Barrington et al. examined the effect of four human diets (American, Mediterranean, Japanese, and Maasai/ketogenic) on metabolic health across four mouse...

Dietary intervention is commonly used for weight loss or to improve health, as diet-induced obesity increases the risk of developing type 2 diabetes, hypertension, cardiovascular disease, stroke, osteoarthritis, and certain cancers. Various dietary patterns are associated with effects on health, yet little is known about the effects of diet at the tissue level. Using untargeted metabolomics, this study aimed to identify changes in water-soluble metabolites in C57BL/6J males and females fed one of five diets (Japanese, ketogenic, Mediterranean, American, and standard mouse chow) for 7 months. Metabolite abundance was examined in liver, skeletal muscle, and adipose tissue for sex, diet, and sex-by-diet interaction. Analysis of variance (ANOVA) suggests that liver tissue has the most metabolic plasticity under dietary changes compared with adipose and skeletal muscle. The ketogenic diet was distinguishable from other diets for both males and females according to partial least-squares discriminant analysis. Pathway analysis revealed that the majority of pathways affected play an important role in amino acid metabolism in liver tissue. Not surprisingly, amino acid profiles were affected by dietary patterns in skeletal muscle. Few metabolites were significantly altered in adipose tissue relative to skeletal muscle and liver tissue, indicating that it was largely stable, regardless of diet alterations. The results of this study revealed that the ketogenic diet had the largest effect on physiology, particularly for females. Furthermore, metabolomics analysis revealed that diet affects metabolites in a tissue-specific manner and that liver was most sensitive to dietary changes.

It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied are the effects of kinship on genetic interaction test statistics. Here, we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using an LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

Background Understanding the genetics of Alzheimer’s disease (AD) has proven difficult in human populations. Using mouse models enables researchers to dissect the genetics of AD‐related pathways. The Diversity Outbred (DO) mice are a heterogenous multi‐parental population created using eight founder strains, mimicking human population structure. The DO provide a new resource to investigate complex diseases, such as AD. Comparing the genetics of individuals with AD to DO mice will enable us to identify AD‐related pathways in mice that can be dissected to better understand AD. Method Genes identified in the hippocampus of 258 male and female DO mice were grouped into 145 paracliques, unique clusters of correlated genes that are closely related. Paracliques were compared with 30 human gene modules related to AD from the Accelerating Medicines Partnership for Alzheimer’s disease (AMP‐AD). Gene content overlaps were calculated between the AMP‐AD modules and DO paracliques by Jaccard index. Additionally, we mapped and characterized quantitative trait loci (QTL) to determine if summary eigengenes from each paraclique were associated with genetic factors. Both SNP associations and database searches were performed to identify potential genes of interest within the QTL. Paracliques and DO genes identified for significant QTL were compared to the AMP‐AD modules and AGORA to determine their relevance to AD. Result Significant QTL were identified for paraclique 56 on chromosome 16. IL6ST, a nominated target in AGORA, was identified within paraclique 56. Additionally, KALRN, a gene that plays a role in decreased anxiety‐related behavior, contextual conditioning, and synapse formation, was identified within the QTL for paraclique 56. KALRN was a member of the AMP‐AD neuronal modules. Furthermore, variants in KALRN were identified in brain eQTL from Alzheimer’s study cohorts and determined to be a candidate gene in AGORA, adding to its potential relevance. Mediation analysis revealed that KALRN is potentially locally regulated by ROGDI. A loss‐of‐function mutation in ROGDI results in Kohlschutter‐Tonz syndrome, which can result in dementia as a symptom. Conclusion This study identified potential candidate genes in DO mice that have relevance to AD. In sum, this study showcases the potential of utilizing DO mice to translationally investigate AD‐related pathways.

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