High-Throughput Computational Mouse Genetic Analysis

The goal of the study was to identify the causal genetic factors impacting important biomedical phenotypes by performing genetic association analysis of inbred mouse strains. More specifically, the aim included the Haplotype-based computational genetic mapping (HbCGM) method based genetic analysis that has previously identified genetic factors affecting many traits of biomedical importance in mice (Mus musculus). With our HbCGM method, by using the whole genome sequencing data of 43 inbred strains to analyze the big phenotypic data (~ 8300 publicly available datasets of biomedical response measurements for panels of inbred mouse strains). The phenotypic susceptibility factors were identified with the automated methods of several important biomedical responses such as eye, metabolism, and infectious diseases and then validated. The novel outcomes of our study included a metabolic protein involved in the regulation of metabolites in FVB and SJL strains. The protein is known for body mass index and affects the mitochondrial organization and metabolism. Upon in-depth inspection, we concluded that the transport mechanism of the protein with which the protein localized into the mitochondrial membrane was compromised due to the unique allelic variations present in the localization region of FVB and SJL strains. Furthermore, an analysis of nuclear-encoded mitochondrial proteins revealed, the previously unknown widespread phenomenon of common variations impacting the mitochondrial localization region of proteins. We identified various examples of allelic effects that are highly light to impact the mitochondrial localization of possible the functions of crucial proteins. Our results suggested the possibility of successful identification of genetic factors impacting biomedical phenotypes by using integrated methods of big biodata analyses