Predicting the multi-omic impact of psychiatric GWAS associations

项目摘要/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 进展迅速。我们已经确定了与这三种疾病相关的多态和基因，尽管 与SCZ和BPD相比，AN仍未得到充分研究。作为全基因组关联研究的样本大小 随着数量的增加，肯定会发现更多的相关变体，特别是对于AN，预计 到2019年，病例将从目前的约3,500例增加到50,000例。然而，这样的研究充其量只能提供很长的清单 相关的基因座，这在生物学上是不容易解释的。因此，我们还不明白 这些疾病背后的关键生物学机制，以及很少有效的治疗或药物是 可用。从这些研究中洞察关联的方法将对进一步推动我们的 了解疾病病因学，并将对公共卫生产生重大影响。 我们建议开发统计模型，将这些研究中现有的关联转化为 与生物相关的信息。这些模式是一种创新的方法，它利用了现有的 成功的基因研究。我们使用大型的、可公开获得的、已证实与以下各项相关的“多组”数据集 SCZ、BPD和AN(例如，脑基因表达、特定细胞类型的组蛋白修饰和肠道 微生物区系)来构建强大的多组学预测因子。这些可用于预测更高级别的衡量标准(用于 例如基因表达)，并测试与疾病的关联。这些类型的关联 可能会增加对潜在生物机制的理解，并有机会 药物和治疗干预措施的发展。 在具体目标1中，我们将对现有的脑基因表达预测模型进行更新和改进， 使用大量来自背外侧前额叶皮质和前额叶的尸检样本 扣带回皮质。这些样本将使我们能够建立大型、功能强大、高度准确的预测模型。 我们将把这些模型应用于SCZ、BPD和AN的现有研究，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，以包括对发育中的大脑基因的预测 表达式，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 基因在整个发育过程中的表达轨迹，并将识别与SCZ、BPD相关的基因 处于不同的发育阶段。 在特定目标3中，我们将创建预测特定细胞类型的组蛋白修饰和肠道的模型 来自基因型的微生物组成，并将这些应用于SCZ、BPD和AN的研究。这些分析 将阐明特定的组蛋白修饰(H3K4me3和H3K27ac)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。

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.