Bayesian causal learning: A novel framework for drug-target discovery using Mendelian randomization on single-cell transcriptomics

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The development of novel therapeutic drugs is a costly and time-consuming endeavour that often fails when tested in large-scale Phase II/III clinical trials which cost tens of millions of dollars. In recent years there has been increasing interest in using evidence from genetics to support the choice of which drug targets to take forward.In this project, we will use a unique resource of single-cell transcriptomics developed at Imperial College which has measured the expression of genes in hundreds of thousands of cells in two regions of the human brain, the hippocampus and pre-frontal cortex, which are critical for memory and behaviour. The unique advantage of this dataset is the ability to zoom in on individual cells, and thereby work out which genes and which types of cells cause disease. This dataset has been linked with genetic information to define genetic regulation of single-cell expression, also known as expression quantitative trait loci (eQTL). However, analytical tools lag behind our ability to generate data and so these datasets are currently not being utilized to their full capacity.To this end, we will combine two causal inference concepts. The first concept called Mendelian randomization (MR) uses the fact that variations in a person's DNA sequence are randomly assigned at conception. MR uses this "natural randomization" to infer the causal effect of an exposure (i.e., the expression of a gene in a cell) on an outcome (neurological disease). The second concept is causal network graphs and structural learning algorithms which consider a group of genes and infer which genes are connected and the directionality of the effect, that is if gene A causes gene B or vice versa. Incorporating the additional information on cell-type specificity from the single-cell transcriptomics measurements will allow us to understand how molecular effects propagate through different cell-types. Working on genetic data allows the integration of publicly available genetic association data from genome-wide association studies. In this proposal, we consider a wide range of more than twenty neurological phenotypes and brain diseases as outcomes, including for example Alzheimer's disease, Parkinson's disease, and epilepsy. Large genome-wide association studies have shown that there is variation in our genome that is associated with more than one disease. From these datasets, we can learn if there are shared molecular mechanisms that cause more than one disease. Here, we propose a novel computational toolkit, called single-cell MR (scMR) for the analysis of single-cell transcriptomics eQTL data combined with genome-wide association studies on the disease outcomes. ScMR will implement computational methodology with the following three aims:1) To identify which genes act in which cells and cause disease. 2) To understand how genes relate with each other and across different cell-types and how molecular processes propagate through different molecular layers.3) To integrate many responses into the model to identify genes that cause more than one disease.Our findings will not be limited to which genes need to be altered to treat neurological disease, but also which specific cell-types need to be targeted. Moreover, we want to learn downstream molecular processes in different cells-types which will help to better design therapeutic interventions. Finally, integrating data on many related neurological disease outcomes will allow us to define shared molecular mechanisms affecting more than one disease outcome. This information can be used for the repurposing of existing drugs, the identification of drug-targets with co-benefits that reduce risk of more than one disease, or highlight the risk of potential side-effects.