Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer’s disease etiopathogenesis

Results

GWAS data: SNP-level analysis

The combined analysis of the three stages (stage I+II+III) (Figure 1 and Supplementary Tables 1–3 and Supplementary Figures 1–3), identified genome-wide significant signals (p<5x10^-8) for APOE, BIN1, CLU, CNTNAP2 and PICALM in the APOE4 stratum; suggestive signals (p<10^-5) in this analysis include a 1.4Mb intergenic region on 4p15. (from 33.3Mb to 34.7 Mb, hg19) with lowest p value for the SNP rs12641122 (p=6.28x10^-7), a 4.5Kb intergenic region on 4q35.2 or the KCNQ3 gene among others. In the APOE3 stratum, ABCA7, BIN1 and PICALM passed the genome-wide significance threshold, with suggestive signals for the HLA-DQ/HLA-DR loci, CTNND2, FBN1, WLS or CSTF1 genes among others. By contrast, no genome wide significant SNPs were found in the APOE2 stratum, nor any known AD gene among suggestive signals.

Figure 1. Manhattan and QQ plots of stage I+II+III APOE2 (A, D), APOE3 (B, E) and APOE4 (C, F) stratified meta-analysis.

An additional validation of stage I+II+III findings was performed using the EADI population (stage IV), where only the APOE locus in the APOE4 stratum reached the genome wide significance threshold (Supplementary Figure 4). In the combined analysis of Stage I+II+III and Stage IV results, ABCA7, BIN1 and PICALM in the APOE3 stratum and APOE, BIN1, CLU and PICALM in the APOE4 stratum reached genome-wide significance (Supplementary Tables 4–6 and Supplementary Figure 5).

Sex stratified meta-analysis (Supplementary Tables 7, 8 and Supplementary Figure 6) identified genome-wide significant signals for BIN1 and APOE as well as suggestive signals for PICALM, MYLK, SOX5 and SCEL in the female population. By contrast in males, only suggestive signals for BIN1, APOE, ZCCHC2, the ABI3BP/IMPG2 locus, ESRRB and the 19q13.4 leukocyte receptor cluster were identified. Stratified analysis by sex and APOE (Supplementary Tables 9–14 and Supplementary Figure 7), yielded genome-wide significant signals for APOE in the APOE4 stratum in both sexes and for a 400kb 13q31.3 intergenic region containing the Ubiquitin Specific Peptidase 7 (Herpes Virus-Associated) (USP7) pseudogene (RP11-464I4.1) for APOE3 males. Among APOE4 males, we found association with AD for a large region of 1.9Mb on 3q12.1 comprising the genes CMSS1, COL8A1, FILIP1L, MIR548G, RPL24 and, in females, a 1.5Mb region on 2q33.2 comprising the ABI3 homologue ABI2, CARF, CYP20A1, FAM117B, FZD7, ICA1L, NBEAL1, RAPH1 and WDR12 genes.

GWAS data: gene-level analysis

Genetic marker-level results were summarized into a single measure of association for each gene. Then, association results from the combined stage I, II and III meta-analysis were used to estimate gene-wide statistics for all genes in each one of the three APOE strata (Supplementary Tables 15–17). Per stratum, genes were ranked in ascending order according to lowest to highest p values derived from the mean χ2 statistics implemented in MAGMA (Table 1). Among previously reported AD genes, APOE was the highest ranked in both APOE2 and APOE4 carriers (ranks 26 and 3 respectively), whereas BIN1 was ranked first in the APOE3 stratum (Supplementary Table 18). Known AD genes were ranked worst in the APOE2 stratum than in the others, with the complement receptor 1 (CR1), ranked in position 1292, being the second most relevant of these genes among APOE2 carriers after APOE.

Table 1. GWAS analysis: gene level data (MAGMA results) by APOE stratum.

Rank E2	HUGO	NSNPS	NPARAM	ZSTAT	PJoint	PSNPwise (mean)	PSNPwise (top)
1	RNF152	609	65	4.62	1.90E-06	4.59E-07	4.63E-04
2	DUOX2	150	26	3.97	3.53E-05	4.72E-06	5.12E-03
3	METRN	140	18	3.88	5.29E-05	2.41E-05	9.43E-04
4	WDR24	197	18	3.83	6.31E-05	2.71E-05	1.21E-03
5	FBXL16	196	18	3.84	6.03E-05	2.85E-05	1.11E-03
6	FAM173A	120	17	3.83	6.29E-05	2.99E-05	1.14E-03
7	JMJD8	193	18	3.79	7.59E-05	3.34E-05	1.32E-03
8	CCDC78	115	17	3.81	6.99E-05	3.68E-05	1.14E-03
9	DUOXA2	145	23	3.63	1.41E-04	3.68E-05	6.05E-03
10	HAGHL	107	15	3.83	6.43E-05	3.88E-05	9.08E-04
11	NARFL	105	15	3.83	6.51E-05	4.00E-05	8.92E-04
12	STUB1	193	18	3.72	1.01E-04	4.61E-05	1.56E-03
13	ABCB4	344	35	3.89	5.11E-05	5.27E-05	5.59E-03
14	APOC1	209	32	3.42	3.16E-04	5.40E-05	4.13E-02
15	IRGC	300	45	4.55	2.72E-06	5.52E-05	3.36E-05
Rank E3	HUGO	NSNPS	NPARAM	ZSTAT	PJoint	PSNPwise (mean)	PSNPwise (top)
1	BIN1	633	70	7.02	1.15E-12	8.93E-07	3.51E-12
2	FBN1	533	41	4.71	1.27E-06	4.82E-06	2.76E-04
3	WNT3	239	38	4.69	1.35E-06	6.85E-06	1.26E-04
4	CLEC4M	424	68	4.07	2.35E-05	1.35E-05	1.46E-03
5	NSF	144	22	4.76	9.48E-07	2.88E-05	3.07E-05
6	CASS4	349	50	5.00	2.80E-07	3.43E-05	4.58E-05
7	APP	998	99	3.96	3.75E-05	3.91E-05	3.43E-03
8	GPR27	306	47	3.21	6.62E-04	4.63E-05	6.29E-02
9	CRHR1	1261	23	4.07	2.39E-05	5.46E-05	2.55E-03
10	CD209	482	71	3.80	7.36E-05	6.93E-05	1.55E-03
11	SPPL2C	556	19	4.03	2.83E-05	7.77E-05	1.68E-03
12	KANSL1	978	20	4.01	2.98E-05	7.79E-05	1.44E-03
13	LRRC37A	115	6	4.01	3.09E-05	7.97E-05	4.41E-04
14	STH	460	17	3.94	4.00E-05	8.35E-05	2.88E-03
15	EIF4E3	516	83	3.01	1.29E-03	9.34E-05	1.41E-01
Rank E4	HUGO	NSNPS	NPARAM	ZSTAT	PJoint	PSNPwise (mean)	PSNPwise (top)
1	TOMM40	293	44	14.90	1.59E-50	1.50E-26	1.00E-50
2	APOC1	247	38	15.05	1.71E-51	3.20E-26	1.00E-50
3	APOE	270	40	14.82	5.36E-50	2.83E-24	1.00E-50
4	PVRL2	361	51	14.31	8.88E-47	1.17E-23	1.00E-50
5	APOC4	241	29	11.30	6.96E-30	1.60E-14	2.52E-33
6	APOC2	232	27	13.20	4.31E-40	8.54E-13	1.00E-50
7	CLPTM1	267	34	12.78	1.06E-37	3.43E-11	5.65E-46
8	CLU	351	46	7.60	1.47E-14	5.85E-10	4.89E-13
9	SCARA3	426	51	7.08	7.06E-13	7.15E-07	3.53E-13
10	PICALM	555	43	5.54	1.49E-08	8.39E-07	9.65E-08
11	AKAP2	601	85	4.12	1.86E-05	2.27E-06	5.58E-03
12	PALM2-AKAP2	1542	159	3.61	1.52E-04	3.72E-05	1.46E-02
13	IL6	414	48	3.72	1.01E-04	4.36E-05	7.17E-03
14	EPHX2	467	56	4.23	1.15E-05	5.26E-05	2.17E-04
15	BIN1	635	70	5.70	5.95E-09	5.72E-05	1.11E-08

Differential expression analysis

Blood APOE stratified DE meta-analysis between AD cases and controls (Supplementary Tables 19–21) included the ADNI and ADDN datasets. In the APOE2 stratum we identified only two upregulated (ISY1 and SRF) and two downregulated (CPT1A, PLCD1) genes below the FDR <0.05 threshold, clearly differing from expression profiles in APOE3 and APOE4 carriers (Figure 2, top 100 genes from each stratum). By contrast, APOE3 and APOE4 stratified analyses identified 1,692 and 3,293 DE genes respectively. Among genes differentially expressed in APOE4 cases versus controls we observed an over-representation of mitochondrial genes, most of them involved in the oxidative phosphorylation pathway. However, several genes from this pathway were differentially expressed in all strata but with opposite expression profiles, such as the electron transport chain genes ATP5F1, UQCRB or NDUFB3 upregulated in APOE2 cases but downregulated in APOE4 cases when compared to controls of the same haplotype. APOE3 genes were mainly cytoplasmatic genes involved in RNA metabolism.

Figure 2. Top candidates from DE analysis in blood datasets.

Cortex APOE-stratified DE included the MAYO, ROSMAP, MSBB, GSE15222 and GSE48350 studies. Meta-analysis of cortex datasets resulted in 518, 7714 and 1717 statistically significant genes (FDR<0.05) for the APOE2, APOE3 and APOE4 strata respectively (Supplementary Tables 22–24). As opposed to blood analyses, the overall picture is of enhanced gene expression in AD in all strata, but more pronounced in APOE2 except for XIST, strongly downregulated in AD APOE2 subjects (Figure 3, top 100 genes from each stratum). The heparan sulfate proteoglycan CD44 and the heparan sulfate lysosomal degradation enzyme IDS encoding genes were differentially expressed in all strata, with CD44 strongly upregulated in APOE2 cases and IDS downregulated in APOE4 cases. APOE2 specific genes were mostly nuclear genes involved in primary metabolic processes, as well as some apoptosis related genes (CFLAR, ATM, MCL1, AKT3 and CTSZ), all of them downregulated in AD cases but CTSZ, with higher expression in AD cases than in controls. APOE3 and APOE4 candidate genes were mainly expressed in the cytoplasm. In all strata, we identified genes involved in neuronal development (such as GFAP, BDNF or CDC42), especially in the APOE3 stratum. For both APOE2 and APOE4 strata, genes involved in vesicle mediated transport were identified, with key genes such as PCSK1, SYTL2 or SVOP downregulated in APOE4 cases.

Figure 3. Top candidates from DE analysis in cortex datasets.

Robust rank aggregation analysis

Integrative analysis was performed independently to include either blood or cortex APOE-stratified DE rankings. Thus, we combined meta-GWAS stage I-III gene-level results with blood meta-GWES results (Supplementary Tables 25–27) or with cortex meta-GWES results respectively (Supplementary Tables 28–30).

In blood 275, 284 and 278 genes passed the FDR threshold in the APOE2, APOE3 and APOE4 strata respectively with 15 common genes, associated with AD irrespective of the APOE haplotype, which include APOC1, CLPTM1, DNAJA1, ING3, LARP7, NGDN, RPA3, RPL36AL, RPS24, SOD1, SRP19, and four mitochondrial proteins (ATP5F1, ATP6V1D, MRPL51 and UQCRH). The list of APOE2 specific genes is the largest one (241 genes) and include mitochondrial transporters such as SLC25A3 and SLC25A4. APOE3 specific signatures included MAPT and APP genes, along with other neuronal genes such as the cholinergic receptors CHRNA10 and CHRNA2. APOE4 specific genes included known AD genes involved in vesicle transport such as CLU and SORL1; SORL1 has been shown to regulate IL6 levels, also identified among APOE4 specific signatures. We found however a large overlap among significant genes for the APOE3 and APOE4 strata (n=51) including BIN1, MS4A4A, MS4A6A, PICALM and SLC24A4 AD genes and a good number of ribosomal and electron transport mitochondrial proteins (ATP5I, ATP5O, ATP6V1E1, COX17, MRPL27, MRPL33, MRPL35, MRPS17, MRPS21, UQCRB). APOE2 and APOE3 shared eleven genes (ACADM, AK2, ENSA, GPR132, GPR137, HSP90AA1, MCL1, NXF1, TAF1C, UBA7, ZC3H15) whereas the overlap between E2 and E4 was the smallest with eight proteins (KTN1, NDST2, POLR1D, RHOT2, STK17B, TOMM40, TSPAN32, UCHL3). At the pathway level, we observed a lower overlap between APOE strata, with only three shared mitochondrial GO categories among the three haplotypes, and little or none overlap between APOE2 and APOE3 or APOE2 and APOE4 (Supplementary Tables 31–33 and Supplementary Figure 8). In contrast, there was considerable overlap between APOE3 and APOE4 which includes mitochondria biology, secretory vesicles and antigen processing and presentation functions.

In cortex, we found 376, 399 and 366 significant genes (FDR<0.05) for the APOE2, APOE3 and APOE4 strata respectively. Seven common AD candidate genes (APOC1, APOC2, CD44, CDC42, CLPTM1, DST, PGM2L1) were significant in all three strata; of them, APOC1 and CLPTM1 were also associated in the three strata in blood. In this analysis, the shortest list of APOE-specific genes was found in the APOE4 stratum, which showed the largest overlap with the APOE3 stratum; among the 45 significant genes shared by these strata, we identified several AD genes, including BIN1, MS4A4A, MS4A6A, PICALM and RIN3 in accordance with blood results. The CR1 gene is included among the 19 top genes from the APOE2 and APOE3 strata (ATPIF1, CACNB2, CDC27, CFLAR, COX15, CR1, DCLK1, GOSR2, KANSL1, KLF12, MAPT, MCL1, NSF, POGK, RUFY3, SCD5, SORBS1, SPEN, TTN), whereas APOE, TOMM40, SLC24A4 or WWOX were included among common genes for the APOE2 and APOE4 strata (21 genes: AHNAK, APOE, ARNT, CRTAP, FBXL16, GART, KALRN, KAT6A, MTMR11, OPA1, PDLIM5, PPFIA1, PURA, RBMS2, SLC24A4, SRGAP1, TOMM40, TSPAN14, UBE2F, WWOX, ZNF264). Enrichment analysis also identified both common and exclusive pathways. Common pathways for AD irrespective of the APOE haplotype were related to adhesion, neuronal development, differentiation, and lipoprotein metabolism; diverse signals related to neuronal death are also present in all three strata. Again, we observed larger overlap between APOE3 and APOE4 pathways (glial cell differentiation and activation, immunological, lipid metabolism, cardiovascular system development and heart function) than for APOE2 and APOE3 (which includes axonogenesis) or APOE2 and APOE4 (mainly phospholipid and lipoprotein metabolism due to APOE, APOC1 and APOC2 genes). (Supplementary Tables 34–36 and Supplementary Figure 9). We observed that APOE2 exclusive pathways include chromatin regulation and telomere maintenance related processes. The APOE3 strata showed the largest number of significant enrichments, but most of them showed a similar annotation in APOE2, or, more frequently, in APOE4 strata, with the exception of antigen processing and presentation, IFNG signalling, astrocyte development and activation and myelin sheath. In APOE4 macrophage activation, fructose metabolism, vitamin D mediated inflammation, inositol phosphate metabolism and cholesterol efflux were the most relevant pathways. Clathrin vesicles, amyloid biology, inflammatory and immune response and glial cell development and differentiation appear as the most relevant categories shared by APOE3 and APOE4 strata.

To identify relevant candidate blood biomarkers tracking brain changes in AD pathology we compared blood and cortex analyses (Figure 4). We identified 68 genes in common for the APOE4 stratum, including CLU, CD2AP, IL6, MS4A2, SLC25A1 or INNPP4A (Figure 4C). In APOE3, 76 common genes were found, including APP, AQP9, ATPAF, CD209, LILRA5, NDUFB3 or PTK2B (Figure 4D). Finally, in the APOE2 stratum, we identified 84 common genes including several ABC receptors (ABCA9, ABCB1, ABCB4, ABCD4), solute carrier molecules (SLC25A3, SLC25A4, SLC35E1, SLC9A9), TLR9 or IL4I1 (Figure 4B). Overlap between APOE strata-specific pathways from blood and cortex showed 6 common pathways for APOE2 (Figure 4F), half of them related to chromatin regulation, 24 common pathways for APOE3 (secretion, regulation of supramolecular fiber organization, site of polarized growth and leukocyte activation involved in inflammatory response, Figure 4G) and 18 shared pathways for APOE4, including clathrin coated vesicles, amyloid-beta processing, mitochondrial transmembrane transport, macrophage activation and monosaccharides and fructose metabolism (Figure 4H). We followed up genes with concordant profiles in both blood and cortex (upregulated or downregulated in AD cases vs controls) and showing opposite profiles in APOE2 and APOE4, which included 34 genes with overrepresentation of the gluconeogenesis and fructose metabolic pathways (FBP1, FBP2, SLC25A1) (Figure 5A). When compared with average expression in normal brains, FBP1, FBP2, RHOH, JPH2, ERAp2 and SCLT1 were upregulated in APOE4 cases when they are usually expressed at low levels, whereas, SNX3 and SUB1, were downregulated in APOE4 cases when they are expressed at very high levels in the normal brain according to GTEx (Figure 5B).

Figure 4. Summary of candidate genes (A–D) and pathways (E–H) from APOE2 (B, F), APOE3 (C, G) and APOE4 (D, H) common candidates from Blood and Cortex RRA analyses.

Figure 5. Blood and cortex biomarkers showing opposite profiles in APOE2 and APOE4 strata. (A) Meta-analysis logFCs from case-control DE analysis in blood and cortex (APOE2 and APOE4 strata); (B) average expression of genes downregulated in APOE2 and upregulated in APOE4 cases by normal tissue (GTEx repository); (C) average expression of genes upregulated in APOE2 and downregulated in APOE4 cases by normal tissue (GTEx repository).

Validation on proteomic datasets

We aimed at investigating if any of our candidate genes were detected and differentially expressed at the proteomic level using blood proteomics data from the ADDN study (931 proteins) and cortex proteomics from four independent datasets (BANNER, BLSA, MAYO and MSBB, 2,658 proteins).

Out of 737 RRA blood candidates, only 38 were present in the ADDN blood proteomic data (Supplementary Table 37 and Supplementary Figure 10). Among them, DE analyses between cases with controls, either overall or stratified by APOE haplotype, identified 8 differentially expressed genes in the unstratified analysis, 8 genes in the APOE4 stratum and 9 in the APOE3 stratum. We could confirm APOE allele-specific effects identified in the RRA analysis for the immune related proteins AIF1, METAP2, NCK1, PRDX1, PRKCZ, RPS27A in the APOE3 stratum, and FCGR2B and SEZ6L2 (involved in SNC development) in the APOE4 stratum. Overall, among these 38 RRA candidates, we identified a cluster of 11 overexpressed proteins in AD cases when compared to controls in the APOE3 stratum, but downregulated APOE4 AD cases including AIF1, APP, GDI2, HSP90AA1, METAP2, NACA, NCK1, PRDX1, RPS27A, SFTPD and UFC1 (Supplementary Figure 11); immunological functions associated to these proteins include leukocyte activation (APP, PRDX1, GDI2), Toll-like receptors (TLRs) cascade (APP, RPS27A, SFTPD) or phagocytosis (NCK1, HSP90AA1, SFTPD, AIF1) in line with our RRA findings.

In cortex, 234 out of 1,039 RRA candidates were present in the proteomics DE meta-analysis, 100 of them showing evidences of association (p<0.05) in at least one stratum or in the unstratified analysis (Supplementary Table 38 and Supplementary Figure 12). Of note, the largest differences between cases and controls were observed among APOE4 carriers, confirming at the proteomic level the role of APOE4 RRA candidates involved in neurogenesis (DPYSL4, EHD1, GABRB3, MAPK8, UNC13A), or more specifically, in glial cell differentiation (CLU, GAP43, GFAP, GSN). Among APOE3 candidates, we confirmed candidates involved in neurotransmission such as RPH3A PTK2B, ALDH5A1, GABRA2 and APP (the later upregulated in all strata) and genes from the electron transport chain (ALDH5A1, NDUFA7, NDUFB3). Confirmed APOE2 candidates included the choline transporter SLC44A1, involved in myelin production, and the myelin basic protein MBP; MAPT was upregulated in all strata but particularly in the APOE2 stratum. We also confirmed the role of CDC42 and DST in all the strata, but we did not observe association of CD44 and PGM2L1 with AD in this analysis.

Cell-type-specific expression profiles: cortex snRNAseq

Since the enrichment analysis showed an over-representation of neuronal development related pathways in all strata, and of cells from the glial lineage in the APOE3 and APOE4 strata, we investigated which cerebral cell types our cortex RRA candidates were mainly expressed in, and which cell types showed largest differences between cases and controls using snRNAseq from the ROSMAP study (Figure 6). We dropped pericytes and endothelial cells from the differential expression analysis because of the low number of cells (≈100 cells, <0.3%).

Figure 6. Cortex snRNAseq data from the ROSMAP study. (A) Cell clustering labelled by reported cell type; (B) APOE expression across cell types; (C) violin plot for APOE expression by cell type; (D) UMAP plot for average expression of cortex RRA APOE2 candidates by total gene expression; (E) UMAP plot for average expression of cortex RRA APOE3 candidates by total gene expression; (F) UMAP plot for average expression of cortex RRA APOE4 candidates by total gene expression; (G) UMAP plot for average expression of blood/cortex biomarkers by total gene expression; (H) expression by cell type of the seven genes in common in all the RRA analyses; (I) APOE expression by cell type and case status.

APOE gene was mainly expressed in astrocytes and microglia (Figure 6). According to previous results, APOE is upregulated in microglia from AD subjects when compared with controls (overall and stratified by APOE genotypes). By contrast, in astrocytes we found higher APOE expression levels in cases than controls in the APOE3 stratum (logFC=0.34, p=1.56x10^-4), but significant lower expression in APOE4 cases than in controls (logFC=-0.14, p=1.83x10^-2, p_interaction<10^-5).

As reported in the original article [19], most neuronal genes were strongly differently expressed in AD cases versus controls. Furthermore, our analysis found this result was consistent irrespective of the APOE haplotype. Given that glial specific signals arose from APOE3 and APOE4 strata, we therefore primarily focused on RRA cortex candidates showing evidence of association with AD in any glial cell type (astrocytes, microglia, oligodendrocytes and oligodendrocyte precursors) within the same APOE stratum (Figure 6 and Supplementary Table 39 and Supplementary Figures 13–16). In fact, RRA candidates were mainly expressed in the glial lineage, showing a lineal decrease in expression from APOE2 to APOE4 in the astrocyte and microglia populations, and an increase in expression in the oligodendrocyte subpopulation (Figure 6). The seven genes in common in all the RRA analyses, were downregulated in all cell types except for APOE and APOC1 in microglia, and CD44 in astrocytes (Figure 6). In the stratified analysis, these 7 genes were predominantly downregulated in AD APOE3 carriers and upregulated among APOE4 AD cases when compared to controls, particularly APOC1, DST and CD44 (Supplementary Figure 9). By stratum, APOE2 RRA cortex candidates were mostly upregulated in all cell types (Supplementary Figure 10), and in particular FXR1 and DNAJB1, the latter only downregulated in microglia. APOE3 RRA candidates showed the largest differences between cases and controls in microglia cells, where APOC1, ALDOA, RPLP0 and DYNLRB1 were strongly upregulated whereas ARL17B was downregulated in AD cases Supplementary Figure 11). Almost all APOE4 candidate genes were downregulated in both excitatory and inhibitory neurons and upregulated in the glial lineage, particularly TMEM163 and CPM in microglia and GFAP, PLCE1, CLU, CALN1, DLG2 and PDE5A in astrocytes Supplementary Figure 12); we also observed a strong downregulation of the Serine/Threonine Kinase 17b (STK17B), involved in apoptosis and autophagy, in microglial cells of APOE4 cases.

Materials and Methods

Table 2 summarizes the datasets and number of individuals by APOE stratum included at each analysis stage (total number of processed samples: 50,737). A flow chart of the analyses performed in this report is shown in Figure 7. Additional information about study datasets is provided as Supplementary Note.

Table 2. Study datasets.

	ApoE2			ApoE3			ApoE4
	Controls	Cases		Controls	Cases		Controls	Cases
GWAS Stage I
ADGC	191	113		810	1070		329	2063
ADDN	22	10		126	104		50	140
ADNI	40	10		161	153		67	303
GNADA	92	35		487	252		164	467
NXC	78	21		510	150		128	150
MAYO	147	24		657	233		286	478
NIA	68	9		353	94		374	297
ROSMAP	51	62		153	353		23	200
TGEN	71	25		281	261		89	420
Total Stage I	760	309		3538	2670		1510	4518
GWAS Stage II
FACE	330	123		2220	1314		639	1115
GERAD	780	145		3572	1090		1574	1634
Total Stage II	1110	268		5792	2404		2213	2749
GWAS Stage III
ARIC	1001	144		4479	688		1666	567
CHS	243	44		1058	240		302	133
FHS	474	27		2346	180		687	88
RS	667	102		2770	548		1001	435
Total Stage III	2385	317		10653	1656		3656	1223
GWAS Stage IV
EADI	819	124		4317	993		1188	1135
Total Stage IV	819	124		4317	993		1188	1135
TOTAL GWAS	5074	1018		24300	7723		8567	9625
Blood GWES
ADDN	14	11		92	79		37	107
ADNI	27	3		118	71		48	127
TOTAL Blood GWES	41	14		210	150		85	234
Cortex GWES
MAYO (TCX)	27	5		112	71		43	118
ROSMAP (DLPFC)	28	26		85	147		13	92
MSBB FP	2	1		10	16		3	18
MSBB OVC	1	3		8	16		4	7
MSBB DLPF	2	0		9	12		5	16
MSBB PCG	0	3		5	14		0	14
MSBB PFC	1	1		9	15		1	17
GSE15222	26	3		114	48		37	108
GSE48350 SFG	9	1		23	8		16	10
GSE48350 EC	8	1		16	5		15	8
GSE48350 PG	10	1		21	8		13	14
TOTAL Cortex GWES	114	45		412	360		150	422
Blood Proteomics
ADDN	12	10		46	76		28	110
TOTAL Blood Proteomics	12	10		46	76		28	110
Brain proteomics
BANNER	6	6		29	35		6	57
BLSA	5	3		7	10		1	7
MAYO	3	4		23	37		4	43
MSBB	6	10		15	88		7	44
TOTAL Cortex Proteomics	20	23		74	170		18	151
Brain snRNAseq
ROSMAP	4	2		7	7		2	9
Total snRNAseq	4	2		7	7		2	9
TOTAL	5265	1112		25049	8486		8850	10551

Figure 7. Integrative analysis workflow.

GWAS data

Study cohorts

Stage I GWAS meta-analysis comprised 13,305 subjects from nine datasets, including the Alzheimer's Disease Neuroimaging Initiative (ADNI), the AddNeuroMed study, the Alzheimer’s Disease Genetics Consortium (ADGC), the Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease (GenADA), the Mayo Clinic Alzheimer's Disease Genetic Study, the Neocodex-Murcia study, the National Institute on Aging (NIA) - Late Onset Alzheimer's Disease Family Study, the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) study and the TGEN study. Stage II meta-analysis (N=14,536 individuals) included the Genome Research at Fundació ACE (GR@ACE) study stage I and the Genetic and Environmental Risk in Alzheimer’s Disease (GERAD) study. The Cohorts for Heart and Ageing Research in Genomic Epidemiology (CHARGE) consortium contributed the Atherosclerosis Risk in Communities (ARIC) study, Cardiovascular Heart Study (CHS), the Framingham study (FS) and the Rotterdam study (RS) for Stage III GWAS meta-analysis (N=11,345 subjects). Additional validation (stage IV) of stage I-III meta-analysis was performed on the European Alzheimer’s Disease Initiative (EADI) dataset (N=8,576 samples). Estimated power for the stage I+II+III meta-analysis was 65.9%, 99.9% and 99.8% for a SNP with MAF=0.2 and OR=1.2 in the E2, E3 and E4 strata respectively, dropping to 28.3%,86.5% and 76.4% for a variant showing MAF=0.2 and OR=1.1.

Whenever possible, clinical information was reviewed to exclude: i) cases not classified as confirmed or probable AD ii) controls with amyloid pathology or history of altered cognition tests.

Quality control (QC) and imputation

A standard QC was applied to all datasets, including removal of individuals with more than 3% missing genotypes, with excess autosomal heterozygosity (>0.35 or more than 3 standard deviations (SD) from population mean), those showing a discrepancy between genotypic and reported sex, as well as individuals of non-European ancestry based on SMARTPCA principal component (PC) analyses (exclusion of subjects more than 6 SDs away from the population mean) [54]. Duplicated and related individuals were identified and removed by means of IBS estimates (IBS>0.1875) both within and across studies. At the genotype level, we removed SNPs with missing genotype rate > 5%, not in Hardy-Weinberg equilibrium (HWE) (p<10^-6 in controls) and SNPs with minor allele frequency (MAF) < 1%. When necessary, datasets were updated to genome build GRCh37/hg19.

Genotype imputation was performed at the University of Michigan server using the minimac3 algorithm and the SHAPEIT tool for haplotype phasing with the Haplotype Reference Consortium (HRC) cohort as reference panel [55]. After imputation, only SNPs with an R² quality estimate higher than 0.3 and MAF >1% were kept for association analysis.

APOE stratified association analysis

Association analysis was performed within each dataset in three independent groups: ε2 stratum (including subjects with APOE genotypes ε2/ε2 and ε2/ε3), ε3 stratum (ε3/ε3 individuals) and ε4 stratum (ε3/ε4 and ε4/ ε4 carriers). The ε2/ε4 genotype was excluded because of the combination of both the protective and deleterious alleles. Association of genotype dosages with the AD case-control status was explored through regression models adjusted by age, sex and the first four PC vectors as covariates using PLINK software [56].

Sex and APOE stratified association analysis

We also explored the effect of both APOE and sex on susceptibility to AD using two approaches. We first performed a sex stratified analysis using logistic regression models adjusted by age, the first four PC vectors and APOE genotype as a quantitative trait, assigning each allele E2 a value of -1, each E3 allele a value of 0 and each E4 allele a value of +1 (full range: from -2 to 2). Additionally, we performed an association analysis stratified by both APOE and sex. For these analyses, eight datasets from Stage I (ADDN, ADGC, ADNI, GNADA, MAYO, NIA, NXC, ROSMAP, N=12,158 individuals) and Stage II (GR@ACE, 5,741 subjects) were used.

Meta-analysis (meta-GWAS)

Within each stage and stratum, association results were combined by meta-analysis using the inverse variance method implemented in METAL [57] or PLINK software programs. SNPs with MAF >1% that were available in at least 60% of the datasets at each stage were included in the meta-analysis. Genomic inflation lambda (λ) was calculated using the GenABEL package [58]. Manhattans and QQ plots were generated with the qqman R package [59].

Gene-level analysis

Gene level analysis was performed using MAGMA software, which compute gene-wise statistics taking into account physical distance and linkage disequilibrium (LD) between markers [60]. All SNPs with MAF above 5% were used in these analyses, setting a distance threshold of 50kb. At each stratum, genes were ranked according to the global p mean value.

Genome-wide expression analysis (GWES) and meta-analysis

Study cohorts

Whole blood expression profiles for meta-analysis were obtained from ADNI and AddNeuroMed studies (N=734). The cortex gene-expression meta-analysis included Mount Sinai Brain Bank (MSBB) dataset (frontal pole, occipital visual cortex, dorsolateral prefrontal cortex, precentral gyrus, prefrontal cortex), ROSMAP (dorsolateral prefrontal cortex) and MAYO (temporal cortex) studies and GSE15222 [61] and GSE48350 [62] (entorhinal cortex, superior frontal cortex, post-central gyrus) datasets from the GEO repository (N=1,503).

QC

For these analyses, we used background corrected and normalized intensity values from expression microarrays distributed by the dataset providers, except for GSE48350. For this GEO dataset, raw. CEL files we downloaded and processed using the Robust Multi-array Average (RMA) algorithm integrated in the affy R package for background correction and normalization [63]. Diagnostic plots included Residuals vs Fitted, Residual vs Leverage, Scale Location, PCA and QQ plots; outlier values identified in these analyses were disregarded. For those datasets provided in different experimental batches, the ComBat function from the sva R package [64] was used to minimize batch effects. A multivariate regression model was fitted to adjust intensity values for covariates, including pH, post-mortem interval (PMI), RNA integrity numbers (RIN), age of death, sex, race and use of lipid lowering medication when available.

Differential expression analysis

As for GWAS data, differential expression (DE) analysis between cases and controls was performed independently in the three APOE subgroups using R package limma [65] by dataset and brain region when available. Limma results were adjusted for multiple testing using the Benjamin and Hochberg’s (BH) method. Volcano plots and heatmaps were produced to assess these results. Probes were annotated to gene symbols using appropriate specific libraries, keeping the most differentially expressed mRNA isoform for those genes showing alternative splicing.

Differential expression meta-analysis (meta-GWES)

Independent APOE stratified meta-analyses were performed for combining DE results from the different datasets into single ranked gene lists for both blood and cortex. For cortex, only genes present in at least a 70% of the datasets were considered for meta-analysis. Individual logFCs were combined using the Random Effect Model (REM). Given that the analysis included data from different brain regions, genes were ranked according to the Fisher statistics to avoid making assumptions about the directionality of the effect, aimed at identifying candidate markers differentially expressed in the “majority” of studies, where Fisher methods has been described to outperform other methods in terms of power detection, biological association, stability and robustness [66]. All the analyses were performed with the metaDE R tool. Heatmap graphs were generated with the Pheatmap R package.

Integrative analysis

In order to obtain per-gene single estimates GWAS and GWES data were combined using the Robust Rank Aggregation (RRA) method [67]. The algorithm, integrated in the RobustRankAggreg R package, uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking. Two independent runs of the RRA algorithm were performed. In all of them we combined stage I+II+III GWAS meta-analysis plus blood or cortex GWES metanalyses (Figure 7). Final gene ranks for blood and cortex were generated according to ascending order of the exact p values generated by the RRA algorithm.

Proteomic data analysis

Proteomic data from blood (ADDN study) and brain (BANNER, BLSA, MAYO and MSBB studies) were collected. Histograms and boxplots were generated to assess the distribution of normalized intensity protein expression values distributed by data providers. Differential protein expression analyses by study and APOE stratum were performed using limma, with PMI, age, sex and, when available, lipid lowering medication as covariates. Meta-analysis of the diverse brain datasets was performed as described for GWES datasets.

Single nuclei RNAseq (snRNAseq) data analysis

Additionally, we explored snRNAseq cortex data from the ROSMAP study [19]. Count matrix provided by ROSMAP study was processed using Seurat package [68]. After QC (filtering out cells that have unique feature counts over 2,500 or less than 200 and cells with >5% mitochondrial counts), data were normalized and scaled. Prior to clustering the cells, we applied the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction technique. Finally, a differential expression analysis between AD cases and controls was performed by each cell type using the edgeR package [69].

Enrichment analysis

Enrichment analysis of RRA results was performed using four different tools: WebGestaltR [70, 71], FUMA [72] and gPROFILER [73], for genes passing the multiple testing correction threshold (p=0.05), and GSEA [74] for full gene ranked lists. The databases being interrogated include GO, KEGG, WikiPathways, and Reactome. Only pathways and GO categories selected by at least two enrichment tools with adjusted p<0.05 and a minimum of three overlapping genes were selected for further exploring.

Data availability statement

Summary statistics are included as Supplementary Tables and will be made available through Synapse repository (https://www.synapse.org/) upon publication.

Most data used in this article are publicly available (see acknowledgement section).

Code availability statement

Code used for this article will be made publicly available through a public Jupyter server (Madrid L, Rubio-Escudero C, Pontes B, González-Pérez A, Riquelme JA, Sáez ME. MOMIC: A Multi-Omics Pipeline for data analysis, integration and interpretation (manuscript in preparation))

Author Contributions

Coordination: M.E.S., A.Ruiz; Data Analyses: L.M.M, A.G.P, P.V.M.A., L.K., M.E.S.; CHARGE analysis and coordination: R.X., Q.Y., J.C.B., X.J., S.A., C.V.D., A.D.S, M.F., M.A.I., C.L.S., E.B., S.S.; EADI analysis and coordination: V.D,. C.B., B.G.-B., P.A., J.C.L.; GERAD analysis and coordination: A.Ramirez; R.S., J.W.; GR@ACE/DEGESCO analysis and coordination: S.M.G., I.R., P.G.G., A.O., A.Ruiz; Core writing group: A.C.S, L.M.M., M.E.S., A.Ruiz. All other authors contributed data and all authors critically reviewed the paper.

Acknowledgments

Funding support for the Alzheimer’s Disease Genetics Consortium (ADGC) was provided through the NIA Division of Neuroscience (U01-AG032984). This study was downloaded from NIH dbGaP repository (phs000372.v1).

The AddNeuroMed study is a public-private partnership supported by EFPIA companies and SMEs as part of InnoMed (Innovative Medicines in Europe), an Integrated Project funded by the European Union of the Sixth Framework program priority FP6-2004-LIFESCIHEALTH-5. Clinical leads responsible for data collection are Iwona Kłoszewska (Lodz), Simon Lovestone (London), Patrizia Mecocci (Perugia), Hilkka Soininen (Kuopio), Magda Tsolaki (Thessaloniki), and Bruno Vellas (Toulouse), imaging leads are Andy Simmons (London), Lars-Olad Wahlund (Stockholm) and Christian Spenger (Zurich) and bioinformatics leads are Richard Dobson (London) and Stephen Newhouse (London). This dataset was downloaded from Synapse (doi:10.7303/syn2790911).

Data collection and sharing for this project was partially funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://www.fnih.org/). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

The Atherosclerosis Risk in Communities (ARIC) study is carried out as a collaborative study supported by the National Heart, Lung, and Blood Institute (NHLBI) contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). The authors thank the staff and participants of the ARIC study for their important contributions. Funding support for “Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium” was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). This project was also funded from R01-NS087541.

The Banner Sun Health Research Institute (BANNER) data were provided by Dr. Levey from Emory University. A portion of these data were generated from samples collected through the Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona. The Brain and Body Donation Program is supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the National Institute on Aging (P30 AG19610 Arizona Alzheimer’s Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson's Disease Consortium) and the Michael J. Fox Foundation for Parkinson's Research. This study was downloaded from Synapse (10.7303/syn7170616).

The Baltimore Longitudinal Study on Aging (BLSA) study data were generated from postmortem brain tissue collected through The National Institute on Aging’s Baltimore Longitudinal Study of Aging and provided by Dr. Levey from Emory University. This study was downloaded from Synapse (10.7303/syn3606086).

Infrastructure for the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium is supported in part by the National Heart, Lung, and Blood Institute grant HL105756 and for the neuroCHARGE phenotype working group through the National Institute on Aging grants R01 AG033193, RF1 AG059421, U01 AG049505 and AG052409.

The Cardiovascular Health Study (CHS) research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC15103, HHSN268200960009C; and NHLBI grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629, and R01AG033193 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The EADI study has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer’s disease) including funding from MEL (Metropole européenne de Lille), ERDF (European Regional Development Fund) and Conseil Régional Nord Pas de Calais. This work was supported by INSERM, the National Foundation for Alzheimer’s disease and related disorders, the Institut Pasteur de Lille and the Centre National de Recherche en Génomique Humaine, CEA, the JPND PERADES, the Laboratory of Excellence GENMED (Medical Genomics) grant no. ANR-10-LABX-0013 managed by the National Research Agency (ANR) part of the Investment for the Future program, and the FP7 AgedBrainSysBio. The Three-City Study was performed as part of collaboration between the Institut National de la Santé et de la Recherche Médicale (Inserm), the Victor Segalen Bordeaux II University and Sanofi-Synthélabo. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also funded by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, Institut de la Longévité, Agence Française de Sécurité Sanitaire des Produits de Santé, the Aquitaine and Bourgogne Regional Councils, Agence Nationale de la Recherche, ANR supported the COGINUT and COVADIS projects. Fondation de France and the joint French Ministry of Research/INSERM “Cohortes et collections de données biologiques” programme. Lille Génopôle received an unconditional grant from Eisai. The Three-city biological bank was developed and maintained by the laboratory for genomic analysis LAG-BRC - Institut Pasteur de Lille. This work was further supported by the CoSTREAM project (http://www.costream.eu/) and funding from the European Union's Horizon 2020 research and innovation program under grant agreement 667375. Pascual Sanchez Juan was supported by grants from, IDIVAL, Instituto de Salud Carlos III (Fondo de Investigacion Sanitario, PI08/0139, PI12/02288, PI16/01652, JPND (DEMTEST PI11/03028) and the CIBERNED program. Belgium samples: Research at the Antwerp site is funded in part by the Belgian Science Policy Office Interuniversity Attraction Poles program, the Belgian Alzheimer Research Foundation, the Flemish government-initiated Flanders Impulse Program on Networks for Dementia Research (VIND) and the Methusalem excellence program, the Research Foundation Flanders (FWO), and the University of Antwerp Research Fund, Belgium. The Antwerp site authors thank the personnel of the VIB Neuromics Support Facility, the Biobank of the Institute Born-Bunge and neurology departments at the contributing hospitals. The authors acknowledge the members of the BELNEU consortium for their contributions to the clinical and pathological characterization of Belgium patients and the personnel of the Diagnostic Service Facility for the genetic testing. Finish sample collection: Financial support for this project was provided by Academy of Finland (grant number 307866), Sigrid Jusélius Foundation and the Strategic Neuroscience Funding of the University of Eastern Finland Swedish sample collection: Financially supported in part by the Swedish Brain Power network, the Marianne and Marcus Wallenberg Foundation, the Swedish Research Council (521-2010- 3134, 2015-02926), the King Gustaf V and Queen Victoria’s Foundation of Freemasons, the Regional Agreement on Medical Training and Clinical Research (ALF) between Stockholm County Council and the Karolinska Institutet, the Swedish Brain Foundation and the Swedish Alzheimer Foundation”.

This work has been partly supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195 and No. HHSN268201500001I) and its contract with Affymetrix, Inc. for genotyping services (Contract No. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This study was also supported by grants from the National Institute of Aging (R01s AG033040, AG033193, AG054076, AG049607, AG008122, AG016495; and U01-AG049505) and the National Institute of Neurological Disorders and Stroke (R01-NS017950). We would like to thank the dedication of the Framingham Study participants, as well as the Framingham Study team, especially investigators and staff from the Neurology group, for their contributions to data collection. Dr. DeCarli is supported by the Alzheimer’s Disease Center (P30 AG 010129). The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.

The genotypic and associated phenotypic data used in the study, Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease (GenADA) study was contributed by Luis Fornazzari. Memory Clinic, St. Michaels Hospital, Toronto, Serge Gauthier. McGill Centre for Studies in Aging, Alzheimer's Disease Research Unit, Verdun, Quebec, Peter H. St. George-Hyslop. University of Toronto, Center for Research in Neurodegenerative Diseases, Toronto, Ontario, Howard Feldman. Vancouver Hospital and Health Sciences Centre, Vancouver, British Columbia, Anthony Guzman. Clinical Trial Unit, SCO Health Service, Ottawa, Ontario, Michael Borrie. Parkwood Hospital, London, Ontario, Andrew Kertesz. St. Joseph's Hospital, London, Ontario, Richard Delisle. Clinique de Neurologie, Trois-Riviéres, Quebec. The study was funded by GlaxoSmithKline, Inc.. Translational Medicine and Genetics, Clinical Imaging Center, R&D Alliances, and Worldwide Epidemiology. The dataset used for analyses described in this manuscript were obtained from NIH dbGaP repository (phs000219.v1.p1).

Data used in the preparation of this article was obtained from the Genetic and Environmental Risk for Alzheimer’s disease (GERAD1) Consortium (Harold et al. 2009). Cardiff University was supported by the Wellcome Trust, Medical Research Council (MRC), Alzheimer’s Research UK (ARUK) and the Welsh Assembly Government. Cambridge University and Kings College London acknowledge support from the MRC. ARUK supported sample collections at the South West Dementia Bank and the Universities of Nottingham, Manchester and Belfast. The Belfast group acknowledges support from the Alzheimer's Society, Ulster Garden Villages, N.Ireland R&D Office and the Royal College of Physicians/Dunhill Medical Trust. The MRC and Mercer’s Institute for Research on Ageing supported the Trinity College group. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer's and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by NIH grants, Barnes Jewish Foundation and the Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the MRC Prion Unit/UCL Department of Neurodegenerative Disease collection was supported by the UCLH/UCL Biomedical Centre and NIHR Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by Lundbeck SA. The Bonn group was supported by the German Federal Ministry of Education and Research (BMBF), Competence Network Dementia and Competence Network Degenerative Dementia, and by the Alfried Krupp von Bohlen und Halbach-Stiftung. The GERAD1 Consortium also used samples ascertained by the NIMH AD Genetics Initiative.

The Genome Research @ Fundació ACE project (GR@ACE) is supported by Grifols SA, Fundación bancaria ‘La Caixa’, Fundació ACE, and CIBERNED. A.R. receive support from the European Union/EFPIA Innovative Medicines Initiative Joint undertaking ADAPTED and MOPEAD projects (grant numbers 115975 and 115985, respectively) and by national grants PI19/01301, PI16/01861, PI17/01474 and PI19/01240. Acción Estratégica en Salud is integrated into the Spanish National R + D + I Plan and funded by ISCIII (Instituto de Salud Carlos III)–Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER–‘Una manera de hacer Europa’). Some control samples and data from patients included in this study were provided in part by the National DNA Bank Carlos III (http://www.bancoadn.org, University of Salamanca, Spain) and Hospital Universitario Virgen de Valme (Sevilla, Spain); they were processed following standard operating procedures with the appropriate approval of the Ethical and Scientific Committee.

The Mayo Clinic Alzheimer's Disease Genetic Studies, led by Dr. Nilüfer Ertekin-Taner and Dr. Steven G. Younkin, Mayo Clinic, Jacksonville, FL using samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer's Disease Research Center, and the Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation, and support from Mayo Foundation. This dataset was downloaded from Synapse (doi:10.7303/syn5550404).

The Mount Sinai Brain Bank (MSBB) study data were provided by Dr. Levey from Emory University based on postmortem brain tissue collected through the Mount Sinai VA Medical Center Brain Bank provided by Dr. Eric Schadt from Mount Sinai School of Medicine. This dataset was downloaded from Synapse (10.7303/syn3159438).

The Neocodex-Murcia study was funded by the Fundación Alzheimur (Murcia), the Ministerio de Educación y Ciencia (Gobierno de España), Corporación Tecnológica de Andalucía and Agencia IDEA (Consejería de Innovación, Junta de Andalucía). The Diabetes Research Laboratory, Biomedical Research Foundation. University Hospital Clínico San Carlos has been supported by CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM); CIBERDEM is an ISCIII Project.

The National Institute on Aging (NIA) - Late Onset Alzheimer's Disease Family Study data were provided by Richard Mayeux, MD, MSc. Columbia University, New York, NY, USA andTatiana Foroud, PhD. National Cell Repository for Alzheimer's Disease and Indiana University, Indianapolis, IN, USA. This study has received funding from HHSN268200782096C. "NIH contract High throughput genotyping for studying the genetic contributions to human disease”. National Institutes of Health, Bethesda, MD, USA. The study was downloaded from dbGap (phs000168p2v2).

The ROS/MAP study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161 (ROS), R01AG15819 (ROSMAP; genomics and RNAseq), R01AG17917 (MAP), R01AG30146, R01AG36042 (5hC methylation, ATACseq), RC2AG036547 (H3K9Ac), R01AG36836 (RNAseq), R01AG48015 (monocyte RNAseq) RF1AG57473 (single nucleus RNAseq), U01AG32984 (genomic and whole exome sequencing), U01AG46152 (ROSMAP AMP-AD, targeted proteomics), U01AG46161(TMT proteomics), U01AG61356 (whole genome sequencing, targeted proteomics, ROSMAP AMP-AD), the Illinois Department of Public Health (ROSMAP), and the Translational Genomics Research Institute (genomic). Additional phenotypic data can be requested at https://www.radc.rush.edu. This dataset was downloaded from Synapse (doi:10.7303/syn3219045).

The Rotterdam Study (RS) is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters, and Carolina Medina-Gomez, for their help in creating the GWAS database, and Karol Estrada, Yurii Aulchenko, and Carolina Medina-Gomez, for the creation and analysis of imputed data. This work has been performed as part of the CoSTREAM project (http://www.costream.eu) and ORACLE project, and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 667375 and No 678543. HHHA was supported by ZonMW grant number 916.19.151.

The TGEN study was supported by Kronos Life Sciences Laboratories, the National Institute on Aging (Arizona Alzheimer’s Disease Center P30 AG19610, RO1 AG023193, Mayo Clinic Alzheimer’s Disease Center P50 AG16574, and Intramural Research Program), the National Alzheimer’s Coordinating Center (U01 AG016976), and the state of Arizona. TGEN investigators provided free access to genotype data to other researchers via Coriell Biorepositories (http://www.coriell.org).

We thank Dr Beatriz Pontes, Dr Cristina Rubio-Escudero and Dr José C. Riquelme for providing support for the implementation and distribution of analysis code as Jupyter notebooks.

The results published here are in part based on data obtained from the AMP-AD Knowledge Portal (https://adknowledgeportal.synapse.org/).

Conflicts of Interest

This study was sponsored by AbbVie Deutschland GmbH & Co. KG. AbbVie contributed to the study design, research, and interpretation of data, writing, reviewing, and approving the abstract. Some authors are currently employed by AbbVie and may own AbbVie stock. Alfredo Cabrera-Socorro is full-time employee of Janssen Pharmaceutical NV, Turnhoutseweg 30, 2340 Beerse, Belgium.

Funding

This work has been supported by the ADAPTED consortium, which has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115975. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations.

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