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; 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

doi:10.1534/genetics.120.303596

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

Surgery

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	905-930
Number of pages	26
Journal	Genetics
Volume	216
Issue number	4
DOIs	https://doi.org/10.1534/genetics.120.303596
State	Published - Dec 2020

Keywords

Chromosomal sex
Diagnostic SNPs
Genetic background
Genetic constructs
Genetic QC
Substrains

ASJC Scopus subject areas

Genetics

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10.1534/genetics.120.303596

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Sigmon, J. S., Blanchard, M. W., Baric, R. S., Bell, T. A., Brennan, J., Brockmann, G. A., Wesley Burks, A., Mauro Calabrese, J., Caron, K. M., Cheney, R. E., Ciavatta, D., Conlon, F., Darr, D. B., Faber, J., Franklin, C., Gershon, T. R., Gralinski, L., Gu, B., Gaines, C. H., ... Manuel de Villena, F. P. (2020). Content and performance of the MiniMUGA genotyping array: A new tool to improve rigor and reproducibility in mouse research. Genetics, 216(4), 905-930. https://doi.org/10.1534/genetics.120.303596

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

In: Genetics, Vol. 216, No. 4, 12.2020, p. 905-930.

Research output: Contribution to journal › Article › peer-review

Sigmon, JS, Blanchard, MW, Baric, RS, Bell, TA, Brennan, J, Brockmann, GA, Wesley Burks, A, Mauro Calabrese, J, Caron, KM, Cheney, RE, Ciavatta, D, Conlon, F, Darr, DB, Faber, J, Franklin, C, Gershon, TR, Gralinski, L, Gu, B, Gaines, CH, Hagan, RS, Heimsath, EG, Heise, MT, Hock, P, Ideraabdullah, F, Charles Jennette, J, Kafri, T, Kashfeen, A, Kulis, M, Kumar, V, Linnertz, C, Livraghi-Butrico, A, Kent Lloyd, KC, Lutz, C, Lynch, RM, Magnuson, T, Matsushima, GK, McMullan, R, Miller, DR, Mohlke, KL, Moy, SS, Murphy, CEY, Najarian, M, O’Brien, L, Palmer, AA, Philpot, BD, Randell, SH, Reinholdt, L, Ren, Y, Rockwood, S, Rogala, AR, Saraswatula, A, Sassetti, CM, Schisler, JC, Schoenrock, SA, Shaw, GD, Shorter, JR, Smith, CM, St. Pierre, CL, Tarantino, LM, Threadgill, DW, Valdar, W, Vilen, BJ, Wardwell, K, Whitmire, JK, Williams, L, Zylka, MJ, Ferris, MT, McMillan, L & Manuel de Villena, FP 2020, 'Content and performance of the MiniMUGA genotyping array: A new tool to improve rigor and reproducibility in mouse research', Genetics, vol. 216, no. 4, pp. 905-930. https://doi.org/10.1534/genetics.120.303596

Sigmon, John Sebastian ; Blanchard, Matthew W. ; Baric, Ralph S. ; Bell, Timothy A. ; Brennan, Jennifer ; Brockmann, Gudrun A. ; Wesley Burks, A. ; Mauro Calabrese, J. ; Caron, Kathleen M. ; Cheney, Richard E. ; Ciavatta, Dominic ; Conlon, Frank ; Darr, David B. ; Faber, James ; Franklin, Craig ; Gershon, Timothy R. ; Gralinski, Lisa ; Gu, Bin ; Gaines, Christiann H. ; Hagan, Robert S. ; Heimsath, Ernest G. ; Heise, Mark T. ; Hock, Pablo ; Ideraabdullah, Folami ; Charles Jennette, J. ; Kafri, Tal ; Kashfeen, Anwica ; Kulis, Mike ; Kumar, Vivek ; Linnertz, Colton ; Livraghi-Butrico, Alessandra ; Kent Lloyd, K. C. ; Lutz, Cathleen ; Lynch, Rachel M. ; Magnuson, Terry ; Matsushima, Glenn K. ; McMullan, Rachel ; Miller, Darla R. ; Mohlke, Karen L. ; Moy, Sheryl S. ; Murphy, Caroline E.Y. ; Najarian, Maya ; O’Brien, Lori ; Palmer, Abraham A. ; Philpot, Benjamin D. ; Randell, Scott H. ; Reinholdt, Laura ; Ren, Yuyu ; Rockwood, Steve ; Rogala, Allison R. ; Saraswatula, Avani ; Sassetti, Christopher M. ; Schisler, Jonathan C. ; Schoenrock, Sarah A. ; Shaw, Ginger D. ; Shorter, John R. ; Smith, Clare M. ; St. Pierre, Celine L. ; Tarantino, Lisa M. ; Threadgill, David W. ; Valdar, William ; Vilen, Barbara J. ; Wardwell, Keegan ; Whitmire, Jason K. ; Williams, Lucy ; Zylka, Mark J. ; Ferris, Martin T. ; McMillan, Leonard ; Manuel de Villena, Fernando Pardo. / Content and performance of the MiniMUGA genotyping array : A new tool to improve rigor and reproducibility in mouse research. In: Genetics. 2020 ; Vol. 216, No. 4. pp. 905-930.

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

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.",

keywords = "Chromosomal sex, Diagnostic SNPs, Genetic background, Genetic constructs, Genetic QC, Substrains",

author = "Sigmon, {John Sebastian} and Blanchard, {Matthew W.} and Baric, {Ralph S.} and Bell, {Timothy A.} and Jennifer Brennan and Brockmann, {Gudrun A.} and {Wesley Burks}, A. and {Mauro Calabrese}, J. and Caron, {Kathleen M.} and Cheney, {Richard E.} and Dominic Ciavatta and Frank Conlon and Darr, {David B.} and James Faber and Craig Franklin and Gershon, {Timothy R.} and Lisa Gralinski and Bin Gu and Gaines, {Christiann H.} and Hagan, {Robert S.} and Heimsath, {Ernest G.} and Heise, {Mark T.} and Pablo Hock and Folami Ideraabdullah and {Charles Jennette}, J. and Tal Kafri and Anwica Kashfeen and Mike Kulis and Vivek Kumar and Colton Linnertz and Alessandra Livraghi-Butrico and {Kent Lloyd}, {K. C.} and Cathleen Lutz and Lynch, {Rachel M.} and Terry Magnuson and Matsushima, {Glenn K.} and Rachel McMullan and Miller, {Darla R.} and Mohlke, {Karen L.} and Moy, {Sheryl S.} and Murphy, {Caroline E.Y.} and Maya Najarian and Lori O{\textquoteright}Brien and Palmer, {Abraham A.} and Philpot, {Benjamin D.} and Randell, {Scott H.} and Laura Reinholdt and Yuyu Ren and Steve Rockwood and Rogala, {Allison R.} and Avani Saraswatula and Sassetti, {Christopher M.} and Schisler, {Jonathan C.} and Schoenrock, {Sarah A.} and Shaw, {Ginger D.} and Shorter, {John R.} and Smith, {Clare M.} and {St. Pierre}, {Celine L.} and Tarantino, {Lisa M.} and Threadgill, {David W.} and William Valdar and Vilen, {Barbara J.} and Keegan Wardwell and Whitmire, {Jason K.} and Lucy Williams and Zylka, {Mark J.} and Ferris, {Martin T.} and Leonard McMillan and {Manuel de Villena}, {Fernando Pardo}",

year = "2020",

month = dec,

doi = "10.1534/genetics.120.303596",

language = "English (US)",

volume = "216",

pages = "905--930",

journal = "Genetics",

issn = "0016-6731",

publisher = "Genetics Society of America",

number = "4",

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TY - JOUR

T1 - Content and performance of the MiniMUGA genotyping array

T2 - A new tool to improve rigor and reproducibility in mouse research

AU - Sigmon, John Sebastian

AU - Blanchard, Matthew W.

AU - Baric, Ralph S.

AU - Bell, Timothy A.

AU - Brennan, Jennifer

AU - Brockmann, Gudrun A.

AU - Wesley Burks, A.

AU - Mauro Calabrese, J.

AU - Caron, Kathleen M.

AU - Cheney, Richard E.

AU - Ciavatta, Dominic

AU - Conlon, Frank

AU - Darr, David B.

AU - Faber, James

AU - Franklin, Craig

AU - Gershon, Timothy R.

AU - Gralinski, Lisa

AU - Gu, Bin

AU - Gaines, Christiann H.

AU - Hagan, Robert S.

AU - Heimsath, Ernest G.

AU - Heise, Mark T.

AU - Hock, Pablo

AU - Ideraabdullah, Folami

AU - Charles Jennette, J.

AU - Kafri, Tal

AU - Kashfeen, Anwica

AU - Kulis, Mike

AU - Kumar, Vivek

AU - Linnertz, Colton

AU - Livraghi-Butrico, Alessandra

AU - Kent Lloyd, K. C.

AU - Lutz, Cathleen

AU - Lynch, Rachel M.

AU - Magnuson, Terry

AU - Matsushima, Glenn K.

AU - McMullan, Rachel

AU - Miller, Darla R.

AU - Mohlke, Karen L.

AU - Moy, Sheryl S.

AU - Murphy, Caroline E.Y.

AU - Najarian, Maya

AU - O’Brien, Lori

AU - Palmer, Abraham A.

AU - Philpot, Benjamin D.

AU - Randell, Scott H.

AU - Reinholdt, Laura

AU - Ren, Yuyu

AU - Rockwood, Steve

AU - Rogala, Allison R.

AU - Saraswatula, Avani

AU - Sassetti, Christopher M.

AU - Schisler, Jonathan C.

AU - Schoenrock, Sarah A.

AU - Shaw, Ginger D.

AU - Shorter, John R.

AU - Smith, Clare M.

AU - St. Pierre, Celine L.

AU - Tarantino, Lisa M.

AU - Threadgill, David W.

AU - Valdar, William

AU - Vilen, Barbara J.

AU - Wardwell, Keegan

AU - Whitmire, Jason K.

AU - Williams, Lucy

AU - Zylka, Mark J.

AU - Ferris, Martin T.

AU - McMillan, Leonard

AU - Manuel de Villena, Fernando Pardo

PY - 2020/12

Y1 - 2020/12

N2 - 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.

AB - 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.

KW - Chromosomal sex

KW - Diagnostic SNPs

KW - Genetic background

KW - Genetic constructs

KW - Genetic QC

KW - Substrains

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