Content and performance of the MiniMUGA genotyping array: A new tool to improve rigor and reproducibility in mouse research

John Sebastian Sigmon, Matthew W. Blanchard, Ralph S. Baric, Timothy A. Bell, Jennifer Brennan, Gudrun A. Brockmann, A. Wesley Burks, J. Mauro Calabrese, Kathleen M. Caron, Richard E. Cheney, Dominic Ciavatta, Frank Conlon, David B. Darr, James Faber, Craig Franklin, Timothy R. Gershon, Lisa Gralinski, Bin Gu, Christiann H. Gaines, Robert S. HaganErnest G. Heimsath, Mark T. Heise, Pablo Hock, Folami Ideraabdullah, J. Charles Jennette, Tal Kafri, Anwica Kashfeen, Mike Kulis, Vivek Kumar, Colton Linnertz, Alessandra Livraghi-Butrico, K. C. Kent Lloyd, Cathleen Lutz, Rachel M. Lynch, Terry Magnuson, Glenn K. Matsushima, Rachel McMullan, Darla R. Miller, Karen L. Mohlke, Sheryl S. Moy, Caroline E.Y. Murphy, Maya Najarian, Lori O’Brien, Abraham A. Palmer, Benjamin D. Philpot, Scott H. Randell, Laura Reinholdt, Yuyu Ren, Steve Rockwood, Allison R. Rogala, Avani Saraswatula, Christopher M. Sassetti, Jonathan C. Schisler, Sarah A. Schoenrock, Ginger D. Shaw, John R. Shorter, Clare M. Smith, Celine L. St. Pierre, Lisa M. Tarantino, David W. Threadgill, William Valdar, Barbara J. Vilen, Keegan Wardwell, Jason K. Whitmire, Lucy Williams, Mark J. Zylka, Martin T. Ferris, Leonard McMillan, Fernando Pardo Manuel de Villena

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 XO 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.

Original languageEnglish (US)
Pages (from-to)905-930
Number of pages26
JournalGenetics
Volume216
Issue number4
DOIs
StatePublished - Dec 2020

Keywords

  • Chromosomal sex
  • Diagnostic SNPs
  • Genetic background
  • Genetic constructs
  • Genetic QC
  • Substrains

ASJC Scopus subject areas

  • Genetics

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