International Journal of Genomics
Volume 2015 (2015), Article ID 735845, 17 pages
http://dx.doi.org/10.1155/2015/735845

Research Article

Validation of Simple Sequence Length Polymorphism Regions of Commonly Used Mouse Strains for Marker Assisted Speed Congenics Screening

Channabasavaiah B. Gurumurthy,¹ Poonam S. Joshi,¹ Scott G. Kurz,¹ Masato Ohtsuka,² Rolen M. Quadros,¹ Donald W. Harms,¹ and K. C. Kent Lloyd³

¹Mouse Genome Engineering Core Facility, Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5915, USA
²Department of Molecular Life Science, Division of Basic Medical Science and Molecular Medicine, Tokai University School of Medicine, 143 Shimokasuya, Isehara, Kanagawa 259-1193, Japan
³Mouse Biology Program, University of California, Davis, 2795 2nd Street, Davis, CA 95618, USA

Received 1 November 2014; Revised 30 December 2014; Accepted 10 January 2015

Academic Editor: Mohamed Salem

Copyright © 2015 Channabasavaiah B. Gurumurthy et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Marker assisted speed congenics technique is commonly used to facilitate backcrossing of mouse strains in nearly half the time it normally takes otherwise. Traditionally, the technique is performed by analyzing PCR amplified regions of simple sequence length polymorphism (SSLP) markers between the recipient and donor strains: offspring with the highest number of markers showing the recipient genome across all chromosomes is chosen for the next generation. Although there are well-defined panels of SSLP makers established between certain pairs of mice strains, they are incomplete for most strains. The availability of well-established marker sets for speed congenic screens would enable the scientific community to transfer mutations across strain backgrounds. In this study, we tested the suitability of over 400 SSLP marker sets among 10 mouse strains commonly used for generating genetically engineered models. The panel of markers presented here can readily identify the specified strains and will be quite useful in marker assisted speed congenic screens. Moreover, unlike newer single nucleotide polymorphism (SNP) array methods which require sophisticated equipment, the SSLP markers panel described here only uses PCR and agarose gel electrophoresis of amplified products; therefore it can be performed in most research laboratories.

1. Introduction

In recent years, there has been a steady increase in the creation and use of genetically engineered mutant mice for use in biomedical research. Frequently, the genetic background of such mice allows only specific experiments and the mutations have to be transferred to different genetic background(s) to facilitate other kinds of experiments. There are many examples where genetic background is shown to influence the phenotype of a transgenic or knockout mouse line [1–12]. Traditionally however, mutant mice are generated using strains that have shown exceptional performance in terms of their suitability for production of transgenic or knockout mice lines. For example, FVB strain and BDF1 strain mice are most commonly used for transgenic mice production [13] and ES (embryonic stem) cells derived from 129 inbred strains are commonly used for knockout mice production. For technical reasons, chimeras developed in knockout mice generation will carry a mixed genetic background (e.g., 129 and B6) adding further complexity to the analysis [14]. Furthermore, ES cells derived from C57BL6/N inbred strain have been used in mouse genetic resources such as KOMP (Knock-Out Mouse Project) and EUCOMM (European Conditional Mouse Mutagenesis) [15]. Also, particular strains of mice seem to have better suitability for a specific research purpose. In addition, mixed background strains between C57BL/6J and DBA/2J were used for ENU mutagenesis projects because the cross between two different inbred strains (one strain (male) was used for ENU exposure and mated with another strain (female)) is useful for mapping and positional cloning of the mutated gene using genetic polymorphisms existing between them [16, 17]. The main disadvantage of a given mutation under a particular strain background is that the strain background may limit its use for a specific research purpose. In such a situation the mutation is needed to be transferred into a strain background of choice through a process called backcrossing.

Backcrossing involves about 10 generations of successive breeding into a recipient strain of choice to achieve 99.9% congenic (genetic composition) for that strain (http://www.informatics.jax.org/silverbook/). This painstaking process consumes about 2.5 to 3 years of time, a fact that often limits its feasibility and usefulness given the pace of scientific research. In some cases, studies are published with animals after only 5 generations of backcrossing, in an attempt to compensate the time required and the need to obtain some results in the new strain [18]. A technique called “marker assisted speed congenic” used for over a decade helps in achieving congenic strain in 5 or less, unlike the usual 10 generations required in traditional backcrossing [19, 20]. The small sequence differences between mouse strains called “microsatellite markers” served as useful tools in detecting the chromosome regions of origin in the offspring when two inbred strains of mice are bred together. To use in these assays, many microsatellite markers have been identified and characterized by various researchers between the donor and recipient strains of their choice ([21] and [22, page 6] and [23–30]). However, the information about the markers that can differentiate between commonly used strains for transgenic and knockout mice generation is not tested sufficiently and is not available readily. In this study, we tested 423 markers, ~10 to 30 per chromosome, using the genomic DNAs from 10 commonly used mice strains—particularly the strains used in transgenic and knockout research. We evaluated the markers that could be used in the agarose gel electrophoresis method which is a simple technique commonly used in most molecular biology laboratories these days. The data presented here will serve as a valuable tool for various investigators in choosing the markers useful for their speed congenic breeding.

2. Materials and Methods

2.1. Selection of Oligonucleotide Primers

The primers were chosen based on the following criteria: (1) evaluation and establishment of at least 6 markers per chromosome, (2) distance between the adjacent markers kept as minimum as 10 to 15 centimorgans (cM), and (3) polymorphic bands appreciable when analyzed in a 4% agarose gel electrophoresis and resolved by electrophoresis distance of up to 10 centimeters from the loading wells. All markers were chosen from the Mouse Genome Informatics (MGI) database links (http://www.informatics.jax.org/searches/probe_report.cgi?_Refs_key=22816 and ftp://ftp.informatics.jax.org/pub/datasets/index.html (numbers 7 and 12 in the list)).

2.2. Mice Strains

The mice were purchased from Charles River Laboratories or The Jackson Laboratory. The strains, the rationale for including these strains in the panel, and the vendors are listed in Table 1.

Table 1

Strain
Vendor, stock number
Rationale

C57BL/6J
JaxMice, 000664
Commonly used strain in biomedical research

FVB/NJ
JaxMice, 001800
Commonly used strain for transgenic mice generation

129 X1/SvJ
JaxMice, 000691
Strains from which highly successful germ line transmission competent ES cells were derived

129S2
Charles River, 476

DBA2/J
JaxMice, 000671
Together with C57BL6, these strains are used as hybrid strains for transgenic mouse production

CBA/J
JaxMice, 000656

SJL/J
JaxMice, 000686

Balb/c
Charles River, 028
Is among the top 2-3 most widely used inbred strains

C3H/HeJ
Charles River 025
Used in a variety of research areas including cancer, infectious disease, and cardiovascular biology

NOD
Charles River

2.3. DNA Extraction, PCR Amplification, and Agarose Gel Electrophoresis

The DNA samples were extracted from the tail pieces of about 3–5 mm length using Gentra Puregene Tissue Kit (Cat. # 158622). Twenty ul PCR reactions were set up using 1X reaction buffer containing 20 mM Tris pH 8.4, 50 mM KCl, 3 mM MgCl₂, and 1 unit of Taq DNA polymerase (New England Biolabs, Cat. # M0273) under the following conditions: one cycle of 95°C –2 min followed by 35 cycles of 95°C –30 sec, 55°C – 30 sec, and 72°C–60 sec and one cycle of 72°C – 5 minutes, followed by a holding temperature at 4°C until the samples were removed from the machine. Fifteen ul of PCR products was resolved using 4% agarose gels for 120 to 150 minutes at 200-constant-volt electric current. The agarose was purchased from Phenix Research Products (Item Number RBA-500: Molecular Biology Grade) and the gels were prepared on 0.5X TAE buffer diluted from a stock of 50X (Fisher Scientific, Cat. # BP1332-20). The gels contained ethidium bromide dye (0.5 μg/mL) to aid the visualization of PCR bands. Each panel of gel included one or more lanes of 100-base-pair molecular weight marker (New England Biolabs, Cat. # N3231) to assess the PCR product sizes. The bromophenol blue dye-front was allowed to run for up to 10 centimeters from the wells and the gels were imaged using BioRad Gel Doc XR system. Wherever necessary, the gels were run longer to resolve the bands.

2.4. Analysis and Interpretation of Polymorphic PCR Bands

The cropped images were imported into an Excel file for analysis and interpretation. Band sizes were assigned numbers 1, 2, 3, or 4 to indicate their sizes compared to the rest of the bands in that set. Number 1 was assigned to the smallest sized polymorphic band, 2 to the next biggest in the group, and so on. The Excel file along with the gel images was converted into a  .pdf file for generating Figure 1.

Figure 1: Agarose gel images of PCR bands and their interpretation. Bands were assigned numbers 1, 2, 3, or 4 to indicate their sizes compared to the rest of the bands in that set. Number 1 was assigned to the smallest sized polymorphic band. 2, 3, and 4 represent the successive bigger sized bands.

3. Results and Discussion

Mutant mice created using transgenic and knockout techniques are available under certain specific backgrounds. In order to best use such mutants for a specific research purpose, they routinely need to be bred into other strain backgrounds through successive breeding of about 10 generations. A quicker way to attain highest recipient genome can be achieved by a process called speed congenic breeding in which the polymorphic markers between the recipient and donor strains are screened among offspring in each generation and the offspring with highest recipient genome is chosen as a breeder for the subsequent generation [19–21]. Although there are a few reports describing the marker sets suitable for certain pairs of strains, there are no well-established marker sets available across the most commonly used strains in transgenic and knockout mouse techniques. Our primary objective of this work was to test the suitability of several markers in an agarose gel electrophoresis method, a technique that uses least expensive equipment and reagents and is readily available in most molecular biology laboratories. We sought to test a large number of microsatellite markers (Tables 2 and 3 and S1 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/735845) among 10 most commonly used mouse strains particularly those used in transgenic and knockout mice techniques.

Table 2: Primer sequences used for PCR amplification of SSLP markers.

Marker
cM
Primer F
Primer R
Product size
Lab code

D1Mit231
6.5
ACCCACAATTGCCTGTGG
GTCTTTGCAAGCCACCAAAT
267
1-1

D1Mit211
10.59
GTTATTCATCAAAATACAGATGGCC
TCTGCTGCTAAGTAGAATGAATGC
135
1-2B

D1Mit213
22.88
TTCTTAGAAGTGATAAAAGTTTCAGCA
AAAATTCCAGAATTCTCACTACGG
108
1-2

D1Mit303
31.79
GGTTTCTATTTCGGTTCTCGG
TCTGTGCTGCAAAACAGAGG
128
1-3

D1Mit46
39.16
AGTCAGTCAGGGCTACATGATG
CACGGGTGCTCTATTTGGAA
253
1-4

D1Mit440
44.98
TCCACACAAGGTGTCCTCTG
GCTCAGGTGACCTCCAAAAC
114
1-5B

D1Mit218
55.76
TGCAAATGTTACTTTAGTCTCTAGTGG
AGTTTTGGTGAGTGATCTTATCCC
145
1-218

D1Mit369
63.51
ACTTGTTTGTTGCTGAGGTTCA
GCTTATGAACCCACCCTCAA
141
1-5Q

D1Mit200
63.84
GCCATGTTCATGTACATAGGTAGG
ATGGATGGATGGTTTTCCTG
199
1-200

D1Mit507
72.38
GTTGGAAAGACTGTTTACAATTCG
GTTCCAGCCTTTGCCCTTAC
115
1-6

D1Mit150
81.08
CTGGTGTCCACACACAGGTC
TGATTGCAATATACCAGGTTTCC
138
1-150

D1Mit407
89.89
GAGAACAACCAGCCACCAAT
ATATTTGCTTTGAAGTTACTTTGTGTG
119
1-407

D1Mit155
98.2
ATGCATGCATGCACACGT
ACCGTGAAATGTTCACCCAT
252
1-7

D2Mit1
2.23
CTTTTTCGTATGTGGTGGGG
AACATTGGGCCTCTATGCAC
123
2-1

D2Mit149
10.04
ATATCATATAGTAGAGAAAGCGTGCTG
TCATTAGACTTGGAAAAAAGTTTGC
199
2-1P

D2Mit152
21.81
CACAGATCTTGTAAGACCACGTG
TGCCATGAGTGTGGGACTAA
109
2-2

D2Mit367
22.5
GCCTGTGCTAAAAAAGAGGTG
GCCCTGAGAACTACCCTCCT
149
2-367

D2Mit323
31.42
AGAATCCTAAGTGGTGGTTAGAGG
ACCCAAAGTTGTCTTTAAGTACACA
125
2-2P

D2Mit328
42.89
CTTTCAATGTTCCGGCATG
AAGACTTGCTTTCATTAGACCACA
235
2-3

D2Mit42
54.85
ATTACTGGGCAGGAACATTTG
GCCAAACTTCCAGACTCCTC
132
2-3B

D2Mit395
59.97
AGGTCAGCCTGGACTATATGG
AGCATCCATGGGATAATGGT
125
2-4

D2Mit106
64.78
GAGGGTTGCCAAAGAGACTG
CACCTCAGGGGAACATTGTG
150
2-4P

D2Mit107
65.13
GGGAGTGAAGCCAGCATAAG
AACTGACTGAGTTTCAAAGTGCC
119
2-107

D2Mit309
75.03
ACAAATGCCACTCTCACATCC
TATTTCTCAGAGTCACTAGGAGTGATG
119
2-309

D2Mit285
75.41
TCAATCCCTGTCTGTGGTAGG
TATGACACTTACAAGGTTTTTGGTG
141
2-5

D2Mit48
77.36
GCTCTGCAGAAGATGCTGC
GCTGAGACGCAGAGTCGC
130
2-5B

D2Mit311
86.02
ACAGGCAGCCTTCCCTTC
TCTGTCCCGCTTCTGTTTCT
126
2-5D

D2Mit265
97.97
AATAATAATCAAGGTTGTCATTGAACC
TAGTCAAAATTCTTTTGTGTGTTGC
105
2-6P

D2Mit266
103.83
GGATCTATGCTCCATTTTAATTGC
TCATCTTCTGGTTTCAACATGG
127
2-6

D2Mit148
100.49
GTTCTCTGATCTACGGGCATG
TTCACTTCTACAAGTTCTACAAGTTCC
115
2-148

D3Mit60
1.96
GACATCCTGGGCAACATTG
GGTGTTGTTTGCTGTTGCTG
170
3-1

D3Mit164
2.01
GCTCCTGGGAAAGGAAGAAT
GATACTTGGGGTTGTGCATACA
135
3-164

D3Mit203
10.82
CTGAATCCTTATGTCCACTGAGG
GGGCACCTGCATTCATGT
150
3-2

D3Mit51
35.2
GGCACTGATAGCAGGCCTAG
TCTCTTCTGGTATTTCCTTCCG
248
3-3

D3Mit 49
39.02
CTTTTCTCGCCCCACTTTC
TCCTTTTAGTTTTTGATCCTCTGG
132
3-49

D3Mit10 F
43.03
CTGGCTTGGTGGAAGTCCT
CCTAAGCCAGCTACCACCAC
142
3 10

D3Mit189
43.89
GTTACCACCCAGAGAAAAGGC
TACTCCTCGCTGCTTCCCTA
133
3-189

D3Mit318
54.58
CTCATTCCTTCTGAGCAATGG
TATGGGATATGCTTTTCATAAAAGG
148
3-318

D3Mit256
63.04
TACATTGCTTTTTGCTTTGAGTG
GTCGAATGTTATCAGAATTTGCA
125
3-4

D3Mit116
79.23
TCACTGCCCATCTTTGTAACC
CCCAGAGACCCGGAATAGAA
259
3-116

D3Mit19
87.6
CAGCCAGAGAGGAGCTGTCT
GAACATTGGGGTGTTTGCTT
159
3-5

D4Mit227
4.43
CTCAGACATGATTTTTTCCAAGG
GCAGTTAAACTGTACTTTCTGTAAACA
181
4-1

D4Mit193
13.99
TATTTTAATTTTAGCCCATCAGGG
AAAGACATACAATTGATCCACAGG
136
4-1B

D4Mit17
33.96
TGGCCAACCTCTGTGCTTCC
ACAGTTGTCCTCTGACATCC
147
4-2

D4Mit27
42.13
GCACGGTAGTTTTTCCAGGA
TGGTGGGCAGGCAATAGT
150
4-2B

D4Mit31
50.04
ACGAGTTGTCCTCTGATCAACA
AGCCAGAGCAAACACCAACT
121
4-2C

D4Mit308
57.66
TATGGATCCACTCTCCAGAAA
CAAAGTCTCCTCCAAGGCTG
88
4-3

D4Mit203
63.26
GAATTCTTCCTGGGCCTTTC
CAAGAGCCCAGGTGTGGTAT
105
4-3B

D4Mit251
69.05
AAAAATCGTTCTTTGACTTCTACATG
TTTAAAAGGGTTTCTTTATCCTGTG
114
4-4

D4Mit354
76.12
TTGATCTGTCGTGGATTCCA
AGACAGACACATAGACACAGACATAGG
111
4-4B

D4Mit42
82.64
CATGTTTGCCACCCTGAAAC
CCTCACTTAGGCAGGTGACTC
100
4-5

D5Mit145
3.37
TATCAGCAATACAGACTCAGTAGGC
TGCCCCTTAAATTCATGGTC
150
5-1

D5Mit73
12.88
GTTTGAGAGGTCCTGAAAGCA
TTTCCATTTACATACATTTGTGCA
113
5-1B

D5Mit352
18.4
CCCAGAGCCCACATCAAG
TAGGTGGGTGTGTCTCTCCC
110
5-2

D5Mit233
28.55
TCCCCTCTGATCTCCTCAGA
CCTCCTAGAATACAATTCAATGTGG
147
5-3

D5Mit307
40.31
AGTGGCATAACTTCATTCAATAATG
GGAATCAAGTTGTTTTTTAAATTTACC
120
5-3P

D5Mit277
51.7
GTGTGTTTGTGCATGGGTATG
ACCATCGGGAAAAAATGTAGC
123
5-4B

D5Mit161
65.34
CACACGCATAGTCTTGTGGG
GCATGTTCAACTGTGCTTTCA
119
5-5B

D5Mit99
79.51
CAGAAAAGAGAAAACGGAGGG
TTCCTGCTGCCTGAAGTTTT
200
5-5C

D6Mit138
1.81
GCTCTTATTAATGAAGAAGAAGGAGG
CAAAGAAAGCATTTCAAGACTGC
135
6-1

D6Mit296
2.25
TCGGGCATCTTTATTTTTGC
TAGTGCAGCACACCCCCT
100
6-296

D6Mit116
11.5
ACATTTCTTTGTGAGGTTCCTTG
CAGGTTTTTTGAAAGACACTCTTG
122
6-1B

D6Mit274
23.7
GCAATGCCAAAATGTTCAAA
TCCTTCTCCATTTACACTTACAACA
113
6-1D

D6Mit8
35.94
TGCACAGCAGCTCATTCTCT
GGAAGGAAGGAGTGGGGTAG
163
6-2B

D6Mit100
41.03
CTTGAGTAGGTCTCAGTGCGG
CACATGCACACACAGAAGCA
84
6-3

D6Mit36
48.93
ACCATCTGCATGGACTCACA
GTTGAAGAGGACGACCAAGTG
196
6-4

D6Mit10
52.75
TCAGAGGAACAAAGCAGCAT
CCTGTGGCTAACAGGTAAAA
200
6 10

D6Mit333
60.55
TCCTCACTACAATTCATCTATTACTGC
TGCTTCTGGTATAGGCAGTTAGG
122
6-333

D6Mit302
67.66
AATGACCCTGGTTAGTGTCAGG
GAATTCCATTCGAGGGGC
113
6-4C

D6Mit14
77.64
ATGCAGAAACATGAGTGGGG
CACAAGGCCTGATGACCTCT
157
6-5

D7Mit21
1.91
GGGTTGAACCTTACAGGGGT
ATCAAACCAGCCCAAGTGAC
192
7-0A

D7Mit152
2.71
GCCTAGCACACGCCAAAG
CCTTGTGCATGGTTGCTATG
129
7-152

D7Mit57
9.94
TTCCCTCTAGAACTCTGACCTCC
AGTTCAGAGCCGAGACTAGGC
148
7.-1

D7Mit267
17.09
CTCTTTCTGTTACATGGTTAGATTTCC
AAAGACAGTTGAAGTTGACTTCTGG
192
7-2

D7Mit158
29.82
CTTCATCTGAGCCTGGGAAG
ACTGTAGACCCATGTTCTGATTAGG
149
7-158

D7Mit317
41.5
ATGTCTCCTTGACATTGGGC
TCTTGTAATCTCACATCTAAGTGTGTG
102
7-2D

D7Mit194
44.83
GTGCAACACACAGAAAAGTTCC
AAAGGCTCACAACGGACTGT
146
7-194

D7Mit220
55.69
AAGCATGCAAGCACACTCAC
ATGCACACAGGCAGTCACTC
135
7-3A

D7Mit101
69.01
TACAGTGTGAACATGTAGGGGTG
TCCCAACATGGATGTGCTAA
111
7-4

D7Mit223
88.85
ATGCACATGAGTGTGTGTATGC
TCCTGTGTCTGACGCTCATC
106
7-5

D8Mit143
11.59
AGCCTGAGGTTATGTTTTTTGC
GGCCCTCAGGTTTTCTCTCT
279
8-143

D8Mit178
34.43
AAAATCAACTGTTTACATTTGAGCC
AGAGCACGCAGTGTGTATGC
148
8-2

D8Mit45
42.16
GAACAGGACCAATAAAATGAAAGC
CTACCTTACCAAACTTCCCGG
121
8-45

D8Mit80
43.06
TGCATTTGTCAGGGCTCTC
ATGACACATGAGCCTCCACA
107
8-080

D8Mit211
52
CAGAACACTGTCCTGAAAAGTCC
TACCCACAAACCTGTATTTAAATTAA
149
8-4

D8Mit215
62.63
AATACACAAGGTTGGCCTCA
ATGTGTGGATATTCATGTGCTC
178
8-5

D8Mit56
76.14
ACACTCAGAGACCATGAGTACACC
GAGTTCACTACCCACAAGTCTCC
162
8-56

D9Mit250
2.46
CCCAAAAACCTATTTGCAGTG
GTGACATGATTCCTTCAGTCTTACC
123
9-1

D9Mit249
2.46
AAGCCCTCTTAGAAGTAGTGTGTATG
AGCCATGAACTAACTTACATGTATCA
125
9-249

D9Mit89
14.79
CACATACAAGGATATACATACACAGGC
TCACAGGAGGTGGCAGAAAT
145
9-89

D9Mit285
21.42
CAAATACATTGCTGATTATATCAGAGA
GGACTCTAGATCTCATCAGGGA
125
9-2

D9Mit336
35.39
AAGTGGTTCACAGAAATGTATACAGG
TTTTCTTTCTGTGGTAAAGGGG
122
9-3

D9Mit8
42.65
GATGAAGACAATAAAGAACCTTAAA
AAGAGCTAACCCATTGCTGC
183
9-3B

D9Mit347
55.11
CCTCCACATGTGCACTGCT
CTGTCCATCTATCATCTATCTGTCTG
122
9-4

D9Mit18
71.49
TCACTGTAGCCCAGAGCAGT
CCTGTTGTCAACACCTGATG
180
9-5

D10Mit28
3.04
CCTCCTGTATGTGTATTTAAAGCA
CTGCCCATCTGACCCTGATA
147
10-1A

D10Mit213
9.75
CTCCTCCTACTGATTGTCCCC
GGGACAAACTTTTAAAAATTGCA
150
10-2

D10Mit108
22.89
TGCCTGTAACCTGCATACCC
GTTTAACACCCAGGACTATACATGG
142
10-108

D10Mit20
34.83
CACCCTCACACAGATATGCG
GCATTGGGAAGTCCATGAGT
234
10-3

D10Mit230
45.28
AGATAGCCTAGGGGGTGCAT
ATCAGTTTCCAATCGCTGCT
115
10-4

D10Mit96
51.19
CTTCTTTGAAGTTAGATGCAGCC
TACGGAGAAGGGAACACCTG
150
10-096

D10Mit178
51.42
ATTGTCAAATATCTTCCTCAGTTGC
TTATTCCTAGGCAGTCTGTCTGG
133
10-4B

D10Mit233
61.58
GTGCTTTATATTGGAGATCATCACA
GTCCCGAATTTCACATACATAGC
130
10-5

D10Mit271
72.31
ACAACCAAAGGTCTTTGTAGAAGA
AATATATAGGCACACCTTAATAGCCA
117
10-6

D11Mit1
2.51
GGGTCTCTGAAGGCTTTGTG
TGAATACAGAAGCCACGGTG
153
11-1P

D11Mit226
5.64
AGGTGAACTCTTTTGAAGTTTGTG
AAAGGAGTGACTGAGAAAGACACC
139
11-226

D11Mit151
15.29
TGGGAATTCTGGGAGTTCTG
GTTGGTCTGTTATGAAGACCAGG
140
11-1Q

D11Mit217
23.1
ACTGGAAAATATGTTTTAAACCTCTG
AAATGGGATTCTGCAAAAACC
135
11-217

D11Mit349
33.29
AGTATCAGAAGATCCAGTTGGAGG
GTAGAAAAAGATACCCAGTGTCAGC
118
11-349

D11Mit298
42.76
AAACAAACAAAAATGCACCTCA
GTACCACCATGCCTAGCCTC
199
11-298

D11Mit39
52.22
TTTCATGACCCCTAATTTCCC
GTGGGTGTGCCTGTCAATC
155
11-39

D11Mit70
58.9
GGAAGTAGCTATGGAGGTGGC
TCTGACCCAGAGCTCAAATACA
140
11-70

D11Mit54
59.82
AGGCTGGTGGCTAGTGTCC
AAGTCTTGCGCTGCATCTTT
144
11-4A

D11Mit132
62.92
GGTCAGAGGACAATCTTACATGC
GTTCCAAGACAATGAGAGACCC
117
11-4B

D11Mit333
71.83
CATGTGGTTATTTTCTAGCCCC
AGGCATCAATAACTATTTTTCAGTG
125
11-5

D11Mit303
82.9
TCAATCTCTCAAGTTTTTCCAGC
GACAACGTTGACCTCCACG
106
11-6

D12Mit12
8.49
TTCAATGCCTTCTGGCTTCT
GATTACCGGGTGTGTGACCT
145
12-1

D12Mit2
18.94
ACACAGGCTAAAACATGGGC
GCATCTGTATTCCACAGGCA
134
12-2

D12Mit114
28.94
TTGACCTTGAACTTGTGACCC
GTTTTCTCCAAATCACTGTCACC
144
12-3

D12Mit149
36.85
CATGGCACACATACATACATGTG
AACATAGCAATGGTATATAGGTATGGG
132
12-3B

D12Mit117
43.32
AATTGAGGAACTTAGAAGAAAAGCC
CCTCTGGCCACCATACATG
127
12-117

D12Mit30
50.43
TATGTGACTGCAATCCCAGC
ATGAACACATCATGCCCAGA
107
12-30

D12Mit99
52.9
CTTACAGAAAATGAAAACCAAAACA
CCTCTGCTTTAGAGGCAAACG
151
12-099

D12Mit263
62.11
TCAGATCTCAGCAGATAAATACTTGG
TCCCCTGGAGCATATTTGAC
113
12-6

D12nDs2
62.22
ACATGGTAATTTATGGGCAA
CTGGATACCTGCAATAGTAGA
195
12-5

D13Mit16
7.26
CCAGCTGAAGGCTTACTCGT
AAAGTTAGAATCAGCCATTCAAGG
207
13-1

D13Mit137
18.87
GAATCAGAGAACCTGGCTGTG
TCTAAAAGAGAGAAAATTGGGGC
131
13-137

D13Mit63
21
GAGATGGAAGGAAGAGATGGC
CAAATGCATGTATCCGTATGTG
139
13-63

D13Mit186
31.87
GAAAGCCCTAGGGGAAGATG
TGCAGTTTCTAAGGTTAAAACTAAAGC
149
13-3

D13Mit282
32.59
TCGCACTTCCTATACAGTTATAAGAG
GGACAGAAAGCATGCAGAGG
125
13-282

D13Mit191
45.05
GCAAATTAGAGAATCATGCATCC
TGGAGAATGCTAAAAGCATGG
119
13-3B

D13Mit51
56.45
TCCTGCAAAAGTGGAGCC
TGGAAACAAGCTCTTGGAGG
146
13-3P

D13Mit213
59.69
GCCTGAAACTCTACATAAAATACATCC
AGTTTCATTGCTTTAGTTACATTTTCA
149
13-5

D13Mit78
67.21
ACAGCACGGGTTTATCATCC
TATGCCTGCCAGGCTTCTAT
229
13-6

D14Mit179
4.92
CCACTTGCAGCATTGACAAT
CAAACATCTGTGACAATAAAATTTCA
144
14-1P

D14Mit126
11.94
CCTGTCCCACAACACCTTTT
TATACATATGGGTAGCACTGAGTGG
140
14-2

D14Mit60
24.6
AGGCTGCCCATAAAAGGG
GTTTGTGCTAATGTTCTCATCTGG
132
14-3

D14Mit259
27.65
TGGTGTCTCCTTCGGAATTT
TAAATGTAAAAGGTAAAGGCAATGG
125
14-259

D14Mit39
35.69
AAAGAGCAACCCCCAATTCT
ACTTTTACCTGGTCTCCAAAAGC
246
14-39

D14Mit68
37.61
GTGGCATGCACAACCGTATA
CCCTTTTGAGGTGCTTGTTT
153
14-4

D14Mit194
45.96
AATATTCTAAATGAAATCCAATGTGTG
TTAATTGCAAGTAACACAATGAGTAGG
92
14-4B

D14Mit95
57.2
TATTTTTAAGTCAGTATACACATGCGC
TTATCCAAGTGTATTTAAAGAAGAGGC
124
14-5

D14Mit97
62.2
TCAGTCCAAACTCTGTTAATCTTCC
CAGCTCCACATTTTTGCTCA
136
14-6A

D15Mit13
1.84
GGAGACAAAAATGAACTCCTGG
TTGTAAGACAAGCATAGCTCAACA
136
15-13

D15Mit265
6.08
ACATTAGTCAACTATGCTGGTACTCTG
TTCCTCTCTAATGTCAATTGTTTCA
198
15-265

D15Mit138
15.68
TTCAATTCCCTTTTGTCAAATG
CAAGACCCTAGATTCAGTCTACCC
147
15-138

D15Mit154
16.82
AGCACTGGGTACACAAACTGG
ATGAAAGCATGTGTAGTCTTTCTCA
150
15-2

D15Mit128
25.58
CAAGTTCTGCAAAGAATTATTTATGC
CCACTTTGAAGTTTTCTTTCTTAGC
134
15-3

D15Mit92
32.19
AGTCTCTCTCCCCCTTCTCTC
TGCCACAAGCACAATAGTATCC
147
15-3B

D15Mit80
38.02
CATTGAGGGTTTGTAGGTTGG
ACCCCTGCAAGTTGTCTTTG
149
15-4

D15Mit34
45.31
TGGACAACCATTTTGGACAA
CTTTCTGTCAGGCATCACCA
147
15-34

D15Mit244
48.65
TCTACCCTCTGTGGAACATCG
CTTTGTGTCCATACACTAATATCAAGG
116
15-4P

D15Mit16
58.05
AGACTCAGAGGGCAAAATAAAGC
TCGGCTTTTGTCTGTCTGTC
119
15-6

D16Mit131
3.41
TGGTGGTGGTGTTGATGGTA
AAGACCATTTCTAATAAACAACACCC
140
16-1

D16Mit136
27.82
AGATAATTCCCGTGAGAATAAAACC
TTGAGAAGTTTGCCCTATAATGG
129
16-2

D16Mit30
33.01
GTGCACATACATACCACAGCG
TCACTGCAGGGAGGTTCAG
152
16-30

D16Mit64
34.22
TACCATGATCAGTCCAAAGGC
ACTTAAGGTTGTCCTGTGGGG
220
16-3

D16Mit140
40.3
ATAGTTGAAAAACTTGAACATGCG
GAAAAGGTTAATGCTGGTCACC
145
16-3B

D16Mit19
45.36
CAGGCATGTGAACAAAGTGG
GTGACTGATGAATGCCTGACA
121
16-3C

D16Mit70
48.81
GGATCTATATGCTATAGAACCATTCA
GTCATCAATTCCATTTCCTAATATAGA
187
16-4

D16Mit71
57.06
TAGAAAATCTTCAAATAGGATCTGTTC
GAGCATTTCCCTTTTACCTGG
154
16-6A

D17Mit164
2.11
AGGCCCTAACATGTAGCAGG
TATTATTGAGACTGTGGTTGTTGTTG
133
17-1

D17Mit133
12.53
TCTGCTGTGTTCACAGGTGA
GCCCCTGCTAGATCTGACAG
188
17-1C

D17Mit51
19.74
TCTGCCCTGTAACAGGAGCT
CTTCTGGAATCAGAGGATCCC
154
17-2

D17Mit68
23.55
GTCCTGACATCATGCTTTGTG
CTACCGTTTGGAAGGCTGAG
130
17-68

D17Mit20
29.73
AGAACAGGACACCGGACATC
TCATAAGTAGGCACACCAATGC
165
17-3

D17Mit205
39.3
TGTGCATGTATATGTGTGTGAATG
GCTAAGTCAGAAGAGTTCTGTAATGG
223
17-4

D17Mit1002
50.97
TCTGAATGCTGACTTCCATCC
ACACATATGTGTAGTGTATGAATGTGC
138
17-4P

D17Mit123
60.67
CACAAGGAGGGAGCCTGTAG
CACCGTAAGAGTCTAATAATAAGGGG
133
17-6

D18Mit222
8.08
AATCCAAGATTGACATGTGGC
CTTAGATGCCCTGTCTTAAAAAAA
113
18-1

D18Mit226
18.18
CAGGCAGGGTGCATATATTATAA
TATCTGTTTATGTGTGTACATTGTGTG
125
18-1C

D18Mit177
21.39
CTGTAGTTTATCAGTTCACCCTGTG
TGTGCTGTTAAACAAATATCTCTGG
171
18-2

D18Mit91
29.67
TCCACAAATGTTGGCAAAGA
TTTCTGGCCATATTGGAAGC
140
18-3

D18Mit124
32.15
CCCAAATGGGGTGTCTTTTA
CTGCCACACATTTGTGTGTATG
150
18-4

D18Mit184
39.7
CACACATGTGTAGGTAGGTAGGTAGG
CGCACAAGGACTACTGAAACA
172
18-4C

D18Mit186
45.63
AAGTGTTGGGCAAAGGCTAA
CTTTAGTATAGTGTGCATGAGTGTGA
125
18-5

D18Mit7
51.92
ACAGGAGAACGGGAACTCAG
GCCAGAGTGGACCAAGATGA
95
18-5B

D18Mit129
53.28
CCAGCACAGAGGCAGTCAT
TGATTCTTGGGTCCTGAATACA
138
18-5C

D19Mit68
3.38
CCAATACAAATCAGACTCAATAGTCG
AGGGTCTCCCCATCTTCCTA
132
19-1

D19Mit60
13.9
CAACACCTCACTGTTTAGGAACC
GCTGAGGTCAATATTTAGCATGC
136
19-60

D19Mit45
16.14
CCATTCATAAAATGGGCTTAGG
ACCATGAATGTGTTTTGAGGTG
145
19-2A

D19Mit30
21.34
GGTGGCTTAGAAATAGTATCGAAA
CCAGCTCTAGGCAGGCATAT
150
19-2B

D19Mit88
32.23
AACAGTGCAACTTTGGAGGC
TCATTGGAACTGTCTTAACAGTGC
148
19-3

D19Mit19
34.08
CCTGTGTCCATACAGGCTCA
ACCATATCAGGAAGCACCATG
138
19-19

D19Mit11
36.26
TCAAAGTCAAGGTGGGCAG
ACTTTCCAGATGTTGGGCAC
146
19-4

D19Mit1
50.32
AATCCTTGTTCACTCTATCAAGGC
CATGAAGAGTCCAGTAGAAACCTC
122
19-5

D19Mit71
56.28
ATGATTCCCGCAGTTTTGTC
TCTCAACTGTTATTCCTCAATAGCC
136
19-6

DXMit136
4.23
ACGGAAACACTCTTATGTGCG
ATTTTGATTACAGCATGTCCCC
182
X-136

DXMit48
25.51
ACCTCCCCCTGCATTACTCT
TTCTCCAGAATCCATGCTCC
105
X-48

DXMit62
34.6
GCAATTGTGATGTTGTAGTAAATATGG
ATAACTGAGGTCTGCGGGG
125
X-62

DXMit110
35.53
TGACATGAAGTATGTGTTCCTGC
TAGGCACATGTTCACATGGG
135
X-110

DXMit170
45.87
TGCAGGCACTAACAGTGAGG
TAGTTTCACTGTGCCATTGTATACA
115
X-170

DXMit179
53.17
TTTGATAGAGCCATGTTTGGC
CAGGCTAGCCTCAAACTCTCA
122
73.26

DXMit79
68.46
AGTCTGCCTTCTCTTTCTGTATCC
TGAAACTATTCCAACATTATTCTTGG
138
53.17

DXMit153
73.26
CAATCAAGCAGATGGAAGCA
AAGGACTGCCAAGAGGACAA
143
68.46

Table 3: The markers that yielded PCR bands but could not be included in the panel due to their not meeting the set criteria.

(a) Category 1: markers that yielded the same size bands in all the strains

Marker
cM
Primer F
Primer R
Lab code

D2Mit74
103.43
CCAAGCTTGCAGTTTGTTAGC
AGGTGTTATTGAGCCCTGTATAGC
2-6A

D10Mit88
28.64
AAGATGAGAAGATAACATGTCAGGC
TTCTGAATTAAGTTCATCTGAACCC
10-2C

D11Mit226
5.64
AGGTGAACTCTTTTGAAGTTTGTG
AAAGGAGTGACTGAGAAAGACACC
11-1

D11Mit60
42.86
AGAGAGGCAAAAATTCCAAGC
CTTCCTGATGGTAGGATTTAGGC
11-60

D16Mit51
53.78
CCTCAGGTCAGTCAGGATTTAA
CCTGTTCACCCTCTCCACAT
16-5

DXMit57
16.24
AGTAGCAAGTAGACTCTCAAAGAGGG
TCTGGCATACATGGGCACT
X-57

DXMit81
20.59
GAGGAGCATCAACCTTCTCG
GAGGTGGGGAGAAACAGAGG
X-81

DXMit63
41.51
TTATAAATTAGTGTTACCACATGCAGC
AACATTTTTTTCCTAGCATGTGTG
X-63

DXMit186
76.75
ATCAATGCATAGTATTTGGGCC
AATTTGTCACTGCGGGTAGG
X-186

(b) Category 2: markers that failed to amplify a PCR band in some strains

Marker
cM
Primer F
Primer R
Lab code

D1Mit375
23.18
TAAATCCATAGATGATAGATCAGTGTG
GTGGAAAAAAAACCTAAGACACC
1-2C

D1Mit76
33.31
ACAAAGGAAACTAAACAGACTCGG
CTCCCTCAAATACATCTTTGGC
1-3C

D1Mit308
50.78
GAGGCTATGAGTCAAATGGACC
TTTATGAGGTGCTGAGATGCA
1-5

D2Mit448
65.66
TACTGTTTGCATTTGAGTGCG
AAAAAGTAATGGTTGGGGCC
2-4B

D3Mit278
32.59
TCTAATATTGGAAAATGAATTTCTCTC
TATGCCCACATGCACACC
3-3P

D5Mit309
42.22
TAGAGCCTATTTCAAACCCCC
GTTGCATCCATAGCAAGCAA
5-4

D10Mit49
1.91
GGAATTTACACTGGAATACAACCC
GTGGGCATTTGCACTGTG
10-1

D11Mit168
78.74
CAGGGATTTGACTTTTAACCTCC
GAAATGGCTCCTACAACCTCC
11-168

D14Mit132
6.03
GAACAGCACCATCCACACAC
GTGGGGTTATATGCAGATACTCG
14-1

D15Mit147
46.85
GATGTGTGAAAAATTTTGTTTCTTG
GTCTCAAAGGAATAAGAAAGAGATGG
15-5

D17Mit3
34.9
GATCTTTTCTTATTCTGGTT
GCAAAGTCATGTACTCTGAG
17-3B

D17Mit39
45.64
CCTCTGAGGAGTAACCAAGCC
CACAGAGTTCTACCTCCAACCC
17-5

D18Mit48
51.89
TTGCACTCACAGGGCACAT
TCAGAGTTTCCAGAAGACACCA
18-6

D18Mit25
59.05
CTGGAAATAAAACCTGGGCA
TTTAGCCTAACTGAGTTCCAGACC
18-7

D19Mit31
8.84
CAATACAGAGTAATGATTGCCTGA
TTCACATTTTGGGATGCTCA
19-1B

D19Mit41
13.18
AGCCCTCCACCCAGTTTC
TCTGGGGAAAAAGGATGAGA
19-2

DXMit124a
4.25
AAGGAGAAGTAGAAGAAAGAAAAGAGG
GGTGTAGCCTCAAAAAAGATGG
X-124a

DXMit68
29.49
TCCTTTGGCCTCCTGCATAT
TGTTCTTACAATGAGCCTCATAGG
X-68

DXMit114
42.82
ATGGCATCCACAGTACCACA
GTAAAATCAATTTGTGAATAAGGAAGC
X-114

DXMit95
45.86
CTGTCAATCTAATTCTCTATGTCTGTG
CTTTCCTGGGTGGCAGTG
X-95

DXMit155
67.49
TGTGCACCACCACCATTC
AGGATCTGAGTGCCCAACC
X-155

(c) Category 3: markers that amplified multiple PCR products in most (or all) strains

Marker
cM
Primer F
Primer R
Lab code

D11Mit86
32.13
TTGACATTGTGACAAAGACTTTCA
AAGGCATCATGAGGTTTTTAGTG
11-3

D12Mit118
44.93
CATCTTCAATAAAATGGAGATGTACA
CGCTTTCCCTTCATGTACTAGC
12-4

D13Mit107
50.2
CAACTGAGCCACATTCCTAGC
CAGCCACAGGATGATGAGG
13-4

DXMit98
72.38
GAGAGCAGGACTATGACTTG
ACACACCAGTTCGCACACAT
X-98

DXMit222
78.31
TTGGTTTGGGGTTTTTTTTG
ATTCCTGATAATGTCTTCTGGACA
X-222

We used Mouse Genome Informatics (MGI) database and short-listed the primers based on the criteria described in Section 2. MGI database lists several of the microsatellite markers identified in various inbred strains. Although the database is extensive, it is difficult to choose marker sets for speed congenic screening because it lacks the critical information such as the sizes of PCR products of various markers in different inbred strains and if the differences among strains can be identified using conventional agarose gel electrophoresis.

As per the information available on MGI database, predicted PCR band size differences among strains for some markers ranged from as few as a few base pairs to over 100 base pairs. Selection criteria for markers and the mice strain are described in Section 2. Taking C57BL6J and 129X1 strains as a comparison pair, for example, we chose markers with differences of 30 bp and above (as per MGI database), a range that can be easily resolved using about 2% agarose gels. We aimed to choose markers in this range for all the chromosomal locations with an interval of 5 to 15 cM. However, we were unable to find suitable markers in some regions with this criterion. In such locations, we tested markers with as less as 8 to 12 bp size difference. Such small differences can be best resolved using polyacrylamide gel electrophoresis (PAGE). However, in a typical speed congenic screening that involves analysis of about 100 markers for each sample, applying PAGE method for screening becomes quite laborious. We used 4% agarose gels to resolve makers even with very small size differences; this seemed to be sufficient to resolve the bands when they were run about 8 to 10 centimeters from the loading well. In order to keep the assay conditions uniform we used 4% agarose gel for all the markers tested.

Table 2 shows the list of the markers tested and found polymorphic in at least one of the 10 strains analyzed. We tested a total of 423 markers of which we present useful data for 195 markers. The gel images and interpretation of polymorphisms are tabulated in Figure 1. The gel images indicate that there were readily appreciable size differences for most markers whereas some markers showed very narrow size difference between some strains. In general, sizes of the majority of the markers matched the information on MGI database but there were some discrepancies noticed. The agarose gel electrophoresis using 4% gels could be useful in detecting differences among the markers in different strains.

The data presented in Figure 1 indicate that some markers could readily distinguish several strains from each other. We initially aimed to identify good panel of markers for every 10 cM. Although we found adjacent markers as close as 10 cM in most locations, we failed to achieve this in some regions. We screened several available markers in such regions and were unable to find markers that matched the criteria set forth in our assay. It should be noted that there were markers that differed very minutely among some strains. If such markers are chosen in an actual speed congenic screening, we recommend including samples from both donor and recipient strains and an equimolar mixture of the two in the panel in order to ensure the proper banding pattern of offspring. The gel images and the interpretation of polymorphism provided in Figure 1 will serve as a comprehensive tool for a researcher who wishes to undertake congenic breeding in a pair of strains from the list.

The data from the markers that did not meet our criteria fell into one of the following categories: it (1) yielded the same size bands in all the strains, (2) failed to amplify a product in some strains, (3) amplified multiple PCR products, or (4) did not yield a reliable PCR product in most/all of the strains. Category 1 means that the bands were not resolvable by 4% agarose gels. We presume that such markers would be <6 base pairs different from one another [31] or they do not differ in size at all. Category 2 would probably mean that the primers did not have perfect binding sites in those strains where there was no PCR product. Category 3 makers may need further optimization of PCR conditions. However, one of our aims was to keep the conditions uniform to all the markers in order to keep the assay simple: it makes the assay cumbersome if PCR conditions have to be varied among different marker sets. It should be noted that the category 4 markers (that did not yield a reliable PCR product in most/all of the strains) might have not worked due to technical errors that may have occurred during some steps like primer synthesis or primer reconstitution; we do not rule out the possibility that some of these primers may yield PCR products if they are resynthesized. The markers that did not meet our criteria because of the above reasons are listed in Table 3 and Supplementary Table S1: providing the list of primers and markers that failed in our hands will help researchers to compare the results if they intend to test more markers to expand the streamlined panel presented here.

There is some debate about the minimum number of markers needed per chromosome for identifying the regions between the donor and recipient strains [32]. A study concluded that as few as three markers per chromosome were sufficient to achieve similarly meaningful results as that of over 6 markers per chromosome [33]. Computer simulation [20] indicated that a relatively modest selection effort of 60 evenly spaced markers with 25 cM spacing (corresponding to 3 markers per chromosome), 16 males per generation, would typically reduce unlinked donor genome contamination to below 1% by four backcross generations (N5). We conclude that the list presented here can serve to choose panels of markers for most two-strain combinations with at least 3 to 4 markers per chromosome (with exception of a very few combinations). Further studies that compare smaller and larger panels of markers in the same set of samples for marker assisted speed congenics are needed to address this question unequivocally.

In recent years there have been advancements in the approaches used for speed congenics. These methods include (i) use of fluorescently labeled primers to amplify SSLP markers followed by resolving the products in sequencing gels [29] and (ii) microarray chips of SNPs [29, 34–36]. With the advent of newer methods particularly those that use SNP based marker analysis, a very high number of markers per chromosome can be screened simultaneously that increases the resolution severalfold compared to the conventional SSLP based markers analysis. Although there are some computer simulation studies for assessing the efficiency of speed congenic screening in general [32] and algorithm based reports to compare SNP and SSLP based speed congenic screens [37] there are no systematic studies to compare the two methods to assess the efficiency and cost effectiveness of each method. Here, we compare the SNP and SSLP based approaches in terms of their adoptability and feasibility to most laboratory settings including their cost. The hands-on-time in performing gel based assays has been reduced greatly by newer methods that use SNP arrays. However these methods have some limitations compared to traditional agarose gel electrophoresis (AGE) based SSLP marker analysis. (1) The newer methods are expensive in terms of the initial investment in reagents and/or operational costs compared to SSLP-AGE method. On the contrary, basic requirements needed for AGE based systems are readily available in any molecular biology laboratory and the only additional investment needed will be to synthesize the required oligonucleotides. (2) Subset of markers to be analyzed in the subsequent generations cannot be skipped from the panel unlike in the AGE based method where the number of markers to be analyzed will become significantly reduced in successive generations and so also the overall cost of the assay. Other advantages of AGE based SSLP marker analysis over these newer methods are as follows: (i) the equipment needed to perform the assay is readily available in most laboratories and (ii) it can be routinely performed by many researchers and technicians without the need of special training as needed for SNP based approaches.

Considering the highest resolution that is possible with the SNP based method, it can be regarded as the superior method of all. However, the microchips that are currently available are expensive and the cost for analyzing each mouse DNA sample runs to about US $100 to $150. Assuming that about 15 mouse DNA samples per generation for 5 generations are analyzed, a typical speed congenic project would cost about $15,000 to $22,500. On the other hand, when using SSLP-AGE approach, since marker analysis is done manually, the markers that were fixed in the previous generation can be skipped in the successive generations; this makes the SSLP-AGE based method cost effective compared to SNP based method. It is estimated that a typical SSLP based speed congenic screen that employs analysis of 15 samples per generation for 5 generations using 5 markers per chromosome would need about 2000 to 2300 PCR reactions. At the rate of $2 to $2.5 per reaction (cost analysis done in our laboratory) it will cost about $4000 to $5750 for one full speed congenic project. Furthermore, SSLP-AGE method can be performed in any simple molecular biology labs compared to SNP method that requires expensive equipment.

4. Conclusions

Although some information about microsatellite marker differences between the commonly used inbred mouse strains was available, there was no systematic study to validate a large panel of markers for SSLP-AGE based speed congenic screening. The panel of marker sets validated and presented in this study serves as a ready reference for researchers who wish to perform cost-effective speed congenic screening in a pair of strains from the panel. The assay can be performed in any standard molecular biology lab. The data in this report is available at ftp://ftp.informatics.jax.org/pub/datasets/index.html#Guru.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Channabasavaiah B. Gurumurthy and Poonam S. Joshi contributed equally. Channabasavaiah B. Gurumurthy designed the study. Poonam S. Joshi, Scott G. Kurz, Rolen M. Quadros, and Donald W. Harms performed experiments. Channabasavaiah B. Gurumurthy and Poonam S. Joshi compiled the data. Channabasavaiah B. Gurumurthy, Poonam S. Joshi, Masato Ohtsuka, and K. C. Kent Lloyd analyzed and interpreted the data. Channabasavaiah B. Gurumurthy and Masato Ohtsuka wrote the paper and all authors read, edited, and approved the final paper.

Acknowledgments

The authors acknowledge the review and comments received from Lluis Montoliu. This work was partially supported by an Institutional Development Award (IDeA) to Channabasavaiah B. Gurumurthy (PI: Shelley Smith) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant no. P20GM103471 and the Center for Humanized Mice Development Award from ORIP/DPCPSI/NIH/1R24OD018546-01 (MPI: Gorantla, S/Poluektova, LY). Rolen M. Quadros, Donald W. Harms, and Channabasavaiah B. Gurumurthy acknowledge the Nebraska Research Initiative and UNMC Vice-Chancellor for Research Office for supporting the mouse genome engineering core facility.

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