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The MiniMUGA genotyping array is a popular tool for genetic quality control of laboratory mice and genotyping samples from most experimental crosses involving laboratory strains, particularly for reduced complexity crosses. The content of the production version of the MiniMUGA array is fixed; however, there is the opportunity to improve the array's performance and the associated report's usefulness by leveraging thousands of samples genotyped since the initial description of MiniMUGA. Here, we report our efforts to update and improve marker annotation, increase the number and the reliability of the consensus genotypes for classical inbred strains and substrains, and increase the number of constructs reliably detected with MiniMUGA. In addition, we have implemented key changes in the informatics pipeline to identify and quantify the contribution of specific genetic backgrounds to the makeup of a given sample, remove arbitrary thresholds, include the Y Chromosome and mitochondrial genome in the ideogram, and improve robust detection of the presence of commercially available substrains based on diagnostic alleles. Finally, we have updated the layout of the report to simplify the interpretation and completeness of the analysis and added a section summarizing the ideogram in table format. These changes will be of general interest to the mouse research community and will be instrumental in our goal of improving the rigor and reproducibility of mouse-based biomedical research., Competing Interests: Conflicts of interest The authors have no conflict of interest to declare. None of the authors have a financial relationship with Neogen Inc. apart from the service contracts listed above., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

Type 2 diabetes (T2D) is a complex metabolic disorder with no cure and high morbidity. Exposure to inorganic arsenic (iAs), a ubiquitous environmental contaminant, is associated with increased T2D risk. Despite growing evidence linking iAs exposure to T2D, the factors underlying inter-individual differences in susceptibility remain unclear. This study examined the interaction between chronic iAs exposure and body composition in a cohort of 75 Diversity Outbred mice. The study design mimics that of an exposed human population where the genetic diversity of the mice provides the variation in response, in contrast to a design that includes untreated mice. Male mice were exposed to iAs in drinking water (100 ppb) for 26 weeks. Metabolic indicators used as diabetes surrogates included fasting blood glucose and plasma insulin (FBG, FPI), blood glucose and plasma insulin 15 min after glucose challenge (BG15, PI15), homeostatic model assessment for [Formula: see text]-cell function and insulin resistance (HOMA-B, HOMA-IR), and insulinogenic index. Body composition was determined using magnetic resonance imaging, and the concentrations of iAs and its methylated metabolites were measured in liver and urine. Associations between cumulative iAs consumption and FPI, PI15, HOMA-B, and HOMA-IR manifested as significant interactions between iAs and body weight/composition. Arsenic speciation analyses in liver and urine suggest little variation in the mice's ability to metabolize iAs. The observed interactions accord with current research aiming to disentangle the effects of multiple complex factors on T2D risk, highlighting the need for further research on iAs metabolism and its consequences in genetically diverse mouse strains., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well-annotated genome, wealth of genetic resources, and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost-effective, informative, and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole-genome sequencing. Here, we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array (MUGA), MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: (1) chromosomal sex determination, (2) discrimination between substrains from multiple commercial vendors, (3) diagnostic SNPs for popular laboratory strains, (4) detection of constructs used in genetically engineered mice, and (5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA, we genotyped 6899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays, both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived, and recombinant inbred lines. Here, we also report the detection of a substantial number of X O and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC, and an important new tool to increase the rigor and reproducibility of mouse research., (Copyright © 2020 by the Genetics Society of America.)