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Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the identification of mutations causing immune phenotypes in mice by creating Candidate Explorer (CE), a machine-learning software program that integrates 67 features of genetic mapping data into a single numeric score, mathematically convertible to the probability of verification of any putative mutation–phenotype association. At this time, CE has evaluated putative mutation–phenotype associations arising from screening damaging mutations in ∼55% of mouse genes for effects on flow cytometry measurements of immune cells in the blood. CE has therefore identified more than half of genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus. The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.

Obesity and diabetes are well known risk factors for nonalcoholic fatty liver disease (NAFLD), but the genetic factors contributing to the development of NAFLD remain poorly understood. Here we describe two semi-dominant allelic missense mutations (Oily and Carboniferous) of Predicted gene 4951 (Gm4951) identified from a forward genetic screen in mice. GM4951 deficient mice developed NAFLD on high fat diet (HFD) with no changes in body weight or glucose metabolism. Moreover, HFD caused a reduction in the level of Gm4951, which in turn promoted the development of NAFLD. Predominantly expressed in hepatocytes, GM4951 was verified as an interferon inducible GTPase. The NAFLD in Gm4951 knockout mice was associated with decreased lipid oxidation in the liver and no defect in hepatic lipid secretion. After lipid loading, hepatocyte GM4951 translocated to lipid droplets (LDs), bringing with it hydroxysteroid 17β-dehydrogenase 13 (HSD17B13), which in the absence of GM4951 did not undergo this translocation. We identified a rare non-obese mouse model of NAFLD caused by GM4951 deficiency and define a critical role for GTPase-mediated translocation in hepatic lipid metabolism.

When applied to immunity, forward genetic studies use meiotic mapping to provide strong statistical evidence that a particular mutation is causative of a particular immune phenotype. Notwithstanding this, co-segregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the selection of authentic causative mutations using a machine learning software tool, Candidate Explorer (CE), which integrates 65 data features into a single numeric score, mathematically convertible to the likelihood of verification of any putative mutation-phenotype association. CE has identified most genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus. The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.