Leveraging proteomics to discover new biology and therapeutic targets

血液中循环的蛋白质来自多种器官和细胞类型，因此提供了这些细胞中发生的不同过程的“快照”。血浆蛋白通常被用作诊断或预后生物标志物，并且鉴于它们可以使用常规小分子或生物制剂直接干扰，它们通常是药物的靶标。然而，成功药物开发的先决条件是有效性，这取决于药物靶标不仅与疾病相关，阐明因果关系的一种方法是通过孟德尔随机化（MR），该方法已成功复制了针对药理学靶点（如PCSK 9）的随机对照试验（RCT）的结果，LpPLA 2和NPC 1 L1，并日益成为一个标准的工具，用于分流药理学治疗的目标。与更复杂的表型相比，使用顺式pQTL的MR具有潜在地对单个蛋白质更特异的优点，这应该限制可能违反MR假设的水平多效性的偏差。最近的技术发展使得能够在大型研究中同时测量数百至数千个循环蛋白质，这为利用全基因组关联研究（GWAS）研究其遗传调控铺平了道路。此外，历史数据表明，具有直接遗传支持的潜在药物靶点获得批准的可能性是没有遗传支持的药物靶点的两倍。结合生物学途径和药物靶点，然后使用MR框架评估因果关系，为评估靶点介导效应的可能性提供了机会目的（a）使用ORCADES中的全基因组序列（WGS）数据和来自合作者的类似数据鉴定影响血浆蛋白水平的罕见变体。(b)在SCALLOP中使用大规模全基因组关联荟萃分析识别trans-pQTL（c）应用最先进的下游分析方法来解开生物学，评估药物再利用并进行因果推断

Proteins circulating in blood are derived from multiple organs and cell types, and therefore provide a "snapshot" of different processes occurring in these cells. Plasma proteins are often used as diagnostic or prognostic biomarkers and given that they can be directly perturbed using conventional small molecules or biologics, they are often targets of medicines.However, a prerequisite for successful drug development is efficacy, which is predicated on the drug target not only being associated with the disease, but also playing a causal role in it. One approach to clarifying causation is through Mendelian randomisation (MR), which has successfully reproduced the outcome of randomised controlled trials (RCT) for pharmacological targets such as PCSK9, LpPLA2 and NPC1L1, and is increasingly becoming a standard tool for triaging pharmacological treatment targets. In contrast to more complex phenotypes, MR using cis-pQTL carries the advantage of being potentially more specific for a single protein, which should limit bias from horizontal pleiotropy that can violate MR assumptions.Recent technological developments have enabled hundreds to thousands of circulating proteins to be measured simultaneously in large studies, which has paved the way for studies of their genetic regulation using genome-wide association studies (GWAS). In addition, historic data has shown that potential drug targets with direct genetic support were two times more likely to be approved than those without the genetic support.The combination of well-powered GWAS for discovery of protein quantitative trait loci (pQTL), integration with biological pathways and drug targets followed by assessment of causality using an MR framework provides the opportunity to evaluate the likelihood of target-mediated effects (including both therapeutic efficacy and on-target adverse outcomes) of ongoing drug development.Aims(a) Identify rare variants influencing plasma protein levels using whole genome sequence (WGS) data in ORCADES and similar data from collaborators.(b) Identify trans-pQTL using large scale genome-wide association meta-analyses in SCALLOP(c) Apply state-of-the-art downstream analysis methods to disentangle the biology, assess drug repurposing and make causal inferences