Inferring gene-to-phenotype and gene-to-disease relationships at Mouse Genome Informatics: challenges and solutions. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Bello, Susan; Eppig, Janan
• Is Part Of: Journal of biomedical semantics. Volume 7:Issue 1(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-12
• Subjects: Phenotype -- Genotype -- Disease -- Mouse -- Annotation; Semantics -- Periodicals; Medicine -- Research -- Periodicals; Biology -- Research -- Periodicals; Computer systems -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1186/s13326-016-0054-4
• Languages: English
• ISSNs: 2041-1480

Abstract

Abstract Background Inferring gene-to-phenotype and gene-to-human disease model relationships from annotated mouse phenotypes and disease associations is critical when researching gene function and identifying candidate disease genes. Filtering the various kinds of genotypes to determine which phenotypes are caused by a mutation in a particular gene can be a laborious and time-consuming process. Methods At Mouse Genome Informatics (MGI, www.informatics.jax.org ), we have developed a gene annotation derivation algorithm that computes gene-to-phenotype and gene-to-disease annotations from our existing corpus of annotations to genotypes. This algorithm differentiates between simple genotypes with causative mutations in a single gene and more complex genotypes where mutations in multiple genes may contribute to the phenotype. As part of the process, alleles functioning as tools (e.g., reporters, recombinases) are filtered out. Results Using this algorithm derived gene-to-phenotype and gene-to-disease annotations were created for 16, 000 and 2100 mouse markers, respectively, starting from over 57, 900 and 4800 genotypes with at least one phenotype and disease annotation, respectively. Conclusions Implementation of this algorithm provides consistent and accurate gene annotations across MGI and provides a vital time-savings relative to manual annotation by curators.