Identifying causal genetic variants and molecular mechanisms impacting mental health

识别影响心理健康的因果遗传变异和分子机制

基本信息

批准号：
10571911
负责人：
Anshul Kundaje
金额：
$ 61.6万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2026-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10571911
关键词：
ATAC-seq Acute Affect Alleles Alzheimer&apos s Disease Biological Assay Biology Brain Brain region Catalogs Cells Chromatin Collaborations Communities Computer software Computing Methodologies DNA Sequence Data Databases Detection Development Disease Future Gene Expression Gene Expression Regulation Gene Frequency Genes Genetic Genetic Diseases Genetic Load Genetic Risk Genetic Variation Genotype-Tissue Expression Project Human Individual Influentials Learning Link Linkage Disequilibrium Machine Learning Maps Masks Mental Health Mental Health Associations Mental disorders Methods Modeling Molecular Molecular Disease Mus National Human Genome Research Institute Neurodevelopmental Disorder Organoids Outcome Parkinson Disease Pathologic Performance Population Quantitative Trait Loci Reporter Reproducibility Research Resolution Resources Risk Tissue Sample Tissues Training Uncertainty Untranslated RNA Variant Weight Work base biobank brain cell causal variant cell type computational pipelines deep learning model disorder risk epigenome epigenomics experimental study functional genomics genetic variant genome resource genome wide association study genomic locus improved insight machine learning method machine learning model novel novel strategies open data polygenic risk score prevent public health relevance regression algorithm risk prediction success therapy development trait

项目摘要

Identifying how genetic variation leads to neurodevelopmental or psychiatric disorders provides new means to study, predict, prevent and treat disease. Identifying the immediate molecular consequences of disease- associated genetic variation has necessitated the development of large-scale, multi-tissue functional genomic resources. Projects such as GTEx, Roadmap Epigenomics Project and PsychENCODE have combined molecular QTL mapping and epigenomic maps in bulk tissues to interpret various disease-associated genetic variants. However, few colocalizations between molecular QTLs and traits have been robustly identified and few causal variants mapped. As tissues like the brain constitute 100s of cell-types, we hypothesize that existing maps may mask the contributions of disease-associated variation in less-abundant cell types. One extremely powerful approach to identify cell-type specific molecular effects and their relationship to genetic diseases is through application of chromatin accessibility data – these data both allow inference of causal cell types and provide base level resolution gene regulation. Our team has considerable expertise in connecting GWAS to molecular functions and predicting causal variants through use of chromatin accessibility data. We have additionally recently collaborated to generate a comprehensive, multi-individual map single cell ATAC- seq map (scATAC-seq) of six different brain regions to detect causal cell types and predict causal variants. This work has been recently demonstrated in our fine-mapping study of Alzheimer’s and Parkinson’s disease (Corces et al, bioRxiv, 2020) but has not been systematically applied to mental health disorders. We propose to develop statistical genetics and machine learning approaches that advance the use of scATAC-seq data to connecting mental health GWAS loci to specific cell types, mechanisms and causal variants. In Aim 1, we will assemble a pipeline that leverages region and cell type-specific scATAC-seq data to identify pathological cell types for 100s of mental health and brain-related traits. We will also enhance the detection of cell-type specific molecular mechanisms by extending and applying a novel GWAS/QTL colocalization approach. Throughout these activities, variants will be validated using massively-parallel reporter assays (MPRA). In Aim 2, we will develop sophisticated machine learning models that learn regulatory grammars and score variants across the allele frequency spectrum. Predicted causal variants in GWAS loci will be further assessed with MPRAs in Aim 1 and applied in Aim 3. In Aim 3, we will demonstrate how improved detection of causal variants using our single-cell informed models aids transferability of polygenic risk scores across populations. We will provide open resources and reproducible computational methods and pipelines that integrate single cell chromatin accessibility data from multiple brain regions. This will allow detection cell-type specific genetic effects and pathological cell types in mental health GWAS, establish robust causal links between variants, genes and disease, and improve prediction of disease risk.

确定遗传变异如何导致神经发育或精神疾病的新手段研究，预测，预防和信任疾病。相关的遗传变异哈斯需要开发大型多组织功能基因组资源。散装组织中的分子QTL映射和表观基因组图来解释各种疾病相关的遗传然而，变异很少很少有因果变体映射为诸如大脑征兵100的组织，我们假设现有的MAS掩模在不多的细胞类型中与疾病相关的贡献。鉴定细胞类型特异性分子效应以及与遗传的关系的极其强大的方法疾病是通过应用染色质可及性数据 - 这些数据BOTA都允许推断因果细胞类型并提供基础水平分辨率基因调控。通过使用染色质访问性数据来预测分子函数的GWA和因果变异最近还合作生成六个不同大脑区域的SEQ MAP（SCATAC-SEQ）检测Cassal细胞细胞类型并预测因果变异。在我们对阿尔茨海默氏病和帕金森氏病的精细映射研究中，这项工作已得到了证明（Corces等人，Biorxiv，2020年），但我们建议的是精神疾病开发统计遗传学和机器学习将SCATAC-SEQ数据接近将心理健康GWAS基因座连接到特定的细胞类型，机制和因果关系。组装利用区域和细胞类型特异性SCATAC-SEQ数据识别病理细胞的管道对于100秒的心理健康和与大脑相关的特征。分子机制通过扩展和应用无新颖的GWAS/QTL共定位方法。这些活动将使用大量并行的记者分析（MPRA）进行有效添加。开发复杂的机器学习模型，以学习常规语法和跨你的评分变体等位基因频谱。 1并应用于AIM3。在AIM 3中，我们将证明如何使用我们的因果变异的检测单细胞知情的模型有助于跨种群的多基因风险评分的转移性。我们将提供开放的资源和声誉的方法和管道来集成单细胞染色质的可访问性数据来自Multighipe Brain区域。心理健康中的遗传效应和病理细胞类型变体，基因和疾病，并改善疾病风险的预测。