bluuebunny/crossref_metadata_2025_split huggingface dataset:
A multiethnic transcriptome for Alzheimer Disease identifies cross‐ancestry and ancestry‐specific expression profiles
Authors: Yang Zikun , Cieza Basilio , Reyes‐Dumeyer Dolly , Lee Annie J. , Dugger Brittany N , Jin Lee‐Way , Murray Melissa E. , Dickson Dennis W. , Pericak‐Vance Margaret A , Vance Jeffery M. , Foroud Tatiana M. , Teich Andrew F , Mayeux Richard , Tosto Giuseppe 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) presents complex molecular heterogeneity, influenced by a variety of factors including heterogeneous phenotypic, genetic, and neuropathologic presentations. Regulation of gene expression mechanisms is a primary interest of investigations aiming to uncover the underlying disease mechanisms and progression.MethodWe generated bulk RNA‐sequencing in prefrontal cortex from 565 AD brain samples (non‐Hispanic Whites, n = 399; Hispanics, n = 113; African American, n = 12) across six U.S. brain banks, and conducted differential gene expression and enrichment analyses. We sought to identify cross‐ancestry and ancestry‐specific differentially expressed genes (DEG) and pathways across Braak stages, adjusting for sex, age at death, and RNA quality metrics. We validated our findings using the Religious Orders Study/Memory and Aging Project study (ROS/MAP, n = 1,095). Lastly, we validated top DEG using publically‐available human single‐nucleus RNA sequencing (snRNAseq) data.ResultAD‐known genes VGF (LFC = ‐0.661, padj = 3.78) and ADAMTS2 (padj = 1.21) were consistently differentially expressed across statistical models, ethnic groups, and replicated in ROS/MAP (Figure 1). Genes from the heat shock protein (HSP) family, e.g. HSPB7 (padj = 3.78), were the top DEG, also replicated in ROS/MAP. Ethnic‐stratified analyses prioritized TNFSF14 and SPOCD1 as top DEG in Hispanic samples. Gene set enrichment analysis highlighted several significantly pathways, including “TYROBP causal network in microglia” (WP3945; padj = 1.68) and “Alzheimer Disease” (WP5124; padj = 4.24). snRNAseq validated several DEG, including VGF downregulated in neurons (padj = 1.1).ConclusionTo our knowledge, this is the largest diverse transcriptome study for AD in post‐mortem tissue. We identified perturbated genes and pathways resulting in cross‐ethnic and ethnic‐specific findings, ultimately highlighting the importance of diversity in AD investigations.
Sex differences for clinical and pathological correlates of APOEe4 in people with Lewy body pathology
Authors: Bayram Ece , Litvan Irene 
Published: January 2025

AbstractBackgroundIn people with Lewy body (LB) pathology, Alzheimer’s (AD) co‐pathology staging has stronger association with cognition for females, and LB pathology staging has stronger association with behavioral and motor symptoms for males. As APOEe4 allele is a risk factor for LB and AD pathologies, in both females and males; we investigated whether APOEe4 association with clinical profile and pathology staging differs by sex in people with LB pathology.MethodsData were obtained from the National Alzheimer’s Coordinating Center Uniform Data Set (UDS), Neuropathology and Genetic Data Set for UDS visits conducted between September 2005 and August 2019. 294 females and 490 males with available APOEe4 count data from 33 Alzheimer’s Disease Research Centers, with brainstem, limbic or neocortical LB pathology were included, excluding those with other neuropathologic diagnoses. Interactions between sex and APOE ε4 count for pathology staging (LB, AD), clinical diagnosis (LB disease, AD), dementia likelihood and severity (CDR® Dementia Staging Instrument‐Sum of Boxes), behavioral symptom severity (Neuropsychiatric Inventory Questionnaire) at last visit were assessed with linear models controlling for age.Results40.5% of females, 40.8% of males had one APOEe4; 12.2% of females, 12.9% of males had two APOEe4 alleles (p = .95). Compared to males, females were older at last visit and died older. APOEe4 was not associated with LB pathology staging. Compared to males, APOEe4 was associated with higher level of AD pathology staging (B = 1.48 for females, B = 0.66 for males; p<.001); higher likelihood of AD clinical diagnosis (B = 1.30 for females, B = 0.49 for males; p<.001); lower likelihood of LB disease clinical diagnosis (B = ‐0.76 for females, B = ‐0.16, p<.001); higher likelihood for dementia (B = 1.76 for females, B = 1.21 for males; p<.001) and more severe dementia (B = 2.69 for females, B = 1.53 for males; p<.001) for females. Compared to females, APOEe4 was associated with more severe behavioral symptoms for males (B = 0.36 for females, B = 1.34 for males; p<.001).ConclusionsIn people with LB pathology, APOEe4 is associated with AD co‐pathology staging, clinical diagnosis, dementia likelihood, dementia and behavioral symptom severity. However, these associations can be different for females and males. Clinical and pathological associations of genetic factors can differ by sex in LB disease.
Integrated analysis approaches to decode functional consequences of molecular quantitative trait loci for complex traits
Authors: Nachun Daniel 
Published: January 2025

AbstractBackgroundGenome‐wide association studies (GWAS) have identified thousands of genomic regions associated with complex diseases but understanding the underlying causal mechanisms remains a significant challenge. The FunGen‐xQTL project has addressed this by generating and harmonizing molecular quantitative trait loci (xQTL) across multiple layers of molecular traits in human brains, cerebrospinal fluid, and blood‐derived cells relevant to neurodegenerative disorders. Existing approaches for integrating xQTL data with GWAS have typically focused on individual molecular traits in individual QTL layers. There is a scarcity of systematic, rigorous computational methods that effectively utilize the diverse data from the FunGen‐xQTL project to understand the convergence of complex disease pathogenesis across various ‘omics levels.MethodsFunGen‐xQTL has evaluated current best practices for xQTL integration with GWAS, creating standardized analysis protocols and bioinformatics workflows for a chosen set of methods. We have developed new computational tools for many tasks: Functional fine mapping for methylation, histone acetylation and chromatin accessibility QTLs (fSuSiE)• Multi‐omic fine mapping for joint analysis of different ‘omics layers (mvSuSiE)• Pairwise enrichment, colocalization, TWAS and Mendelian randomization (pecotmr)• Multi‐omic colocalization with gradient boosting (colocBoost)• Multi‐omic and multi‐trait TWAS and Mendelian randomization (mr.mash, CTWAS)• Multi‐omic discovery of transQTL effects on gene networks (transCCA)• Improved predictive models for identifying rare variants that impact function genomics (Watershed2)• Improved quantification of splicing for splicing QTL mapping (leafcutter2).ResultsIn both simulated and real data, we have found that our novel methods outperform existing methods for discovering associations between xQTL and GWAS when applied to different xQTL types. We have prepared precompiled packages for the conda family of package managers which are bundled together into containers for each workflow that can be deployed on cloud providers or local computing clusters. We have designed different schemes for xQTL integration, including disease‐centric, gene‐centric, and event‐centric analysis, to characterize the functional landscape of xQTLs around AD loci.ConclusionsOur novel tools have improved our ability to identify shared genetic etiology between multiple related molecular traits, metabolic pathways, and Alzheimer’s disease (AD) that are highlighted by the other talks in this session.
Fine‐mapping of existing and novel functional quantitative trait locireveals local genetic influences of epigenetic and transcriptomic regulation in human brains
Authors: Marcora Edoardo , Sun Hao 
Published: January 2025

AbstractBackgroundThe FunGen‐xQTL project has significantly advanced genetics by developing and exploring novel quantitative trait loci (QTL) types in human brains, enriching our understanding of complex neurological disease etiology. We broadened the scope of epigenomic QTL analysis, integrating histone acetylation QTLs (haQTLs) and methylation QTLs (mQTLs) that affect multiple histone acetylation peaks or methylation CpG sites spatially. Additionally, we investigated a new category of splicing QTLs (sQTLs) implicated in nonsense‐mediated decay (NMD).MethodUtilizing unique computational methods developed by FunGen‐xQTL for genome‐wide statistical fine‐mapping, we dissected intricate gene regulation networks at single‐base resolution, effectively accounting for linkage disequilibrium confounding.ResultOur research has unveiled complex multi‐omic regulations via fine‐mapped epigenomic QTLs using our new method called fSuSiE. We identified specific genetic variants performing dual roles as both epigenomic and eQTLs across different genes, indicative of 3D multi‐gene interactions. For instance, cis‐variant rs62324750 for TMEM131L uniquely acts as a negative eQTL, negative haQTL, and positive mQTL. This complex interaction extends to nearby genes TRIM2 and MND1, where rs11728062 impacts gene expression as a negative haQTL and positive mQTL through epigenomic markers spanning gene bodies. Oligodendrocyte‐specific Hi‐C data further support these findings, suggesting novel regulatory mechanisms involving disruption of transcriptional repressor bindings. Moreover, neuronal Hi‐C and ChiP‐Seq data uncover a unique xQTL interaction pattern in APOL2, indicative of 3D protein‐chromatin structures in transcription regulation.Employing LeafCutter2 methods from FunGen‐xQTL, we investigated the genetic basis of NMD by identifying premature termination codons from RNA‐seq data and fine‐map the corresponding sQTL for such unproductive splicing events. In the ROSMAP DLPFC dataset, we distinguished 45,999 unproductive from 130,664 productive splicing events for NMD. We observed a higher colocalization ratio of unproductive sQTLs with eQTLs, illuminating new insights into AD etiology. Notably, an unproductive sQTL in TSPAN14, colocalized with an AD GWAS signal, revealed significant gene expression and intron usage differences across genotypes, suggesting RNA expression of TSPAN14 may be mediated by genetically regulated NMD events.ConclusionOur work demonstrates the complex interplay of novel QTL types in gene regulation and offers significant insights for future research into the genetic mechanisms of complex diseases.
Cognitive and behavioral decline predicted by perimenopausal symptoms: A CAN‐PROTECT study
Authors: Crockford Jasper F. E. , Guan Dylan X. , Einstein Gillian , Smith Eric E. , Ismail Zahinoor 
Published: January 2025

AbstractBackgroundMenopause has been associated with greater dementia risk. We investigated the relationship between the presence of specific symptoms experienced during menopause (i.e., perimenopausal symptoms) and later‐life emergence of cognitive and behavioral symptoms that are linked to elevated dementia risk.MethodParticipant data were from the Canadian Platform for Research Online to Investigate Health, Quality of Life, Cognition, Behaviour, Function, and Caregiving in Aging (CAN‐PROTECT) study. The sample for this analysis comprised 896 post‐menopausal participants, who recalled the presence or absence of 11 perimenopausal symptoms. Symptoms were categorized as vasomotor, weight changes, vaginal dryness, irregular periods, sleep problems, mood changes, brain fog, and other unnamed symptoms. Currently experienced subjective cognitive symptoms were measured with the Everyday Cognition (ECog‐II) scale. Emergent and persistent neuropsychiatric symptoms were measured with the Mild Behavioral Impairment Checklist (MBI‐C). Higher scores reflected greater severity on both measures. A negative binomial regression examined the association between perimenopausal symptoms and cognitive function. A zero‐inflated negative binomial regression examined the association between perimenopausal symptoms and MBI symptoms. All models adjusted for age of menopausal onset, menopausal hormone therapy (MHT), menopause type (i.e., spontaneous, or due to medical reasons), age, and years of education.ResultSymptoms of brain fog, weight changes, and mood changes were associated with poorer current ECog‐II score (b = 74.8, 95%CI [47.2, 108.0], p<.001; b = 24.4, 95%CI [8.9, 42.2], p = .001; b = 36.2, 95%CI [17.3, 58.3], p<.001, respectively). Comparatively, use of estrogen and non‐estrogen‐based MHT during menopause was not significantly associated with current ECog‐II scores (b = ‐11.0,95%CI[‐25.3, 6.5], p = .2; b = 16.9, 95%CI[‐10.9, 56.1], p = .3, respectively). Weight and mood symptoms of perimenopause were significantly associated with poorer current MBI‐C score (b = 24.4,95%CI[2.4,51.1], p = .03; b = 68.4%,95%CI[36.3,108.1], p<.001, respectively). MBI‐C scores differed based on type of MHT used. Estrogen‐based MHT was associated with a statistically significant 26.9% lower MBI‐C score (95%CI[‐43.3, ‐5.7], p = .02), while score did not differ significantly with non‐estrogen‐based MHT (b = ‐19.1,95%CI[‐44.6, 18.1], p = .3).ConclusionThe experience of brain fog, weight changes, and mood changes during perimenopause may predict greater risk for cognitive and behavioral changes later on. Use of estrogen‐based MHT may mitigate the relationship between perimenopause symptoms and MBI symptoms. However, longitudinal data are required to explore mechanisms.
Multi‐ancestry whole genome sequencing analysis of 36,361 individuals from the Alzheimer’s Disease Sequencing Project (ADSP)
Authors: Kunkle Brian W. , Farrell John J. , Akgun Bilcag , Wang Dongyu , Zhu Congcong , Hamilton‐Nelson Kara L. , Dietrich Sven‐Thorsten , Zhang Xiaoling , Rampersaud Andy , Yang Jiemin , Naj Adam C. , Martin Eden R. , DeStefano Anita L. , Choi Seung Hoan ,   the Alzheimer’s Disease Sequencing Project (ADSP)
Published: January 2025

AbstractBackgroundThe Alzheimer’s Disease Sequencing Project (ADSP) aims to identify genetic variation contributing to the development or protection of Alzheimer’s disease (AD) in diverse ancestral populations. The latest ADSP whole genome sequencing (WGS) data release includes over 36,000 individuals from 37 datasets (NIAGADS NG00067.v11 ADSP R4). Quality control (QC) of these data has been performed and association analyses are underway.MethodWe performed QC filtering based on metrics including GATK VQSR, genotype missingness rate (<20%), comtamination rate, singleton rate, heterozygous/homozygous ratio, ABHet ratio ([0.3, 0.7]), Ti/Tv ratio, and a differential allele frequency between PCR‐free and PCR‐amplification library protocol. After filtering, two genome‐wide association analytical pipelines are being implemented: 1) a population‐specific analysis using a Gaussian mixture model genetic clustering approach to identify genetic similarity clusters for defining populations, and 2) a pooled analysis of all populations accounting for population structure and empirical relationships using GENESIS KING, PC‐Air and PC‐Relate methods. Using SAIGE, planned analyses include single variant logistic mixed model analysis and set‐based association tests (Burden, SKAT, SKAT‐O) for coding and noncoding rare variant groups.ResultAfter QC, we retained over 293 million single nucleotide variants (SNVs), 15 million small deletions, 6 million small insertions, and over 11,000 AD and related dementia (ADRD) cases, for downstream association analysis. With high‐quality non‐correlated variants (n = xx), we defined 8 genetically similar population clusters for population‐specific analyses comprising ∼800 Amish, ∼12,200 European, ∼5,400 African/African American, ∼5,700 Hispanic/Amerindian/African, ∼2,500 Hispanic/Amerindian, ∼1,200 Ashkenazi Jewish, ∼2,600 Indian, and ∼200 other individuals. In the principal component analysis, the Ashkenazi and Amish population revealed distinct clusters separate from the European reference population.ConclusionA major goal of the ADSP is to fully reveal the genetic architecture of AD/ADRD across diverse ancestral populations with the hope that new discoveries and the therapeutic or prevention strategies they enable will benefit all ancestral groups. Complementary pooled‐population and population‐specific analyses of the ADSP’s latest WGS data release are underway and new insights from these investigations are anticipated.
Heterogeneity of brain endothelial cell transcriptome in Alzheimer’s disease brain tissue
Authors: Yue Qian , Hoi Pui Man Maggie 
Published: January 2025

AbstractBrain endothelial cell (BEC) dysfunction in Alzheimer’s disease (AD) have attracted much more attention in recent years. A series of multi‐omics study is carried out to indicate the specific endothelial state during AD progression. We have summarized the most recent AD multi‐omics study on AD BECs. This figure shows multiple characterization of transcriptomic information. BEC transcriptomic alteration shows region‐specific and subpopulation‐specific manner, in which different groups of differentially expressed genes (DEGs) are detected in each category, as well as related signaling pathways and biological processes. DEGs and related biological processes also shows changing trends as the disease progresses from no neuritic plaques (Braak0‐II) to severe neuritic plaques (BraakVI), which indicates that the pathologic process in AD is dynamic. Then we show the potential downstream and upstream factors related to BEC DEGs, including BEC receptors, transcription factors and AD GWAS loci. Finally, we show the DEGs as well as related biological processes in BECs near the Aβ plaques, or in cerebral amyloid angiopathy (CAA). An overlap between microglia risk GWAS and BMECs DEG is noticed and shown. In addition, BEC DEGs also indicate the activation of BEC receptors that may be related to several AD ligands produced by other brain cells like oligodendrocytes, astrocytes, pericytes, and neurons, showing the close relationship between BEC pathology and other cell pathology. Taken together, BEC transcriptomic information strongly indicated the close relationship between BEC DEGs and AD pathogenesis and is valuble for the exploration of therapeutic strategies for AD by targeting BEC dysfunction.
APOE Genotypes and Alzheimer’s Disease Risk in Older Ugandans
Authors: Lwere Kamada 
Published: January 2025

AbstractBackgroundApolipoprotein E (APOE) gene has three common alleles: ԑ2, ԑ3, and ԑ4, which influence Alzheimer’s disease (AD) risk. APOE ԑ4 allele increases AD risk, while APOE ԑ2 allele may protect against AD. This study aimed to examine the distribution and prevalence of APOE genotypes and their association with AD in older Ugandans.MethodForty‐three participants aged ≥ 65 years were recruited from two villages in Wakiso district, Uganda. Suspected AD cases were identified using the Montreal Cognitive Assessment (MoCA) tool. APOE genotypes were determined from blood samples using molecular biology techniques.Preliminary resultsThe participants (n = 43) had a mean age of 79.0 ± 11.4 years, with a range from 62 to 120 years, and 86% were female. The distribution and prevalence of APOE genotypes varied across the sample, with ԑ3ԑ3 being the most common (53.5%), followed by ԑ2ԑ3 (23.3%), ԑ2ԑ2 (4.7%), and ԑ3ԑ4 (2.3%). The frequency of ԑ3ԑ3 genotype was consistent across different age groups, reflecting its dominance in the studied population. Females displayed a broader variety of APOE genotypes than males, including ԑ2ԑ3, ԑ2ԑ2, and ԑ3ԑ4. APOE ԑ4 allele was more prevalent among participants with higher MoCA scores, suggesting lower AD risk.ConclusionThis study reveals the APOE genotyping patterns and their relation to AD risk in older Ugandans. The low frequency of APOE ԑ4 allele and its inverse association with AD risk are intriguing and warrant further investigation. Future studies should include larger and more representative samples, as well as other biomarkers and clinical measures of AD.
Alzheimer’s Disease Polygenic Risk in the LEADS Cohort
Authors: Nudelman Kelly N. , Pentchev Julian V. , Jackson Trever , Eloyan Ani , Dage Jeffrey L. , Foroud Tatiana M. , Hammers Dustin B. , Carrillo Maria C. , Dickerson Bradford C. , Rabinovici Gil D. , Apostolova Liana G. ,   the LEADS Consortium
Published: January 2025

AbstractBackgroundCurrently, it is unclear to what extent late‐onset Alzheimer’s disease (AD) risk variants contribute to early‐onset AD (EOAD). One method to clarify the contribution of late‐onset AD genetic risk to EOAD is to investigate the association of AD polygenic risk scores (PRS) with EOAD. We hypothesize that in the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS), EOAD participants will have greater PRS than early‐onset amyloid‐negative cognitively‐impaired participants (EOnonAD) and controls, and investigate the association of AD PRS with age of disease onset (AoO) and cognitive performance.MethodsGWAS data was generated for LEADS participants, including those with EOAD, EOnonAD, and controls, with the Illumina Global Screening Array. A PRS was calculated using the 31 SNPs and weights published previously by Desikan et al. (2017) for LEADS participants with imputed GWAS data (N = 369). Logistic regression models including age, sex, PRS, and genetic ancestry principal components were tested to identify predictors of EOAD (N = 210) vs. EOnonAD (N = 69) and controls (N = 89). ANCOVA models were used to assess group differences in PRS scores. Kaplan‐Meier regression was used to assess differences in EOAD AoO for tertile‐binned PRS groups. Within EOAD, pre‐calculated cognitive domain scores for speed and attention, working memory, episodic memory, language, and visuospatial performance were assessed for correlation with PRS.ResultsThe AD PRS was a predictor of EOAD (p = 0.014), with the model explaining 10.5% of variance (X2 = 40.971, p<0.001). EOAD participants had higher PRS scores (mean = 0.0012, standard deviation (SD) = 0.015) compared to EOnonAD and controls (mean = ‐0.0018, SD = 0.015) (F = 6.602, p = 0.011). Survival analysis indicated no significant differences in EOAD AoO between PRS groups (X2 = 3.396, p = 0.183). In the EOAD group, PRS was associated with cognitive scores for speed and attention (r = 0.204, p = 0.007), language (r = 0.230, p = 0.002), and visuospatial performance (r = 0.166, p = 0.037).ConclusionsIn the LEADS cohort, AD PRS is a predictor for EOAD, and is associated with cognitive performance, but does not predict EOAD AoO. This suggests that while late onset AD‐associated genetic variants contribute to disease risk and processes, they do not account for a large portion of disease risk, and do not explain differences in disease AoO in the LEADS cohort.
Molecular Mechanism of Substrate Recognition and Cleavage by Human γ‐Secretase
Authors: Guo Xuefei 
Published: January 2025

AbstractBackgroundSuccessive cleavages of amyloid precursor protein C‐terminal 99 residues (APP‐C99) by human γ‐secretase result in amyloid‐β peptides (Aβs) of varying lengths, the main constituents of amyloid plaques in Alzheimer’s disease patients. Most cleavages have a step size of three residues, as exemplified by sequential generation of Aβ49, Aβ46, Aβ43, and Aβ40.MethodTo elucidate the mechanism of substrate cleavage, we determined atomic structures of human γ‐secretase bound individually to APP‐C99, Aβ49, Aβ46, and Aβ43.ResultRemarkably, in all cases, the substrate has the same structural features: a transmembrane α‐helix that binds PS1, a three‐residue linker, and a β‐strand that forms a hybrid β‐sheet with two β‐strands of PS1. Proteolytic cleavage occurs at the peptide bond just preceding the substrate β‐strand.ConclusionTherefore, each cleavage is followed by unwinding of the substrate α‐helix by one turn, translocation of the α‐helix towards the C‐terminus, and formation of a new β‐strand. This mechanism is consistent with existing biochemical data and may also explain the cleavages of other substrates by human γ‐secretase.