Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes

Genome-wide association studies (GWAS) have identified more than 40 loci associated with Alzheimer’s disease (AD), but the causal variants, regulatory elements, genes and pathways remain largely unknown, impeding a mechanistic understanding of AD pathogenesis. Previously, we showed that AD risk alleles are enriched in myeloid-specific epigenomic annotations. Here, we show that they are specifically enriched in active enhancers of monocytes, macrophages and microglia. We integrated AD GWAS with myeloid epigenomic and transcriptomic datasets using analytical approaches to link myeloid enhancer activity to target gene expression regulation and AD risk modification. We identify AD risk enhancers and nominate candidate causal genes among their likely targets (including AP4E1, AP4M1, APBB3, BIN1, MS4A4A, MS4A6A, PILRA, RABEP1, SPI1, TP53INP1, and ZYX) in twenty loci. Fine-mapping of these enhancers nominates candidate functional variants that likely modify AD risk by regulating gene expression in myeloid cells. In the MS4A locus we identified a single candidate functional variant and validated it in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Taken together, this study integrates AD GWAS with multiple myeloid genomic datasets to investigate the mechanisms of AD risk alleles and nominates candidate functional variants, regulatory elements and genes that likely modulate disease susceptibility

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction

Abstract Background Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. Method To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype–phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. Results We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer’s disease). Conclusion We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use

The relationship between dopamine receptor D1 and cognitive performance.

BackgroundCognitive impairment cuts across traditional diagnostic boundaries and is one of the most typical symptoms in various psychiatric and neurobiological disorders.AimsThe objective of this study was to examine the genetic association between 94 candidate genes, including receptors and enzymes that participate in neurotransmission, with measures of cognition.MethodsThe Clinical Dementia Rating (CDR), a global measure of cognition, and genotypes derived from a custom array of 1,536 single-nucleotide polymorphisms (SNPs) in 94 genes were available for a large postmortem cohort of Caucasian cases with Alzheimer's disease (AD), schizophrenia and controls (n=727). A cohort of healthy young males (n=1,493) originating from the LOGOS project (Learning On Genetics Of Schizophrenia Spectrum) profiled across multiple cognitive domains was available for targeted SNP genotyping. Gene expression was quantified in the superior temporal gyrus of control samples (n=109). The regulatory effect on transcriptional activity was assessed using the luciferase reporter system.ResultsThe rs5326-A allele at the promoter region of dopamine receptor D1 (DRD1) locus was associated with: (i) poorer cognition (higher CDR) in the postmortem cohort (P=9.325×10(-4)); (ii) worse cognitive performance relevant to strategic planning in the LOGOS cohort (P=0.008); (iii) lower DRD1 gene expression in the superior temporal gyrus of controls (P=0.038); and (iv) decreased transcriptional activity in human neuroblastoma (SH-SY5Y) cells (P=0.026).ConclusionsAn interdisciplinary approach combining genetics with cognitive and molecular neuroscience provided a possible mechanistic link among DRD1 and alterations in cognitive performance

Transcriptomic profiling of prefrontal cortex reveals shared mechanisms underlying Alzheimer’s and Parkinson’s Diseases

Abstract Background Alzheimer’s disease (AD) and Parkinson’s disease (PD) have overlapping characteristics in terms of symptoms and brain pathology. Understanding the similarities in these diseases could lead to more effective treatments. While there is only limited evidence for genetic overlap between the two, enrichment of heritability in genetic regions that are related to microglia has been found in both diseases. However, most of the transcriptomics studies published on AD and PD employed postmortem brains, which obscures the identification of alteration of gene expression within individual cell types. Method Here we used single‐nucleus transcriptomics to examine over eight million nuclei generated from prefrontal cortex samples of over a thousand postmortem donors with AD, PD, both diseases and controls. Then, we built gene regulatory networks (GRNs) to detect the shared gene expression alterations in all major cell types and detected key drivers within each module. Result Examining multiple levels of transcriptomic organization, such as gene expression and GRNs for both protein‐coding and noncoding genes, provided a comprehensive understanding of the molecular pathology of AD and PD. Our analysis revealed cell‐specific modules linked to shared and distinct mechanisms of both diseases. Network‐derived signatures showed cell‐type specificity, implicating regulatory regions within AD and PD genetic risk loci that participate in a variety of biological pathways as well as changes in TF regulation. Conclusion The integration of transcriptomic and genetic data from AD and PD provides a quantitative, genome‐wide resource for mechanistic insight and potential therapeutic development. Funding : U01NS125580, R01AG067025, AARF‐21‐72220

Multi-omic atlas of the parahippocampal gyrus in Alzheimer’s disease

Abstract Alzheimer’s disease (AD) is the most common form of dementia worldwide, with a projection of 151 million cases by 2050. Previous genetic studies have identified three main genes associated with early-onset familial Alzheimer’s disease, however this subtype accounts for less than 5% of total cases. Next-generation sequencing has been well established and holds great promise to assist in the development of novel therapeutics as well as biomarkers to prevent or slow the progression of this devastating disease. Here we present a public resource of functional genomic data from the parahippocampal gyrus of 201 postmortem control, mild cognitively impaired (MCI) and AD individuals from the Mount Sinai brain bank, of which whole-genome sequencing (WGS), and bulk RNA sequencing (RNA-seq) were previously published. The genomic data include bulk proteomics and DNA methylation, as well as cell-type-specific RNA-seq and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) data. We have performed extensive preprocessing and quality control, allowing the research community to access and utilize this public resource available on the Synapse platform at https://doi.org/10.7303/syn51180043.2

The stability of the myelinating oligodendrocyte transcriptome is regulated by the nuclear lamina

Summary: Oligodendrocytes are specialized cells that insulate and support axons with their myelin membrane, allowing proper brain function. Here, we identify lamin A/C (LMNA/C) as essential for transcriptional and functional stability of myelinating oligodendrocytes. We show that LMNA/C levels increase with differentiation of progenitors and that loss of Lmna in differentiated oligodendrocytes profoundly alters their chromatin accessibility and transcriptional signature. Lmna deletion in myelinating glia is compatible with normal developmental myelination. However, altered chromatin accessibility is detected in fully differentiated oligodendrocytes together with increased expression of progenitor genes and decreased levels of lipid-related transcription factors and inner mitochondrial membrane transcripts. These changes are accompanied by altered brain metabolism, lower levels of myelin-related lipids, and altered mitochondrial structure in oligodendrocytes, thereby resulting in myelin thinning and the development of a progressively worsening motor phenotype. Overall, our data identify LMNA/C as essential for maintaining the transcriptional and functional stability of myelinating oligodendrocytes