Predicting the multi-omic impact of psychiatric GWAS associations

预测精神病学 GWAS 关联的多组学影响

• 批准号: 10320945
• 负责人: Laura Marianne Huckins
• 金额: $ 69.37万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10320945
• 关键词: Address; Algorithms; Anorexia Nervosa; Anterior; Autopsy; Biological; Bipolar Disorder; Brain; Bypass; Cell Line; Collection; Complex; Data; Data Set; Development; Disease; Emotional; Etiology; Family; Financial Hardship; Gene Expression; Genes; Genetic; Genetic Polymorphism; Genetic study; Genotype; Goals; High Prevalence; Human; Impairment; Lead; Measures; Mental disorders; Methodology; Methods; Modeling; Morbidity - disease rate; Multiomic Data; Neurons; Pharmaceutical Preparations; Prefrontal Cortex; Public Health; Regulation; Research; Research Personnel; Risk; Role; Sample Size; Sampling; Schizophrenia; Statistical Models; Testing; Therapeutic Intervention; Time; Tissue Banks; Tissue Sample; Tissues; Translating; Update; Variant; beta diversity; brain tissue; case control; cell type; cingulate cortex; disorder risk; effective therapy; epigenome; falls; genome wide association study; genome-wide; genomic locus; gut microbiota; histone modification; improved; induced pluripotent stem cell; innovation; insight; microbial; microbial composition; microbiome; mortality; multiple omics; novel; predictive modeling; prenatal; sample collection; social; success; trait; transcriptome; transcriptomics

PROJECT SUMMARY / ABSTRACT Our understanding of schizophrenia (SCZ), bipolar disorder (BPD) and anorexia nervosa (AN) is advancing rapidly. We have identified polymorphisms and genes associated with all three disorders, although AN is still understudied compared to SCZ and BPD. As sample sizes for genome-wide association studies increase, larger numbers of associated variants will surely be identified, particularly for AN, which is projected to increase to 50,000 cases from ~3,500 currently, by 2019. However, such studies provide, at best, long lists of associated loci, which are not easily biologically interpretable. Consequently, we do not yet understand the key biological mechanisms underlying these diseases, and few effective treatments or medications are available. Methods that provide insight into the associations from these studies will be vital to furthering our understanding of disease etiology, and will have substantial public health impacts. We propose to develop statistical models to translate existing associations from these studies into biologically relevant information. These models are an innovative approach that capitalize on existing successful genetic studies. We use large, publicly available ‘multi-omic’ datasets with proven relevance to SCZ, BPD, and AN (for example brain gene expression, cell-type specific histone modifications, and gut microbiota) to build powerful multi-omic predictors. These may be used to predict higher-level measures (for example gene expression) from genotype, and test for association with disease. These types of associations may lead to increased understanding of underlying biological mechanisms, and opportunities for development of medications and therapeutic interventions. In specific aim 1, we will update and improve on our existing brain gene expression prediction models, using a large collection of post-mortem brain samples from the dorso-lateral pre-frontal cortex and anterior cingulate cortex. These samples will allow us to build large, well-powered, highly accurate prediction models. We will apply these models to existing studies of SCZ, BPD, and AN to provide disease-associated genes. In specific aim 2, we will extend our approach to include prediction of developmental brain gene expression, and again will apply our models to studies of SCZ, BPD, and AN. These analyses will provide trajectories of gene expression throughout development, and will identify genes associated with SCZ, BPD and AN at distinct developmental stages. In specific aim 3, we will create models predicting cell-type specific histone modifications and gut microbial composition from genotype, and will apply these to studies of SCZ, BPD, and AN. These analyses will elucidate the role of specific histone modifications (H3K4me3 and H3K27ac), in neurons and non-neurons, as well as the role of microbial diversity and specific bacterial species, in SCZ, BPD, and AN.

项目总结/摘要 我们对精神分裂症（SCZ）、双相情感障碍（BPD）和神经性厌食症（AN）的理解是 快速推进。我们已经确定了与这三种疾病相关的多态性和基因， 与SCZ和BPD相比，AN仍然研究不足。作为全基因组关联研究的样本量 增加，更大数量的相关变体肯定会被识别，特别是对于AN，预计 到2019年，从目前的约3,500例增加到50,000例。然而，这些研究充其量提供了一个很长的清单， 相关的基因座，这是不容易生物学解释。因此，我们还不明白 这些疾病的关键生物学机制，以及一些有效的治疗或药物， available.从这些研究中深入了解相关性的方法对于进一步研究 了解疾病的病因，并将产生重大的公共卫生影响。 我们建议开发统计模型，将这些研究中现有的关联转化为 生物相关信息。这些模式是一种创新的方法，利用现有的 成功的基因研究我们使用大型的、公开可用的“多组学”数据集，这些数据集已被证明与以下内容相关： SCZ、BPD和AN（例如脑基因表达、细胞类型特异性组蛋白修饰和肠基因修饰）。 微生物群）来构建强大的多组学预测器。这些可以用于预测更高级别的测量（对于 例如基因表达），并测试与疾病的关联。这些类型的协会 可能导致对潜在生物机制的理解增加， 开发药物和治疗干预措施。 在具体目标1中，我们将更新和改进我们现有的大脑基因表达预测模型， 使用大量的死后大脑样本，从背外侧前额叶皮层和前额叶皮层， 扣带皮层这些样本将使我们能够构建大型，功能强大，高度准确的预测模型。 我们将把这些模型应用到SCZ、BPD和AN的现有研究中，以提供疾病相关基因。 在具体目标2中，我们将扩展我们的方法，包括预测发育脑基因 表达，并再次将我们的模型应用于SCZ，BPD和AN的研究。这些分析将提供 轨迹的基因表达在整个发展，并将确定基因与SCZ，BPD 和AN处于不同的发育阶段。 在具体目标3中，我们将创建预测细胞类型特异性组蛋白修饰和肠 微生物组成的基因型，并将这些应用于SCZ，BPD和AN的研究。这些分析 将阐明特定组蛋白修饰（H3 K4 me 3和H3 K27 ac）在神经元和非神经元中的作用， 以及微生物多样性和特定细菌种类在SCZ、BPD和AN中的作用。