² · Norbert Scherbaum⁹¹ ·

Matthias Schmid⁹² · Anja Schneider¹⁵,¹⁶ · Geir Selbæk²⁵,⁴⁸,⁶² ID · Per Selnes⁴⁹ · Alexey Shadrin²² · Ingmar Skoog¹³,⁶¹ ·

Hilkka Soininen⁵⁹ · Lluís Tárraga⁸,⁹ · Stefan Teipel⁹³,⁹⁴ · The GR@ACE study group · Betty Tijms¹,² ID · Magda Tsolaki⁹⁵ ·

Christine Van Broeckhoven¹⁸,⁹⁶ · Jasper Van Dongen¹⁷,¹⁸ ID · John C. van Swieten⁷⁸ · Rik Vandenberghe⁹⁷,⁹⁸ ·

Jean-Sébastien Vidal²³ · Pieter J. Visser¹,²,⁹⁹,¹⁰⁰ · Jonathan Vogelsgang¹⁰¹,¹⁰² · Margda Waern¹³,¹⁰³ ID ·

Michael Wagner¹⁵,¹⁶ ID · Jens Wiltfang¹⁰¹,¹⁰⁴,¹⁰⁵ · Mandy M. J. Wittens¹⁸,¹⁰⁶ · Henrik Zetterberg²⁸,²⁹,¹⁰⁷,¹⁰⁸,¹⁰⁹ ID ·

Miren Zulaica⁹,⁷³,⁷⁴ · Cornelia M. van Duijn⁷⁶,¹¹⁰ ID · Maria Bjerke¹⁸,¹⁰⁶,¹¹¹ · Sebastiaan Engelborghs¹⁸,¹⁰⁶,¹¹¹,¹¹² ID ·

Frank Jessen¹⁶,¹¹³,¹¹⁴ · Charlotte E. Teunissen²,¹¹⁵ · Pau Pastor¹¹⁶ · Mikko Hiltunen¹¹⁷ · Martin Ingelsson¹¹⁸,¹¹⁹,¹²⁰ ·

Ole A. Andreassen²²,¹²¹ ID · Jordi Clarimón⁹,¹⁹ · Kristel Sleegers¹⁷,¹⁸ ID · Agustín Ruiz⁸,⁹ ·

Alfredo Ramirez¹⁰,¹⁵,¹⁶,¹¹⁴,¹²² ID · Carlos Cruchaga⁵,⁶,⁷ ID · Jean-Charles Lambert¹² · Wiesje van der Flier¹,² ID

☒ Iris E. Jansen
i.e.jansen@vu.nl

☒ Wiesje van der Flier
wm.vdflier@amsterdamumc.nl

¹ Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands

² Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands

³ Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands

⁴ Section Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands

⁵ Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA

⁶ NeuroGenomics and Informatics, Washington University School of Medicine, St Louis, MO, USA

⁷ Hope Center for Neurological Disorders, Washington University School of Medicine, St Louis, MO, USA

⁸ Research Center and Memory Clinic, Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain

⁹ CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain

¹⁰ Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

¹¹ Neurosciences and Complex Systems Unit (ENyS), CONICET, Hospital El Cruce, National University A. Jauretche (UNAJ), Florencio Varela, Argentina

¹² Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE / Labex DISTAL - Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, F-59000 Lille, France

¹³ Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden

¹⁴ University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, 33000 Bordeaux, France

¹⁵ Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty, Bonn, Germany

¹⁶ German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

¹⁷ Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium

¹⁸ Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium

¹⁹ Sant Pau Memory Unit, Department of Neurology, Institut d'Investigacions Biomèdiques Sant Pau - Hospital de Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain

²⁰ Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Terrassa, Spain

²¹ Fundació per a la Recerca Biomèdica i Social Mútua de Terrassa, Terrassa, Spain
22 NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health, Oslo, Norway

23 Université Paris Cité, EA4468, Maladie d'Alzheimer, F-75013 Paris, France

24 The Research-Centre for Age-Related Functional Decline and Disease, Innlandet Hospital Trust, Brumunddal, Norway

25 Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway

26 Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany

27 Department of Neurology, UMC Utrecht Brain Center, Utrecht, The Netherlands

28 Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden

29 Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden

30 Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, 91057 Evry, France

31 German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany

32 Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany

33 Grupo de Medicina Xenómica, Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

34 Fundación Pública Galega de Medicina Xenómica-CIBERER-IDIS, Santiago de Compostela, Spain

35 Department of Neurology, CHU de Bordeaux, 33000 Bordeaux, France

36 Radboudumc Alzheimer Center, Department of Geriatrics, Radboud University Medical Center, Nijmegen, The Netherlands

37 Donders Center for Medical Neuroscience, Nijmegen, The Netherlands

38 Department of Neurology, Boston University School of Medicine, Boston, MA 2115, USA

39 Department of Neurology and Alzheimer Center Groningen, University Medical Center Groningen, Groningen, The Netherlands

40 Center for Cognitive Disorders, Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich, School of Medicine, Munich, Germany

41 kbo-Inn-Salzach-Hospital, Wasserburg am Inn, Germany

42 Department of Medical Genetics, Oslo University Hospital, Oslo, Norway

43 Department of Clinical Science, NORMENT Centre, University of Bergen, Bergen, Norway

44 Pôle de Santé Publique Centre Hospitalier Universitaire (CHU) de Bordeaux, Bordeaux, France

45 Université Paris-Saclay. Inserm 1178 MOODS, Paris, France

46 German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany

47 Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany

48 Institute of Clinical Medicine, University of Oslo, Oslo, Norway

49 Department of Neurology, Akershus University Hospital, Lorenskog, Norway

50 Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg, Germany

51 Cognitive Impairment Unit, Neurology Service, “Marqués de Valdecilla” University Hospital, Institute for Research “Marques de Valdecilla” (IDIVAL), University of Cantabria, Santander, Spain, and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain

52 Division of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria

53 A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

54 Alzheimer Precision Medicine (APM), Sorbonne University, AP-HP, Pitié-Salpêtrière Hospital, Paris, France

55 Neurology Business Group, Eisai Inc, 100 Tice Blvd, Woodcliff Lake, NJ 07677, USA

56 Service gériatrie, Centre Mémoire de Ressources et Recherches Ile de France-Broca, AP-HP, Hôpital Broca, F-75013 Paris, France

57 Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, 53127 Bonn, Germany

58 Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland

59 Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland

60 Department of Neurology, Akershus University Hospital, Lorenskog, Norway

61 Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden

62 Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway

63 Department of Neurology, Kuopio University Hospital, Kuopio, Finland

64 Department of Neurology, Helsinki University Hospital, Helsinki, Finland

65 Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, and Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany

66 Bioinformatics Center, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland

67 Atlantic Fellow at the Global Brain Health Institute (GBHI) -, University of California, San Francisco, USA

<figure><img src="image_11.png" /></figure>
68 German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany

69 Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany

70 Institute of Clinical Medicine, Neurosurgery, University of Eastern Finland, Kuopio, Finland

71 Department of Neurosurgery, Kuopio University Hospital, Kuopio, Finland

72 Department of Neurodegeneration Diagnostics, Medical University of Białystok, Białystok, Poland

73 Hospital Universitario Donostia-OSAKIDETZA, Donostia, Spain

74 Instituto Biodonostia, San Sebastián, Spain

75 University of The Basque Country, San Sebastian, Spain

76 Department of Epidemiology, ErasmusMC, Rotterdam, The Netherlands

77 Department of Genetics and CNR-MAJ, Normandie Univ, UNIROUEN, Inserm U1245 and CHU Rouen, Rouen, France

78 Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, Rotterdam, The Netherlands

79 Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany

80 Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany

81 Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK

82 German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany

83 Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich and University of Zürich, Zurich, Switzerland

84 Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland

85 Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany

86 Department of Psychiatry and Psychotherapy, Klinikum rechts der isar, Technical University Munich, 81675 Munich, Germany

87 Department of Psychiatry and Neuropsychologie, Alzheimer Center Limburg, Maastricht University, Maastricht, The Netherlands

88 Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

89 Department of Geriatrics, St Olav Hospital, University Hospital of Trondheim, Trondheim, Norway

90 Alzheimer's Centre Reina Sofía-CIEN Foundation-ISCIII, 28031 Madrid, Spain

91 Department of Psychiatry and Psychotherapy, Medical Faculty, LVR-Hospital Essen, University of Duisburg-Essen, Essen, Germany

92 Institute of Medical Biometry, Informatics and Epidemiology, University Hospital of Bonn, Bonn, Germany

93 German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany

94 Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany

95 1st Department of Neurology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Makedonia, Greece

96 Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium

97 Neurology, University Hospitals Leuven, Leuven, Belgium

98 Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium

99 Alzheimer Center Limburg, School for Mental Health and Neuroscience Maastricht University, Maastricht, The Netherlands

100 Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics Karolinska Institutet, Stockholm, Sweden

101 Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Göttingen, Germany

102 Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, USA

103 Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Psychosis Clinic, Gothenburg, Sweden

104 German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany

105 Medical Science Department, iBiMED, Aveiro, Portugal

106 Center for Neurosciences (C4N), Vrije Universiteit Brussel, Brussels, Belgium

107 Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK

108 UK Dementia Research Institute at UCL, London, UK

109 Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China

110 Nuffield Department of Population Health, Oxford University, Oxford, UK

111 Laboratory of Neurochemistry, Universitair Ziekenhuis Brussel, Brussels, Belgium

112 Department of Neurology, Universitair Ziekenhuis Brussel, Brussels, Belgium

113 Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

114 Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany

115 Neurochemistry Lab, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
¹¹⁶ Unit of Neurodegenerative diseases, Department of Neurology, University Hospital Germans Trias i Pujol and The Germans Trias i Pujol Research Institute (IGTP) Badalona, Barcelona, Spain

¹¹⁷ Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland

¹¹⁸ Department of Public Health and Caring Sciences, Molecular Geriatrics, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden

¹¹⁹ Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada

¹²⁰ Department of Medicine and Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada

¹²¹ Addiction, Oslo University Hospital, 0407 Oslo, Norway

¹²² Department of Psychiatry, Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA