Leveraging proteomics to discover new biology and therapeutic targets

点击翻译按钮获取中文摘要

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