The DAWN study aims to investigate Alzheimer's disease (AD) genetics and underlying biological markers in a global population including African Americans (AA: 4,000) and Hispanic/Latinos (HI: 4,000) ascertained in the US and indigenous Africans (AF: 5,000) through collaboration with the African Dementia Consortium (AfDC) from 10 African counties. These 13,000 participants include AD cases and individuals with mild (MCI) or no cognitive impairment (NCI). To understand the underlying blood-based AD biomarkers profile of this unique cohort, we are analyzing the plasma levels of pTau181, neurofilament light chain (NFL), and Glial fibrillary acidic protein (GFAP). We measured pTau181 and NFL, and GFAP with Simoa chemistry using the pTau181 AdvantageV2 and NEUROLOGY 4-PLEX A assays, respectively, on the Quanterix HD-X instrument. Our preliminary cohort consisted of 174 AF (86 AD; 88 NCI) from Nigeria and Ghana study sites, 254 AA (24 AD; 102 MCI; 85 NCI), and 166 HI (44 AD; 61 MCI; 62 NCI). Linear mixed-effect regression models adjusted for age, sex, population substructure and relatedness followed by Bonferroni correction were applied to identify biomarker differences. There were no significant differences between the ancestral groups within the diagnostic categories for any of the biomarkers measured. Plasma pTau181 concentrations were increased in AD relative to NCI in all three populations ( p  = 5.1x10 -4 , 9.6x10 -5 , 5.1x10-4 in AA, AF, and HI respectively) and AD relative to MCI ( p  = 0.012, 0.0014 in AA and HI respectively), though no differences were noted between MCI and NCI. Interestingly, GFAP and NFL were highly significantly increased in AF AD vs NCI ( p  = 4.5x10 -9 , 7.9x10 -7 for GFAP and NFL respectively) and in HI ( p  = 7.9x10 -8 , 1.6x10 -6 for GFAP and NFL respectively), but no differences noted in AA. These results suggest AD biomarkers are generalizable across global populations, with baseline values being consistent. However, there are notable differences, particularly in NFL and GFAP levels, which may reflect underlying differences in environmental or genetic influences on AD. Ultimately, increasing sample sizes and combining genomic, biomarker, and social and environmental data will increase understanding of genetic risk of AD.

GPR132 to DRP1 can be dissected pharmacologically

GPR132 affects HIF2a and lipid metabolism

Cognitive losses resulting from severe brain trauma have long been associated with the focal region of tissue damage, leading to devastating functional impairment. For decades, researchers have focused on the sequelae of cellular alterations that exist within the perilesional tissues; however, few pharmacological therapies are available to patients. To examine whether expansive global synaptic damage underlies cognitive losses associated with brain injury, we evaluated the influence of D-serine on synaptic damage in male and female wild-type mice as well as mice deficient in microglial serine racemase (TMEM119creErt2:SRRfl/fl) or neuronal GluN2B (CamKIIcreErt2:Grin2bfl/fl). We measured biochemical alterations in synaptic proteins, dendritic spine numbers and morphology, electrophysiological responses, and learning and memory behaviour. Single-cell analysis was employed to examine cell-type specific contributions, and perilesional tissues from 41 traumatic brain injury (TBI) patients were analysed for mRNA and/or protein differences. Our findings demonstrate that synaptic damage results from the prolonged increase in D-serine release from activated microglia and astrocytes, which leads to hyperactivation of perisynaptic N-methyl-D-aspartate receptors and tagging of damaged synapses by complement components. We show that this mechanistic pathway for synaptic pruning is reversible at several stages within the acute period of brain injury, and that these key factors are also present in human brain injury. We conclude that prolonged glial D-serine release after brain injury leads to the reactivation of developmental pruning processes that underlie synaptic losses. Targeting specific molecules in this pathway may represent a new therapeutic strategy for protecting TBI patients from cognitive dysfunction.

Alzheimer disease (AD) risk differs across ancestral populations, yet most genetic studies have focused on non-Hispanic White (NHW) cohorts. We conducted a multi-population transcriptome-wide association study (TWAS) using whole-blood RNA sequencing (RNA-seq) and genotype data from NHW (n = 235), African American (AA; n = 224), and Hispanic (HISP; n = 292) Multi-Ancestry Genomics, Epigenomics, and Transcriptomics of Alzheimer’s (MAGENTA) participants. Using sum of shared single effects (SuShiE) for multi-population cis-eQTL fine-mapping, we identified credible sets for 8,748 genes, improving fine-mapping precision relative to analyses using fewer populations. cis-eQTL effects were largely shared across populations, with a subset showing population-specific regulation. We performed population-stratified TWAS of AD and inverse-variance-weighted meta-analysis, followed by gene-level TWAS fine-mapping (MA-FOCUS), prioritizing nine genes (false discovery rate [FDR] <0.05, posterior inclusion probability [PIP] >0.8), including established AD loci (BIN1, PTK2B, DMPK) with broadly consistent effects across populations. At BIN1, fine-mapped cis-eQTL variants used in the TWAS prediction model highlighted rs11682128, which is only modestly correlated with the genome-wide association study (GWAS) index SNP rs6733839 (r2 ≈ 0.34), demonstrating how integrating eQTL fine-mapping with TWAS can refine signals beyond sentinel GWAS variants. We also identified an association between COG4 expression and AD in NHW, implicating Golgi-related pathways. Using independent SuShiE-derived models from TOPMed MESA (PBMC), several signals replicated directionally across ancestries, with the strongest statistical support in NHW. Overall, multi-population eQTL fine-mapping improves model interpretability and helps resolve shared and population-specific regulatory mechanisms relevant to AD.

Background:Sequence-to-function (S2F) deep learning models are increasingly used to prioritize non-coding regulatory variants, but their behavior across ancestrally diverse populations remains unclear. Because both training data and reference resources are heavily European-centered, multi-ancestry benchmarks are needed to determine whether S2F scores capture regulatory effects consistently across populations with different allele-frequency and LD patterns. Methods:We evaluated Borzoi and AlphaGenome using whole blood eQTL data from the MAGENTA cohort, including African American (AA; N = 224), Caribbean Hispanic (CH; N = 209), and Non-Hispanic White (NHW; N = 235) participants. Model predictions were benchmarked against sampled nominal eQTLs and ancestry-stratified SuSiE fine-mapped variants using Spearman correlation, direction concordance, inter-model convergence, and distance-matched AUROC, with sensitivity analyses for minor allele frequency and comparison-set definition. We also compared FILER functional annotation overlap among high-Posterior Inclusion Probability (PIP) variants across ancestries. Results:Both models showed weak agreement with nominal eQTL effect sizes across ancestries and TSS-distance bins (ρ ≤ 0.138), with direction concordance only marginally above chance. Agreement and discrimination improved for high-confidence fine-mapped variants, and Borzoi and AlphaGenome showed stronger inter-model convergence on fine-mapped variants than on nominal eQTLs, consistent with enrichment for regulatory variants whose effects are more apparent to sequence-based models. In distance-matched AUROC analyses at PIP ≥ 0.9 using PIP < 0.01 variants as low-PIP comparison variants, the AA high-PIP variant set yielded the highest discrimination for both Borzoi (0.837 [95% CI: 0.790-0.870]) and AlphaGenome (0.820 [0.793-0.845]). The CH-versus-NHW ordering was model-dependent: Borzoi yielded higher AUROC in NHW than CH, whereas AlphaGenome produced nearly identical CH and NHW estimates. AUROC values were lower when intermediate-PIP variants were used as comparison variants, but the AA set retained the highest discrimination. MAF-stratified sensitivity analyses attenuated some ancestry contrasts but did not eliminate the higher AA discrimination pattern. Functional annotation analysis showed that AA high-PIP variants more often overlapped chromatin accessibility and chromatin-contact annotations than NHW variants, despite lower overlap with prior eQTL and sQTL annotation catalogs. Conclusions:Borzoi and AlphaGenome showed limited agreement with nominal eQTL effect sizes, but better distinguished high-confidence fine-mapped eQTLs from low-PIP variants. These results support using S2F scores as prioritization evidence for fine-mapped regulatory variants, especially promoter-proximal high-PIP variants, rather than as standalone predictors of eQTL effect size. The strongest discrimination was observed for the AA high-PIP variant set. Overall, the AA result is best interpreted as stronger separation of high-PIP variants from lower-PIP comparison variants, shaped by fine-mapping resolution, LD, the choice of comparison variants, and annotation composition.

High levels of mitochondrial DNA (mtDNA) deletions have been described in the substantia nigra. However, the mechanisms involved are poorly understood. We found that transient expression of a mitochondrial targeted restriction endonuclease (mitoPstI) in mice leads to an accumulation of mtDNA rearrangements that involve both the PstI cleavage sites and unrelated specific regions of the mtDNA, including the MTERF1 binding site and the edge of the D-loop. This pattern of rearrangements after double-strand breaks supports the presence of recombination hotspots in the mtDNA. Transient expression of mitoPstI in dopaminergic neurons led to further accumulation of mtDNA rearrangements in dopaminergic neurons after expression was suppressed, a pattern that was not observed in glutamatergic neurons. This accumulation was also blunted when a mtDNA replisome factor was absent, suggesting that robust mtDNA replication is required for the accumulation of preexisting mtDNA rearrangements in dopaminergic neurons over time.

CNS diseases are a prevailing cause of morbidity and mortality worldwide, and are influenced by environmental and biological factors, including genetic risk. Here, we generated genome-wide genetic data on a large cohort of brain tissue donors with in-depth clinical and neuropathological phenotyping, allowing for broad investigations into the risk and mechanisms of these neurological, neurodevelopmental and psychiatric conditions. This resource consists of 9663 donors with array-based genotyping and 9543 donors with whole-genome sequencing completed. The clinical diagnoses of these donors include 148 CNS diseases clustered into 15 broad categories by International Classification of Diseases-10 (ICD-10) coding. These donors were collected by six repositories comprising the National Institutes of Health NeuroBioBank, with an average participant age of 60 years. While primarily older individuals of European descent, the cohort also contains younger donors and individuals from non-European backgrounds. Variants were detected in whole-genome sequencing, normalized and annotated to describe their functional impact, resulting in 171 121 209 unique variants and 1 078 774 non-silent variants. These raw and normalized data have been made available as a neurogenomics resource in the National Institute of Mental Health Data Archive (nda.nih.gov), combined with donor-matched deep demographic and phenotypic data from the NeuroBioBank Portal (neurobiobank.nih.gov). To illustrate applications, we replicated the strong association observed in previous studies between pathogenic CAG nucleotide repeat expansions in the HTT gene with the clinical diagnosis of Huntington's disease, as well as associations of the APOE gene with Alzheimer's disease, and examined the association of polygenic risk scores with the three most common disease diagnoses in the cohort.

Objective:Both the phosphatidylinositol binding clathrin assembly protein gene (PICALM) and the embryonic ectoderm development gene (EED) have been implicated as causal genes driving a genome-wide association for Alzheimer disease (AD) risk. We employed a new virtual approach using genome-wide chromatin interactions (Hi-C) called enhanced Hi-C Capture Analysis (eHiCA) to identify the genes and regulatory regions that are driving this important AD risk association. Methods:Hi-C data from the frontal cortex of eight AD patients, as well as inducible pluripotent stem cell-derived microglia and spheroids of AD and control patients were used. We applied 14 eHiCA baits each containing a GWAS SNP to identify the cis regulatory interactions in this GWAS locus at a 5kb resolution. Results:The baits derived from the GWAS associated haplotype primarily interacted with the PICALM promoter and the large cis-regulatory elements cluster (CREe) lying upstream of the EED promoter. The EED promoter interacts with PICALM gene body and promoter region but not directly with the associated risk haplotype. Although the AD-associated variants segregate together as a haplotype in the population, each bait exhibited distinct functional chromatin interactions. Interpretation:The PICALM gene is the primary driver of the association in microglia along with the CREe locus. Different SNPs in a segregating haplotype can display different physical Hi-C interactions. This study demonstrates that eHiCA can help resolve the casual genes driving complex GWAS associations, opening new pathways to study Alzheimer disease and other disorders.

Abstract Despite the presence of oxygen, tumors frequently preferentially perform fermentative glycolysis, producing lactate and acidifying the tumor microenvironment (TME). Although studies have observed that high concentrations of lactate in the TME help tumors gain a proliferative advantage, a detailed understanding of the molecular mechanisms is needed to uncover strategies to overcome lactate-mediated growth. In this study, we investigated how lactate exerts progrowth effects in clear cell renal cell carcinoma (ccRCC), a highly glycolytic tumor primarily caused by alterations in the von Hippel–Lindau tumor suppressor and constitutive activation of HIF signaling. High lactate concentrations activated GPR132, a lactate sensor highly expressed by ccRCC, which conferred protumor growth signaling by elevating mitochondrial respiration through the ERK/STAT3/JAK2 pathway. Furthermore, GPR132 facilitated the uptake of lactate through elevation of HIF signaling downstream of AKT/mTOR to fuel mitochondrial respiration in a feed-forward manner. Treatment with a small-molecule GPR132 antagonist demonstrated the essentiality of GPR132 to support ccRCC growth in vivo. Together, these findings reveal that GPR132 signaling promotes ccRCC by sustaining mitochondrial integrity and elevating lactate import. The cross-talk between lactate and tumor cells is a metabolic vulnerability that can be disrupted by targeting GPR132, providing a potential treatment strategy for ccRCC. Significance: Lactate sensing through GPR132 represents a tumor dependency mechanism that reprograms metabolic signaling to support clear cell renal cell carcinoma growth, suggesting GPR132 could represent a potential target for developing cancer therapies.

Poor cardiovascular health is a risk factor for cognitive decline and dementia, including Alzheimer's disease (AD). Development of ancestry-informed blood-based biomarkers for the small vessel diseases of the brain that contribute to vascular cognitive impairment across multiple populations is critical to identify individuals at risk. To begin addressing this, we examined the associations of blood-based biomarkers of endothelial function and vascular disease with AD biomarkers and genetic ancestry in 1,534 admixed Hispanic and African American individuals from the PRADI and READD-ADSP. Biomarkers of endothelial function and vascular disease included VEGF, PlGF, bFGF, VCAM-1, ICAM-1, HbA1C, and plasma lipid levels. AD biomarkers included Aβ40, Aβ42, and p -tau181. Adjusting for age and sex as covariates, linear regressions were modeled separately predicting the three AD biomarkers by all 7 cardiovascular biomarkers, as well as degree of African ancestry. Additional regression models were run predicting each AD biomarker by each cardiovascular biomarker individually while controlling for age and sex and examining the interaction between the endothelial/vascular biomarker and African ancestry. Aβ40 is associated with VEGF, PlGF, VCAM-1, HbA1c, and African ancestry (Table 1); Aβ42 is associated with VEGF, PlGF, HbA1c, and African ancestry (Table 2); and p -tau181 is associated with VEGF and African ancestry (Table 3). In addition, an interaction between cardiovascular biomarker and degree of African ancestry was observed for bFGF (β = 0.83, P  = .03) while predicting Aβ40, ICAM-1 (β = -0.006, P  = .04) while predicting Aβ42, and VEGF (β = 0.03, P  = .02) and total cholesterol (β = -0.11, P  = .002) while predicting p -tau181. Levels of VEGF, PlGF, VCAM-1, and HbA1c predict levels of AD biomarkers in admixed individuals, and these associations are influenced by degree of African ancestry. While these findings need additional validation, they support the notion that endothelial disease contributes to cognitive impairment and that the assessed biomarkers may be valuable biomarkers for clinical settings. These results underscore the importance of biomarker research and validation in individuals from multiple populations.

Background: The incidence of Alzheimer's disease (AD) increases with age, is associated with insulin resistance (IR), and has a greater prevalence in women. Genome‑wide technologies can yield novel biomarkers and may help identify aspects of AD pathophysiology. Individual AD blood transcriptomic studies are small, preventing the study of sex, while meta‑analyses are technically challenging. Relationships between AD biomarkers and IR are also underexplored, largely due to a lack of metabolic phenotyping in AD cohorts. Methods: We generated 1,021 new whole blood transcriptomic profiles from the AddNeuroMed cohort (including 317 technical replicates) and 410 whole-blood transcript profiles (metabolic cohort). We aligned this data, and our large AD RNA-seq whole-blood transcriptome study, to a common genomic and transcriptomic reference and modelled blood cell composition. Bias was assessed using randomly sampled gene-sets and cross-validated classifiers. Further, a 62‑gene IR signature was generated using our metabolic cohort studies, providing a surrogate IR RNA score to retrospectively phenotype the AD cohort. Machine learning was used to develop AD classifiers from the data and evaluate multimodal integration with magnetic resonance imaging. Sex stratified differential expression and pathway analysis were used to explore sex differences in AD. Results: The new data was more robust than the original AddNeuroMed data, with a lower 'sampling at random' score (AUC=0.61 vs 0.73-0.79). A novel AD classification signature was cross‑ and externally evaluated, achieving higher performance in women. Identification of AD‑associated disease pathways, in whole-blood, was influenced by variation in blood cell composition. Notably, B‑cell pathways were modified in AD (including genes BLNK and MS4A1), and this was relatively consistent across sexes and ethnicity. Previous reports that mitochondrial-DNA-encoded RNAs were upregulated, and nuclear-encoded mitochondrial transcripts were consistently downregulated, were not substantiated, with only women showing modest evidence for loss of nuclear-encoded mitochondrial transcripts. Conclusions: We provide a large‑scale, technically robust blood AD transcriptomic dataset that enhances legacy AD resources. Analysis revealed robust immune signatures and the statistical transfer of a classification signature across technologies and ethnicities. We add to the evidence for a role of altered B‑cell biology in AD, while delivering an updateable transcriptomic resource for future machine learning and genomic studies. ### Competing Interest Statement NMI, MM, HC, BEP, JAS, CK, TG, RJB, CW, PJA, JPC, WEK, KS, AJG, CHV, GS and JAT declare no conflicts of interest related to this project. JAT is the major shareholder in Augur Precision Medicine LTD which contributed resources to this project but has no commercial links. GS serves as an advisor to BioAI Health. CW is co-founder of Epigenetix Inc., Jupiter Neurosciences Inc., and CuRNA Health Inc., and serves as an advisor for Ribocure AB, SeqLL Inc., and Sylentis S.A. CHV is a consultant for Jupiter Neurosciences Inc. KS is an employee of MSD (Merck Sharp & Dohme) Research and Development LTD. None of the other authors declare any conflict of interest. ### Funding Statement This work was supported by the National Institute on Aging (NIA), NIH grant 1R56AG061911-01, the Medical Research Council (MRC) grant G1100015 and BBSRC grant BB/Y513593/1. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Blood samples for the RNA analyses were obtained from individuals taking part in the biomarker studies (coordinated by Dr Angela Hodges; the AddNeuroMed study and the Maudsley Biomedical Research Centre Dementia Case Register) as previously reported . The clinical study obtained informed consent according to the Declaration of Helsinki (1991) and received ethical approval at each of the six clinical centres (London, Kuopio, Lodz, Perugia, Thessaloniki, and Toulouse). For the independent RNA‑seq whole‑blood transcriptomic study described by Griswold et al. , all participants, or their legally authorised representatives, gave informed consent, and the Institutional Review Board of the University of Miami gave ethical approval for this work. The multi‑centre META‑PREDICT cohort was funded by a Seventh Framework Programme (FP7) grant and approved by local ethics committees at each centre (the University of Nottingham Medical School Ethics Committee, the Regional Ethical Review Board of Stockholm, the ethics committee of the municipality of Copenhagen and Frederiksberg in Denmark, the Comite Etico de Investigacion Humana of the Universidad de Las Palmas de Gran Canaria, and the Loughborough University Ethics Approvals (Human Participants) Sub‑Committee), each of which gave ethical approval for this work. The STRRIDE AT‑RT study protocol was approved by the Institutional Review Boards at Duke University and East Carolina University, and the STRRIDE‑PD study protocol was approved by the Institutional Review Board at Duke University, which gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The raw gene expression data reported in this paper are available at E MTAB 15140, along with the processed data files and novel CDFs. The probes for the raw data can be realigned to the current genome and transcriptome each year to remain current. Code for the various informatics analyses can be readily obtained by contacting the authors or via https://github.com/Nasim-MI/Affy-ANMerge-ML. The version of the realigned raw count RNA seq data used in the present study, together with the associated processed matrices and phenotype data for the Miami cohort, can be accessed via Zenodo (https://doi.org/10.5281/zenodo.20269433). National Institute on Aging (NIA), National Institutes of Health (NIH), R56AG061911, R01AG079373, R01AG070935 Seventh Framework Programme (FP7), HEALTH‑F2‑2012‑277936 Medical Research Council, https://ror.org/03x94j517, G1100015 Biotechnology and Biological Sciences Research Council, BB/Y513593/1 National Heart, Lung, and Blood Institute (NHLBI), HL‑057354 National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), DK‑081559 Engineering and Physical Sciences Research Council, EP/Y009800/1 Responsible AI UK, KP0016 National Institute for Health Research (NIHR) Barts Biomedical Research Centre, NIHR203330

BackgroundEducation promotes cognitive reserve (CR), potentially buffering Alzheimer's disease pathology (ADP). However, the education-CR relationship may differ by population and genetic background.ObjectiveTo examine education, APOE ε4, and functional scores in a Puerto Rican (PR) cohort with varying plasma pTau181, an ADP biomarker.MethodsA subset of 514 PR older adults with "high" (>mean+1SD) or "low" pTau181 ( 0) and (2) severity among impaired participants, adjusting for age and sex.ResultsHigh EA was associated with better CDR-FUNC than low EA within the high pTau181 group (n = 80; MedianLow_EA = 7, MedianHigh_EA = 0; p = 0.011). The hurdle model similarly showed that each additional year of education reduced the odds of any functional impairment in the high-pTau181 group (OR = 0.89, 95% CI [0.79-0.99]). No significant education × APOE ε4 interaction was observed, though a negative trend suggested that increasing education attenuated ε4-related impairment.ConclusionsGreater education is linked to functional preservation in PR older adults, particularly in those with elevated ADP, suggesting potential CR-mediated resilience. APOE ε4 may worsen outcomes with decreasing education, suggesting both educational and genetic factors should inform strategies to mitigate cognitive decline globally.

Importance:APOE*ε4 is the strongest genetic risk factor for Alzheimer's disease (AD), yet its effect varies across ancestral populations. As blood-based biomarkers increasingly inform AD diagnosis, failure to account for both APOE genotype and ancestry could lead to misinterpretation of biomarker profiles and inaccurate diagnostic classification. Understanding how ancestry modulates APOE effects is crucial for ensuring accurate biomarker-based assessments and AD diagnosis. Objective:To determine whether genetic ancestry modulates APOE association with cognitive function, brain morphometry, and plasma biomarkers. Design Setting Participants:Cross-sectional analysis of community-dwelling older adults from the Health and Aging Brain Study-Health Disparities (HABS-HD) cohort (N = 2733). Participants spanning the cognitive spectrum underwent cognitive assessment, neuroimaging, plasma biomarker collection, and genome-wide genotyping from 2018 to 2023. Main Outcomes and Measures:Cognitive performance (global cognition, memory, executive function, verbal ability), brain morphometry (cortical thickness, hippocampal volume), and plasma biomarkers (Aβ42/Aβ40, pTau181, pTau217, total tau, NfL). Results:In the full cohort, APOE ε4+ was associated with worse cognitive performance across all domains, reduced cortical thickness and hippocampal volume, lower Aβ42/Aβ40, and elevated pTau181 and pTau217. APOE ε2+ was associated with lower pTau217. Ancestry-stratified analyses revealed attenuated ε4+ effects on pTau217 and pTau181 in African compared with European participants (~2.5-fold for both), with the pTau217 difference surviving FDR correction. Compositional analysis confirmed that ε4+ effects on pTau181 and pTau217 strengthened with increasing European ancestry proportion. Local ancestry analysis showed ε4+ effects on pTau217 were significantly attenuated in individuals with African local ancestry at the APOE locus. In contrast, ε4+ effects on Aβ42/Aβ40, cognition, and neuroimaging were largely consistent across ancestry groups. Meta-analysis with an independent multi-ancestry cohort replicated the attenuated pTau181 findings. Conclusions and Relevance:Genetic ancestry modifies the effect of APOE on AD endophenotypes. In particular, African ancestry attenuates the association between APOE ε4+ and pTau181 and pTau217. Accurate AD diagnosis requires consideration of both APOE genotype and ancestry to avoid misclassification in biomarker-based evaluations.

List of noncoding guide RNAs for GPR132, MCT1 and DRP1

Several genes guide inner ear development, and mutations in these genes can cause malformations that result in congenital hearing loss. However, the contribution of noncoding regulatory elements remains largely unclear. This study investigates the function of distal enhancer elements in the transcriptional regulation of GDF6 , a gene implicated in cochlear development. Using mouse models with targeted deletions, human inner ear organoids, and CRISPR interference (CRISPRi), we identified a downstream regulatory interval harboring a developmental enhancer required to maintain GDF6 expression during otic epithelial maturation and cochlear morphogenesis. Deletion of this regulatory region or targeting of CRISPRi-based repressors to these regions resulted in decreased GDF6 expression, failure of otic-epithelium development, and prevention of hair cell-like differentiation, reflecting cochlear aplasia observed in patients with corresponding genomic deletions. These findings highlight the contribution of long-range regulatory elements to auditory development and illustrate how their disruption contributes to human deafness.