Informing Osteoporosis GWAS Using Networks

项目概述：骨质疏松症是一种以低骨密度为特征的复杂疾病， 骨质疏松和骨折风险增加。BMD的全基因组关联研究（GWAS） 发现了1100多个协会。这个新的遗传信息的"宝库"有可能 彻底改变我们对骨生物学和骨疾病治疗的理解;然而， 相关的致病基因已经被确定。我实验室的长期目标是减少这种情况 利用创新的分析和实验战略，消除知识差距。有许多方法可以 识别与GWAS相关的基因。然而，大多数依赖于基于人口的"组学"数据， 这在人类骨骼中是稀缺的，将变异与分子改变联系起来。此外，大多数方法 没有提供关于因果基因如何影响"系统水平"功能的信息。为了解决这些限制， 我们最近使用从小鼠骨转录组数据集生成的共表达网络来告知BMD GWAS。这个想法很简单--在复杂性状的调控中起核心作用的基因通常是 功能相关的基因和功能相关的基因通常共表达。我们证明了通过识别 骨组织中的共表达基因模块富集了GWAS所涉及的基因，我们 能够预测靶基因并推断它们如何影响BMD。我们已经通过实验证明， 基因（MARK 3、PPP 6R3等）使用这种方法确定的是BMD的真正调节者。然而，迄今为止， 分析仅基于无向网络中GWAS所涉及的基因的富集 从有限细胞多样性的异质大块骨转录组学数据集产生。在这里，我们解决 这些限制使用创新的分析方法，结合贝叶斯网络和关键驱动程序 分析（KDA），并将该策略应用于原代骨细胞培养和成骨细胞的转录组学数据 由单细胞RNA-seq（scRNA-seq）定义的亚型。我们假设我们整合GWAS的方法 与代表所有主要骨细胞类型的定向网络将确定新的基因与直接和中央 在调节BMD中的作用，并阐明这些基因如何影响网络结构。我们将测试这个 通过三个具体目标的假设。在目标1中，我们将通过基因工程发现新的BMD GWAS靶基因。 骨和骨细胞网络与GWAS数据的整合。在目标2中，我们将发现新的BMD GWAS 通过整合成骨细胞类型特异性网络和GWAS数据，在目标3中，我们 确定新的BMD GWAS靶基因对BMD和骨网络稳态的影响。我们的创新 一种告知GWAS的方法将确定致病的BMD基因，并导致发现推定的治疗基因。 预防和治疗骨脆性的目标。

Project Summary: Osteoporosis is a complex disease characterized by low bone mineral density (BMD), bone fragility, and an increased risk of fracture. Genome-wide association studies (GWASs) for BMD have identified over 1100 associations. This “treasure trove” of novel genetic information has the potential to revolutionize our understanding of bone biology and the treatment of bone diseases; however, few of the causal genes underlying associations have been identified. The long-term goal of my lab is to reduce this knowledge gap using innovative analytical and experimental strategies. A number of approaches exist to identify genes responsible for GWAS associations. However, most rely on population-based “-omics” data, which are scarce for human bone, to connect variants to molecular alterations. Furthermore, most approaches do not provide information on how causal genes impact “systems-level” function. To address these limitations, we recently used co-expression networks generated from mouse bone transcriptomic datasets to inform BMD GWAS. The idea is simple – genes that play a central role in the regulation of a complex trait are often functionally-related and functionally-related genes are often co-expressed. We demonstrated that by identifying modules of co-expressed genes in bone tissue that were enriched for genes implicated by GWAS, we were able to predict target genes and infer how they impact BMD. We have shown experimentally that several genes (MARK3, PPP6R3, etc.) identified using this approach are true regulators of BMD. However, to date our analyses have been based only on enrichment of genes implicated by GWAS in undirected networks generated from heterogenous bulk bone transcriptomic datasets of limited cellular diversity. Here, we address these limitations using an innovative analytical approach incorporating Bayesian networks and key driver analysis (KDA) and apply this strategy to transcriptomic data from primary bone cell cultures and osteoblast subtypes defined by single cell RNA-seq (scRNA-seq). We hypothesize that our approach of integrating GWAS with directed networks representing all major bone cell types will identify novel genes with direct and central roles in regulating BMD and elucidate how such genes impact network architecture. We will test this hypothesis through three specific aims. In Aim 1, we will discover novel BMD GWAS target genes through the integration of bone and bone cell networks and GWAS data. In Aim 2, we will discover novel BMD GWAS target genes through the integration of osteoblast cell-type specific networks and GWAS data. In Aim 3, we will define the impact of novel BMD GWAS target genes on BMD and bone network homeostasis. Our innovative approach for informing GWAS will identify causal BMD genes and lead to the discovery of putative therapeutic targets for the prevention and treatment of bone fragility.