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The genetic component of early-onset Alzheimer disease (EOAD), accounting for ~10% of all Alzheimer’s disease (AD) cases, is largely unexplained. Recent studies suggest that EOAD may be enriched for variants acting in the lipid pathway. The current study examines the shared genetic heritability between EOAD and the lipid pathway using genome-wide multi-trait genetic covariance analyses. Summary statistics were obtained from the GWAS meta-analyses of EOAD by the Alzheimer’s Disease Genetics Consortium ( n =19,668) and five blood lipid traits by the Global Lipids Genetics Consortium ( n =1,320,016). The significant results were compared between the EOAD and lipids GWAS and genetic covariance analyses were performed via SUPERGNOVA. Genes in linkage disequilibrium (LD) with top EOAD hits in identified regions of covariance with lipid traits were scored and ranked for causality by combining evidence from gene-based analysis, AD-risk scores incorporating transcriptomic and proteomic evidence, eQTL data, eQTL colocalization analyses, DNA methylation data, and single-cell RNA sequencing analyses. Direct comparison of GWAS results showed 5 loci overlapping between EOAD and at least one lipid trait harboring APOE , TREM2 , MS4A4E , LILRA5 , and LRRC25 . Local genetic covariance analyses identified 3 regions of covariance between EOAD and at least one lipid trait. Gene prioritization nominated 3 likely causative genes at these loci: ANKDD1B , CUZD1 , and MS4A64 .The current study identified genetic covariance between EOAD and lipids, providing further evidence of shared genetic architecture and mechanistic pathways between the two traits.

INTRODUCTION Neuropsychiatric symptoms (NPS) are highly prevalent in Alzheimer's disease (AD). There are no effective treatments targeting these symptoms. METHODS To facilitate identification of causative mechanistic pathways, we initiated an effort (NIH: U01AG079850) to collate, harmonize, and analyze all available NPS data (≈ 100,000 samples) of diverse ancestries with whole‐genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP). RESULTS This study will generate a genomic resource for Alzheimer's disease with both harmonized whole‐genome sequencing and NPS phenotype data that will be publicly available through NIAGADS. Primary analyses will (1) identify novel genetic risk factors associated with NPS in AD, (2) characterize the shared genetic architecture of NPS in AD and primary psychiatric disorders, and (3) assess the role of ancestry effects in the etiology of NPS in AD. DISCUSSION Expansion of the ADSP to harmonize and refine NPS phenotypes coupled with the proposed core analyses will lay the foundation to disentangle the molecular mechanisms underlying these detrimental symptoms in AD in diverse populations. Highlights Neuropsychiatric symptoms (NPS) are highly prevalent in Alzheimer's disease (AD). There are no effective treatments targeting NPS in AD. The current effort aims to collate, harmonize, and analyze all NPS data from the Alzheimer's Disease Sequencing Project. Core analyses will identify underlying genetic factors and mechanistic pathways. The harmonized genomic and phenotypic data from this initiative will be available through National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site.

Background Alzheimer's disease (AD) and kidney function share multiple risk factors and pathogenic mechanisms, particularly through impaired clearance of AD‐related biomarkers. Recent clinical evidence suggests kidney function directly influences AD pathophysiology independent of cardiovascular factors. We aimed to explore this relationship through genetic analyses to minimize confounders like age and lifestyle. Method We investigated genome‐wide and local genetic correlations (rg) between AD and estimated glomerular filtration rate (eGFR) in European (EUR) and African (AFR) ancestries using LDSC, cond/conjFDR, and LAVA utilizing summary statistics from large‐scale genome‐wide association studies. LAVA measures the strength and direction of local correlations, while cond/conjFDR increases power for pleiotropic variant detection by leveraging conditional false discovery rates. Result Genome‐wide correlations between AD and eGFR were nonsignificant (rg values ≈ 0.07) in both ancestries, with regions of strong local correlations observed. In EUR, 52 regions showed significant local correlations (Bonferroni‐corrected), and heritability (AD: h² = 0.05, p ≈ 0.025; eGFR: h² = 0.10, p < 1E‐10). These regions exhibited balanced bidirectional correlations (26 loci each; positive range: 0.32 to 1.00; negative range: ‐1.00 to ‐0.69), with strongest signals on chr9 (rg = 0.77, p = 2.9E−10) and chr5 (rg = −0.66, p = 1.34E‐10). The identified loci were linked to genes involved in vascular, cognitive, and inflammatory pathways. Cond/conjFDR analysis identified 15 loci containing 372 SNPs with significant pleiotropy (conjFDR < 0.05), mapped to genes like PICALM, SPI1, and TOMM40 with previously reported AD associations. In AFR, three loci showed significant negative local correlations (range: ‐0.9 to ‐1), with one AFR‐specific locus on chr11 containing several key genes: CD81 (inflammation), STIM1 (calcium signaling and neurodegeneration), KCNQ1 (potassium channels), and RRM1 (nephron development and neuronal DNA repair). Conclusion Our findings reveal components of shared genetic architecture between AD and kidney function, characterized by a mixture of concordant and discordant associations that are likely driven by specific biological pathways rather than genome‐wide effects. We will focus on refining these associations to determine directionality and causality, identifying key genes driving strong local genetic correlations and their biological pathways, and integrating chronic kidney disease associations alongside eGFR to capture phenotypic variability.

Alzheimer's disease (AD) and kidney function share multiple risk factors and pathogenic mechanisms, particularly through impaired clearance of AD-related biomarkers. Recent clinical evidence suggests kidney function directly influences AD pathophysiology independent of cardiovascular factors. We aimed to explore this relationship through genetic analyses to minimize confounders like age and lifestyle. We investigated genome-wide and local genetic correlations (r ) between AD and estimated glomerular filtration rate (eGFR) in European (EUR) and African (AFR) ancestries using LDSC, cond/conjFDR, and LAVA utilizing summary statistics from large-scale genome-wide association studies. LAVA measures the strength and direction of local correlations, while cond/conjFDR increases power for pleiotropic variant detection by leveraging conditional false discovery rates. Genome-wide correlations between AD and eGFR were nonsignificant (r values ≈ 0.07) in both ancestries, with regions of strong local correlations observed. In EUR, 52 regions showed significant local correlations (Bonferroni-corrected), and heritability (AD: h² = 0.05, p ≈ 0.025; eGFR: h² = 0.10, p < 1E-10). These regions exhibited balanced bidirectional correlations (26 loci each; positive range: 0.32 to 1.00; negative range: -1.00 to -0.69), with strongest signals on chr9 (r = 0.77, p = 2.9E-10) and chr5 (r = -0.66, p = 1.34E-10). The identified loci were linked to genes involved in vascular, cognitive, and inflammatory pathways. Cond/conjFDR analysis identified 15 loci containing 372 SNPs with significant pleiotropy (conjFDR < 0.05), mapped to genes like PICALM, SPI1, and TOMM40 with previously reported AD associations. In AFR, three loci showed significant negative local correlations (range: -0.9 to -1), with one AFR-specific locus on chr11 containing several key genes: CD81 (inflammation), STIM1 (calcium signaling and neurodegeneration), KCNQ1 (potassium channels), and RRM1 (nephron development and neuronal DNA repair). Our findings reveal components of shared genetic architecture between AD and kidney function, characterized by a mixture of concordant and discordant associations that are likely driven by specific biological pathways rather than genome-wide effects. We will focus on refining these associations to determine directionality and causality, identifying key genes driving strong local genetic correlations and their biological pathways, and integrating chronic kidney disease associations alongside eGFR to capture phenotypic variability.

Genome-wide association studies (GWAS) of complex traits often implicate genetic loci spanning thousands of variants. Statistical fine-mapping refines a GWAS locus to a smaller set of likely causal variants facilitating interpretation and functional validation. Ancestry-informed fine-mapping can further improve resolution by capitalizing on the genomic diversity across ancestries (e.g., smaller LD blocks in African populations). We employed cross-ancestry fine-mapping at top loci recently identified in a transethnic meta-analysis of non-Mendelian early-onset Alzheimer's Disease (EOAD; Bradley et al. 2025) to identify population-specific causative variants associated with this type of AD. Bradley et al. (2025) identified 13 distinct disease-associated loci, 8 of these for the first time reported in EOAD. Capitalizing on the individual ancestry-specific summary statistics from individuals of European (NHW) and African American (AA) ancestry (NHW: 6,282 cases, 13,386 controls; AA: 782 cases, 3,663 controls) for the top loci, we employed MESuSiE to model shared and ancestry-specific causal variants. MESuSie accounts for LD structure in multiple ancestries and accommodates causal effect size similarity and heterogeneity. Loci prioritized by MESuSiE were followed up in whole-genome sequencing data of NHW (N = 6,225) and AA individuals (N = 4,376) from the ADSP. Multi-ancestry fine-mapping identified regulatory variant rs6733839 in BIN1 (transethnic EOAD GWAS p-value: 1.73E-10) as the most likely causative variant associated with EOAD at this locus and suggests that it is shared across NHW and AA populations (PIP EUR: 0.0002; PIP AFR: 0.0001; PIP EUR_AFR: 0.99). Notably, while the associated haplotypes in both ancestries overlapped, the AA haplotype was much narrower, in line with the smaller extent of LD in individuals of African ancestry and pinpointing a much more concise locus harboring the causative variant. Individual analysis of WGS data from individuals of European and African ancestry from the ADSP validated rs6733839 as disease-associated in both ancestral population groups. Multi-ancestry fine-mapping pinpoints rs6733839 as a likely causative variant associated with non-Mendelian EOAD and suggests that it is shared across individuals of European and African ancestry. These findings elucidate the etiology of this type of AD in these populations and demonstrate the value of ancestry-informed fine-mapping for identification of population-specific causative variants.

Background Genome‐wide association studies (GWAS) of complex traits often implicate genetic loci spanning thousands of variants. Statistical fine‐mapping refines a GWAS locus to a smaller set of likely causal variants facilitating interpretation and functional validation. Ancestry‐informed fine‐mapping can further improve resolution by capitalizing on the genomic diversity across ancestries (e.g., smaller LD blocks in African populations). We employed cross‐ancestry fine‐mapping at top loci recently identified in a transethnic meta‐analysis of non‐Mendelian early‐onset Alzheimer's Disease (EOAD; Bradley et al. 2025) to identify population‐specific causative variants associated with this type of AD. Method Bradley et al. (2025) identified 13 distinct disease‐associated loci, 8 of these for the first time reported in EOAD. Capitalizing on the individual ancestry‐specific summary statistics from individuals of European (NHW) and African American (AA) ancestry (NHW: 6,282 cases, 13,386 controls; AA: 782 cases, 3,663 controls) for the top loci, we employed MESuSiE to model shared and ancestry‐specific causal variants. MESuSie accounts for LD structure in multiple ancestries and accommodates causal effect size similarity and heterogeneity. Loci prioritized by MESuSiE were followed up in whole‐genome sequencing data of NHW (N = 6,225) and AA individuals (N = 4,376) from the ADSP. Result Multi‐ancestry fine‐mapping identified regulatory variant rs6733839 in BIN1 (transethnic EOAD GWAS p‐value: 1.73E‐10) as the most likely causative variant associated with EOAD at this locus and suggests that it is shared across NHW and AA populations (PIP EUR: 0.0002; PIP AFR: 0.0001; PIP EUR_AFR: 0.99). Notably, while the associated haplotypes in both ancestries overlapped, the AA haplotype was much narrower, in line with the smaller extent of LD in individuals of African ancestry and pinpointing a much more concise locus harboring the causative variant. Individual analysis of WGS data from individuals of European and African ancestry from the ADSP validated rs6733839 as disease‐associated in both ancestral population groups. Conclusion Multi‐ancestry fine‐mapping pinpoints rs6733839 as a likely causative variant associated with non‐Mendelian EOAD and suggests that it is shared across individuals of European and African ancestry. These findings elucidate the etiology of this type of AD in these populations and demonstrate the value of ancestry‐informed fine‐mapping for identification of population‐specific causative variants.

Neuropsychiatric Symptoms (NPS) (e.g., aggression, psychosis, anxiety, apathy, depression, agitation, sleep disturbances, repetitive behaviors) occur in 85% of AD patients, and are associated with accelerated decline, out-of-home placement, increased costs, and greatly increased suffering of patients and families. Our understanding of the etiology of NPS in AD is inadequate, with treatments for NPS often being ineffective and associated with serious adverse effects. Pharmacological treatments have generally been borrowed from their indications for psychiatric illness, despite limited understanding to what extent the molecular and genetic architectures underlying AD-associated NPS overlap with that of primary psychiatric disorders. To characterize the genetic overlap between AD and major psychiatric disorders and identify potentially shared biological processes we conducted local genetic correlation analyses between AD and bipolar disorder, depression, and schizophrenia using LAVA (Local Analysis of [co]Variant Association), capitalizing on the largest most recent GWAS summary statistics for these traits (AD: n = 401,577; bipolar disorder: n = 413,466; depression: n = 1,154,267; schizophrenia: n = 130,644). Local genetic correlation analyses identified fifteen loci shared between Alzheimer's disease and depression and two loci shared between AD and bipolar disorder after adjusting for multiple testing. Finemapping and functional analyses of the identified loci are in process. These findings support the notion of genetic overlap between AD and bipolar disorder, and AD and depression, respectively. Functional characterization of these loci will be critical to characterize the specific underlying causative genes and biological processes.

Background Neuropsychiatric Symptoms (NPS) (e.g., aggression, psychosis, anxiety, apathy, depression, agitation, sleep disturbances, repetitive behaviors) occur in 85% of AD patients, and are associated with accelerated decline, out‐of‐home placement, increased costs, and greatly increased suffering of patients and families. Our understanding of the etiology of NPS in AD is inadequate, with treatments for NPS often being ineffective and associated with serious adverse effects. Pharmacological treatments have generally been borrowed from their indications for psychiatric illness, despite limited understanding to what extent the molecular and genetic architectures underlying AD‐associated NPS overlap with that of primary psychiatric disorders. Method To characterize the genetic overlap between AD and major psychiatric disorders and identify potentially shared biological processes we conducted local genetic correlation analyses between AD and bipolar disorder, depression, and schizophrenia using LAVA (Local Analysis of [co]Variant Association), capitalizing on the largest most recent GWAS summary statistics for these traits (AD: n = 401,577; bipolar disorder: n = 413,466; depression: n = 1,154,267; schizophrenia: n = 130,644). Result Local genetic correlation analyses identified fifteen loci shared between Alzheimer’s disease and depression and two loci shared between AD and bipolar disorder after adjusting for multiple testing. Finemapping and functional analyses of the identified loci are in process. Conclusion These findings support the notion of genetic overlap between AD and bipolar disorder, and AD and depression, respectively. Functional characterization of these loci will be critical to characterize the specific underlying causative genes and biological processes.

Background Sex differences in progression and pathology of Alzheimer's disease (AD) suggest sex‐specific factors influencing its development. While studies have established APOE‐genotype as contributing to AD risk differently in men and women, few have searched for additional genetic sex differences in AD. To identify sex‐specific AD genetic associations, we conducted genome‐wide sex‐aware meta‐analyses in the Alzheimer's Disease Genetics Consortium (ADGC) and Alzheimer's Disease Sequencing Project (ADSP) datasets. Method Sex‐interaction and sex‐stratified analyses were performed in multi‐ancestry genome‐wide imputed AD datasets from the ADGC (N = 25,284 cases, 60% female and 33,455 controls, 63% female; ancestry/ethnicity distribution: 63.7% European, 15.6% African, 15.2% Hispanic/Latino, 5.5% Asian). STAAR aggregation‐based rare‐variant testing was also conducted in coding and non‐coding genomic regions on an ancestrally diverse sample of 8,697 AD cases and 14,758 controls with whole‐genome sequencing from the ADSP. Result Cross‐ancestry sex‐stratified analyses identified several loci with evidence of interaction (p <0.05) and suggestive significance in one sex (p <5x10‐6) and not the other (p >0.05). These include male specific variants in STXBP6, MAP4K5 and the known AD locus PICALM. Genetic loci associated in females and not males include NPAS3, ZNF438 and a genome‐wide result in NECTIN2 near the APOE locus. Top ancestry‐specific results include associations at COL4A2 in Asian‐ancestry males and C16orf96 in African‐ancestry females. Cross‐ancestry rare‐variant aggregation‐based testing revealed four novel genome‐wide significant associations in females including with missense variants in SH3BP1. Five population specific associations were also discovered including with missense variants in SERTAD4 in a genetically‐defined Hispanic/Amerindian/African ancestral population cluster and promoter variants of PSMA5 in a European population cluster. Seven male‐specific effects were also discovered including genome‐wide significant cross‐ancestry associations with an enhancer region of MORC1 and a promoter of ITPKA. Conclusion We identified sex‐specific AD associations at loci with AD‐relevant genes including STXBP6 (involved in AD relevant processes such as endolysosomal transport and synaptic transmission), NPAS3 (neurogenesis), COL4A2 (cerebral vasculature), ITPKA (learning/memory processes), PAQR3 (cholesterol homeostatis and neuronal function), and MORC1 (recently associated in a UK Biobank exome‐wide sequencing study of dementia (Zhang et al. Alz & Dementia 2024). Understanding the nature of these associations could help explain sex differences in risk and progression for AD.

Sex differences in progression and pathology of Alzheimer's disease (AD) suggest sex-specific factors influencing its development. While studies have established APOE-genotype as contributing to AD risk differently in men and women, few have searched for additional genetic sex differences in AD. To identify sex-specific AD genetic associations, we conducted genome-wide sex-aware meta-analyses in the Alzheimer's Disease Genetics Consortium (ADGC) and Alzheimer's Disease Sequencing Project (ADSP) datasets. Sex-interaction and sex-stratified analyses were performed in multi-ancestry genome-wide imputed AD datasets from the ADGC (N = 25,284 cases, 60% female and 33,455 controls, 63% female; ancestry/ethnicity distribution: 63.7% European, 15.6% African, 15.2% Hispanic/Latino, 5.5% Asian). STAAR aggregation-based rare-variant testing was also conducted in coding and non-coding genomic regions on an ancestrally diverse sample of 8,697 AD cases and 14,758 controls with whole-genome sequencing from the ADSP. Cross-ancestry sex-stratified analyses identified several loci with evidence of interaction (p <0.05) and suggestive significance in one sex (p <5x10-6) and not the other (p >0.05). These include male specific variants in STXBP6, MAP4K5 and the known AD locus PICALM. Genetic loci associated in females and not males include NPAS3, ZNF438 and a genome-wide result in NECTIN2 near the APOE locus. Top ancestry-specific results include associations at COL4A2 in Asian-ancestry males and C16orf96 in African-ancestry females. Cross-ancestry rare-variant aggregation-based testing revealed four novel genome-wide significant associations in females including with missense variants in SH3BP1. Five population specific associations were also discovered including with missense variants in SERTAD4 in a genetically-defined Hispanic/Amerindian/African ancestral population cluster and promoter variants of PSMA5 in a European population cluster. Seven male-specific effects were also discovered including genome-wide significant cross-ancestry associations with an enhancer region of MORC1 and a promoter of ITPKA. We identified sex-specific AD associations at loci with AD-relevant genes including STXBP6 (involved in AD relevant processes such as endolysosomal transport and synaptic transmission), NPAS3 (neurogenesis), COL4A2 (cerebral vasculature), ITPKA (learning/memory processes), PAQR3 (cholesterol homeostatis and neuronal function), and MORC1 (recently associated in a UK Biobank exome-wide sequencing study of dementia (Zhang et al. Alz & Dementia 2024). Understanding the nature of these associations could help explain sex differences in risk and progression for AD.