Enabling AI-based Mouse Genetic Discovery

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Abstract No model organism has contributed more than the laboratory mouse to improving human health. Many genetic factors and therapies for human diseases were initially discovered or characterized in mice, before they were transitioned to human use. Large-scale efforts are underway to integrate recent advances in artificial intelligence (AI) into human healthcare, but very few AI advances have been used for analysis of the data produced using the model organism that has formed the foundation for many healthcare innovations. We recently developed an AI-based computational pipeline that could identify causative genetic factors for murine genetic models of human biomedical traits and diseases. After assessing the strength of allelic associations with the phenotypic response pattern exhibited by the inbred strains; this AI pipeline uses a machine-learning trained method to analyze 29M published papers and assess candidate gene-phenotype relationships; and the information obtained from assessment of their protein-protein interaction network and protein sequence features of the candidate genes are also incorporated into the graph neural network-based analysis. This project will produce a markedly enhanced AI pipeline (AIv2) that will greatly accelerate the pace of genetic discovery using murine genetic models. First, long read genomic sequencing (LRS) and computational tools are used to produce a more complete map of the pattern of genetic variation among the inbred strains, which also includes alleles for two major types of genetic variation (structural variants, tandem repeats), which are poorly characterized using conventional sequencing methods. Second, we develop two additional computational tools for the AI, which facilitate candidate gene prioritization through the evaluation of: (i) the phenotypes exhibited by 8200 mouse lines with individual gene knockouts (KOs); and (ii) the results of 5700 human GWAS covering many biomedical phenotypes to determine if alleles within the human homologues of candidate murine genes affect an analyzed trait. The ability of AIv2 to accelerate genetic discovery will be demonstrated by using it to identify new genetic factors through analysis of a public database with >10,307 datasets, which measure biomedical or disease-related responses in panels of inbred strains. Since it is critical to experimentally confirm some of the computational findings, genetic factors for two murine models of human diseases that are major public health problems (cancer, diabetes/obesity), which were identified by the AI pipeline, will be experimentally validated. CRISPR engineering is used to revert the causative mutation(s) to wildtype on the genetic background of the strain exhibiting the disease phenotype, and the genome engineered mice are analyzed to assess the contribution of the genetic factor to the disease phenotype.