Fig. 3: A computational framework for rapid gene-level validation of causative genetic factors for biomedical traits from analysis of mouse GWAS data.
From: Twenty-first century mouse genetics is again at an inflection point
The pipeline analyzes GWAS data and identifies candidate genes whose allelic pattern correlates with the responses exhibited by the inbred strains. Then, it builds a knowledge graph for the candidate genes based upon analysis of gene–phenotype (φ) associations in the published literature, a PPI network and protein structural features. Analysis of this graph identifies the candidate genes that are most strongly related to the analyzed phenotype (φ). The effect of a KO of a prioritized candidate gene on the analyzed phenotype can then be assessed by examining the IMPC database. This database indicates if a phenotype was altered in an IMPC gene KO line through the P value determined by comparing the KO line’s phenotype with that of the wild-type strain.
