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 Hagan
Ernest G Heimsath
Mark T Heise
Pablo Hock
Folami Ideraabdullah
J Charles Jennette
Tal Kafri
Anwica Kashfeen
Mike Kulis
Vivek Kumar, The Jackson Laboratory
Colton Linnertz
Alessandra Livraghi-Butrico
K C Kent Lloyd
Cathleen Lutz, The Jackson Laboratory
Rachel M Lynch
Terry Magnuson
Glenn K Matsushima
Rachel McMullan
Darla R Miller
Karen L Mohlke
Sheryl S Moy
Caroline Murphy
Maya Najarian
Lori O'Brien
Abraham A Palmer
Benjamin D Philpot
Scott H Randell
Laura G Reinholdt, The Jackson Laboratory
Yuyu Ren
Steve Rockwood, The Jackson Laboratory
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, The Jackson Laboratory
Jason K Whitmire
Lucy Williams
Mark J Zylka
Martin T Ferris
Leonard McMillan
Fernando Pardo-Manuel de Villena


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, 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 6,899 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 the increase rigor and reproducibility of mouse research.