Informing Osteoporosis GWAS Using Networks

项目简介：骨质疏松症是一种以低骨密度(BMD)为特征的复杂疾病， 骨骼脆弱，骨折风险增加。关于BMD的全基因组关联研究(GWASs) 确定了1100多个协会。这一新型基因信息的“宝库”有可能 彻底改变了我们对骨生物学和骨疾病治疗的理解；然而，很少有 潜在关联的因果基因已经被确定。我的实验室的长期目标是减少这种情况 使用创新的分析和实验策略的知识差距。有许多方法可以实现以下目的 确定与GWA关联有关的基因。然而，大多数依赖于基于人口的“组学”数据， 对于人类骨骼来说是稀有的，可以将变异与分子变化联系起来。此外，大多数方法 不提供因果基因如何影响“系统水平”功能的信息。为了解决这些限制， 我们最近使用了从小鼠骨转录数据集产生的共表达网络来告知BMD GWARD。这个想法很简单--在复杂性状的调节中发挥核心作用的基因通常是 功能相关基因和功能相关基因通常是共表达的。我们通过确定 骨组织中共表达的基因模块，这些基因富含GWAS所涉及的基因，我们是 能够预测目标基因并推断它们如何影响骨密度。我们已经通过实验证明，有几个 基因(Mark3、PPP6R3等)使用这种方法确定的是BMD的真正调节者。然而，到目前为止，我们的 分析仅基于在非定向网络中GWAS所涉及的基因的丰富 从细胞多样性有限的异质批量骨转录数据集生成。在这里，我们解决了 使用结合了贝叶斯网络和关键驱动因素的创新分析方法 分析(KDA)并将此策略应用于原代骨细胞培养和成骨细胞的转录数据 单细胞RNA-seq(scRNA-seq)定义的亚型。我们假设我们整合GWAs的方法 代表所有主要骨细胞类型的定向网络将识别具有直接和中心的新基因 在调节BMD中的作用，并阐明这些基因如何影响网络结构。我们将对此进行测试 通过三个具体目标提出假设。在目标1中，我们将通过以下方式发现新的BMD GWA靶基因 骨和骨细胞网络和GWAs数据的整合。在目标2中，我们将发现新的骨密度 通过整合成骨细胞类型特异性网络和GWAS数据获得靶基因。在《目标3》中，我们将 明确新的BMD GWA靶基因对BMD和骨网络动态平衡的影响。我们的创新 通知GWAs的方法将识别导致BMD的基因并导致假定的治疗方法的发现 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.