Abstract
Introduction
Genes implicated by genome‐wide association studies and family‐based studies of Alzheimer's disease (AD) are largely discordant. We hypothesized that genes identified by sequencing studies like the Alzheimer's Disease Sequencing Project (ADSP) may bridge this gap and highlight shared biological mechanisms.
Methods
We performed structured literature review of genes prioritized by ADSP studies, genes underlying familial dementias, and genes nominated by genome‐wide association studies. Gene set enrichment analyses of each list identified enriched pathways.
Results
The genes prioritized by the ADSP, familial dementia studies, and genome‐wide association studies minimally overlapped. Each gene set identified dozens of enriched pathways, several of which were shared (e.g., regulation of amyloid beta clearance).
Discussion
Alternative study designs provide unique insights into AD genetics. Shared pathways enriched by different genes highlight their relevance to AD pathogenesis, while the patterns of pathway enrichment unique to each gene set provide additional targets for functional studies.
Keywords: Alzheimer's disease, genetic architecture, genome, networks, pathways
1. BACKGROUND
Alzheimer's disease (AD) is the leading cause of dementia in the United States, estimated to affect 5.8 million Americans in 2020. 1 AD is a complex and highly heritable trait 2 for which there is no efficacious treatment. Drug targets supported by human genetic evidence are much more likely to be approved by the Food and Drug Administration for therapeutic use, 3 demonstrating the need for continued genetics research into AD and an improved understanding of the biological processes underlying the disease.
The known genetic architecture of AD implicates causal and risk variants at dozens of loci. 4 Family studies have illustrated that rare early‐onset autosomal dominant AD (ADAD) can be caused by highly penetrant variants in APP, 5 PSEN1, 6 and PSEN2. 7 Although these autosomal dominant variants explain the cause of AD in < 1% of cases, 8 their discovery provided a direct link between AD genetics and pathogenesis through rare coding changes 9 in genes underlying the generation of amyloid beta (Aβ), a neuropathological hallmark of AD. 10 The apolipoprotein E (APOE) ε2 and ε4 alleles defined by two missense variants were first associated with AD in family studies and underlie the strongest signal across genome‐wide association studies (GWAS) of AD. 11 , 12 , 13 , 14 Rare variant association studies have also identified protein coding changes associated with AD, 15 though many of these studies have been restricted to analyses of known variants (e.g., ABI3, PLCG2 16 ) or small samples of whole exome sequence (WES) data (e.g., AKAP9, 17 TREM2 18 ). Large GWAS of common variants have implicated dozens of loci but do not implicate the ADAD genes. 13 , 19 Many of the AD GWAS loci are intergenic, and the specific genes influencing AD risk and pathogenesis within those loci are mostly unresolved. 19 The genes implicated by family studies and GWAS approaches are largely discordant, influenced in part by their study design: family‐based studies have better power to detect rare variants with large effect sizes, while GWAS are better powered to identify common variants associated with modest effect sizes but typically representing a single ancestry. Large‐scale sequencing efforts like the Alzheimer's Disease Sequencing Project (ADSP 20 ) may resolve the link between GWAS locus and functional variation by directly testing sequence variation rather than genetic markers or imputed genotypes. We hypothesize that the genes implicated in AD risk by these different analytical strategies may represent shared biological pathways.
Instead of relying on a single gene's story, pathway analyses identify enrichment in biological functions among members of a gene set. 21 These approaches have connected genes near GWAS loci to biological processes that may influence AD pathogenesis. 12 , 13 Pathway analyses are frequently restricted to the genes or loci implicated by a single study rather than the field as a whole and may miss connections with genes implicated by alternative study designs. If the support for a given pathway is strong, one could imagine targeting therapeutic interventions or treatments to those pathways, as opposed to a single gene. 20
Here, we summarize the genes implicated by the ADSP Discovery Phase publications and place them into the larger context of AD genetics. We compare the genes implicated by the ADSP with genes underlying familial dementias and genes prioritized in a recent meta‐analysis of AD GWAS representing > 90,000 subjects (35,274 cases and 59,163 controls) 13 or an AD genetics literature review. 22 Gene set enrichment analyses identify biological processes implicated by these three different avenues of AD genetics research. We hypothesize that the genes implicated by the ADSP will provide greater resolution within established AD pathways and may implicate new pathways relevant to disease.
RESEARCH IN CONTEXT
Systematic review: Genes implicated by the Alzheimer's Disease Sequencing Project (ADSP) underwent a literature review to identify prior evidence for a relationship to Alzheimer's disease (AD). Gene set enrichment analyses compared the pathways implicated by the subset of ADSP genes with independent support to those implicated in familial dementias or genome‐wide or association studies.
Interpretation: While the ADSP, familial dementia, and genome‐wide association study gene sets are largely discordant, they are enriched in genes representing similar biological pathways (e.g., regulation of amyloid beta clearance). Gene set–specific pathways highlight the utility of alternative strategies for identifying genetic variation influencing AD risk and pathogenesis.
Future directions: The genes and pathways highlighted here present targets for further functional and neuropathological studies, as well as pathway‐specific genetic risk scores. Increasingly diverse study populations and approaches within AD research are expected to identify novel genes that may provide support for these pathways or nominate others.
HIGHLIGHT
Exome and genome‐based Alzheimer's disease studies nominate novel genes/pathways
Common and rare variant studies support genes within several biological pathways
APOE, AKAP9, MAPT, ABCA7, CSF1R, and TREM2 contributed to the most ADSP pathways
Functional studies support most Alzheimer's Disease Sequencing Project genes
2. MATERIALS AND METHODS
2.1. AD GWAS gene set
The curated AD GWAS gene list includes the genes summarized in two recent publications: a literature review of sporadic or late‐onset AD risk loci implicated by linkage and/or association studies 22 (N = 16 studies, sample size = 40–113,600) and a meta‐analysis of 94,437 clinically diagnosed AD subjects. 13 These two references represent samples with European ancestry and do not include stratified analyses or studies of biomarkers, endophenotypes, or family history of dementia. Most of these associations involve single‐variant tests of common, non‐coding markers, although a handful of rare variant studies were included. 22 The 31 genes extracted from the review paper were restricted to a single gene at each locus prioritized by the authors of the review. The meta‐analysis combined evidence from coding changes, gene expression, pathway analyses, and clinical expression to nominate 53 candidate genes across 24 genome‐wide significant loci, including most of the genes extracted from the review paper (17/31 = 55%).
2.2. Familial dementia gene set
Genes underlying AD, dementias which can clinically mimic AD such as frontotemporal dementia (MIM:600274), and distinct dementias such as leukoencephalopathy with vanishing white matter (MIM:603896) were extracted from a clinical neurodegenerative disease gene panel followed by literature review 9 (Table S1 in supporting information). C9ORF72, a gene underlying frontotemporal dementia 23 previously associated with AD, 24 was added to complete the familial dementia gene set (N = 36). Most of these gene–phenotype relationships were identified by the co‐segregation of the phenotype with rare coding changes in small, family‐based studies.
2.3. The AD sequencing project gene set
The ADSP, supported jointly by the National Institute on Aging and the National Human Genome Research Institute, gathers and analyzes WES and whole genome sequence (WGS) data to detect novel AD risk variants. 20 The ADSP Discovery Phase was a collaboration between the Alzheimer's Disease Genetics Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. 20 The ADSP Discovery Phase produced eight gene‐discovery publications: three using WGS data from 582 individuals from 111 families with either European American or Caribbean Hispanic ancestry 9 , 25 , 26 and five publications based upon WES representing > 10,000 subjects with primarily non‐Hispanic White ancestry. 27 , 28 , 29 , 30 , 31 Sample sizes within these studies range from 5740 cases and 5096 controls with European American or Caribbean Hispanic ancestry 27 to 164 cases and 33 controls within 42 families with non‐Hispanic European ancestry. 25
Genes with evidence for a relationship with AD risk were extracted from ADSP Discovery Phase publications using permissive filters. Genes from the family‐based WGS studies were extracted if they met one or more of the following conditions: (1) variation in genes belonging to the familial dementia gene set which either was previously reported as pathogenic or co‐segregated with AD in at least one family within the ADSP, (2) variation within genes from the AD GWAS gene set with either evidence for association with AD or co‐segregation in 2+ families, or (3) variation co‐segregating with AD in 2+ families within a multi‐family linkage region. Genes from the ADSP WES studies were extracted if their support met at least one of the following conditions: (1) variation with exome‐wide significant evidence of association at the variant or gene level or (2) variation includes rare coding variants in 10+ cases and no controls. All gene names were verified using the multi‐symbol checker developed by the HUGO Gene Nomenclature Committee (HGNC) multi‐symbol checker.
Genes meeting these permissive criteria underwent structured literature reviews by two investigators, and the two earliest references supporting a link between AD and the gene were recorded where available. First, we searched for “gene” AND “Alzheimer” in PubMed and reviewed the entries from oldest to newest. We then reviewed the Online Mendelian Inheritance in Man (OMIM 32 ) for each gene for a connection to AD. Finally, we searched for ‘“gene” and “Alzheimer”’ and reviewed the first two pages of matches for references supporting the gene to AD link using https://scholar.google.com (last accessed March 22, 2021). Papers were included as evidence of a connection between the gene and AD if the gene was associated with AD‐specific changes in genotype or gene expression, or AD‐specific endophenotypes, pathology, or biomarkers in humans or animal models at a study‐wide statistical significance level. References were excluded from the review if the research was an abstract for a conference, part of a dissertation, not published in English, or linked only to an AD risk factor (e.g., aging). Genes with at least one external publication supporting a link to AD were included in the ADSP‐derived gene set (ADSP+) used for pathway analysis.
2.4. Gene set enrichment analysis
Gene sets were provided to STRING‐db (v11.0 33 ) to test for protein–protein interaction (PPI) enrichment using most default parameter settings but dropping text mining of PubMed abstracts and neighborhood of the genome as sources of interaction. Genes in our gene sets have been published together by definition, and the gene list derived from GWAS provided multiple gene candidates at a single locus, both of which would bias results if text mining or gene neighborhood were allowed as a source. Tests for PPI applied a significance threshold of P < .05. Gene set enrichment analyses were performed using the eXploring Genomic Relations for enhanced interpretation (XGR) software 34 to identify significantly enriched pathways among familial dementia, GWAS, and ADSP+ gene sets. Each gene set was tested for enrichment in Gene Ontology (GO) biological processes using a hypergeometric test accounting for ontological structure and redundant pathways, excluding gene sets with fewer than two genes, and using all human genes as the reference. The significance threshold was set to a false discovery rate (FDR) < 0.05. 33 Using the GeneOverlap R package (v3.12), 35 Fisher's exact test was used to test for evidence of significant overlap between genes driving the enrichment of each pair of pathways, with a significance threshold of P < .05.
3. RESULTS
3.1. ADSP+, AD GWAS, and familial dementia gene sets
Across the eight ADSP Discovery Phase studies, 9 , 25 , 26 , 27 , 28 , 29 , 30 , 31 64 genes met our permissive criteria (Table S2 in supporting information). Independent support for a link to AD was identified for the majority of these genes (43/64, 67%), defining the ADSP+ gene set (Table 1). Most of these genes were reported in a single ADSP Discovery Phase study, though TREM2 appeared in four studies. 27 , 28 , 30 , 31 Much of the literature support for the ADSP+ genes come from functional studies, rather than statistical associations (Figure 1, Table S2). Studies identifying genes differentially expressed in AD supported the highest number of genes (15 genes), closely followed by studies of genes related to changes in AD pathology (12 genes) or animal models (12 genes), GWAS or single nucleotide polymorphism (SNP) association studies (9 genes), linkage analyses (5 genes), and WES/WGS studies (3 genes). The relatively sparse support from WES/WGS studies almost certainly reflects the relative scarcity of large sequencing studies of AD prior to the ADSP.
TABLE 1.
Origins of genes belonging to the ADSP+, familial dementia, and GWAS gene sets
| Gene Set | Source | Data | Genes |
|---|---|---|---|
| ADSP+ | Bis et al. (2020) 27 | ADSP WES | ABCA7, APOE, BCAM, CBLC, GAS2L2, MS4A6A, OPRL1, PILRA, TREM2, ZNF655 |
| Ma et al. (2019) 28 | ADSP WES | GPAA1, MAPT, NSF, OR8G5, SLC24A3, TREM2 | |
| Patel et al. (2019) 29 | ADSP WES | ABCD4, CELSR1, GIMAP2, GTSE1, L3MBTL2, NOTCH3, QRICH2, SCFD1, SPHK2, SUV420H1, UBAP2 | |
| Tosto et al. (2019) 30 | ADSP WES | PINX1, TREM2 | |
| Zhang et al. (2019) 31 | ADSP WES | CASP7, HTR3A, KANSL3, KCNK13, NPC1, SCN4A, STAB1, TMEM87A, TREM2 | |
| Beecham et al. (2018) 25 | ADSP WGS | DDR2, FERMT2, TTC3 | |
| Blue et al. (2018) 9 | ADSP WGS | ARSA, CHMP2B, CSF1R, GRN | |
| Vardarajan et al. (2018) 26 | ADSP WGS | AKAP9 | |
| Familial dementia | Dementia gene panel 9 | Clinical test | APOE, APP, ARSA, ATP13A2, C9orf72, CHCHD10, CHMP2B, CSF1R, DNMT1, EIF2B1, EIF2B2, EIF2B3, EIF2B4, EIF2B5, FUS, GALC, GRN, HEXA, ITM2B, LMNB1, MAPT, NOTCH3, NPC1, NPC2, PDGFB, PDGFRB, PRNP, PSEN1, PSEN2, SLC20A2, SLC25A12, TARDBP, TBP, TREM2, TYROBP, VCP |
| GWAS | Kunkle et al. (2019), 13 Figure 2 | GWAS and annotation | ABCA7, ACP2, ADAM10, ADAMTS1, AGFG2, ARHGAP45 (HMHA1), BIN1, C1QTNF4, C4A, CASS4, CD2AP, CD55, CELF1, CLU, CNN2, CR1, ECHDC3, EED, EPHB4, FAM131B, GAL3ST4, GPSM3, HLA‐DPA1, HLA‐DQA1, HLA‐DRA, HLA‐DRB1, HLA‐DRB5, INPP5D, IQCK, MAF, MAP11 (C7orf43), MS4A4A, MS4A6A, MS4A7, MTCH2, NYAP1, NDUFS3, NUP160, PICALM, PILRA, PSMB8, PSMB9, PSMC3, PSMC5, PTK2B, RIN3, SORL1, SPI1, STYX, TREM2, WDR18, WWOX, YOD1, ZKSCAN1 |
| Naj et al. (2017) 22 review of 16 publications, Table 2 | GWAS and linkage analysis | ABCA7, ACE, APOE, APP, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, CR1, DSG2, EPHA1, FERMT2, HLA‐DRB1, INPP5D, MEF2C, MS4A gene cluster, NME8, PICALM, PLD3, PTK2B, RIN3, SLC24A4, SORL1, TREM2, TREML2, TRIP4, ZCWPW1 |
Abbreviations: ADSP, Alzheimer's Disease Sequencing Project; GWAS, genome‐wide association study; WES, whole exome sequence; WGS, whole genome sequence.
FIGURE 1.
Sources of literature support for Alzheimer's Disease Sequencing Project (ADSP) Discovery Phase candidate genes. Differentially expressed genes (N = 15) include ABCD4, CELSR1, GAS2L2, GIMAP2, GPAA1, GRN, KANSL3, NPC1, QRICH2, SCFD1, SCN4A, SLC24A3, SPHK2, STAB1, SUV420H1/KMT5B. Mouse/animal model genes (N = 12) include ABCA7, CELSR1, CHMP2B, CSF1R, DDR2, GTSE1, HTR3A, NSF, TMEM87A, TREM2, TTC3, UBAP2. Pathology/biomarkers genes (N = 12) include APOE, CASP7, CBLC, CHMP2B, DDR2, KCNK13, MAPT, NOTCH3, OPRL1, PINX1, STAB1, ZNF655. Genome‐wide association study (GWAS)/single nucleotide polymorphism (SNP) association genes (N = 9) include ABCA7, APOE, ARSA, CASP7, FERMT2, L3MBTL2, MS4A6A, NPC1, PILRA. Linkage analysis genes (N = 5) include ABCD4, CSF1R, NOTCH3, OR8G5, TTC3. Whole genome sequence (WGS)/Whole exome sequence (WES) genes (N = 3) include AKAP9, BCAM, CBLC. Complete details available in Table S1 in supporting information
The GWAS gene set includes 70 genes derived from 17 publications (Table 1). 13 , 22 Six of the GWAS genes (9%) overlap with the ADSP+ gene set: ABCA7, APOE, FERMT2, MS4A6A, PILRA, and TREM2. The familial dementia gene set includes 36 genes derived from a clinical testing panel for neurodegenerative disease supplemented with literature review (Table 1). 9 Nine of the familial dementia genes (25%) overlap with the ADSP+ gene set: APOE, ARSA, CHMPB, CSF1R, GRN, MAPT, NOTCH3, NPC1, and TREM2. The familial dementia and AD GWAS gene sets are largely discordant, sharing only APOE, APP, and TREM2.
3.2. Gene set enrichment analysis
The genes within the ADSP+ gene list exhibit significant evidence of interaction and represent many biological pathways. The ADSP+ genes exhibit significant PPI enrichment (P = 8.36E‐03), with seven PPI edges observed between 43 nodes when two edges were expected under the null hypothesis. These edges form four clusters: (1) CSF1R is co‐expressed with TREM2, MS4A6A, and STAB1 with the latter two also co‐expressed with each other; (2) ABCA7 and ABCAD4 are co‐expressed and associated with each other in a curated database; as are (3) ARSA and GRN; while (4) NSF and SCFD1 are co‐expressed, associated in a curated database, and their proteins physically interact as measured with biochemical data. 33 XGR analyses of the ADSP+ genes identified 45 significantly enriched biological processes (Table 2). The top two ADSP+ pathways, regulation of Aβ clearance (GO:1900221, FDR = 2.60E‐05) and cholesterol efflux (GO:0033344, FDR = 9.00E‐05), have much stronger support than the remaining 43 pathways (0.05 > FDR > 0.005). Both the familial dementia gene set (FDR = 8.80E‐05; APOE, TREM2) and the GWAS gene set (FDR = 2.70E‐07; ABCA7, APOE, CLU, TREM2) were significantly enriched in genes belonging to the regulation of Aβ clearance (GO:1900221) pathway. The familial dementia gene set is also enriched in genes belonging to the cholesterol efflux pathway (GO:0033344; FDR = 1.00E‐05; ABCA7, APOE, NPC1, NPC2), while the AD GWAS gene set is not (FDR > 0.05).
TABLE 2.
Pathways identified by ADSP+ gene set enrichment analysis
| GO ID | Term Name | FDR | Genes |
|---|---|---|---|
| GO:1900221 | Regulation of amyloid beta clearance | 2.60E‐05 | ABCA7, APOE, TREM2 |
| GO:0033344 | Cholesterol efflux | 9.00E‐05 | ABCA7, APOE, NPC1 |
| GO:0051651 | Maintenance of location in cell | 2.60E‐03 | AKAP9, APOE, GPAA1 |
| GO:0031116 | Positive regulation of microtubule polymerization | 2.60E‐03 | AKAP9, MAPT |
| GO:0016242 | Negative regulation of macroautophagy | 2.60E‐03 | NPC1, SCFD1 |
| GO:0070374 | Positive regulation of ERK1 and ERK2 cascade | 2.70E‐03 | ABCA7, APOE, CSF1R, TREM2 |
| GO:0019068 | Virion assembly | 2.70E‐03 | APOE, CHMP2B |
| GO:0030316 | Osteoclast differentiation | 2.70E‐03 | CSF1R, TREM2 |
| GO:0007613 | Memory | 2.90E‐03 | ABCA7, APOE, MAPT |
| GO:0007080 | Mitotic metaphase plate congression | 3.00E‐03 | CHMP2B, PINX1 |
| GO:0007160 | Cell‐matrix adhesion | 3.10E‐03 | BCAM, DDR2, FERMT2 |
| GO:0061024 | Membrane organization | 3.90E‐03 | ABCA7, APOE, CHMP2B, NPC1, NSF, SCFD1, TREM2 |
| GO:0048844 | Artery morphogenesis | 3.90E‐03 | APOE, NOTCH3 |
| GO:0048278 | Vesicle docking | 4.30E‐03 | NSF, SCFD1 |
| GO:0034765 | Regulation of ion transmembrane transport | 4.60E‐03 | AKAP9, HTR3A, KCNK13, OPRL1, SCN4A |
| GO:1900182 | Positive regulation of protein localization to nucleus | 4.60E‐03 | GTSE1, PINX1 |
| GO:0010948 | Negative regulation of cell cycle process | 4.90E‐03 | GTSE1, L3MBTL2, PINX1, ZNF655 |
| GO:0006813 | Potassium ion transport | 4.90E‐03 | KCNK13, NSF, SLC24A3 |
| GO:1902749 | Regulation of cell cycle G2/M phase transition | 5.10E‐03 | AKAP9, GTSE1, PINX1 |
| GO:0043407 | Negative regulation of MAP kinase activity | 5.80E‐03 | APOE, CBLC |
| GO:0035725 | Sodium ion transmembrane transport | 7.90E‐03 | SCN4A, SLC24A3 |
| GO:0032414 | Positive regulation of ion transmembrane transporter activity | 9.60E‐03 | AKAP9, HTR3A |
| GO:0007267 | Cell‐cell signaling | 1.00E‐02 | AKAP9, APOE, CELSR1, FERMT2, HTR3A, MAPT, STAB1 |
| GO:0050848 | Regulation of calcium‐mediated signaling | 1.00E‐02 | MAPT, TREM2 |
| GO:0042327 | Positive regulation of phosphorylation | 1.10E‐02 | ABCA7, AKAP9, APOE, CSF1R, DDR2, MAPT, TREM2 |
| GO:0051656 | Establishment of organelle localization | 1.10E‐02 | CHMP2B, MAPT, NSF, PINX1, SCFD1 |
| GO:0006664 | Glycolipid metabolic process | 1.10E‐02 | ARSA, GPAA1 |
| GO:0042391 | Regulation of membrane potential | 1.30E‐02 | AKAP9, HTR3A, KCNK13, MAPT, SCN4A |
| GO:0006897 | Endocytosis | 1.40E‐02 | ABCA7, APOE, NPC1, STAB1, TREM2 |
| GO:0006475 | Internal protein amino acid acetylation | 1.40E‐02 | KANSL3, MAPT |
| GO:0043269 | Regulation of ion transport | 1.60E‐02 | ABCA7, AKAP9, APOE, HTR3A, KCNK13, OPRL1, SCN4A |
| GO:0051348 | Negative regulation of transferase activity | 1.60E‐02 | APOE, CBLC, MAPT, PINX1 |
| GO:0022604 | Regulation of cell morphogenesis | 2.00E‐02 | APOE, CSF1R, FERMT2, MAPT |
| GO:0007626 | Locomotory behavior | 3.10E‐02 | APOE, CELSR1, NPC1 |
| GO:0040017 | Positive regulation of locomotion | 3.20E‐02 | CHMP2B, CSF1R, DDR2, GRN, GTSE1 |
| GO:0006643 | Membrane lipid metabolic process | 3.20E‐02 | ARSA, GPAA1, SPHK2 |
| GO:0050795 | Regulation of behavior | 3.30E‐02 | APOE, OPRL1 |
| GO:0018108 | Peptidyl‐tyrosine phosphorylation | 3.50E‐02 | CSF1R, DDR2 |
| GO:0051174 | Regulation of phosphorus metabolic process | 4.40E‐02 | ABCA7, AKAP9, APOE, CBLC, CSF1R, DDR2, MAPT, TREM2 |
| GO:0061097 | Regulation of protein tyrosine kinase activity | 4.50E‐02 | CBLC, CSF1R |
| GO:0016192 | Vesicle‐mediated transport | 4.70E‐02 | ABCA7, APOE, ARSA, CHMP2B, GRN, NPC1, NSF, SCFD1, STAB1, TMEM87A, TREM2 |
| GO:0006644 | Phospholipid metabolic process | 4.90E‐02 | CSF1R, GPAA1, SPHK2 |
| GO:0006874 | Cellular calcium ion homeostasis | 4.90E‐02 | APOE, OPRL1, SLC24A3 |
| GO:0099177 | Regulation of trans‐synaptic signaling | 4.90E‐02 | AKAP9, APOE, MAPT |
| GO:0006942 | Regulation of striated muscle contraction | 4.90E‐02 | AKAP9, SCN4A |
Abbreviations: ADSP, Alzheimer's Disease Sequencing Project; FDR, false discovery rate; GO, Gene Ontology.
Note: Significant results were defined as with FDR < 0.05.
The intersection of pathways enriched by ADSP+ genes with those enriched by the familial dementia genes (N = 116, Table S3 in supporting information) and AD GWAS genes (N = 102, Table S4 in supporting information) provides insight into the genetic architecture of AD. Nine pathways are enriched by both the ADSP+ and familial dementia genes, seven are enriched by both the ADSP+ and AD GWAS genes, and four are enriched in analyses of all three gene sets (Table 3). For some of these shared pathways, the ADSP+ gene set contributes unique genes absent from the familial dementia and AD GWAS sets, fleshing out pathways previously implicated in AD. In addition to ABCA7, APOE, NPC1, and TREM2, endocytosis (GO:0006897) is also supported by the ADSP+ gene STAB1. The ADSP+ genes also add AKAP9 and DDR2 to the list of genes implicating regulation of phosphorous metabolic process (GO:0051174) and CBLC (in the APOE region) to regulation of protein tyrosine kinase activity (GO:0061097).
TABLE 3.
Pathways significantly enriched in genes from ADSP+ gene list that overlap with those enriched in the familial dementia gene list, the GWAS gene list, or both
| ADSP+ gene set | Familial dementia gene set | GWAS gene set | |||||
|---|---|---|---|---|---|---|---|
| GO ID | GO term name | FDR | Genes | FDR | Genes | FDR | Genes |
| GO:1900221 | Regulation of amyloid beta clearance | 2.60E‐05 | ABCA7, APOE, TREM2 | 8.80E‐05 | APOE, TREM2 | 2.70E‐07 | ABCA7, APOE, CLU, TREM2 |
| GO:0006897 | Endocytosis | 1.40E‐02 | ABCA7, APOE, NPC1, STAB1 , TREM2 | 7.60E‐03 | APOE, APP, C9orf72, NPC1, TREM2 | 3.20E‐03 | ABCA7, APOE, APP, BIN1, PICALM, RIN3, SORL1, TREM2 |
| GO:0051174 | Regulation of phosphorus metabolic process | 4.40E‐02 | ABCA7, AKAP9 , APOE, CBLC, CSF1R, DDR2 , MAPT, TREM2 | 8.70E‐03 | APOE, APP, C9orf72, CSF1R, MAPT, PDGFB, PDGFRB, PRNP, PSEN1, SLC25A12, TARDBP, TREM2, VCP | 2.80E‐02 | ABCA7, ACE, APOE, APP, CASS4, CLU, EPHA1, MEF2C, PTK2B, SORL1, STYX, TREM2 |
| GO:0061097 | Regulation of protein tyrosine kinase activity | 4.50E‐02 | CBLC , CSF1R | 3.20E‐06 | APP, CSF1R, PDGFB, PRNP, PSEN1 | 3.10E‐03 | ACE, APP, CASS4 |
| GO:0022604 | Regulation of cell morphogenesis | 2.00E‐02 | APOE, CSF1R, FERMT2, MAPT | NA | NA | 2.20E‐02 | ADAM10, APOE, CASS4, FERMT2, PTK2B |
| GO:0099177 | Regulation of trans‐synaptic signaling | 4.90E‐02 | AKAP9 , APOE, MAPT | NA | NA | 1.70E‐02 | APOE, APP, MEF2C, PSMC5, PTK2B |
| GO:0006874 | Cellular calcium ion homeostasis | 4.90E‐02 | APOE, OPRL1 , SLC24A3 | NA | NA | 3.90E‐02 | APOE, APP, CD55, PTK2B, SLC24A4 |
| GO:0033344 | Cholesterol efflux | 9.00E‐05 | ABCA7, APOE, NPC1 | 1.00E‐05 | APOE, NPC1, NPC2 | NA | NA |
| GO:0070374 | Positive regulation of ERK1 and ERK2 cascade | 2.70E‐03 | ABCA7, APOE, CSF1R, TREM2 | 1.60E‐05 | APOE, APP, CSF1R, PDGFB, PDGFRB, TREM2 | NA | NA |
| GO:0019068 | Virion assembly | 2.70E‐03 | APOE, CHMP2B | 7.90E‐04 | APOE, CHMP2B | NA | NA |
| GO:0048844 | Artery morphogenesis | 3.90E‐03 | APOE, NOTCH3 | 1.80E‐04 | APOE, NOTCH3, PDGFRB | NA | NA |
| GO:0042391 | Regulation of membrane potential | 1.30E‐02 | AKAP9, HTR3A , KCNK13 , MAPT, SCN4A | 2.90E‐02 | APP, CHCHD10, MAPT, PSEN1, VCP | NA | NA |
Abbreviations: ADSP, Alzheimer's Disease Sequencing Project; FDR, false discovery rate; GWAS, genome‐wide association study; NA, not applicable.
The ADSP+ pathway analyses identified significant enrichment of 33 GO Biological Processes that were not significantly enriched in either the familial dementia or AD GWAS pathway analyses (Table 2). Among these, maintenance of location in cell (GO:0051651; AKAP9, APOE, GPAA1), positive regulation of microtubule polymerization (GO:0031116; AKAP9, MAPT), and negative regulation of macroautophagy (GO:0016242; NPC1, SCFD1) share the strongest evidence of enrichment among the pathways (FDR = 0.0026). Glial cell development (GO:0021782; FDR = 5.70E‐10) and regulation of Aβ formation (GO:1902003; FDR = 3.80E‐12) were the most significantly enriched biological processes in the familial dementia and AD GWAS gene sets, respectively.
Many of the 45 pathways identified in the ADSP+ pathway enrichment analysis share contributing genes: 21 pathways involve APOE, 12 pathways involve AKAP9 and/or MAPT, 10 pathways involve ABCA7, and 9 pathways involve CSF1R and/or TREM2 (Table 2). The right matrix in Figure 2 summarizes contribution of each of these genes to each pathway, while the left matrix illustrates the evidence for significant overlap between the genes driving enrichment of each pathway, where P < .05 is shown in purple (Figure 2, Figures S1 and S2 in supporting information). APOE, AKAP9, and MAPT are involved in 30/45 ADSP+ enriched biological processes. Across the most frequent ADSP+ contributors to pathway enrichment, AKAP9 is the only gene absent from the familial dementia and AD GWAS gene sets. AKAP9 appears in 12 ADSP+ enriched pathways, second only to APOE. The genes contributing to the enrichment of 277 of 990 pairs of pathways implicated by the ADSP+ gene set significantly overlap (P < .05). As expected, some pairs of pathways describe similar functions (e.g., positive regulation of phosphorus metabolic process and regulation of phosphorus metabolic process). However, other pairs of pathways share similar genetic profiles yet may implicate distinct mechanisms for AD pathogenesis (e.g., membrane organization and endocytosis).
FIGURE 2.
Heatmap of relationships between pathways implicated by Alzheimer's Disease Sequencing Project (ADSP)+ pathway analysis. Left: matrix of pathways significantly enriched in members of the ADSP+ gene set (false discovery rate [FDR] < 0.05) which involve the genes with broadest membership across the ADSP+ pathways. Fisher's exact tests were used to test for overlap in the genes driving the enrichment of each pathway, with P‐value encoded by color: P > .01 are shown in white, P‐values between 0.05 and 0.1 are shown in gray, and P‐values between 0 and 0.05 are purple. The gray and purple values are divided into thirds, with darker colors representing smaller values. Right: Matrix indicating the presence/absence of a listed gene (x‐axis) and a pathway (y‐axis). An extended version of this figure including all 45 pathways implicated by the ADSP+ pathway analysis is available in Figures S1 and S2 in supporting information
4. DISCUSSION
While the genetic architecture and etiology of AD remains only partially understood, our structured literature review and gene set enrichment analyses suggest that WGS and WES studies may fill in some of these gaps while also providing support for pathways previously implicated in AD. Although each gene set provided a long list of candidate genes with few overlapping genes, the ADSP+ gene set was enriched in biological processes also implicated by the familial dementia genes, AD GWAS genes, or both. This suggests the alternative strategies used to associate these genes with AD point to shared mechanisms of disease.
The presence of pathways associated with regulation of Aβ clearance, endocytosis, regulation of phosphorous metabolic process, immune system process, and regulation of MAPK cascade in all three gene sets support candidate and gene pathways nominated by AD GWAS. 36 , 37 , 38 The relationship between regulation of Aβ clearance (GO:1900221) and cholesterol efflux (GO:0033344) pathways and AD are well established. 39 , 40 The regulation of Aβ clearance is directly related to the hallmark pathologic features of AD and offers a connection between the genes implicated in late‐onset AD 41 and ADAD. Similarly, the relationship between cholesterol efflux and AD has been of interest since the association between APOE and AD was first reported. 11 The ADSP+ studies also provide unique genes to these commonly implicated pathways, further elucidating the mechanisms by which these pathways contribute to the progression of AD.
Among the pathways significantly enriched only by the ADSP+ gene set, one of the most strongly associated processes is positive regulation of microtubule polymerization (GO:0031116; FDR = 0.0026; AKAP9 and MAPT; Table 2). Microtubule polymerization events play important roles in synaptic plasticity and function, 42 biological processes highlighted by a recent family‐based WGS study of AD. 43 Tau stabilizes microtubule polymerization, promoting microtubule assembly, 44 and neurofibrillary tangles of tau are another hallmark of AD pathology. 1 Post‐translational modifications of tau are known to contribute to neurodegenerative aggregation and affect the ability of tau to promote microtubule polymerization. 45 Microtubule deficiencies in brain tissue are significantly associated with clinical AD status, 46 and variation at the MAPT locus has been associated with AD among APOE ε4 negative subjects. 47
Although AKAP9 is specific to the ADSP+ gene set in this study, it was evaluated by the ADSP as a candidate gene with prior evidence of association with AD. 26 Other AD sequencing studies have identified rare variants with large effect sizes in AKAP9, 17 , 48 and variants in AKAP9 were nominally associated with AD in a recent GWAS of African American samples. 14 AKAP9 mutations enhance phosphorylation of tau, 49 directly influence the development of neurofibrillary tangles, 17 and the gene is upregulated in the hippocampi of patients in early stages of AD. 49 Among the ADSP+ enriched pathways, AKAP9 often appears alongside APOE and MAPT in pathways including cell–cell signaling (GO:0007267), positive regulation of phosphorylation (GO:0042327), regulation of phosphorous metabolic process (GO:0051174), and regulation of trans‐synaptic signaling (GO:0099177). These pathways echo results from a recent study using Bayesian networks to model relationships between epigenomic and transcriptomic data to identify AD networks, where protein phosphorylation and synaptic signaling were identified as differential subnetworks associated with AD. 50
We have shown that large‐scale sequencing studies like the ADSP bring attention to new genes and biological processes implicated in AD while providing support for biological processes previously nominated by GWAS and family studies. Furthermore, the frequency with which AKAP9 contributed to both new and established AD pathways and evidence from functional studies that it relates to tau‐mediated AD pathology strengthens the evidence it may play a role in AD risk and pathogenesis.
Our study has several limitations. The ADSP study design included a complicated ascertainment strategy, favoring families with many cases and few APOE ε4 alleles, while age, sex, and APOE genotype were used to select cases and controls with reduced risk of developing AD. 20 The sample size of the ADSP Discovery Phase was much smaller than the large‐scale GWAS conducted in recent years. 12 , 13 The WGS data in the ADSP Discovery Phase was limited to hundreds of samples representing fewer families; as most AD GWAS signals fall outside of the exome, this may partially explain the minimal overlap between the ADSP+ and GWAS gene sets. It is also important to note that many of the studies that contributed samples to the ADSP are also represented in other AD genetics studies, meaning some samples contribute to both ADSP and GWAS publications. The ADSP Follow‐up study is generating WGS data for thousands of additional subjects with a focus on diverse populations. This increase in diversity and sample size in WES/WGS analyses may provide further insights into the complex genetic architecture of AD. Our analytical approach also has its own limitations. The gene or genes underlying a GWAS or linkage signal are not always clear; gene sets prioritizing different genes within these loci may implicate different pathways. Gene sets which include genes implicated by studies of AD endophenotypes, biomarkers, or studies better representing non‐European ancestry may also implicate additional pathways in AD. While gene set enrichment analysis is a useful tool for providing biological context for genes, there is no single gold‐standard approach. This study focused on GO: Biological Processes, as our approach accounted for the ontological relationships between processes and this approach has been widely used in AD genetics studies (e.g., Jansen et al. 12 and Kunkle et al. 13 ). GO: Biological Processes have complex relationships and can be broadly defined; alternative pathway analysis strategies using a different source for pathway definitions or requiring a different number of genes to contribute to an enrichment signal will yield different results. Despite the limitations, gene set analysis and other pathway analysis tools provide a mechanism of hypothesis generation for disease susceptibility.
ADSP BANNER AUTHOR LIST
Members of the Discovery Phase of the Alzheimer's Disease Sequencing Project included: Michelle Bellair, Huyen Dinh, Harsha Doddapeneni, Shannon Dugan‐Perez, Adam English, Richard A Gibbs, Yi Han, Jianhong Hu, Joy Jayaseelan, Divya Kalra, Ziad Khan, Viktoriya Korchina, Sandra Lee, Yue Liu, Xiuping Liu, Donna Muzny, Waleed Nasser, William Salerno, Jireh Santibanez, Evette Skinner, Simon White, Kim Worley, Yiming Zhu, Alexa Beiser, Yuning Chen, Jaeyoon Chung, L Adrienne Cupples, Anita DeStefano, Josee Dupuis, John Farrell, Lindsay Farrer, Daniel Lancour, Honghuang Lin, Ching Ti Liu, Kathy Lunetta, Yiyi Ma, Devanshi Patel, Chloe Sarnowski, Claudia Satizabal, Sudha Seshadri, Fangui Jenny Sun, Xiaoling Zhang, Seung Hoan Choi, Eric Banks, Stacey Gabriel, Namrata Gupta, William Bush, Mariusz Butkiewicz, Jonathan Haines, Sandra Smieszek, Yeunjoo Song, Sandra Barral, Phillip L. De Jager, Richard Mayeux, Christiane Reitz, Dolly Reyes, Giuseppe Tosto, Badri Vardarajan, Shahzad Amad, Najaf Amin, M Afran Ikram, Sven van der Lee, Cornelia van Duijn, Ashley Vanderspek, Helena Schmidt, Reinhold Schmidt, Alison Goate, Manav Kapoor, Edoardo Marcora, Alan E. Renton, Kelley Faber, Tatiana Foroud, Michael Feolo, Adam Stine, Lenore J. Launer, David A. Bennett, Li Charlie Xia, Gary Beecham, Kara Hamilton‐Nelson, James Jaworski, Brian Kunkle, Eden Martin, Margaret Pericak‐Vance, Farid Rajabli, Michael Schmidt, Thomas H. Mosley, Laura Cantwell, Micah Childress, Yi‐Fan Chou, Rebecca Cweibel, Prabhakaran Gangadharan, Amanda Kuzma, Yuk Yee Leung, Han‐Jen Lin, John Malamon, Elisabeth Mlynarski, Adam Naj, Liming Qu, Gerard Schellenberg, Otto Valladares, Li‐San Wang, Weixin Wang, Nancy Zhang, Jennifer E. Below, Eric Boerwinkle, Jan Bressler, Myriam Fornage, Xueqiu Jian, Xiaoming Liu, Joshua C. Bis, Elizabeth Blue, Lisa Brown, Tyler Day, Michael Dorschner, Andrea R. Horimoto, Rafael Nafikov, Alejandro Q. Nato Jr., Pat Navas, Hiep Nguyen, Bruce Psaty, Kenneth Rice, Mohamad Saad, Harkirat Sohi, Timothy Thornton, Debby Tsuang, Bowen Wang, Ellen Wijsman, Daniela Witten, Lucinda Antonacci‐Fulton, Elizabeth Appelbaum, Carlos Cruchaga, Robert S. Fulton, Daniel C. Koboldt, David E. Larson, Jason Waligorski, Richard K. Wilson.
Supporting information
Supporting Information
Supporting Information
Supporting Information
ACKNOWLEDGMENTS
This work was supported by the National Institutes of Health (NIH) National Institute on Aging (NIA) grants U01AG058589 and T32AG052354, along with P30AG066546, R01AG048927, R01AG054076, RF1AG057519, U01AG032984, U01AG052409, U01AG052410, U01AG058635, U01AG058654, and U01AG062602. The research team thanks the study participants and their families for their contributions to this work. The Alzheimer's Disease Sequencing Project (ADSP) is comprised of two Alzheimer's disease (AD) genetics consortia and three National Human Genome Research Institute (NHGRI) funded Large Scale Sequencing and Analysis Centers (LSAC). The two AD genetics consortia are the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) funded by NIA (R01 AG033193), the National Heart, Lung, and Blood Institute (NHLBI), other National Institute of Health (NIH) institutes, and other foreign governmental and non‐governmental organizations. The Discovery Phase analysis of sequence data is supported through UF1AG047133 (to Drs. Schellenberg, Farrer, Pericak‐Vance, Mayeux, and Haines); U01AG049505 to Dr. Seshadri; U01AG049506 to Dr. Boerwinkle; U01AG049507 to Dr. Wijsman; and U01AG049508 to Dr. Goate and the Discovery Extension Phase analysis is supported through U01AG052411 to Dr. Goate, U01AG052410 to Dr. Pericak‐Vance, and U01 AG052409 to Drs. Seshadri and Fornage. Data generation and harmonization in the Follow‐up Phases is supported by U54AG052427 (to Drs. Schellenberg and Wang). The ADGC cohorts include: Adult Changes in Thought (ACT), the Alzheimer's Disease Centers (ADC), the Chicago Health and Aging Project (CHAP), the Memory and Aging Project (MAP), Mayo Clinic (MAYO), Mayo Parkinson's Disease controls, University of Miami, the Multi‐Institutional Research in Alzheimer's Genetic Epidemiology Study (MIRAGE), the National Cell Repository for Alzheimer's Disease (NCRAD), the National Institute on Aging Late Onset Alzheimer's Disease Family Study (NIA‐LOAD), the Religious Orders Study (ROS), the Texas Alzheimer's Research and Care Consortium (TARC), Vanderbilt University/Case Western Reserve University (VAN/CWRU), the Washington Heights‐Inwood Columbia Aging Project (WHICAP) and the Washington University Sequencing Project (WUSP), the Columbia University Hispanic‐ Estudio Familiar de Influencia Genetica de Alzheimer (EFIGA), the University of Toronto (UT), and Genetic Differences (GD).
The CHARGE cohorts are supported in part by National Heart, Lung, and Blood Institute (NHLBI) infrastructure grant HL105756 (Psaty), RC2HL102419 (Boerwinkle) and the neurology working group is supported by the National Institute on Aging (NIA) R01 grant AG033193. The CHARGE cohorts participating in the ADSP include the following: Austrian Stroke Prevention Study (ASPS), ASPS‐Family study, and the Prospective Dementia Registry‐Austria (ASPS/PRODEM‐Aus), the Atherosclerosis Risk in Communities (ARIC) Study, the Cardiovascular Health Study (CHS), the Erasmus Rucphen Family Study (ERF), the Framingham Heart Study (FHS), and the Rotterdam Study (RS). ASPS is funded by the Austrian Science Fond (FWF) grant number P20545‐P05 and P13180 and the Medical University of Graz. The ASPS‐Fam is funded by the Austrian Science Fund (FWF) project I904), the EU Joint Programme ‐ Neurodegenerative Disease Research (JPND) in frame of the BRIDGET project (Austria, Ministry of Science) and the Medical University of Graz and the Steiermärkische Krankenanstalten Gesellschaft. PRODEM‐Austria is supported by the Austrian Research Promotion agency (FFG) (Project No. 827462) and by the Austrian National Bank (Anniversary Fund, project 15435). ARIC research is carried out as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Neurocognitive data in ARIC is collected by U01 2U01HL096812, 2U01HL096814, 2U01HL096899, 2U01HL096902, 2U01HL096917 from the NIH (NHLBI, NINDS, NIA and NIDCD), and with previous brain MRI examinations funded by R01‐HL70825 from the NHLBI. CHS research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and grants U01HL080295 and U01HL130114 from the NHLBI with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG023629, R01AG15928, and R01AG20098 from the NIA. FHS research is supported by NHLBI contracts N01‐HC‐25195 and HHSN268201500001I. This study was also supported by additional grants from the NIA (R01s AG054076, AG049607 and AG033040) and NINDS (R01 NS017950). The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG‐CT‐2006‐01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007‐2013)/grant agreement HEALTH‐F4‐2007‐201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no. QLG2‐CT‐2002‐01254). High‐throughput analysis of the ERF data were supported by a joint grant from the Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO‐RFBR 047.017.043). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, the Netherlands Organization for 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. Genetic data sets are also supported by the Netherlands Organization of Scientific Research NWO Investments (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), and the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project 050‐060‐810. All studies are grateful to their participants, faculty, and staff. The content of these manuscripts is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the U.S. Department of Health and Human Services. The four LSACs are: the Human Genome Sequencing Center at the Baylor College of Medicine (U54 HG003273), the Broad Institute Genome Center (U54HG003067), The American Genome Center at the Uniformed Services University of the Health Sciences (U01AG057659), and the Washington University Genome Institute (U54HG003079). Biological samples and associated phenotypic data used in primary data analyses were stored at Study Investigators institutions, and at the National Cell Repository for Alzheimer's Disease (NCRAD, U24AG021886) at Indiana University funded by NIA. Associated Phenotypic Data used in primary and secondary data analyses were provided by Study Investigators, the NIA funded Alzheimer's Disease Centers (ADCs), and the National Alzheimer's Coordinating Center (NACC, U01AG016976) and the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA, and at the Database for Genotypes and Phenotypes (dbGaP) funded by NIH. This research was supported in part by the Intramural Research Program of the National Institutes of health, National Library of Medicine. Contributors to the Genetic Analysis Data included Study Investigators on projects that were individually funded by NIA, and other NIH institutes, and by private US organizations, or foreign governmental or nongovernmental organizations.
Xue D, Bush WS, Renton A, et al. Large‐scale sequencing studies expand the known genetic architecture of Alzheimer's disease. Alzheimer's Dement. 2021;13:e12255. 10.1002/dad2.12255
REFERENCES
- 1. Alzheimer's Association . 2020 Alzheimer's disease facts and figures. Alzheimer's and Dementia. 2020;16:391‐460. [Google Scholar]
- 2. Gatz M, Reynolds CA, Fratiglioni L, et al. Role of genes and environments for explaining Alzheimer's disease. Arch Gen Psychiatry. 2006;63:168‐174. [DOI] [PubMed] [Google Scholar]
- 3. Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47:856‐860. [DOI] [PubMed] [Google Scholar]
- 4. Cuyvers E, Sleegers K. Genetic variations underlying Alzheimer's disease: evidence from genome‐wide association studies and beyond. Lancet Neurol. 2016;15:857‐868. [DOI] [PubMed] [Google Scholar]
- 5. Goate A, Chartier‐Harlin MC, Mullan M, et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer's disease. Nature. 1991;349:704‐706. [DOI] [PubMed] [Google Scholar]
- 6. Sherrington R, Rogaev EI, Liang Y, et al. Cloning of a gene bearing missense mutations in early‐onset familial Alzheimer's disease. Nature. 1995;375:754‐760. [DOI] [PubMed] [Google Scholar]
- 7. Levy‐Lahad E, Wasco W, Poorkaj P, et al. Candidate gene for the chromosome 1 familial Alzheimer's disease locus. Science. 1995;269:973‐977. [DOI] [PubMed] [Google Scholar]
- 8. Bekris LM, Yu CE, Bird TD, Tsuang DW. Genetics of Alzheimer's disease. J Geriatr Psychiatry Neurol. 2010;23:213‐227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Blue EE, Bis JC, Dorschner MO, et al. Genetic variation in genes underlying diverse dementias may explain a small proportion of cases in the Alzheimer's disease sequencing project. Dement Geriatr Cogn Disord. 2018;45:1‐17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Rayaprolu S, Higginbotham L, Bagchi P, et al. Systems‐based proteomics to resolve the biology of Alzheimer's disease beyond amyloid and tau. Neuropsychopharmacology. 2021;46:98‐115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Corder EH, Saunders AM, Strittmatter WJ, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science. 1993;261:921‐923. [DOI] [PubMed] [Google Scholar]
- 12. Jansen IE, Savage JE, Watanabe K, et al. Genome‐wide meta‐analysis identifies new loci and functional pathways influencing Alzheimer's disease risk. Nat Genet. 2019;51:404‐413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Kunkle BW, Grenier‐Boley B, Sims R, et al. Genetic meta‐analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nat Genet. 2019;51:414‐430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Kunkle BW, Schmidt M, Klein HU, et al. Novel Alzheimer's disease risk loci and pathways in African American individuals using the African genome resources panel: a meta‐analysis. JAMA Neurol. 2021;78:102‐113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Kamboh MI. A brief synopsis on the genetics of Alzheimer's disease. Curr Genet Med Rep. 2018;6:133‐135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Sims R, van der Lee SJ, Naj AC, et al. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial‐mediated innate immunity in Alzheimer's disease. Nat Genet. 2017;49:1373‐1384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Logue MW, Schu M, Vardarajan BN, et al. Two rare AKAP9 variants are associated with Alzheimer's disease in African Americans. Alzheimers Dement. 2014;10:609‐618e11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Guerreiro R, Wojtas A, Bras J, et al. TREM2 variants in Alzheimer's disease. N Engl J Med. 2013;368:117‐127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Andrews SJ, Fulton‐Howard B, Goate A. Interpretation of risk loci from genome‐wide association studies of Alzheimer's disease. Lancet Neurol. 2020;19:326‐335. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Beecham GW, Bis JC, Martin ER, et al. The Alzheimer's disease sequencing project: study design and sample selection. Neurol Genet. 2017;3:e194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Nguyen TM, Shafi A, Nguyen T, Draghici S. Identifying significantly impacted pathways: a comprehensive review and assessment. Genome Biol. 2019;20:203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Naj AC, Schellenberg GD. Alzheimer's disease genetics consortium. Genomic variants, genes, and pathways of Alzheimer's disease: an overview. Am J Med Genet B Neuropsychiatr Genet. 2017;174:5‐26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Rohlfing FW, Tu RK. Genetics of frontotemporal dementia. AJNR Am J Neuroradiol. 2017;38:10‐11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Kohli MA, John‐Williams K, Rajbhandary R, et al. Repeat expansions in the C9ORF72 gene contribute to Alzheimer's disease in Caucasians. Neurobiol Aging. 2013;34:1519e5‐12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Beecham GW, Vardarajan B, Blue E, et al. Rare genetic variation implicated in non‐Hispanic white families with Alzheimer's disease. Neurol Genet. 2018;4:e286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Vardarajan BN, Barral S, Jaworski J, et al. Whole genome sequencing of Caribbean Hispanic families with late‐onset Alzheimer's disease. Ann Clin Transl Neurol. 2018;5:406‐417. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Bis JC, Jian X, Kunkle BW, et al. Whole exome sequencing study identifies novel rare and common Alzheimer's‐Associated variants involved in immune response and transcriptional regulation. Mol Psychiatry. 2020;25:1859‐1875. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Ma Y, Jun GR, Zhang X, et al. Analysis of whole‐exome sequencing data for Alzheimer's disease stratified by APOE genotype. JAMA Neurol. 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Patel D, Mez J, Vardarajan BN, et al. Association of rare coding mutations with Alzheimer's disease and other dementias among adults of European ancestry. JAMA Netw Open. 2019;2:e191350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Tosto G, Vardarajan B, Sariya S, et al. Association of variants in PINX1 and TREM2 with late‐onset Alzheimer's disease. JAMA Neurol. 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Zhang X, Zhu C, Beecham G, et al. A rare missense variant of CASP7 is associated with familial late‐onset Alzheimer's disease. Alzheimers Dement. 2019;15:441‐452. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Online Mendelian Inheritance in Man , OMIM (R). Baltimore, MD: McKusick‐Nathans Institute of Genetic Medicine, Johns Hopkins University. [Google Scholar]
- 33. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein‐protein association networks with increased coverage, supporting functional discovery in genome‐wide experimental datasets. Nucleic Acids Res. 2019;47:D607‐D13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Fang H, Knezevic B, Burnham KL, Knight JC. XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits. Genome Med. 2016;8:129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Shen L, GeneOverlap: test and visualize gene overlaps. 1.26.0 ed: GitHub; 2020.
- 36. Jones L, Holmans PA, Hamshere ML, et al. Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease. PLoS One. 2010;5:e13950. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Munoz L, Ammit AJ. Targeting p38 MAPK pathway for the treatment of Alzheimer's disease. Neuropharmacology. 2010;58:561‐568. [DOI] [PubMed] [Google Scholar]
- 38. Baig S, Joseph SA, Tayler H, et al. Distribution and expression of picalm in Alzheimer's disease. J Neuropathol Exp Neurol. 2010;69:1071‐1077. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Tarasoff‐Conway JM, Carare RO, Osorio RS, et al. Clearance systems in the brain‐implications for Alzheimer's disease. Nat Rev Neurol. 2015;11:457‐470. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Rebeck GW. Cholesterol efflux as a critical component of Alzheimer's disease pathogenesis. J Mol Neurosci. 2004;23:219‐224. [DOI] [PubMed] [Google Scholar]
- 41. Cruchaga C, Haller G, Chakraverty S, et al. Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late‐onset Alzheimer's disease families. PLoS One. 2012;7:e31039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Dent EW. Of microtubules and memory: implications for microtubule dynamics in dendrites and spines. Mol Biol Cell. 2017;28:1‐8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Prokopenko D, Morgan SL, Mullin K, et al. Whole‐genome sequencing reveals new Alzheimer's disease‐associated rare variants in loci related to synaptic function and neuronal development. Alzheimers Dement. 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Panda D, Goode BL, Feinstein SC, Wilson L. Kinetic stabilization of microtubule dynamics at steady state by tau and microtubule‐binding domains of tau. Biochemistry. 1995;34:11117‐11127. [DOI] [PubMed] [Google Scholar]
- 45. Lindwall G, Cole RD. Phosphorylation affects the ability of tau protein to promote microtubule assembly. J Biol Chem. 1984;259:5301‐5305. [PubMed] [Google Scholar]
- 46. Cash AD, Aliev G, Siedlak SL, et al. Microtubule reduction in Alzheimer's disease and aging is independent of tau filament formation. Am J Pathol. 2003;162:1623‐1627. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Jun G, Ibrahim‐Verbaas CA, Vronskaya M, et al. A novel Alzheimer's disease locus located near the gene encoding tau protein. Mol Psychiatry. 2016;21:108‐117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Cukier HN, Kunkle BK, Hamilton KL, et al. Exome sequencing of extended families with Alzheimer's disease identifies novel genes implicated in cell immunity and neuronal function. J Alzheimers Dis Parkinsonism. 2017;7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Ikezu T, Chen C, DeLeo AM, et al. Tau phosphorylation is impacted by rare AKAP9 mutations associated with Alzheimer's disease in African Americans. J Neuroimmune Pharmacol. 2018;13:254‐264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Klein HU, Schafer M, Bennett DA, Schwender H, De Jager PL. Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks. PLoS Comput Biol. 2020;16:e1007771. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting Information
Supporting Information
Supporting Information