Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving treatment of diseases. We employed a cross-disciplinary approach to identify potential treatments for metabolic dysfunction associated steatotic liver disease (MASLD) using humans as a model organism. We identified 212 putative causal genes associated with MASLD using data from a large multi-ancestry genetic association study, of which 158 (74.5%) are novel. From this set we identified 57 genes that encode for druggable protein targets, and where the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. These potential targets were then evaluated for evidence of efficacy using Mendelian randomization, pathway analysis, and protein structural modeling. Using these approaches, we present compelling evidence to suggest activation of FADS1 by icosopent ethyl as well as S1PR2 by fingolimod could be promising therapeutic strategies for MASLD.

DG is the Chief Executive Officer of Sequoia Genetics, a private limited company that works with investors, pharma, biotech, and academia by performing research that leverages genetic data to help inform drug discovery and development. DG has interests in several biotech companies. JAL reports grants from Alnylam Pharmaceuticals, Inc., AstraZeneca Pharmaceuticals LP, Biodesix, Inc, Celgene Corporation, Janssen Pharmaceuticals, Inc., Novartis International AG, and Parexel International Corporation through the University of Utah or Western Institute for Veteran Research outside of the submitted work. MGL receives grant support to the University of Pennsylvania from MyOme, and consulting fees from BridgeBio outside the submitted work. The remaining authors declare no competing interests.

Support provided by T32GM145734-01 (HMS), BX003362 (Chang/Tsao), K12AR084232 (JNH & MMS), VA BLR&D IK2-BX006551 and Doris Duke Foundation 2023-0224 (MGL), CSP2012 (KB, KL, JAL), T32HL00773 (ATA), and R01DK134575 (MV). JAL, KL, and MV are also supported by MVP003.

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The GWAS data used in this study are available through dbGAP under accession number phs001672.v7.p1 (Veterans Administration MVP Summary Results from Omics Studies). https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.v7.p1

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