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Despite decades of genetic studies on late-onset Alzheimer’s disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer’s pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer’s-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer’s disease.

Chromatin immunoprecipitation (ChIP) is an antibody-based approach that is frequently utilized in chromatin biology and epigenetics. The challenge in experimental variability by unpredictable nature of usable input amounts from samples and undefined antibody titer in ChIP reaction still remains to be addressed. Here, we introduce a simple and quick method to quantify chromatin inputs and demonstrate its utility for normalizing antibody amounts to the optimal titer in individual ChIP reactions. For a proof of concept, we utilized ChIP-seq validated antibodies against the key enhancer mark, acetylation of histone H3 on lysine 27 (H3K27ac), in the experiments. The results indicate that the titration-based normalization of antibody amounts improves assay outcomes including the consistency among samples both within and across experiments for a broad range of input amounts.

SummaryDespite decades of genetic studies on late onset Alzheimer’s disease (LOAD), the molecular mechanisms of Alzheimer’s disease (AD) remain unclear. Furthermore, different cell types in the central nervous system (CNS) play distinct roles in the onset and progression of AD pathology. To better comprehend the complex etiology of AD, we used an integrative approach to build robust predictive (causal) network models which were cross-validated over multiple large human multi-omics datasets in AD. We employed a published method to delineate bulk-tissue gene expression into single cell-type gene expression and integrated clinical and pathologic traits of AD, single nucleotide variation, and deconvoluted gene expression for the construction of predictive network models for each cell type in AD. With these predictive causal models, we are able to identify and prioritize robust key drivers of the AD-associated network state. In this study, we focused on neuron-specific network models and prioritized 19 predicted key drivers modulating AD pathology. These targets were validated via shRNA knockdown in human induced pluripotent stem cell (iPSC) derived neurons (iNs), in which 10 out of the 19 neuron-related targets (JMJD6, NSF, NUDT2, YWHAZ, RBM4, DCAF12, NDRG4, STXBP1, ATP1B1, andFIBP) significantly modulated levels of amyloid-beta and/or phosphorylated tau peptides in the postmitotic iNs. Most notably, knockdown ofJMJD6significantly altered the neurotoxic ratios of Aβ42 to 40 and p231-tau to total tau, indicating its potential therapeutic relevance to both amyloid and tau pathology in AD. Molecular validation by RNA sequencing (RNAseq) in iNs further confirmed the network structure, showing significant enrichment in differentially expressed genes after knockdown of the validated targets. Interestingly, our network model predicts that these 10 key drivers are upstream regulators of REST and VGF, two recently identified key regulators of AD pathogenesis.

Background: Dementia, vascular disease, and cancer increase with age, enabling complex comorbid interactions. Understanding vascular and cancer contributions to dementia risk and neuropathology in oldest-old may improve risk modification and outcomes. Objective: Investigate the contributions of vascular factors and cancer to dementia and neuropathology. Methods: Longitudinal clinicopathologic study of prospectively followed Mayo Clinic participants dying≥95 years-old who underwent autopsy. Participants were stratified by dementia status and compared according to demographics, vascular risk factors, cancer, and neuropathology. Results: Participants (n = 161; 83% female; 99% non-Hispanic whites)≥95 years (95–106 years-old) with/without dementia did not differ based on demographics. APOE ɛ2 frequency was higher in no dementia (20/72 [28%]) versus dementia (11/88 [12%]; p = 0.03), but APOE ɛ4 frequency did not differ. Coronary artery disease was more frequent in no dementia (31/72 [43%]) versus dementia (23/89 [26%]; p = 0.03) associated with 56% lower dementia odds (odds ratio [OR] = 0.44 [confidence interval (CI) = 0.19–0.98]; p = 0.04) and fewer neuritic/diffuse plaques. Diabetes had an 8-fold increase in dementia odds (OR = 8.42 [CI = 1.39–163]; p = 0.02). Diabetes associated with higher cerebrovascular disease (Dickson score; p = 0.05). Cancer associated with 63% lower dementia odds (OR = 0.37 [CI = 0.17–0.78]; p

Neurogenesis relates to the brain resilience and is reduced in Alzheimer’s disease (AD). Restoring healthy levels of neurogenesis could have beneficial effects for coping with neurodegeneration. However, molecular mechanisms that could enhance neurogenesis from astroglial progenitors in AD pathology are largely unknown. We used lentiviruses to expressNgfrin the hippocampus of the APP/PS1dE9 mouse model of AD, histologically analyzed the changes in proliferation of neural stem cells and neurogenesis; performed single-cell transcriptomics, spatial proteomics, and functional knockdown studies. We found that expression ofNgfr, a neurogenic determinant in pathology-induced neuroregeneration in zebrafish, stimulated proliferative and neurogenic outcome in the APP/PS1dE9 AD mouse model. Ngfr suppressed reactive astrocyte marker Lipocalin-2 (Lcn2) in astroglia. Blockage of Lcn2 receptor, Slc22a17, recapitulated the neurogenic effects of NGFR, and long-term Ngfr expression reduced amyloid plaques and Tau phosphorylation. Furthermore, immunostaining on postmortem human hippocampi with AD or primary age-related Tauopathy and 3D human astroglial cultures showed that elevated LCN2 levels correlate with gliosis. By comparing transcriptional changes in mouse hippocampus, zebrafish brain, and human AD brains in terms of cell intrinsic differential gene expression analyses as well as weighted gene co-expression network analysis, we observed common potential downstream effectors of NGFR signaling,C4BandPFKP, that are relevant to AD. Our study links the regulatory role of an autocrine molecular mechanism in astroglia to the neurogenic ability and modulatory effects on amyloid and tau phosphorylation, opening new research avenues and suggesting that neurogenesis-oriented therapeutic approaches could be a potential clinical intervention for AD.

Background: Alterations in innate immunity are pathologically associated with and genetically implicated in Alzheimer’s disease (AD). In the whole exome sequence (WES) dataset generated by the Alzheimer’s Disease Sequencing Project (ADSP), only the previously identified p.R47H variant in the innate immunity gene, TREM2, shows study-wide association with risk of AD. Using a novel approach, we searched the ADSP WES data to identify additional immune pathway genes with deleterious variants that, like TREM2.pR47H, show strong association with AD. Methods: Using polygenic risk scores (PRS) to analyze association with AD, we evaluated deleterious variants (CADD Phred-scaled score > 20) with a minor allele count of 20 or more in 228 genes comprising an immune co-expression network containing TREM2 (CENTREM2). A significant polygenic component composed of deleterious stop-gain and non-synonymous variants was identified, and false discovery rates were determined for the variants in this component. In genes harboring a significant variant, PRS for all variants in the genes were then analyzed. Results: The PRS for the 182 deleterious variants in CENTREM2 showed significant association with AD that was driven by 142 deleterious variants (136 non-synonymous, 6 stop-gain). In the 142 variant polygenic component, four variants had significant AD risk association: TREM2.pR47H, two deleterious stop-gain variants (FCGR1A.pR92X, and LILRB1.pY331X) in novel AD genes and 1 non-synonymous variant (ATP8B4.pG395S). Remarkably, PRS for the 36 additional variants in these four genes also showed significant association with AD. The PRS for all 40 variants in the 4 genes, showed significant, replicable association with AD and 3 additional variants in this polygenic component had significant false discovery rates: ATP8B4.pR1059Q, LILRB1.pP7P, and LILRB1.pY327Y. Conclusions: Here, we identify 3 immune pathway genes (ATP8B4, LILRB1, and FCGR1A) with a variant that associates with AD. Like TREM2.pR47H, each of the variants has a minor allele frequency less than 1% and is a deleterious, protein altering variant with a strong effect that increases or decreases (LILRB1.pY331X) risk of AD. Additional variants in these genes also alter risk of AD. The variants identified here are ideally suited for studies aimed at understanding how the innate immune system may be modulated to alter risk of AD.

Background The S209F variant of Abelson Interactor Protein 3 (ABI3) increases risk for Alzheimer’s disease (AD), but little is known about its function in relation to AD pathogenesis. Methods Here, we use a mouse model that is deficient in Abi3 locus to study how the loss of function of Abi3 impacts two cardinal neuropathological hallmarks of AD—amyloid β plaques and tau pathology. Our study employs extensive neuropathological and transcriptomic characterization using transgenic mouse models and adeno-associated virus-mediated gene targeting strategies. Results Analysis of bulk RNAseq data confirmed age-progressive increase in Abi3 levels in rodent models of AD-type amyloidosis and upregulation in AD patients relative to healthy controls. Using RNAscope in situ hybridization, we localized the cellular distribution of Abi3 in mouse and human brains, finding that Abi3 is expressed in both microglial and non-microglial cells. Next, we evaluated Abi3−/− mice and document that both Abi3 and its overlapping gene, Gngt2, are disrupted in these mice. Using multiple transcriptomic datasets, we show that expression of Abi3 and Gngt2 are tightly correlated in rodent models of AD and human brains, suggesting a tight co-expression relationship. RNAseq of the Abi3-Gngt2−/− mice revealed upregulation of Trem2, Plcg2, and Tyrobp, concomitant with induction of an AD-associated neurodegenerative signature, even in the absence of AD-typical neuropathology. In APP mice, loss of Abi3-Gngt2 resulted in a gene dose- and age-dependent reduction in Aβ deposition. Additionally, in Abi3-Gngt2−/− mice, expression of a pro-aggregant form of human tau exacerbated tauopathy and astrocytosis. Further, using in vitro culture assays, we show that the AD-associated S209F mutation alters the extent of ABI3 phosphorylation. Conclusions These data provide an important experimental framework for understanding the role of Abi3-Gngt2 function and early inflammatory gliosis in AD. Our studies also demonstrate that inflammatory gliosis could have opposing effects on amyloid and tau pathology, highlighting the unpredictability of targeting immune pathways in AD.

Background Alzheimer’s disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis. Methods Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables. Results We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways. Conclusions Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.

BackgroundAlterations in innate immunity are pathologically associated with and genetically implicated in Alzheimer’s disease (AD). In the whole exome sequence (WES) dataset generated by the Alzheimer’s Disease Sequencing Project (ADSP), only the previously identified p.R47H variant in the innate immunity gene,TREM2, shows study-wide association with risk of AD. Using a novel approach, we searched the ADSP WES data to identify additional immune pathway genes with deleterious variants that, likeTREM2.pR47H, show strong association with AD.MethodsUsing polygenic risk scores (PRS) to analyze association with AD, we evaluated deleterious variants (CADD Phred-scaled score > 20) with a minor allele count of 20 or more in 228 genes comprising an immune co-expression network containingTREM2(CENTREM2). A significant polygenic component composed of deleterious stop-gain and non-synonymous variants was identified, and false discovery rates were determined for the variants in this component. In genes harboring a significant variant, PRS for all variants in the genes were then analyzed.ResultsThe PRS for the 182 deleterious variants in CENTREM2showed significant association with AD that was driven by 142 deleterious variants (136 non-synonymous, 6 stop-gain). In the 142 variant polygenic component, four variants had significant AD risk association:TREM2.pR47H, two deleterious stop-gain variants (FCGR1A.pR92X, andLILRB1.pY331X) in novel AD genes and 1 non-synonymous variant(ATP8B4.pG395S). Remarkably, PRS for the 36 additional variants in these four genes also showed significant association with AD. The PRS for all 40 variants in the 4 genes, showed significant, replicable association with AD and 3 additional variants in this polygenic component had significant false discovery rates:ATP8B4.pR1059Q,LILRB1.pP7P, andLILRB1.pY327Y.ConclusionsHere, we identify 3 immune pathway genes (ATP8B4, LILRB1, andFCGR1A) with a variant that associates with AD. LikeTREM2.pR47H, each of the variants has a minor allele frequency less than 1% and is a deleterious, protein altering variant with a strong effect that increases or decreases (LILRB1.pY331X) risk of AD. Additional variants in these genes also alter risk of AD. The variants identified here are ideally suited for studies aimed at understanding how the innate immune system may be modulated to alter risk of AD.

The apolipoprotein E (APOE) gene is the strongest genetic risk factor for Alzheimer’s disease (AD); however, how it modulates brain homeostasis is not clear. The apoE protein is a major lipid carrier in the brain transporting lipids such as cholesterol among different brain cell types. Here, we show that APOE deficiency in human iPSC-derived cerebral organoids impacts brain lipid homeostasis by modulating multiple cellular and molecular pathways. Molecular profiling through single cell RNA-sequencing revealed that APOE deficiency leads to changes in cellular composition of isogenic cerebral organoids likely by modulating the EIF2 signaling pathway as these events were alleviated by the treatment of a pathway inhibitor ISRIB. APOE deletion also leads to activation of the Wnt/β-catenin signaling pathway with concomitant decrease of SFRP1 expression in glia cells. Importantly, the critical role of apoE in cell type-specific lipid homeostasis was observed upon APOE deletion in cerebral organoids with a specific upregulation of cholesterol biosynthesis in excitatory neurons and excessive lipid accumulation in astrocytes. Relevant to human AD, APOE4 cerebral organoids show altered neurogenesis and cholesterol metabolism compared to those with APOE3. Our work demonstrates critical roles of apoE in brain homeostasis and offers critical insights into the APOE4-related pathogenic mechanisms.

Alzheimer’s disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. Main protein components of these hallmarks include Aβ40, Aβ42, tau, phospho-tau and APOE. With the exception of the APOE-ε4 variant, genetic risk factors associated with brain biochemical measures of these proteins have yet to be characterized. We performed a genome-wide association study in brains of 441 AD patients for quantitative levels of these proteins collected from three distinct fractions reflecting soluble, membrane-bound and insoluble biochemical states. We identified 123 genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD- related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways. Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.

Alzheimer's disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD-associated metabolic changes in the brain has been missing.We metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late-life cognitive performance and AD neuropathology measures.We observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD-relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation, and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than amyloid beta pathology.This work provides a comprehensive reference map of metabolic brain changes in AD that lays the foundation for future mechanistic follow-up studies.

BackgroundRisk stratification for hospitalized adults with COVID-19 is essential to inform decisions for individual patients and allocation of potentially scarce resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Additionally, existing risk models have been limited to either small sample sizes, or modeling mortality over an entire hospital admission. Further, previous models were developed on data from early in the pandemic, before improvements in COVID-19 treatment, the SARS-CoV-2 delta variant, and vaccination. There remains a need for early, accurate identification of patients who may need invasive mechanical ventilation (IMV) or die, considering multiple time horizons.MethodsThis retrospective study analyzed data from 6,906 hospitalized adults with COVID-19 from a community health system with 51 hospitals and 1085 clinics across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data collected available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. The relative importance of predictive risk factors features for all models was determined using Shapley additive explanations.FindingsThe percentage of patients who required mechanical ventilation or died within seven days of admission to the hospital due to COVID-19 was 10.82%. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.80 (95% CI: 0.70-0.89) and models for age ≥ 50 years reached AUROC 0.83 (95% CI: 0.79-0.88). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients, including age, BMI, vital signs, and laboratory results. In addition, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results.InterpretationFor hospitalized adults, baseline data that is readily available within one hour after hospital admission or a first positive inpatient SARS-CoV-2 test can predict critical illness within one day, and up to 56 days later. Further, the relative importance of risk factors differs between older and younger patients.

Risk stratification for hospitalized adults with COVID-19 is essential to inform decisions about individual patients and allocation of resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Models also need to be updated to reflect improvements in COVID-19 treatments. This retrospective study analyzed data from 6906 hospitalized adults with COVID-19 from a community health system across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. For the seven-day interval, models for age ≥ 18 and

Blood-brain barrier (BBB) dysfunction is well-known in Alzheimer’s disease (AD), but the precise molecular changes contributing to its pathophysiology are unclear. To understand the transcriptional changes in brain vascular cells, we performed single nucleus RNA sequencing (snRNAseq) of temporal cortex tissue in 24 AD and control brains resulting in 79,751 nuclei, 4,604 of which formed three distinct vascular clusters characterized as activated pericytes, endothelia and resting pericytes. We identified differentially expressed genes (DEGs) and their enriched pathways in these clusters and detected the most transcriptional changes within activated pericytes. Using our data and a knowledge-based predictive algorithm, we discovered and prioritized molecular interactions between vascular and astrocyte clusters, the main cell types of the gliovascular unit (GVU) of the BBB. Vascular targets predicted to interact with astrocytic ligands have biological functions in signalling, angiogenesis, amyloid ß metabolism and cytoskeletal structure. Top astrocytic and vascular interacting molecules include both novel and known AD risk genes such as APOE, APP and ECE1. Our findings provide information on transcriptional changes in predicted vascular-astrocytic partners at the GVU, bringing insights to the molecular mechanisms of BBB breakdown in AD.Graphical AbstractPericytes (yellow), endothelia (salmon) and astrocytes (purple) that form the gliovascular unit (GVU) at the blood brain barrier (BBB) were interrogated for their differentially expressed genes (DEG) and vascular cell (pericyte or endothelia) to astrocyte interactions using single nucleus RNA sequencing (RNAseq) transcriptome obtained from brains of Alzheimer’s disease (AD) patients and controls. We identified many upregulated (red) or downregulated (blue) DEGs in AD brains in these cell types. These genes have known biological functions in amyloid ß (Aß) clearance, immune modulation, astrogliosis and neuronal death. Novel predicted interactions were identified between vascular cells and astrocytic DEGs. Collectively, our findings highlight the vast transcriptome changes that occur at the GVU and provide mechanistic insights into BBB dysfunction in AD. This figure was created with Biorender.com.

African Americans (AA) remain underrepresented in Alzheimer's disease (AD) research, despite the prevalence of AD being double in AA compared to non-Hispanic whites. To address this disparity, our group has established the Florida Consortium for African American Alzheimer's Disease Studies (FCAUtilizing FCAAssociation with higher plasma CLU in CU vs. AD remained significant after Bonferroni correction. Study-wide significant eQTL associations were observed with 105 WES variants in cis with 22 genes, including variants in genes previously associated with AD risk in AA such as ABCA7 and AKAP9. Results from this plasma eQTL analysis identified AD-risk variants in ABCA7 and AKAP9 that are significantly associated with lower and higher plasma mRNA levels of these genes, respectively. Receiver operating characteristic analysis of age, sex APOE-ε4 dosage, CLU, APP, CD14, ABCA7, AKAP9 and APOE mRNA levels, and ABCA7 and AKAP9 eQTLs, achieved 77% area under the curve to discriminate AD vs. CU, an 8% improvement over a model that only included age, sex and APOE-ε4 dosage.Incorporating plasma mRNA levels could contribute to improved predictive value of AD biomarker panels.This work was supported by the National Institute on Aging [RF AG051504, U01 AG046139, R01 AG061796 to NET; P30 AG062677 to JAL and NGR]; Florida Health Ed and Ethel Moore Alzheimer's Disease grants [5AZ03 and 7AZ17 to NET; 7AZ07 to MMC; 8AZ08 to JAL].

A few copy number variations (CNVs) have been reported for Alzheimer's disease (AD). However, there is a lack of a systematic investigation of CNVs in AD based on whole genome sequencing (WGS) data.We used four methods to identify consensus CNVs from the WGS data of 1,411 individuals and further investigated their functional roles in AD using the matched transcriptomic and clinicopathological data.We identified 3,012 rare AD-specific CNVs whose residing genes are enriched for cellular glucuronidation and neuron projection pathways. Genes whose mRNA expressions are significantly correlated with common CNVs are involved in major histocompatibility complex class II receptor activity. Integration of CNVs, gene expression, and clinical and pathological traits further pinpoints a key CNV that potentially regulates immune response in AD.We identify CNVs as potential genetic regulators of immune response in AD. The identified CNVs and their downstream gene networks reveal novel pathways and targets for AD.

Mutations in the gene encoding the presenilin-1 protein (PSEN1) were first discovered to cause Alzheimer’s disease (AD) 20 years ago. Since then more than 200 different pathogenic mutations have been reported, including a p.Gly206Ala founder mutation in the Hispanic population. Here we report mutation analysis of known AD genes in a cohort of 27 early-onset (age of onset ≤65, age of death ≤70) Hispanic patients ascertained in Florida. The PSEN1 p.Gly206Ala mutation was identified in 13 out of 27 patients (48.1%), emphasizing the importance of this specific mutation in the etiology of early-onset AD in this population. One other patient carried the known PSEN1 p.Gly378Val mutation. Genotyping of the PSEN1 p.Gly206Ala and p.Gly378Val mutations in 63 late-onset Hispanic AD patients did not identify additional mutation carriers. All p.Gly206Ala mutation carriers shared rare alleles at two microsatellite markers flanking PSEN1 supporting a common founder. This study confirms the p.Gly206Ala variant as a frequent cause of early onset AD in the Hispanic population and for the first time reports the high frequency of this mutation in Hispanics in Florida.

Objective: Recent genome-wide association studies (GWAS) of late-onset Alzheimer disease (LOAD) identified 9 novel risk loci. Discovery of functional variants within genes at these loci is required to confirm their role in Alzheimer disease (AD). Single nucleotide polymorphisms that influence gene expression (eSNPs) constitute an important class of functional variants. We therefore investigated the influence of the novel LOAD risk loci on human brain gene expression. Methods: We measured gene expression levels in the cerebellum and temporal cortex of autopsied AD subjects and those with other brain pathologies (∼400 total subjects). To determine whether any of the novel LOAD risk variants are eSNPs, we tested their cis -association with expression of 6 nearby LOAD candidate genes detectable in human brain ( ABCA7, BIN1, CLU, MS4A4A, MS4A6A, PICALM ) and an additional 13 genes ±100 kb of these SNPs. To identify additional eSNPs that influence brain gene expression levels of the novel candidate LOAD genes, we identified SNPs ±100 kb of their location and tested for cis -associations. Results: CLU rs11136000 ( p = 7.81 × 10 −4 ) and MS4A4A rs2304933/rs2304935 ( p = 1.48 × 10 −4 –1.86 × 10 −4 ) significantly influence temporal cortex expression levels of these genes. The LOAD-protective CLU and risky MS4A4A locus alleles associate with higher brain levels of these genes. There are other cis -variants that significantly influence brain expression of CLU and ABCA7 ( p = 4.01 × 10 −5 –9.09 × 10 −9 ), some of which also associate with AD risk ( p = 2.64 × 10 −2 –6.25 × 10 −5 ). Conclusions: CLU and MS4A4A eSNPs may at least partly explain the LOAD risk association at these loci. CLU and ABCA7 may harbor additional strong eSNPs. These results have implications in the search for functional variants at the novel LOAD risk loci.