Identifying potential drug targets for non-alcoholic fatty liver disease: a drug target Mendelian randomization study

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Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Research and Innovation Training Grant: Centre for Doctoral Training in AI-enabled Healthcare Systems Background Non-alcoholic fatty liver disease (NAFLD), a range of conditions caused by the build-up of fat in the liver, is the most common form of chronic liver disease, with an estimated prevalence of ~25% globally. NAFLD is associated with increased risk of all-cause mortality, with CVD emerging as the primary cause of death amongst affected individuals. The aetiology of NAFLD is not yet clearly understood, and currently no drugs exist for its treatment, with therapeutics aimed at symptom management instead. Purpose To identify and prioritise candidate protein drug targets and pathways relevant for therapeutic intervention in NAFLD. Methods To identify plasma proteins causally associated with NAFLD, we analysed genetic associations on 1,561 proteins from 35,559 participants in the deCODE study, and compared these to associations from a GWAS of biopsy confirmed NAFLD cases (1,483 cases, 17,781 controls). We sourced genetic variants from a 400kbp region around each protein-coding gene, pruning variants on an F-statistic of 15, minor allele frequency threshold of 1% and linkage disequilibrium r-squared of 0.4. cis-Mendelian randomization (MR) was used to identify proteins associated with NAFLD. Bias due to pleiotropy was limited by excluding variants with leverage statistic more than 3 times the mean, or outlier statistic larger than 10.83. We used colocalization analysis to identify proteins with a posterior probability of at least 0.8, indicating the presence of common causal variants. We further prioritised proteins by annotating them with information on druggability (i.e. whether a protein is a target for a known drug, or predicted to be a target for a drug), mRNA expression in pathology-relevant tissues, and involvement in known biological pathways. Results We identified 5 proteins with a multiplicity-corrected (p<3.2×10-5) association with NAFLD, and strong evidence of genetic colocalization: CYB5A, NT5C, NCAN, TGFBI, DAPK2. TGFBI and DAPK2 are potentially druggable, with all five found to be expressed in both liver and adipose tissues. Pathway analysis indicated the involvement of NT5C, CYB5A, NCAN in Reactome pathway R-HSA-1430728 reflecting cellular energy metabolism, including mitochondrial lipid metabolism, which is strongly implicated with NAFLD. Conclusion We identified a prioritised set of five plasma proteins influencing NAFLD, including druggable proteins informing de novo and ongoing drug development programs. Figure: MR effect estimates of proteins on NAFLD A: Effect of each protein on NAFLD. Each point represents a protein, effect estimates are reported in log(odds ratio) and statistical significance in -log(p-value). Coloured points represent proteins passing the multiplicity-corrected p-value threshold B: MR effect estimates of prioritised proteins. Estimates are reported in log(odds ratio). Proteins are annotated according to their druggability based on the British National Formulary and ChEMBL

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