Learning the Regulatory Code of Alzheimer's Disease Genomes

学习阿尔茨海默病基因组的调控密码

基本信息

批准号：
10471969
负责人：
David Arthur Knowles
金额：
$ 113.32万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10471969
关键词：
ATAC-seq Address Affect Aging Alzheimer&apos s Disease Alzheimer&apos s disease brain Alzheimer&apos s disease patient Alzheimer&apos s disease risk Amyloid beta-Protein Architecture Autopsy Base Sequence Biological Brain Cell physiology Cells ChIP-seq Chromatin Clinical Data Code Computer Models DNA DNA Sequence Data Data Collection Data Set Development Disease Family Frequencies Future Gene Expression Genes Genetic Genetic Risk Genome Genomics Genotype-Tissue Expression Project Goals Health Care Costs Histones Human Genetics Immune Individual Investments Learning Life Link Linkage Disequilibrium Machine Learning Maps Medical Meta-Analysis Methods Microglia Modality Modeling Molecular Multiomic Data Mutagenesis Neighborhoods Neurodegenerative Disorders Nucleic Acid Regulatory Sequences Pathogenesis Pathway interactions Peripheral Personal Satisfaction Persons Population Post-Transcriptional Regulation Quantitative Trait Loci RNA RNA Processing RNA Splicing Regulation Research Signal Transduction Single Nucleotide Polymorphism Statistical Models Susceptibility Gene Techniques Testing Therapeutic Studies Training Untranslated RNA Vacuum Variant Work abeta accumulation case control causal variant cell type deep learning deep learning model diverse data endophenotype epigenomics exome sequencing frontal lobe functional genomics gene network gene regulatory network genetic analysis genetic architecture genetic variant genome sequencing genome wide association study genome-wide genomic data in silico insertion/deletion mutation insight large scale data mRNA Expression machine learning algorithm machine learning method molecular phenotype monocyte multi-ethnic new therapeutic target novel novel diagnostics novel therapeutic intervention novel therapeutics protective allele protein aggregation rare variant risk variant side effect single-cell RNA sequencing therapeutic development therapeutic target tool trait transcriptome transcriptome sequencing transcriptomics whole genome

项目摘要

With ageing populations world-wide, neurodegenerative diseases are placing an ever increasing burden on long- term well-being, healthcare costs and family life. Despite decades of research and enormous investment, no disease-modifying treatment is available for the most common of these diseases: Alzheimer’s (AD). The majority of these, to-date unsuccessful, efforts have focused on one potential cause of AD: amyloid-β aggregation. Combining population-scale data collection, human genetics and machine learning provides a way forward to uncover and characterize new causal cellular processes involved in AD. This would provide an array of potential therapeutic targets, increasing the chance that one will be more easily modulated than the amyloid-β pathway. AD-specific genomic datasets of unprecedented scale are being actively collected: whole genome sequencing (WGS) from ~20k individuals, gene expression (RNA-seq) and epigenomics (ATAC-seq, histone ChIP-seq) from >1000 post-mortem AD brains, single-cell transcriptomes and similar modalities in peripheral and brain-resident innate immune cells (which we and others have shown to be AD-relevant). Effectively integrating these diverse data to better understand AD represents a substantial computational challenge, both in terms of data scale and analysis complexity. This proposal leverages state-of-the-art deep learning (DL) and machine learning (ML), combined with human genetic analyses, to address this challenge. We will train DL models to predict epigenomic signals and RNA splicing from genomic sequence, enabling in silico mutagenesis to estimate the functional impact (a “delta score”) of any genetic variant. The delta scores will be used in genetic analyses that distinguish causal associations: cellular changes that drive AD pathogenesis rather than downstream/side effects of disease. Delta scores will aid in associating both rare and common variants to AD. To achieve sufficient power, rare variants must be aggregated (e.g. for a gene): delta scores will allow filtering out many likely non-functional (particularly non-coding) variants. Most common variants from AD Genome Wide Association Studies (GWAS) are simply correlated with the causal variant due to linkage disequilibrium (LD). Delta scores, combined with trans-ethnic GWAS, will enable estimation of the likely causal variant(s). These analyses will highlight variants and genes involved in AD. However, genes do not operate in a vacuum so robust probabilistic ML will be used to learn cell-type and disease-specific gene regulatory networks from sorted bulk and single-cell RNA-seq. The detected networks will be integrated with our genetic findings to discover network neighborhoods/pathways especially enriched in AD variants. Such pathways will be prime candidates for future functional and therapeutic studies of AD.