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Integrated target discovery in Alzheimer's disease

阿尔茨海默病的综合靶标发现

基本信息

批准号：
9285327
负责人：
Christopher David Brown
金额：
$ 294.58万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9285327
关键词：
ATAC-seq Address Affect Age Alleles Alzheimer&apos s Disease Alzheimer&apos s disease risk American Basic Science Biological Assay Brain Brain region Chromatin Code Collection Complex Data Dimensions Discipline Disease Environment Epigenetic Process Etiology Future Gene Expression Gene Targeting Genes Genetic Genome engineering Genotype-Tissue Expression Project Haplotypes Hereditary Disease Heritability Link Linkage Disequilibrium Maps Molecular Molecular Conformation Neurodegenerative Disorders Neurons Outcome Pathway interactions Pharmaceutical Preparations Population Prevalence Prevention Proteins Protocols documentation Quantitative Trait Loci Randomized Regulatory Element Reporter Risk Sampling Statistical Methods Statistical Models Tissues Translating Untranslated RNA Update Validation Variant analytical tool base brain tissue disorder risk experimental study follow-up genetic association genetic variant genome wide association study improved risk variant therapeutic development trait translational impact

项目摘要

Project Summary Alzheimers disease (AD) is a highly heritable, progressive neurodegenerative disorder. However, the genetic basis of AD is complex and the molecular basis of disease risk remains poorly understood. As a result, AD is essentially untreatable. Available drugs for AD are only marginally effective. AD risk increases exponentially with age with a prevalence of 3 5% by 65 69 years increasing to 30 40% by 85 89 years. A speciﬁc promise of the genome-wide association study (GWAS) era was that genetic associations would translate into improved disease prediction, prevention, and therapeutic development, but we have not seen this promise fulﬁlled. While GWAS have rapidly and reproducibly identiﬁed genetic loci associated with complex diseases such as AD, critical limitations restrict their translational impact. The vast majority of identiﬁed disease risk loci are not within protein- coding genes, but rather lie within cis-regulatory elements (CREs), many of which are tissue speciﬁc. This suggests that most complex disease risk variants modify the function of a CRE, which impacts gene expression, which, in turn, affect disease risk. This has impeded our understanding of complex disease mechanisms because it is typically difﬁcult to identify the relevant genes and pathways from non-coding GWAS associations alone. Moreover, follow up experimental characterization of candidate regions has proven expensive and laborious. In this proposal, we will address these critical limitations by taking a combined statistical and experimental approach to identify the precise genetic variants and genes that affect AD risk. In Aim 1, we will identify AD-associated CREs and their target genes across ten brain regions. In Aim 2, we will develop statistical methods to integrate gene expression and disease association data to identify the genes whose expression levels causally affect AD risk. Finally, in Aim 3, we propose to validate the predictions made in Aims 1 and 2 with a combination of high throughput reporter assays and targeted genome engineering. Throughout this proposal, we will develop, evaluate, and make public new analytic tools that take advantage of many-core computing environments, and will make publicly available all of the genetic and epigenetic data generated from the brain samples.

项目总结