Selected Grantee Publications
GenomeMUSter Mouse Genetic Variation Service Enables Multitrait, Multipopulation Data Integration and Analysis
Ball et al., Genome Research. 2024.
https://genome.cshlp.org/content/34/1/145.long
Advances in genetics, including transcriptome-wide and phenome-wide association analysis methods, create compelling new opportunities for using fully reproducible and widely studied inbred mouse strains to characterize the polygenetic basis for individual differences in disease-related traits. Investigators developed an imputation approach and implemented data service to provide a broad and more comprehensive mouse variant resource. They evaluated the strain-specific imputation accuracy on a “held-out” test set that was not used in the imputation process. The authors present its application to multipopulation and multispecies analyses of complex trait variation in type 2 diabetes and substance use disorders and compare these results to human genetics studies. Supported by ORIP (U42OD010921, P40OD011102, R24OD035408), NCI, NIAAA, NIDA, and NIDCD.
The Monarch Initiative in 2024: An Analytic Platform Integrating Phenotypes, Genes and Diseases Across Species
Putman et al., Nucleic Acids Research. 2024.
https://pubmed.ncbi.nlm.nih.gov/38000386/
The Monarch Initiative aims to bridge the gap between the genetic variations, environmental determinants, and phenotypic outcomes critical for translational research. The Monarch app provides researchers access to curated data sets with information on genes, phenotypes, and diseases across species and advanced analysis tools for such diverse applications as variant prioritization, deep phenotyping, and patient profile matching. Researchers describe upgrades to the app, including scalable cloud-based infrastructure, simplified data ingestion and knowledge graph integration systems, enhanced data mapping and integration standards, and a new user interface with novel search and graph navigation features. A customized plugin for OpenAI’s ChatGPT allows the use of large language models to interrogate knowledge in the Monarch graph and increase the reliability of the responses of Monarch’s analytic tools. These upgrades will enhance clinical diagnosis and the understanding of disease mechanisms. Supported by ORIP (R24OD011883), NLM, and NHGRI.