Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer's disease susceptibility.
paper
Cited
Public
- Authors
- Raj, Towfique; Li, Yang I; Wong, Garrett; Humphrey, Jack; Wang, Minghui; Ramdhani, Satesh; Wang, Ying-Chih; Ng, Bernard; Gupta, Ishaan; Haroutunian, Vahram; Schadt, Eric E; Young-Pearse, Tracy; Mostafavi, Sara; Zhang, Bin; Sklar, Pamela; Bennett, David A; De Jager, Philip L
- Year
- 2018
- Journal
- Nature genetics
- PMID
- 30297968
- DOI
- 10.1038/s41588-018-0238-1
- PMCID
- PMC6354244
Here we use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLPFC) of 450 subjects from two aging cohorts. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimer's disease. We also generate a catalog of splicing quantitative trait loci (sQTL) effects: splicing of 3,006 genes is influenced by genetic variation. We report that altered splicing is the mechanism for the effects of the PICALM, CLU and PTK2B susceptibility alleles. Furthermore, we performed a transcriptome-wide association study and identified 21 genes with significant associations with Alzheimer's disease, many of which are found in known loci, whereas 8 are in novel loci. These results highlight the convergence of old and new genes associated with Alzheimer's disease in autophagy-lysosomal-related pathways. Overall, this study of the transcriptome of the aging brain provides evidence that dysregulation of mRNA splicing is a feature of Alzheimer's disease and is, in some cases, genetically driven.
Overview of the study.RNA was sequenced from the gray matter of the dorsal lateral prefrontal cortex (DLPFC) of 542 samples (450 remained after QC and matching for genotype data) from the ROS/MAP cohort. RNA-Seq data were processed, aligned and quantified by our parallelized pipeline. The intronic usage ratios for each cluster were then computed using LeafCutter20, standardized (across individuals) and quantile normalized. The intronic usage ratios were used for differential splicing analysis, for calling splicing QTLs, and for transcriptome-wide association studies (TWAS). TWAS was performed on summary statistics from IGAP Alzheimer’s disease GWAS of 74,046 individuals14.
Differential splicing analysis in relation to Alzheimer’s disease diagnosis and neuropathology.(a) Heat map of top 35 differently excised intron association with burden of tangles in ROSMAP. Each column is one subject, who are ordered by their tangles burden (yellow row at the top of the panel). The association’s Z-score strength and direction are denoted using the key at the bottom of the panel. (b) Variance explained (%) of top 5 differently excised introns association for four different traits. (c) The left two panels present the mean and distribution of intron usage for differently excised introns in NDRG2 in relation to a clinical diagnosis of Alzheimer’s disease in ROSMAP and in MSBB. The right two panels display the association of amyloid or tangle burden to intron usage in NDRG2. (d) Differentially excised intron in APP upon Tau overexpression in iPSC Neurons.
Enrichment of splicing QTLs in epigenomic marks and in Alzheimer’s disease GWAS.(a) Splicing QTLs are enriched in regions (or chromatin states) associated with active transcription and genic enhancers, and they are depleted in polycomb regions that are transcriptionally repressed in the DLPFC. (b) Left: P-value distribution of ROSMAP sQTLs that are significant in CMC (FDR < 0.05). The majority (78%) of sQTLs in ROSMAP are also discovered in CMC. Right: The direction of effect is consistent for the majority (93%) of the significant (FDR < 0.05) lead sQTLs in CMC and in ROSMAP. (c) P-value distribution of ROSMAP eQTLs that are significant sQTLs (FDR < 0.05). (d) SNPs that drive QTLs in H3K9ac and DNA methylation data in the same ROSMAP brains are more likely to be sQTLs than matched SNPs within H3K9ac domains (left) and near DNA methylated CG (right). (e) QQ-plot for Alzheimer’s disease GWAS suggests that sQTLs are enriched among Alzheimer’s disease GWAS (IGAP study14) compared to other types of QTLs. (f) Fold-enrichment of Alzheimer’s disease GWAS SNPs (GWAS P < 10−5) among QTL SNPs driving variation in gene expression, splicing, histone acetylation, and DNA methylation in primary monocytes15,31,50, T-cells15,31, or DLFPC29.
Enrichment of RNA-binding protein (RBP) binding sites among sQTLs.(a) RBP enrichment (expected vs. observed) among the lead sQTLs. Significant RBSs are in bold and shown with an “*”.(b) Association of hnRNPA2B1 (left) and hnRNPC (right) gene expression levels with differential intron usage in TBC1D7 (left) and in PICALM (right). (c) Regional plot of sQTL results for SNPs in the vicinity of TBC1D7 (6:13306759:13307828). SNPs driving splicing QTLs for TBC1D7 overlap CLIP binding sites (from CLIPdb34) for several splicing factors. The top SNP (rs2439540, red) overlaps motifs for a number of RBPs. Splicing QTL results are highly consistent between ROSMAP (orange) and CommonMind (blue) data.
Transcriptome-wide association study of Alzheimer’s Disease.(a) Transcriptome-wide results using the IGAP GWAS summary statistics; each dot is one gene. The dotted green line denotes the threshold of significance (FDR 0.05). Genes for which there is evidence of significant differential intron usage are highlighted in blue. In green, we highlight those genes where the TWAS using total gene expression results are significant. (b) Replication of ROSMAP TWAS in CMC DLFPC data. The red triangles denote genes where the replication analysis is significant. (c) Replication of IGAP Alzheimer’s disease TWAS using the UK BioBank GWAS based on an independent set of subjects. (d) PTK2B gene structure (top): clusters of differential splicing events are noted with the colored curves. The panel then zooms to highlight differential intronic usage for chr8:27308412–27308560 stratified by rs2251430 genotypes (right). On the left, we show the same data use a box plot. (e) Conditional analysis of IGAP GWAS results for two splicing effects for PTK2B and CLU in Alzheimer’s disease GWAS data. As noted in the top aspect of the panel, these two Alzheimer’s disease genes are located close to one another. The intronic excision events for PTK2B and CLU are present in both ROSMAP (blue) and in CMC (green) dataset. When the Alzheimer’s disease GWAS is conditioned on the PTK2B (chr8:27308412–27308560) splicing effect, the CLU effect remained significant, demonstrating its independence from the PTK2B association. The reciprocal analysis conditioning on the CLU (chr8:27461909:27462441) effect, the PTK2B association remained significant.
TWAS prioritizes Alzheimer’s disease genes in endocytosis and autophagy-related pathway.(a) Differential intronic usage for chr6: 13306759:13307828 (TBC1D7) stratified by rs2439540 genotypes (left). Box plot for the same data (right). (b) Regional plot showing the IGAP P-values in TBC1D7 locus. Two intronic excision events at TBC1D7 are present in both ROSMAP (blue) and in CMC (green) dataset. The Alzheimer’s disease GWAS effect is mostly explained by intronic usage of chr6:13306759:13307828. The AD GWAS at TBC1D7 is suggestive in the original IGAP study (p<10−5). (c) The product of three of the novel Alzheimer’s disease genes (AP2A2, AP2A1, and MAP1B) are members of the same PPI network (P < 0.006). The genes in this network and others not in the network (i.e., TBC1D7, PACS2, and RABEP1) are significantly enriched in genes annotated as being involved in endocytosis (blue; P < 0.0002) and autophagy-related pathways (green; P < 0.003). (d) The novel Alzheimer’s disease genes (AP2A2, AP2A1, and MAP1B) form a significant PPI sub-network (P < 4.3 ×10−4) with known Alzheimer’s disease genes (i.e., PICALM, BIN1, and PTK2B).
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| ABCA7 | gene |
| Affymetrix GeneChip 6.0 platform local | drug |
| age at death | phenotype |
| Agilent 2100 Bioanalyzer | drug |
| aging brains local | cohort |
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| alternative splicing | drug |
| Alzheimer’s cases local | cohort |
| Alzheimer's disease | phenotype |
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| amyloid burden local | phenotype |
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| amyloid-β local | phenotype |
| amyloid-β burden local | phenotype |
| amyotrophic lateral sclerosis | phenotype |
| AP2A1 local | gene |
| AP2A2 local | gene |
| APP | gene |
| autism | phenotype |
| BIN1 | gene |
| Braak stages local | phenotype |
| brain | anatomy |
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| CD33 | gene |
| CD3316 local | cohort |
| cell type proportion local | phenotype |
| chromatin states | drug |
| Clu | gene |
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| CommonMind Consortium | cohort |
| control | cohort |
| controls | cohort |
| cortex | anatomy |
| CPSF7 | gene |
| CR1 | gene |
| differentially excised introns local | variant |
| disease-associated splicing changes local | phenotype |
| DLFPC | anatomy |
| dlPFC | anatomy |
| DNA methylation | drug |
| dorsolateral prefrontal cortex | anatomy |
| EIGENSTRAT | drug |
| ELAVL local | gene |
| ELAVL1 | gene |
| eQTLGen Consortium | cohort |
| European ancestry | cohort |
| false discovery rate (FDR) local | phenotype |
| fastQTL local | drug |
| Frontotemporal lobar dementia local | phenotype |
| frozen post-mortem brain tissue local | anatomy |
| FUS local | gene |
| GTEx | cohort |
| GTEx project | cohort |
| H3K27me3 | drug |
| H3K36me3 | drug |
| H3K4me1 | drug |
| H3K4me3 | drug |
| H3K9me3 | drug |
| Haplotype Reference Consortium | cohort |
| haQTL local | variant |
| histone H3K9Ac local | drug |
| hnRNP local | drug |
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| human brain | anatomy |
| human subjects | cohort |
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| IGAP | cohort |
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| incident Alzheimer’s disease local | phenotype |
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| intronic SNP in TBC1D7 local | variant |
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| intron ratio local | phenotype |
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| late-onset Alzheimer’s disease | phenotype |
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| lead eQTL local | phenotype |
| lead sQTL SNP local | variant |
| lead sQTL SNPs local | variant |
| Leafcutter20 local | drug |
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| MAP local | cohort |
| MAP1B | gene |
| MAP sample local | cohort |
| MAP subject local | phenotype |
| Mapt | gene |
| Memory and Aging Project local | cohort |
| Michigan Imputation Server | drug |
| Mild cognitive impairment | phenotype |
| minimac3 | drug |
| monocytes | cohort |
| Mount Sinai Brain Bank local | cohort |
| mQTL | variant |
| MSBB local | cohort |
| MTCH2 | gene |
| myeloid cells | phenotype |
| Nanodrop | drug |
| NDRG2 local | gene |
| neocortex | anatomy |
| neuritic plaques | phenotype |
| neurofibrillary tangles | phenotype |
| neuronal loss | phenotype |
| neuropathology traits local | phenotype |
| neutrophils local | cohort |
| Non-impaired local | phenotype |
| non-Latino whites local | cohort |
| Parkinson’s disease | phenotype |
| PD | phenotype |
| PFKP local | gene |
| PHKB local | gene |
| phosphorylated tau | drug |
| PICALM | gene |
| Plink | drug |
| Poly-A selection local | drug |
| postmortem interval | phenotype |
| prefrontal cortex | anatomy |
| protein degradation machinery local | drug |
| PTBP1 local | gene |
| PTK2B | gene |
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In this knowledge base
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| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Brain aging mediates region-specific transcriptomic alterations. | Alharbi AB | — | 2026 | → |
| GENEasso: a curated resource of credible disease-gene associations across complex diseases from GWAS summary statistics. | Jiang T et al. | — | 2026 | → |
| Implications of virus-induced stress granules in tauopathies. | Sharma S et al. | — | 2026 | → |
| <i>PICALM</i> Genetic Variant Alters mRNA Expression Without Affecting Protein Levels or Tau Spreading in Alzheimer's Disease. | Ando K et al. | — | 2026 | → |
| Mapping isoforms and regulatory mechanisms from spatial transcriptomics data with SPLISOSM. | Su J et al. | — | 2026 | → |
| Modest rescue of RBFOX1 splicing function attenuates Huntington's disease features. | Lozano-Muñoz D et al. | — | 2026 | → |
| Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures. | Shrestha HK et al. | — | 2026 | → |
| Synaptic pruning genes networks in Alzheimer's disease: correlations with neuropathology and cognitive decline. | Sanfilippo C et al. | — | 2026 | → |
| Translated retained intron 11 sequence confers pathological properties to Tau in Alzheimer's disease. | Tan YY et al. | — | 2026 | → |
| Unveiling the mechanism of abdominal and transcranial ultrasound stimulation against DSS-induced colitis based on proteomic analysis. | Yang FY et al. | — | 2026 | → |
| A computational framework for detecting inter-tissue gene-expression coordination changes with aging. | Briller S et al. | — | 2025 | → |
| Aging-related alternative splicing drive neoantigen emergence revealed by transcriptome analysis of 1,255 human blood samples. | Li S et al. | — | 2025 | → |
| Alternative splicing in Alzheimer's disease: Mechanisms, therapeutic implications, and 3D modeling approaches. | Pasteris M et al. | — | 2025 | → |
| An integrative systems-biology approach defines mechanisms of Alzheimer's disease neurodegeneration. | Leventhal MJ et al. | — | 2025 | → |
| AP2A1 modulates cell states between senescence and rejuvenation. | Chantachotikul P et al. | — | 2025 | → |
| A single-cell, long-read, isoform-resolved case-control study of FTD reveals cell-type-specific and broad splicing dysregulation in human brain. | Belchikov N et al. | — | 2025 | → |
| A spatial long-read approach at near-single-cell resolution reveals developmental regulation of splicing and polyadenylation sites in distinct cortical layers and cell types. | Foord C et al. | — | 2025 | → |
| Assessing the diagnostic impact of blood transcriptome profiling in a pediatric cohort previously assessed by genome sequencing. | Hou H et al. | — | 2025 | → |
| Behavioural pharmacology predicts disrupted signalling pathways and candidate therapeutics from zebrafish mutants of Alzheimer's disease risk genes. | Kroll F et al. | — | 2025 | → |
| Beyond Clathrin: Decoding the Mechanism of Ultrafast Endocytosis. | Imoto Y et al. | — | 2025 | → |
| Cannabinerol Restores mRNA Splicing Defects Induced by β-Amyloid in an In Vitro Model of Alzheimer's Disease: A Transcriptomic Study. | Lui M et al. | — | 2025 | → |
| Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox. | Schertzer MD et al. | — | 2025 | → |
| Combined single-cell profiling of chromatin-transcriptome and splicing across brain cell types, regions and disease state. | Hu W et al. | — | 2025 | → |
| Comprehensive characterization of the transcriptional landscape in Alzheimer's disease (AD) brains. | Chen C et al. | — | 2025 | → |
| Computational and functional prioritization identifies genes that rescue behavior and reduce tau protein in fly and human cell models of Alzheimer disease. | Stephens MC et al. | — | 2025 | → |
| Dysregulation of Inositol Polyphosphate 5-Phosphatase OCRL in Alzheimer's Disease: Implications for Autophagy Dysfunction. | Ando K et al. | — | 2025 | → |
| Extracellular RNAs as Messengers and Early Biomarkers in Neurodegeneration. | Lu K et al. | — | 2025 | → |
| From variants to mechanisms: Neurogenomics in the post-GWAS era. | Margolis MP et al. | — | 2025 | → |
| Genetic regulation of nascent RNA maturation revealed by direct RNA nanopore sequencing. | Choquet K et al. | — | 2025 | → |
| Higher Intron Retention Levels in Female Alzheimer's Brains May Be Linked to Disease Prevalence. | Choo CT et al. | — | 2025 | → |
| Identifying Alzheimer's disease-related pathways based on whole-genome sequencing data. | Wang Y et al. | — | 2025 | → |
| Impact of Confounding Factors in Human Postmortem Brain Tissues on Gene Expression Profiles: A Comparison of Patients With Schizophrenia, Bipolar Disorder, and Nonpsychiatric Controls. | Hatano M et al. | — | 2025 | → |
| Integrating Iso-seq and RNA-seq data for the reannotation of the killifish telencephalon transcriptome. | Ayana R et al. | — | 2025 | → |
| Integrating whole genome and transcriptome sequencing to characterize the genetic architecture of isoform variation. | Liu C et al. | — | 2025 | → |
| Integrative Approaches Identify Genetic Determinants of Levodopa Induced Dyskinesia. | Wan Y et al. | — | 2025 | → |
| Long-read RNA-seq demarcates cis- and trans-directed alternative RNA splicing. | Quinones-Valdez G et al. | — | 2025 | → |
| Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing. | Humphrey J et al. | — | 2025 | → |
| Long-read RNA-sequencing reveals transcript-specific regulation in human-derived cortical neurons. | Xu J et al. | — | 2025 | → |
| Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits. | Okamoto J et al. | — | 2025 | → |
| Multi Layered Omics Approaches Reveal Glia Specific Alterations in Alzheimer's Disease: A Systematic Review and Future Prospects. | İş Ö et al. | — | 2025 | → |
| NAD<sup>+</sup> reverses Alzheimer's neurological deficits via regulating differential alternative RNA splicing of <i>EVA1C</i>. | Ai R et al. | — | 2025 | → |
| Nuclear ribonucleoprotein condensates as platforms for gene expression regulation. | Choi S et al. | — | 2025 | → |
| Organelle abnormalities in Alzheimer's disease. | Gao J et al. | — | 2025 | → |
| Phosphorylated-tau associates with HSV-1 chromatin and correlates with nuclear speckles decondensation in low-density host chromatin regions. | D'Aiuto L et al. | — | 2025 | → |
| PICALM Alzheimer's risk allele causes aberrant lipid droplets in microglia. | Kozlova A et al. | — | 2025 | → |
| Post-Transcriptional Regulation of Gene Expression and the Intricate Life of Eukaryotic mRNAs. | Lancaster CL et al. | — | 2025 | → |
| Profiling RNA Cargo in Extracellular Vesicles From hiPSC-Derived Neurons of Alzheimer's Disease Patients. | Sagar R et al. | — | 2025 | → |
| Proteostasis and lysosomal repair deficits in transdifferentiated neurons of Alzheimer's disease. | Chou CC et al. | — | 2025 | → |
| Revolutionizing multi-omics analysis with artificial intelligence and data processing. | Yetgin A | — | 2025 | → |
| Systematic review and meta-analysis of bulk RNAseq studies in human Alzheimer's disease brain tissue. | Aguzzoli Heberle B et al. | — | 2025 | → |
| Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits. | Yan Y et al. | — | 2025 | → |
| Transcriptome-wide alternative splicing and transcript-level differential expression analysis of post-mortem Lewy body dementia brains. | Goddard TR et al. | — | 2025 | → |
| Translational Remodeling of the Synaptic Proteome During Aging. | Caterino C et al. | — | 2025 | → |
| Whole-genome sequencing reveals the impact of lipid pathway and APOE genotype on brain amyloidosis. | Patel M et al. | — | 2025 | → |
| A clathrin mediated endocytosis scaffolding protein, Intersectin 1, changes in an isoform, brain region, and sex specific manner in Alzheimer's disease. | Jaye S et al. | — | 2024 | → |
| Alternative splicing in prostate cancer progression and therapeutic resistance. | Rawat C et al. | — | 2024 | → |
| A multi-omics study of brain tissue transcription and DNA methylation revealing the genetic pathogenesis of ADHD. | Wang J et al. | — | 2024 | → |
| APOER2 splicing repertoire in Alzheimer's disease: Insights from long-read RNA sequencing. | Gallo CM et al. | — | 2024 | → |
| A review and analysis of key biomarkers in Alzheimer's disease. | Zhang Z et al. | — | 2024 | → |
| Clathrin mediated endocytosis in Alzheimer's disease: cell type specific involvement in amyloid beta pathology. | Jaye S et al. | — | 2024 | → |
| Codes between Poles: Linking Transcriptomic Insights into the Neurobiology of Bipolar Disorder. | Garcia JPT et al. | — | 2024 | → |
| Comprehensive transcriptome analysis reveals altered mRNA splicing and post-transcriptional changes in the aged mouse brain. | Kumar NH et al. | — | 2024 | → |
| Cryptic splicing of stathmin-2 and UNC13A mRNAs is a pathological hallmark of TDP-43-associated Alzheimer's disease. | Agra Almeida Quadros AR et al. | — | 2024 | → |
| Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms. | Patowary A et al. | — | 2024 | → |
| Emerging role of senescent microglia in brain aging-related neurodegenerative diseases. | Rim C et al. | — | 2024 | → |
| Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes. | Bao J et al. | — | 2024 | → |
| From genetic associations to genes: methods, applications, and challenges. | Qi T et al. | — | 2024 | → |
| Genetic regulation of nascent RNA maturation revealed by direct RNA nanopore sequencing | Choquet K et al. | — | 2024 | — |
| Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes. | Farrell K et al. | — | 2024 | → |
| Genome-wide CRISPR screen identifies neddylation as a regulator of neuronal aging and AD neurodegeneration. | Saurat N et al. | — | 2024 | → |
| Integrative Proteogenomics for Differential Expression and Splicing Variation in a DM1 Mouse Model. | Solovyeva EM et al. | — | 2024 | → |
| Large-scale deep proteomic analysis in Alzheimer's disease brain regions across race and ethnicity. | Seifar F et al. | — | 2024 | → |
| Long-read transcript sequencing identifies differential isoform expression in the entorhinal cortex in a transgenic model of tau pathology. | Leung SK et al. | — | 2024 | → |
| Loss of DNA glycosylases improves health and cognitive function in a C. elegans model of human tauopathy. | Tiwari V et al. | — | 2024 | → |
| Metabolomic and Proteomic Analysis of ApoE4-Carrying H4 Neuroglioma Cells in Alzheimer's Disease Using OrbiSIMS and LC-MS/MS. | Lu L et al. | — | 2024 | → |
| Microglia contribute to polyG-dependent neurodegeneration in neuronal intranuclear inclusion disease. | Zhong S et al. | — | 2024 | → |
| Multilayer Analysis of RNA Sequencing Data in Alzheimer's Disease to Unravel Molecular Mysteries. | Uzuner D et al. | — | 2024 | → |
| Multiome-wide Association Studies: Novel Approaches for Understanding Diseases. | Shao M et al. | — | 2024 | → |
| Population-level exploration of alternative splicing and its unique role in controlling agronomic traits of rice. | Zhang H et al. | — | 2024 | → |
| Profiling genetically driven alternative splicing across the Indonesian archipelago. | Ibeh N et al. | — | 2024 | → |
| Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry. | Shrestha HK et al. | — | 2024 | → |
| Psychosocial experiences are associated with human brain mitochondrial biology. | Trumpff C et al. | — | 2024 | → |
| Tau, RNA, and RNA-Binding Proteins: Complex Interactions in Health and Neurodegenerative Diseases. | Lester E et al. | — | 2024 | → |
| The ABC's of Alzheimer risk gene ABCA7. | Duchateau L et al. | — | 2024 | → |
| The broken Alzheimer's disease genome. | Gouveia Roque C et al. | — | 2024 | → |
| The clinical utility and diagnostic implementation of human subject cell transdifferentiation followed by RNA sequencing. | Li S et al. | — | 2024 | → |
| Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches. | Firdaus Z et al. | — | 2024 | → |
| ZCCHC17 Modulates Neuronal RNA Splicing and Supports Cognitive Resilience in Alzheimer's Disease. | Bartosch AMW et al. | — | 2024 | → |
| Aberrant splicing of mutant huntingtin in Huntington's disease knock-in pigs. | Tong H et al. | — | 2023 | → |
| Alternative polyadenylation transcriptome-wide association study identifies APA-linked susceptibility genes in brain disorders. | Cui Y et al. | — | 2023 | → |
| Alternative splicing in mouse brains affected by psychological stress is enriched in the signaling, neural transmission and blood-brain barrier pathways. | Wang F et al. | — | 2023 | → |
| Alternative splicing in neurodegenerative disease and the promise of RNA therapies. | Nikom D et al. | — | 2023 | → |
| Alzheimer's disease pathogenetic progression is associated with changes in regulated retained introns and editing of circular RNAs. | Arizaca Maquera KA et al. | — | 2023 | → |
| Analyzing alternative splicing in Alzheimer's disease postmortem brain: a cell-level perspective. | Farhadieh ME et al. | — | 2023 | → |
| Anti-malaria drug artesunate prevents development of amyloid-β pathology in mice by upregulating PICALM at the blood-brain barrier. | Kisler K et al. | — | 2023 | → |
| A post-transcriptional regulatory landscape of aging in the female mouse hippocampus. | Winsky-Sommerer R et al. | — | 2023 | → |
| A Splicing Transcriptome-Wide Association Study Identifies Candidate Altered Splicing for Prostate Cancer Risk. | Sun Y et al. | — | 2023 | → |
| Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. | de Klein N et al. | — | 2023 | → |
| Clusterin/apolipoprotein J, its isoforms and Alzheimer's disease. | Milinkeviciute G et al. | — | 2023 | → |
| Comprehensive transcript-level analysis reveals transcriptional reprogramming during the progression of Alzheimer's disease. | Wu H et al. | — | 2023 | → |
| Copy number variation as a tool for implementing pregnancy as an aging model. | Andrawus M et al. | — | 2023 | → |
| Cytosolic condensates rich in polyserine define subcellular sites of tau aggregation. | Lester E et al. | — | 2023 | → |
| Differential splicing of neuronal genes in a Trem2*R47H mouse model mimics alterations associated with Alzheimer's disease. | Pandey RS et al. | — | 2023 | → |
| Dissecting Detergent-Insoluble Proteome in Alzheimer's Disease by TMTc-Corrected Quantitative Mass Spectrometry. | Zaman M et al. | — | 2023 | → |
| Enrichment of novel Tau isoform with altered biochemical properties in Alzheimer's disease. | Ong CT | — | 2023 | → |
| Farnesyl diphosphate synthase promotes cell proliferation by regulating gene expression and alternative splicing profiles in HeLa cells. | Wang L et al. | — | 2023 | → |
| Fine-mapping and replication of EWAS loci harboring putative epigenetic alterations associated with AD neuropathology in a large collection of human brain tissue samples. | Palma-Gudiel H et al. | — | 2023 | → |
| Genetic insights into immune mechanisms of Alzheimer's and Parkinson's disease. | Nott A et al. | — | 2023 | → |
| Genetic Regulation of SMC Gene Expression and Splicing Predict Causal CAD Genes. | Aherrahrou R et al. | — | 2023 | → |
| Genome- and transcriptome-wide splicing associations with alcohol use disorder. | Huggett SB et al. | — | 2023 | → |
| Genome-Wide Association Analysis across Endophenotypes in Alzheimer's Disease: Main Effects and Disease Stage-Specific Interactions. | Rosewood TJ et al. | — | 2023 | → |
| Genome-Wide Splicing Quantitative Expression Locus Analysis Identifies Causal Risk Variants for Non-Small Cell Lung Cancer. | Jin M et al. | — | 2023 | → |
| Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data. | Sun Y et al. | — | 2023 | → |
| Inhibition of NLRP1 inflammasome improves autophagy dysfunction and Aβ disposition in APP/PS1 mice. | Li X et al. | — | 2023 | → |
| Integrated DNA Methylation/RNA Profiling in Middle Temporal Gyrus of Alzheimer's Disease. | Piras IS et al. | — | 2023 | → |
| Integration of transcriptome-wide association study with neuronal dysfunction assays provides functional genomics evidence for Parkinson's disease genes. | Li J et al. | — | 2023 | → |
| Integrative splicing-quantitative-trait-locus analysis reveals risk loci for non-small-cell lung cancer. | Wang Y et al. | — | 2023 | → |
| Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease. | Gonçalves RL et al. | — | 2023 | → |
| <i>Schizosaccharomyces pombe</i> Rtf2 is important for replication fork barrier activity of <i>RTS1</i> via splicing of <i>Rtf1</i>. | Budden AM et al. | — | 2023 | → |
| Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective. | Zhu Z et al. | — | 2023 | → |
| Moving beyond amyloid and tau to capture the biological heterogeneity of Alzheimer's disease. | Young-Pearse TL et al. | — | 2023 | → |
| mRNA isoform balance in neuronal development and disease. | LaForce GR et al. | — | 2023 | → |
| OTTERS: a powerful TWAS framework leveraging summary-level reference data. | Dai Q et al. | — | 2023 | → |
| Patterns of Gene Expression, Splicing, and Allele-Specific Expression Vary among Macular Tissues and Clinical Stages of Age-Related Macular Degeneration. | Shwani T et al. | — | 2023 | → |
| RNA alternative splicing impacts the risk for alcohol use disorder. | Li R et al. | — | 2023 | → |
| RNA binding proteins in senescence: A potential common linker for age-related diseases? | Varesi A et al. | — | 2023 | → |
| Single-nucleus RNA-sequencing of autosomal dominant Alzheimer disease and risk variant carriers. | Brase L et al. | — | 2023 | → |
| The landscape of expression and alternative splicing variation across human traits. | García-Pérez R et al. | — | 2023 | → |
| The Q/R editing site of AMPA receptor GluA2 subunit acts as an epigenetic switch regulating dendritic spines, neurodegeneration and cognitive deficits in Alzheimer's disease. | Wright AL et al. | — | 2023 | → |
| Tip60's Novel RNA-Binding Function Modulates Alternative Splicing of Pre-mRNA Targets Implicated in Alzheimer's Disease. | Bhatnagar A et al. | — | 2023 | → |
| Unveiling the Molecular Footprint: Proteome-Based Biomarkers for Alzheimer's Disease. | Jain M et al. | — | 2023 | → |
| Widespread dysregulation of mRNA splicing implicates RNA processing in the development and progression of Huntington's disease. | Tano V et al. | — | 2023 | → |
| Aberrant RNA Splicing Is a Primary Link between Genetic Variation and Pancreatic Cancer Risk. | Tian J et al. | — | 2022 | → |
| Accounting for nonlinear effects of gene expression identifies additional associated genes in transcriptome-wide association studies. | Lin Z et al. | — | 2022 | → |
| Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer's disease-associated genetic variants. | He L et al. | — | 2022 | → |
| Alpha adaptins show isoform-specific association with neurofibrillary tangles in Alzheimer's disease. | Srinivasan S et al. | — | 2022 | → |
| Alzheimer's disease-associated U1 snRNP splicing dysfunction causes neuronal hyperexcitability and cognitive impairment. | Chen PC et al. | — | 2022 | → |
| Analysis of modular gene co-expression networks reveals molecular pathways underlying Alzheimer's disease and progressive supranuclear palsy. | Iohan LDCC et al. | — | 2022 | → |
| An Alzheimer's Disease Patient-Derived Olfactory Stem Cell Model Identifies Gene Expression Changes Associated with Cognition. | Rantanen LM et al. | — | 2022 | → |
| An Introduction to the Special Issue "Protein Glycation in Food, Nutrition, Health and Disease". | Rabbani N et al. | — | 2022 | → |
| Bioinformatics detection of modulators controlling splicing factor-dependent intron retention in the human brain. | Chen SX et al. | — | 2022 | → |
| Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease. | Castaldi PJ et al. | — | 2022 | → |
| Emerging Glycation-Based Therapeutics-Glyoxalase 1 Inducers and Glyoxalase 1 Inhibitors. | Rabbani N et al. | — | 2022 | → |
| Fine-mapping of Parkinson's disease susceptibility loci identifies putative causal variants. | Schilder BM et al. | — | 2022 | → |
| Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. | Lopes KP et al. | — | 2022 | → |
| Genetic control of RNA splicing and its distinct role in complex trait variation. | Qi T et al. | — | 2022 | → |
| Impaired type I interferon signaling activity implicated in the peripheral blood transcriptome of preclinical Alzheimer's disease. | Song L et al. | — | 2022 | → |
| Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder. | Wingo TS et al. | — | 2022 | → |
| Integrating whole-genome sequencing with multi-omic data reveals the impact of structural variants on gene regulation in the human brain. | Vialle RA et al. | — | 2022 | → |
| Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery. | Ji Y et al. | — | 2022 | → |
| Microexon alternative splicing of small GTPase regulators: Implication in central nervous system diseases. | Lee JS et al. | — | 2022 | → |
| Mitochondrial respiratory chain protein co-regulation in the human brain. | Trumpff C et al. | — | 2022 | → |
| Molecular Quantitative Trait Locus Mapping in Human Complex Diseases. | Olayinka OA et al. | — | 2022 | → |
| Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms. | Schilder BM et al. | — | 2022 | → |
| Neuronal Rubicon Represses Extracellular APP/Amyloid β Deposition in Alzheimer's Disease. | Espinoza S et al. | — | 2022 | → |
| Omics-based biomarkers discovery for Alzheimer's disease. | Aerqin Q et al. | — | 2022 | → |
| Overlap between Central and Peripheral Transcriptomes in Parkinson's Disease but Not Alzheimer's Disease. | Hooshmand K et al. | — | 2022 | → |
| PICALM and Alzheimer's Disease: An Update and Perspectives. | Ando K et al. | — | 2022 | → |
| Poly(A) RNA sequencing reveals age-related differences in the prefrontal cortex of dogs. | Sándor S et al. | — | 2022 | → |
| Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes. | Bykova M et al. | — | 2022 | → |
| RETRACTED: Parallel bimodal single-cell sequencing of transcriptome and methylome provides molecular and translational insights on oocyte maturation and maternal aging. | Zhang FL et al. | — | 2022 | → |
| RNA splicing regulators play critical roles in neurogenesis. | Fisher E et al. | — | 2022 | → |
| SNP-by-CpG Site Interactions in <i>ABCA7</i> Are Associated with Cognition in Older African Americans. | Chaar DL et al. | — | 2022 | → |
| Subcutaneous adipose tissue splice quantitative trait loci reveal differences in isoform usage associated with cardiometabolic traits. | Brotman SM et al. | — | 2022 | → |
| SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. | Zhang Z et al. | — | 2022 | → |
| The different autophagy degradation pathways and neurodegeneration. | Fleming A et al. | — | 2022 | → |
| The three-dimensional landscape of cortical chromatin accessibility in Alzheimer's disease. | Bendl J et al. | — | 2022 | → |
| Transcriptome-wide association study identifies multiple genes and pathways associated with thyroid function. | Ke X et al. | — | 2022 | → |
| Truncated Tau caused by intron retention is enriched in Alzheimer's disease cortex and exhibits altered biochemical properties. | Ngian ZK et al. | — | 2022 | → |
| Weakly activated core neuroinflammation pathways were identified as a central signaling mechanism contributing to the chronic neurodegeneration in Alzheimer's disease. | Li F et al. | — | 2022 | → |
| A gene-level methylome-wide association analysis identifies novel Alzheimer's disease genes. | Wu C et al. | — | 2021 | → |
| Aggregating multiple expression prediction models improves the power of transcriptome-wide association studies. | Zeng P et al. | — | 2021 | → |
| Alternative splicing: Human disease and quantitative analysis from high-throughput sequencing. | Jiang W et al. | — | 2021 | → |
| Alternative splicing in aging and Alzheimer's disease: Highlighting the role of tau and estrogen receptor α isoforms in the hypothalamus. | Ishunina TA | — | 2021 | → |
| Alternative splicing in Alzheimer's disease. | Biamonti G et al. | — | 2021 | → |
| Alternative Splicing Mechanisms Underlying Opioid-Induced Hyperalgesia. | Zhang P et al. | — | 2021 | → |
| A new non-aggregative splicing isoform of human Tau is decreased in Alzheimer's disease. | García-Escudero V et al. | — | 2021 | → |
| Atlas of RNA editing events affecting protein expression in aged and Alzheimer's disease human brain tissue. | Ma Y et al. | — | 2021 | → |
| A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk. | Sun Y et al. | — | 2021 | → |
| A transcriptome-wide association study of Alzheimer's disease using prediction models of relevant tissues identifies novel candidate susceptibility genes. | Sun Y et al. | — | 2021 | → |
| Beyond association: successes and challenges in linking non-coding genetic variation to functional consequences that modulate Alzheimer's disease risk. | Novikova G et al. | — | 2021 | → |
| Brain proteome-wide association study implicates novel proteins in depression pathogenesis. | Wingo TS et al. | — | 2021 | → |
| Brain-Specific Gene Expression and Quantitative Traits Association Analysis for Mild Cognitive Impairment. | Yuan SX et al. | — | 2021 | → |
| Consideration of Gut Microbiome in Murine Models of Diseases. | Zhang C et al. | — | 2021 | → |
| Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases. | Feleke R et al. | — | 2021 | → |
| DEF8 and Autophagy-Associated Genes Are Altered in Mild Cognitive Impairment, Probable Alzheimer's Disease Patients, and a Transgenic Model of the Disease. | Leyton E et al. | — | 2021 | → |
| Differential transcript usage unravels gene expression alterations in Alzheimer's disease human brains. | Marques-Coelho D et al. | — | 2021 | → |
| Do Fragile X Syndrome and Other Intellectual Disorders Converge at Aberrant Pre-mRNA Splicing? | Shah S et al. | — | 2021 | → |
| Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease. | Leo L et al. | — | 2021 | → |
| Exploring Common Therapeutic Targets for Neurodegenerative Disorders Using Transcriptome Study. | Dharshini SAP et al. | — | 2021 | → |
| Expression Profiling of Rectal Biopsies Suggests Altered Enteric Neuropathological Traits in Parkinson's Disease Patients. | Cossais F et al. | — | 2021 | → |
| Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing. | Leung SK et al. | — | 2021 | → |
| Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes. | Schwartzentruber J et al. | — | 2021 | → |
| Genomics of Alzheimer's disease implicates the innate and adaptive immune systems. | Li Y et al. | — | 2021 | → |
| Global Profiling of Lysine Accessibility to Evaluate Protein Structure Changes in Alzheimer's Disease. | Yu K et al. | — | 2021 | → |
| G-quadruplexes originating from evolutionary conserved L1 elements interfere with neuronal gene expression in Alzheimer's disease. | Hanna R et al. | — | 2021 | → |
| Hippocampal transcriptome-wide association study and neurobiological pathway analysis for Alzheimer's disease. | Liu N et al. | — | 2021 | → |
| Huntington's disease-specific mis-splicing unveils key effector genes and altered splicing factors. | Elorza A et al. | — | 2021 | → |
| Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome. | Garrido-Martín D et al. | — | 2021 | → |
| Inactive rhomboid proteins RHBDF1 and RHBDF2 (iRhoms): a decade of research in murine models. | Burzenski LM et al. | — | 2021 | → |
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| Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer's disease pathogenesis. | Wingo AP et al. | — | 2021 | → |
| Integration of Alzheimer's disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. | Novikova G et al. | — | 2021 | → |
| Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease. | Volpato V | — | 2021 | → |
| Isoform cell-type specificity in the mouse primary motor cortex. | Booeshaghi AS et al. | — | 2021 | → |
| Machine learning identifies candidates for drug repurposing in Alzheimer's disease. | Rodriguez S et al. | — | 2021 | → |
| Meta-Analyses of Splicing and Expression Quantitative Trait Loci Identified Susceptibility Genes of Glioma. | Patro CPK et al. | — | 2021 | → |
| Mitochondrial Permeability Transition: A Pore Intertwines Brain Aging and Alzheimer's Disease. | Jia K et al. | — | 2021 | → |
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