Echt et al. BMC Genetics 2011, 12:17
http://www.biomedcentral.com/1471-2156/12/17

RESEARCH ARTICLE

Open Access

An annotated genetic map of loblolly pine basedon microsatellite and cDNA markers
Craig S Echt1*, Surya Saha2,3, Konstantin V Krutovsky4, Kokulapalan Wimalanathan6, John E Erpelding5, Chun Liang6,
C Dana Nelson1

Abstract
Background: Previous loblolly pine (Pinus taeda L.) genetic linkage maps have been based on a variety of DNApolymorphisms, such as AFLPs, RAPDs, RFLPs, and ESTPs, but only a few SSRs (simple sequence repeats), also
known as simple tandem repeats or microsatellites, have been mapped in P. taeda. The objective of this study was
to integrate a large set of SSR markers from a variety of sources and published cDNA markers into a composite
P. taeda genetic map constructed from two reference mapping pedigrees. A dense genetic map that incorporates
SSR loci will benefit complete pine genome sequencing, pine population genetics studies, and pine breeding
programs. Careful marker annotation using a variety of references further enhances the utility of the integrated
SSR map.
Results: The updated P. taeda genetic map, with an estimated genome coverage of 1,515 cM(Kosambi) across 12
linkage groups, incorporated 170 new SSR markers and 290 previously reported SSR, RFLP, and ESTP markers. The
average marker interval was 3.1 cM. Of 233 mapped SSR loci, 84 were from cDNA-derived sequences (EST-SSRs)
and 149 were from non-transcribed genomic sequences (genomic-SSRs). Of all 311 mapped cDNA-derived markers,
77% were associated with NCBI Pta UniGene clusters, 67% with RefSeq proteins, and 62% with functional Gene
Ontology (GO) terms. Duplicate (i.e., redundant accessory) and paralogous markers were tentatively identified byevaluating marker sequences by their UniGene cluster IDs, clone IDs, and relative map positions. The average genediversity, He, among polymorphic SSR loci, including those that were not mapped, was 0.43 for 94 EST-SSRs and0.72 for 83 genomic-SSRs. The genetic map can be viewed and queried at http://www.conifergdb.org/pinemap.
Conclusions: Many polymorphic and genetically mapped SSR markers are now available for use in P. taeda
population genetics, studies of adaptive traits, and various germplasm management applications. Annotating
mapped genes with UniGene clusters and GO terms allowed assessment of redundant and paralogous EST markers
and further improved the quality and utility of the genetic map for P. taeda.

Background
Loblolly pine (Pinus taeda L.) is an economically important native forest tree species in the southern UnitedStates, accounting for approximately 16% of the harvested industrial wood in the US and 5% worldwide[1,2]. Over 40% of this production occurs on 12 million
hectares of sustainably managed forests, where essentially all of the planting stock is the product of genetic
improvement [1,3]. In addition to its economic significance, P. taeda is ecologically important, as it is a
* Correspondence: cecht@fs.fed.us
1Southern Institute of Forest Genetics, Southern Research Station, USDA
Forest Service, Saucier, MS 39574, USA
Full list of author information is available at the end of the article

frequent or predominant species on about 25 million
hectares of non-planted forested land in the southern
US [1]. Pinus taeda also is considered one of the major
bioenergy feedstocks [4] and recently was selected as thefirst pine species for complete genome sequencing with
the intent of using genome information to enhance its
bioenergy potential [5]. Genetic maps, when integrated
with physical maps, have played an important role ingenome assembly for other plant species [6,7] and, whenannotated for gene function, are important for mapping
cellular regulatory and signalling networks [8].
Prior P. taeda genetic maps were based on multiple
types and combinations of marker systems: RFLPs [9],AFLPs [10], RFLPs and RAPDs [11,12], ESTPs [13],© 2011 Echt et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.

Echt et al. BMC Genetics 2011, 12:17
http://www.biomedcentral.com/1471-2156/12/17

ESTPs, RFLPs, and RAPDs [14], SSRs [15], and RFLPs,
ESTPs and SNPs [16]. The RFLP, ESTP, and SNP markers in these maps were derived from cDNA sequence,
although a few of the RFLP markers also were derived
from genomic sequence. All mapping of RFLP and
ESTP markers was conducted in one or both P. taeda
reference mapping pedigrees, base and qtl. Of all themaps, only the AFLP map of 508 loci [10] and the
RFLP-ESTP-SNP map of 373 loci [16] consolidated into
12 linkage groups (LGs) that correspond to the 12 chromosomes of P. taeda [17].
Comparative mapping with P. taeda using ESTP markers has been conducted with other pines, such as Pinus
elliottii Englm. [14], Pinus pinaster Ait. [18], Pinus sylvestris L. [19], and with other conifers, such as Pseudotsuga menziesii Mirb. [20] and Picea species [21,22].
Pinus taeda maps were essential in comparative mapping studies that helped to understand chromosomal
evolution and identify syntenic regions in Pinaceae(reviewed in [23,24]). Putative single-copy genes in conserved ortholog sets (COS) have been identified in
P. taeda and Picea glauca (white spruce) [25,26] andsegregating markers were developed [26]. As these priorreports point out, the development and mapping of
COS markers is expected to be an additional mapping
resource that would be particularly useful for more
extensive comparative maps and detailed studies of syntenic and phylogenetic relationships between P. taeda
and other Pinus and Picea species.
Simple sequence repeat (SSR) markers were integral
components of the genetic maps used in prior plant
genome projects [6,7] and are expected to be so for thepine genome project. Over 300 SSR markers for
P. taeda previously were developed from EST sequences[27,28] and genomic DNA sequences [29-31]. While a
dedicated SSR mapping study placed 51 genomic-SSR
markers into 15 linkage groups [15], only a few SSR locifrom any source were integrated into the more complete
cDNA-based maps [16,19,32].
Here we describe SSR marker development, mapping,
and annotation in P. taeda. From SSR-enriched pine
genomic DNA libraries and from pine EST databases,
we obtained sequences for SSR discovery and primer
design. The resulting EST-SSR and genomic-SSR markers were evaluated for specificity and quality of amplification. Those segregating in one or both P.taeda
mapping pedigrees, base and qtl, were used for genome
mapping. We combined new SSR marker segregation
data with prior ESTP and RFLP marker segregation datato construct a new integrated genetic map of P. taeda.
We assigned UniGene and GO (gene ontology) annotations to all classes of cDNA markers (e.g., EST-SSRs,
ESTPs and RFLP). We did so because transcript mapping with UniGene clusters and GO terms can be used

Page 2 of 16

to facilitate gene discovery and help integrate positionaland functional information for most genes [33], andthus aid future pine genome project efforts. We used
the annotations to help identify redundant markers andpotentially paralogous loci on the map. The result is thefirst functionally annotated, SSR-based, genetic map fora conifer. Map and annotation data were loaded into a
new database with an enhanced graphic interface, PineMap [34], that was developed to contribute to the goalsof the Pine Genome Initiative [35] and provide publicaccess to the map, marker, and associated sequence
information on ConiferGDB [36].

Results and discussion
Evaluating SSR sequences, primer pairs, andpolymorphism

Using 197,931 pine EST sequences that were available
July 2004 and represented in 52,911 assembled contigs
and singletons, we found 1,485 (2.8%) that contained atleast one SSR as defined by our selection criteria (seeMethods - SSR selection and primer design). From SSRenriched genomic DNA libraries, we selected andsequenced 887 SSR-positive clones from P. taeda
libraries and 619 from P. radiata libraries. Marker
amplification in P. taeda was evaluated for 830 EST-SSR
primer pairs, 566 P. taeda genomic-SSR primer pairs,
and 107 P. radiata genomic-SSR primer pairs. In addition, we evaluated 43 previously reported EST-SSRs [27]and 131 previously reported genomic-SSRs [29-31].
Determination of PCR amplification quality and marker polymorphism used different screening and evaluation strategies in different laboratories as each set ofprimer pairs became available. The final round of evaluation reported here, however, included all previously
selected primer pairs, and used one PCR protocol to
produce dye-labelled amplicons for allele detection. Following this evaluation, we submitted to NCBI’s UniSTS
database 165 EST-SSR markers and 203 genomic-SSR
markers, as listed in Additional file 1. Detailed marker
data for 517 SSR, ESTP and RFLP loci are in Additional
file 2.
We genotyped 14 P. taeda individuals originatingfrom across the natural geographic range of the species
to obtain allele diversity heterozygosity estimates for 185
SSR loci (Additional file 3). This set included eightmonomorphic EST-SSR loci that may prove useful forinter-species investigations if subsequent surveys findthem to be polymorphic among species. We estimatedmean unbiased gene diversity (He) as 0.57 for the 177
SSR loci that were polymorphic, 46 of which did notsegregate in either mapping pedigree. When examinedby SSR class, Heestimates for 94 polymorphic EST-SSRs
and 83 polymorphic genomic-SSRs were 0.43 and
0.72, respectively (difference of H e means = 0.29, 95%

Echt et al. BMC Genetics 2011, 12:17
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Page 3 of 16

CI = 0.08 - 0.49, t = 8.16, d.f. = 175, P < 0.0000). This
demonstrated that P. taeda SSRs contained within
expressed genes have lower genetic diversity than thosecontained in anonymous genomic regions presumed to
be selectively neutral. Prior studies that used many
fewer P. taeda SSR markers to examine polymorphism
in P. radiata or P. contorta did not observe this notabledifference in gene diversity between transcribed and presumably non-transcribed SSR loci [27,28]. The general
constraint on polymorphism that we observed most
likely results from background selection or positive
selection acting on the associated genes. The observed
disparity in the level of polymorphism between the twoclasses of SSR markers is a clear signature of selection
acting on expressed genes as a group [37]. SSRs markers
linked with genes of known function and harbouring
unusually low levels of polymorphism could thus help
to identify candidate genes for adaptively important phenotypes. When considering the general level of polymorphism among all 177 SSR markers, we believe there
is sufficient allelic diversity to allow selection of particularly informative sets of markers for use in detailed
population genetic studies or tree improvement
programs.
Genetic map

From each pedigree, base and qtl, we found one progeny
sample that had over 40% missing genotype data (Additional file 4, Tables S1 - S4). We excluded these two
samples from linkage analyses, leaving 97 base samples
and 170 qtl samples for map construction. Numbers ofsegregating and mapped loci, categorized by pedigreeand marker type, are listed in Table 1. Segregation datafor 590 marker loci (Additional file 4, Tables S1 - S4)
were evaluated for linkage mapping based on criteria
described under Methods - Linkage analysis and mapping. SSR markers NZPR0300, NZPR0440, and
PtSIFG_0715 cosegregated on linkage group 5 (LG-5),
as did PtRIP_0621 and PtRIP_0165 on LG-10. Because

the JOINMAP program automatically retained only one
marker from each set for subsequent map construction,
we manually reinserted those loci in the final maps after
confirming that the marker sequences in each set were
not homologous. In total, 154 markers were in common
between the two pedigrees to permit map integration,with genomic-SSR markers comprising the largest class(Table 1). Summaries, by pedigree, of the reasons for
excluding individual markers from integration mapping
are in Additional file 4, Tables S5 and S6.
Based upon the third round (round-3) of JOINMAP mapping calculations for integrated mapping, which included
ancillary markers, we retained 460 non-redundant loci in
12 linkage groups that extended 1,416 cM, which provided
an average marker interval of 3.1 cM. SSR loci comprised
50% of mapped loci. Approximately equal numbers of SSR
primer pairs were evaluated for each marker type (Table1), however, 53% fewer of the evaluated EST-SSR primer
pairs produced mapped markers than did the genomicSSR primer pairs (18% conversion to mapped loci forgenomic-SSR primer pairs vs. 9.6% for EST-SSR primer
pairs). We attributed this lower value to the lower genetic
diversity observed for transcribed SSR loci, as discussed
above.
Results from the second round (round-2) of JOINMAP
mapping calculations, which exclude poorer fitting ancillary markers, are depicted in Figure 1 with details in Additional file 5. The round-2 map spanned 1,429 cM with 429
loci in 12 linkage groups, providing an average genomic
marker interval of 3.3 cM (or 3.6 cM if averaged fromindividual linkage groups). The numbers of mapped loci ineach integrated map, categorized by pedigree and marker
type, are in Table 1. Using the more conservatively constructed round-2 map data, we estimated genome lengthas 1,515 cM using the (m + 1)/(m - 1) method. Our
round-2 map of 429 loci therefore covers 94% of the estimated genome length and provides a 99.6% probability
that any locus in the genome lies within 10 cM of one of
the mapped markers (as determined from c, defined inTable 1 Summary counts of Pinus taeda marker evaluation and mapping results
Marker type

SSR eval 1

base seg2

qtl seg2

Total seg2

base map3

qtl map3

Both map4

Rnd 3 integ map5

Rnd 2 integ map6

genomic SSR

804

138

134

168

121

113

86

149

133

EST SSR

873

72

70

97

61

60

37

84

78

ESTP

n/a

69

104

159

49

65

10

104

98

RFLP

n/a

56

139

166

44

100

21

123

120

total

1677

335

447

590

275

338

154

460

429

n/a - not applicable.
1
SSR primer pairs that were evaluated.
2
Markers that segregated in the base pedigree, qtl pedigree, and either (Total seg).
3
Markers in the respective pedigree maps used for map integration.
4
Markers common to both base and qtl pedigrees and used for map integration.
5
Non-redundant marker loci in the round-3 integrated map, which included ancillary markers, as listed in Additional File 2.
6
Non-redundant marker loci in the round-2 integrated map, which excluded ancillary markers, as shown in Figure 1 and listed in Additional file 5.

Echt et al. BMC Genetics 2011, 12:17
http://www.biomedcentral.com/1471-2156/12/17

LG-1
0
5
10

PtIFG_2086_13
PtIFG_1626_c
PtIFG_653_d
PtIFG_2697_A

PtRIP_0211

20

PthCAB

25

PtSIFG_0566
PtRIP_0790

30

PsUME_Ps3_A

35

NZPR1699

40

PtIFG_2253_A
estPtIFG_1576_a

45

PtRIP_0540
NZPR1680
PtSIFG_4218
PtTX4030

50
55
60
65
70
75
80
85
90
95
100
105
110
115
120
125
130

LG-2
GO
GO
GO

PtSIFG_0463

15

GO

GO

PtSIFG_4438
PtSIFG_0437
PtSIFG_4378

GO

20
25

PtIFG_3006_1
estPtIFG_8939_a

GO

PtTX2037

60
65
70
75

GO

GO

GO

95

PtSIFG_1005

100

PtIFG_2413_B

GO
GO
GO
GO

125

GO

GO

PtIFG_846

GOGO

PtIFG_2718_2
GO
PtIFG_1A2_C
estPtNCS_6C12A_a GO
estPtIFG_8429_a

estPtIFG_8887_a
PtAGP
PtNCS_3H6z5_A
PrCHS1
estPtIFG_8738_a
PstASU_APX
estPtNCS_2N7G_a
PtIFG_1636_2

PtIFG_1916_4

PtTX2091
PtSIFG_4213

PtRIP_0841
PtRIP_0846

estPtIFG_1956_a GOGO

PtIFG_669
PtIFG_1593_21
estPtIFG_624_a

PtSIFG_0408
PtSIFG_1062
PtTX4079

GO

GO
GO

GO

PtIFG_2568_A
GO
estPtIFG_2781_a

GO

PtSIFG_1190
GO

PtSIFG_0587
estPtIFG_9113_a GO

PtSIFG_1262
PtRIP_0700

PtSIFG_0203

PtIFG_2738_B
GO
GO

PtSIFG_4446
PtIFG_2538_5
PtIFG_149_2

GO
GO
GO

GO
GO
GO
GO

PtRIP_0305
PtIFG_503_A
GO
estPtIFG_C4H-2_a GO

PtIFG_2145_28

GO

PtRIP_0171
GO

PtSIFG_4394
PtTX3047
PtRIP_0647
PtRIP_0135

PtNCS_ptCadA GO
estPtIFG_8415_a
PtIFG_2899_A
GO

GO
GO
GO
GO
GO
GO
GO
GO

PtIFG_1635_A

PrMADS3

PtMTU_lpPAL
PtIFG_1918_A

estPtIFG_1950_a
PtIFG_2723_Aa
estPtIFG_739_a
PtIFG_2090_1

GO

GO
GO

NZPR0116
estPtIFG_9044_a

estPtIFG_1454_a
estPtNCS_22C8_a GO
estPtIFG_1955_a
PtIFG_2220_A
GO

GO

PtTX4062
PtSIFG_4315
estPtIFG_8702_a
GO
PtLP15GO
PtIFG_4D4_A
estPtNCS_CCoAOMT_a GO

PtRIP_0627
PtRIP_0814
GO

PtRIP_0960

PtTX3097
PtIFG_2574_2
PtIFG_3026_A
PtIFG_2295_2

GO

PpSIFG_3147

PtSIFG_1055

PtSIFG_1318

GOGO

PtTX4137
PtSIFG_0635
NZPR0290
PtRIP_0990

GO

PtIFG_138_A

GO

GO

PtTX3049

PtIFG_2574_c

GO
GO

PtRIP_0619

GO

PtIFG_2782_2
PtIFG_1454_A
estPtIFG_9008_a

estPtIFG_8647_a
estPtIFG_8531_a
PtIFG_2802_3

PtIFG_2291_A

NZPR0440
NZPR0300
PtSIFG_0715
PsyGPDGO

PtRIP_0609
GO
GO
GO

PtTX4058

estPtIFG_SAHH_a
estPpINR_RN01G04_a

PtIFG_2615_1

PtIFG_606_1

GO
GO
GO

GO

PtTX3045
PtIFG_2009_A

GO

LG-12
GO
GO

PtTX3052
PtIFG_1588_A

PtSIFG_4415

GO

PtIFG_2986_B

PtSIFG_1113
estPtNCS_6N3C_a
estPtIFG_9076_a
PtIFG_2086_2

GO
GO
GO

PtRIP_0263
PtSIFG_4493
GO

estPtIFG_2615_aGO

PtTX4092
PtRIP_0165
PtRIP_0621
PtRIP_1037

PtTX3001
PtSIFG_0592
PtSIFG_4282

GO

PtIFG_1916_1

GO

NZPR0599
PtSIFG_0564

GO

PtIFG_2963_1

PtTX4215
PtRIP_0560
PtIFG_719_3

SsrPt_ctg946
NZPR0458

PtIFG_653_3

GO
PtSIFG_1018
GO
estPtIFG_1750_aGO
estPtIFG_COMT-2_a GO

PtRIP_0106
PtTX3117
PitaIFG_2020_1

GO

PitaIFG_2361_1

GO

PitaIFG_1A7_6

GO

PtIFG_2197_1

PtSIFG_4447
PtRIP_1023
PtRIP_9138
PtSIFG_0424
PtRIP_0117
PtRIP_0860

GO

PtSIFG_0237
PtSIFG_4304
PtSIFG_0596

GO

PtRIP_0905

PtIFG_2969_1

PtTX3063

SsrPt_ctg7444
PtSIFG_4472
GO

PtTX2123
PtTX3081

PtIFG_616

estPtIFG_8837_a
estPtIFG_0893_a
estPtIFG_9092_a

estPtNCS_23C5_a

PtRIP_0287

estPtIFG_1643_a

PtRIP_0388
PtIFG_459_1
PtIFG_1633_c

PtIFG_1889_1
estPtIFG_8580_a

PtIFG_1918_f

NZPR1004

GO
GO

PtIFG_2933_12

LG-11

PtRIP_1035
PtSIFG_0209

PtIFG_2323_A

GO
GO

PtTX3027

PtSIFG_0349

estPtIFG_500_a

PtTX4093

estPtIFG_8898_a
PtIFG_2220_B

GO
GO

PtIFG_1869_2

estPtIFG_2290_a GO

PtIFG_1917_AGO
estPtIFG_9036_a GO
PtSIFG_0245
GO

GO

PtSIFG_0166

PrE79

GO

PtSIFG_4458

PtSIFG_0745
PtSIFG_0551

PtIFG_2479_1
estPtIFG_9156_a GO
estPtIFG_9164_a GOGO

NZPR0413
NZPR0947

GO

LG-10

PtSIFG_0440

PtRIP_0022
PtRIP_0103

PtIFG_149_A
estPtIFG_8886_a

GO

PtSIFG_0629

PtRIP_0852
PtSIFG_1260
PtRIP_0683

PtIFG_1599
GO

105

120

GO

LG-9

PtSIFG_0186
PtSIFG_4249
PtRIP_0067
PtTX3029

PtIFG_2022_A

PtIFG_2718_3

PtRIP_0958

PtTX3107
PtTX2093
PtTX3_lp3-1

GO

PtTX3055

NZPR0206

PtSIFG_0625
NZPR0473

estPtIFG_1934_a
PtIFG_2006_C

PtSIFG_4222
PtSIFG_1123
PtSIFG_0219

GO

GO

PtTX3105
PtSIFG_0167

PtIFG_3008_1
GO
PtIFG_719_A
estPtIFG_8732_a GO

PtIFG_1672_A
PtIFG_2361_2
PsUME_S43_63

PtIFG_2564_B

GO

estPpINR_AS01C10-1_a GO

PtNCS_HLH1

GO

estPtNCS_6C5A_a

PtIFG_2145_1

PtTX3114

GO

estPmaLU_SB12_aGO

PtTX4214

estPmaLU_SB49_a
PtIFG_975_3
PtIFG_2897_d
PtIFG_2068_A
GO

PtTX2094

GO

PtIFG_2113_1

PtIFG_2530_A

PtTX3020
GO

estPtIFG_9157_a

estPtIFG_2166_a

PtIFG_138_B
estPtIFG_8500_a

PtIFG_1165_a

PtNCS_1CA4G
estPtIFG_dhn-1

PtNCS_p9myb1_21 GO

PtRIP_0032

GO
GO
GO
GO
GO
GO
GO
GO

PtRIP_0496
PtRIP_0630
PtSIFG_1212
PtIFG_2090_4
estPtIFG_8471_a

PtRIP_0791

PtTX3120

NZPR1078

GO

NZPR0269

GO

estPtIFG_2358_a

PtNCS_PtaAGP6

PtIFG_2145_3

estPtNCS_22C5_a GO
PtIFG_2588_1
GO

PtIFG_2986_A
PtIFG_1D11_A

PtSIFG_0168
PtTX4147

PtRIP_1077
PtRIP_0376

GO
GO

LG-6

PtIFG_2963_3

GO
GO

PtSIFG_0652

PtRIP_0066
NZPR0351
PtTX4112

90

115

PtSIFG_1052
PtTX4033

PtIFG_2957_A

PtTX3019

PtTX4056

110

PtIFG_66_1

PtRIP_0789
PtTX3032

85

GO
GO

PpSIFG_3145
SsrPt_ctg7141
NZPR0826
PtTX2146

PtIFG_1623_A

LG-5

PtIFG_975_4

PtIFG_2745_1

GO

LG-8

PtTX4090
PtTX3011

80

LG-4

PtTX4228

PtIFG_851_1

estPtIFG_9151_a

GO
GO

GO

estPmaLU_SB58_a GO
PtIFG_2393_1
PtIFG_2931_A
GO
estPtNCS_6N3E_a GO

PtSIFG_4233

PtRIP_0126
PtSIFG_0594
PtSIFG_0740
PtSIFG_0640

PtRIP_0508

PtTX3058

PtIFG_1916_2
estPtIFG_4CL_a

GO
GO

NZPR0143

50
55

GO

estPtIFG_lp3-3
estPmaLU_SB32a_a

35

45

estPtIFG_C4H-1_a
estPtNCS_22B8_a
PtIFG_2090_2
estPtIFG_8612_a
PtIFG_1636_54
PtIFG_1918_3

PtRIP_0941
PtRIP_0968

30

40

PtIFG_1A7_A
PtIFG_2564_A

estPtIFG_0464_a
estPaINR_PAXY13_a

PtRIP_0064

PpSIFG_3129
NZPR0917
NZPR1702

PtRIP_9315

GO

GO

PtSIFG_4245

PtTX4036
PtRIP_1040

515

estPtIFG_2889_a
PtIFG_2718_1
PtIFG_2988_21

LG-7

10

GO

GO
GO
GO

PtIFG_2146_2
PtIFG_658_A
estPmaLU_SB41_a GO
PtIFG_2441_1

estPtIFG_149_a

PtTX2164
PtRIP_0134
PtSIFG_1166

GO

135

0

GO
GO

PtSIFG_0493
PtRIP_0065
PtSIFG_4454
PtTX3025

PtIFG_2150_A
estPtIFG_8569_a
PtIFG_2538_B
estPtIFG_9022_a

PtRIP_0984
NZPR0943

PtRIP_0158
PtRIP_1072
PtSIFG_2229

PtIFG_3012_3

PtSIFG_1207
PtSIFG_1325

PtTX4181

estPtIFG_0107_a

PbRAMS
PtIPST_pLP2
PtIFG_1D9_2
PtIFG_2145_76
estPtIFG_8781_a

GO

PtRIP_0367
PtRIP_1027
PtSIFG_0145

estPtIFG_9102_a

LG-3

PtTX2189
PtSIFG_1252

PtIFG_2885_1

PtIFG_2782_31

PtIFG_1457_A
estPtIFG_9198_a

Page 4 of 16

PtTX3013
estPtIFG_8542_a

PtRIP_1036
PtIFG_2723_1

PtSIFG_0126
estPtIFG_674_a

GOGO

estPtIFG_8565_a

GO

NZPR0102c
PtSIFG_0174
PtSIFG_1065
estPtIFG_SB32b_a GO
PtIFG_3012_2
GO
PtIFG_653_2
GO

NZPR0274
PtSIFG_0561
PtTX2080

PtRIP_0255

PtTX3037
estPtIFG_8725_a GO

Figure 1 A genetic map of Pinus taeda. The round-2 map of 429 marker loci constructed from the base and qtl pedigrees. The scales to the left
are in cM units, marker names in bold font denote genomic-SSR loci, bold underline denotes EST-SSR loci, and plain font denotes ESTP and RFLP
loci. A “GO” tag to the right of a locus denotes assignment of a functional Gene Ontology term. Supporting data are in Additional files 2, 5, 8.

Methods - Linkage analysis and mapping). The observed
distribution of markers among 10 cM genomic intervals
was not different from the expected random distribution(K-S test D = 0.25, P = 0.786). Interactive graphical displaysof these maps, and comparisons between them, can beviewed and queried from the PineMap database (Figure 2)[34]. The round-3 map is represented in PineMap as
the map set named Pinus_Taeda_Base_&_QTL_SSR_
Version_1, while the round-2 map is in map set Pinus_
Taeda_Base_&_QTL_SSR_Version_2.

Echt et al. BMC Genetics 2011, 12:17
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Page 5 of 16

Figure 2 PineMap user interface. A web page screen capture of the CMap graphic display for LG-5 of the Map Set
Pinus_Taeda_Base_&_QTL_SSR_Version_1 in PineMap [34]. Markers are colour-coded according to marker type. Each marker is hyperlinked to its
mapping, annotation, and DNA sequence data. Map, annotation, and sequence data for all markers, and for map metadata, can be downloaded
from links available through the Map Sets tab found on the top menu bar.

The prior integrated map of P. taeda from the base and qtl
pedigrees contains 302 ESTP, RFLP, and isozyme markers
in 12 linkage groups that span 1,274 cM [20], while a
recent qtl pedigree map of 373 ESTP, RAPD, RFLP, SNP,
SSR, and isozyme markers spans 1,228 cM [16]. A

previous base pedigree map of 51 SSR markers spans 795
cM across 15 linkage groups [15]. Our two-pedigree integrated round-2 map of 429 markers incorporated 227 of
the RFLP and ESTP markers and 30 of the SSR markers
used in those prior studies. We observed that inclusion ofEcht et al. BMC Genetics 2011, 12:17
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mapped loci that had been assigned functional GO
terms, that is, those with a GO term other than the stubassignment of “molecular_function”. GO terms and UniGene IDs assigned to mapped markers can be queried
as “free text” searches in PineMap [34].
PineMap users also can follow links from a marker in a
PineMap or CMap display to its assigned UniGene cluster and associated ConiferGDB EST clusters and contigs[38]. ConiferGDB P. taeda EST clusters take advantageof NCBI P. taeda UniGene clusters (Build #11). EachNCBI UniGene cluster is intended to include mRNA
sequences transcribed from a unique locus in the genome. Because of the particular clustering algorithm
adopted by NCBI, however, it is possible that a UniGene
cluster contains transcripts from paralogous loci. Conversely, transcript isoforms (alternative splice variants)
derived from the same gene could possibly be grouped
into different UniGene clusters. ConiferGDB adopts a
unique approach to EST clustering by implementing thefollowing protocol: (1) retrieve EST component lists ofall UniGene clusters from NCBI, (2) use cleaned EST
sequences annotated with cDNA termini, which delimit
transcript ends, and (3) conduct EST clustering (ConiferEST clusters) for every individual UniGene cluster

level-3 GO category

203 previously unmapped SSR markers extended the
mapped P. taeda genome by about 150 cM. In particular,
SSR loci extended one end of LG-7 by 52 cM and one end
LG-12 by 42 cM compared to the prior two-pedigree integrated map of 302 markers [20]. Comparisons of the current and prior integrated maps are shown in Additional
file 6. No RFLP or ESTP loci were included in these
extended intervals, except for the terminal marker on LG7, estPtIFG_0149_a. This marker is not present in priormaps, presumably because it had not been linked to other
RFLP and ESTP markers [16,17].
Our round-2 map depicted in Figure 1 had overallcolinearity with the prior two-pedigree integrated map(Additional file 6). Deviations in the order of loci generally occurred only among closely linked loci. A notable
exception was at the end of LG-6, in which three markers in a ~28 cM interval on our map shifted positions
relative to three other markers and mapped within a ~8
cM interval on the prior map (Additional file 6). We
could not trace the cause of the discrepancy to the mapping process and attributed it to the addition of three
SSR markers (PtTX_3055, PtRIP_0609, PtRIP_0619) andthe exclusion of an ESTP marker (estPtIFG_8972_a)that altered linkage estimates in the region. Similartypes of deviations from colinearity also occur between
prior P. taeda maps [16,20], as shown in Additional file
7. We suggest that these types of deviations are not specific to our SSR markers or mapping protocols and may
be a function of unresolved genotyping errors.
Obtaining consistent linkages and orders of loci were
problematic for the distal half of LG-10 in our initial mapping sessions. We resolved the discrepancies by first mapping only the SSR markers in LG-10 using the round-2
JOINMAP map calculations, then adding ESTP and RFLP
markers one by one, retaining only those that passed our
exclusion criteria (see Methods - Linkage analysis andmapping). Difficulties in ordering RFLP and ESTP markers
in this linkage group also arose in prior mapping studies[16,20], as is evident by the flipped orientation of six markers in the distal half of LG-10 (Additional file 7).

Page 6 of 16

unknown function
catalytic activity (F)binding (F)structural molecule activity (F)transporter activity (F)response to stimulus (P)cellular process (P)establishment of localization (F)developmental process (P)cell (C)enzyme regulator activity (F)metabolic process (P)Marker curation

Each of the 369 cDNA-derived marker sequences wascurated, i.e., manually annotated, for a P. taeda UniGene
cluster, reference protein, and GO term (Additional
file 8). We assigned UniGene clusters to 240 of the 311(77%) mapped cDNA loci and assigned RefSeq proteinsto 208 (67%). Based on these assignments GO terms ofa known function, process, or cell compartment wereassigned to 193 loci (62%). Comparison of the relative
proportions of the hierarchical level-3 GO categories forthese mapped genes revealed that the categories “catalytic activity” and “binding” together included 46% of all
GO term assignments (Figure 3). We flagged in Figure 1

0

5

10

15

20

25

30

35

% mapped cDNA lociFigure 3 Comparison of GO categories for mapped cDNA
sequences. Percentages of general GO term category assignments
found among mapped cDNA marker loci in P. taeda. GO terms for
similar functions, processes or cell compartments were grouped bystandardizing them to level-3 of the GO lineage hierarchy, wherelevel-1 is ‘all’ and level-2 comprises the three ontologies:‘molecular_function’ (F), ‘biological_process’ (P) and‘cellular_compartment’ (C). Assigned GO terms and the parental GO
lineages (levels-3, -4 and -5) for individual markers are in Additional
file 8.

Echt et al. BMC Genetics 2011, 12:17
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using CAP3 to create contig consensus sequences[38,39]. Consequently, one UniGene cluster could contain zero (i.e., no suitable EST sequence are found), one,or multiple ConiferEST clusters and contigs. The result
is that for functionally annotated cDNA loci displayedon PineMap, users can quickly obtain information on amarker’s UniGene cluster, its ConiferEST cluster, andthe consensus sequence of that cluster.
Redundant markers and paralogous lociWe identified 28 mapped marker pairs that appeared to
be derived from the same locus (as defined in Methods Linkage analysis and mapping) and therefore would haveprovided redundant mapping information (Table 2). We
excluded these markers from the final map integration
session, although provide their segregation data to permit
independent analysis (Additional file 4, Tables S3 and S4).
We found it interesting that the duplicate marker
PtTX3118, a SSR marker derived from low-copy, highCoT, genomic DNA [40], appeared to be derived fromthe same gene as PtSIFG_1325, which was an EST-SSR
marker derived from a root cDNA library, and which
mapped to LG-2. The two clone sequences are nearly
identical (99% identity over 188 bp, E-value = 2 × 10-94),
differing only in the microsatellite region, and putatively
code for a Ras-like GTP-binding protein (Additional
file 8). Even though a number of the PtTX markers
were developed with the intent of targeting gene regions[40], this match was the only instance that we found of
a genomic-SSR marker for a transcribed gene.
We identified 21 sets of tentative paralogs that
included 58 markers (Table 3). We assigned markers as
paralogs if they shared a UniGene ID (or derived fromthe same DNA clone) and mapped greater than 3 cM
apart. In fact, only two markers, found in paralog set#14 of Table 3, approached this distance limit:
PtIFG_2574_c and PtIFG_2574_2 were separated by
8 cM near the end of LG-5. The next most closely separated markers assigned as paralogs, PtIFG_1636_3 and
PtIFG_1636_54 in paralog set #11, spanned a distanceof 13 cM on LG-3. All paralog sets contained markers
that mapped to two or more LGs. While most of theparalog sets contained RFLP markers derived from thesame clone, which share the clone ID in the marker
name, some of the paralogous associations were not soeasily identifiable. For example, the following five markers mapped to different LGs, but all were assigned toparalog set #16 and share the same UniGene cluster ID(Pta.598) and GO term (glyceraldehyde-3-phosphate
dehydrogenase activity): PsyGPD (LG-5), PtIFG_1165_a(LG-2), PtIFG_2068_A (LG-3), PtIFG_2538_5 (LG-8),
estPtIFG_1950_a (LG-6). This further exemplifies theutility of annotating P. taeda genetic maps with UniGene and GO assignments to explore the genetic

Page 7 of 16

structure of complex gene families that are a characteristic of pine genomes [41].
The threshold map distance of 3 cM and other criteriathat we used to distinguish redundant markers for a single locus from independent markers for paralogous lociwere conservatively chosen to minimize redundant
genetic information on our maps. We cannot exclude
the possibility, however, that we wrongly categorizedsome markers as derived from the same gene sequencethat were in fact derived from closely linked paralogous
genes. Given the available P. taeda marker segregation
data, we expect that higher density and higher resolution genetic maps, or nearly complete physical maps,
will be required to make definitive statements about thefine scale organization of paralogous loci in P. taeda.

Conclusions
We developed 170 SSR markers and mapped 233 in
P. taeda, many more SSR markers than has beenreported for any other conifer species. The integration ofthese markers with previously mapped ESTP and RFLP
markers significantly extended the coverage of two
P. taeda LGs, LG-7 and LG-12. With the map and marker information that we are providing here, there are
now sufficient numbers of different classes of informative
SSR markers for immediate use in a variety of areas:
P. taeda population genetics, evolutionary analysis ofcandidate adaptive trait genes, and germplasm management applications that require unambiguous identification of parental and clonal genotypes. Throughannotation of mapped genes with UniGene cluster IDs
and GO terms, we initiated an assessment of redundantand paralogous EST markers, further improving the quality and utility of this and future P. taeda genetic maps.
The only two P. taeda reference mapping pedigree
populations currently available in the public domain (baseand qtl) are limited to 75 and 85 clonally archived full-sib
progeny, respectively. The limited mapping resolution provided by this resource is insufficient to provide an extensive genetic mapping scaffold that could assist withphysical assembly of the anticipated P. taeda genome
sequence. For this purpose, a public domain high-resolution genetic mapping resource will be required. Nonetheless, the current map can provide immediate genome
ordering of assembled sequence scaffolds by establishinghomology between the sequence tagged markers reported
here and their corresponding scaffold sequence.
Methods
SSR marker development

SSR-enriched genomic DNA libraries of P. taeda andPinus radiata were obtained through the service provider GIS (Genetic Information Services, Inc, CA USA).
Additional P. taeda libraries were obtained by theEcht et al. BMC Genetics 2011, 12:17
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Page 8 of 16

Table 2 Marker pairs with redundancy for a locus
Redundant marker

GenBank accession

UniSTS ID

Cita

Mapped marker

LG

Evidenceb

estPtIFG_0066_a

H75018n/a[13]

PtIFG_66_1

2

B

estPtIFG_0606_a

n/a

n/a

[13]

PtIFG_606_1

6B

estPtIFG_1165_a

H75179n/a[13]

estPtIFG_1950_a

6A

estPtIFG_1635_a

H75110n/a[13]

PtIFG_1635_A

10

B

estPtIFG_2009_a

n/a

n/a

[13]

PtIFG_2009_A

6B

estPtIFG_2253_a

H75063n/a[13]

PtIFG_2253_A

1

B

estPtIFG_2610E(S)H75220n/a[13]

estPtIFG_1950_a

6A

estPtIFG_8496_a
estPtIFG_8596_a

AA739536
AA739594

516058516051[14][14]

estPtIFG_9198_a
estPtIFG_9092_a

1
5

A
A

estPtIFG_8843_a

AA739759

516049[12]

estPtIFG_9053_a

1A

estPtIFG_8907_a

AA739797

516254[13]

PtSIFG_0219

8A

estPtIFG_AGP-3

n/a

n/a

[12]

SsrPt_ctg7444

9C

estPtNCS_20G2_a

AI812850

n/a

[12]

estPtIFG_9053_a

1A

estPtNCS_6C12F_a

AA556811

516381[14]

PpSIFG_3145

3

C

PtIFG_1576_1

n/a

n/a

[11]

estPtIFG_1576_a

1

B

PtIFG_1635_C
PtIFG_1A2_1

H75110
n/a

n/a
n/a

[11]
[11]

PtIFG_1635_A
PtIFG_1A2_C

10
4

B
B

PtIFG_2145_5

H75056n/a[18]

PtIFG_2145_76

3

B

PtIFG_2146_31

n/a

n/a

[11]

PtIFG_2146_2

2

B

PtIFG_2540_2

H75131n/a[11]

estPtIFG_0893_a

5

A

PtIFG_2610_A

H75221n/a[11]

estPtIFG_1950_a

6

A

PtIFG_2889_21

H75234n/a[11]

estPtIFG_2889_a

3

B

PtIFG_2931_b

H75250n/a[11]

PtIFG_2931_A

1

B

PtTX3118
SsrPt_AA739797

AF277845
AA739797

508481516382[40][27]

PtSIFG_1325
PtSIFG_0219

2
8

C
A

SsrPt_ctg4487a

DR159792

n/a

[27]

SsrPt_ctg7141

3

A

SsrPt_ctg4487b

DR159792

516389[27]

SsrPt_ctg7141

3

A

SsrPt_ctg4698

BG275886

516390[27]

PtSIFG_1325

2An/a = not available.
a
The citation in which the redundant marker was first reported. Additional file 2 lists the citations, GenBank accession numbers, and UniSTS IDs of the mappedmarkers.
b
Evidence for assignment of redundant status: all markers mapped within 3 cM of each other, “A” denotes marker pairs assigned to the same UniGene, “B”
denotes marker pairs derived from the same DNA clone, “C” denotes marker pair sequences with a BLASTn E-value less than 4 × 10-43.

enrichment protocol of Ostrander et al. [42] as
described by Echt et al. [43]. SSR motifs targeted forenrichment were: AC, AAC, AAG, AAT, ACC, ACG,
AGG, ATC, AAAC, AAAT and AGAT. Protocols for
DNA sequencing, PCR primer design and amplification,and preliminary evaluations of primer pairs were similar
to those previously described [43].
Pinus taeda EST sequences and contigs were acquiredin July 2004 from the University of Georgia Laboratoryof Genomics and Bioinformatics [44]. Additional
P. taeda ESTs were acquired at the same time from
GenBank dbEST. Pinus pinaster ESTs were obtained
from the INRA, Bordeaux, Laboratory of Forest Genetics
and Tree Improvement [45]. From all sources, 197,931
ESTs with 52,911 assembled contigs were used.
Sequences were analyzed with the Gramene SSRIT PERL
script SSR.pl [46] to find SSRs that met the following SSR
length criteria (bp length); dinucleotides (12), trinucleotides(15), tetranucleotides (16), pentanucleotides (20) and hexanucleotides (24). Only perfect, that is, uninterrupted, SSRs
were counted. Thus compound repeats, such as (TC)8(TA)6, were counted as two repeats, as were imperfect repeats,such as (TA)21G(AT)6. Parallel analyses were run with the
web application BatchPrimer3 [47]. Both analyses gave thesame SSR tallies when identical input parameters wereused. From the SSR.pl reports, we did not count redundant
entries for SSRs that were listed under multiple motif
classes. For example, the dinucleotide (AT)nalso would bereported by SSR.pl as a tetranucleotide of (ATAT)n and asa hexanucleotide of (ATATAT) n , but we counted all
instances as a single dinucleotide SSR. Nomenclature for
SSR motifs followed the convention of the alphabeticallyminimum form [48,49]. For example, all SSRs of the type(AAT)n, (ATA)n, (TAA)n and their reverse complements(ATT)n, (TAT)n, and (TTA)nwere classified as an (AAT)
nmotif, or equivalently as AAT.

Echt et al. BMC Genetics 2011, 12:17
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Page 9 of 16

Table 3 Sets of assigned paralogous markers

Table 3 Sets of assigned paralogous markers (Continued)

MarkerPtIFG_975_4

17

4

6

H75119PtIFG_138_A

19

5

102

H75134

PtIFG_138_B

19

3

95

H75135

PtIFG_2090_1
PtIFG_2090_2

20
20

6
3

77
50

H75048
H75048

PtIFG_2090_4

20

5

26

H75048

PtIFG_2022_A

21

5

54

H75128

PtIFG_2113_1

21

4

27

H75052

Paralog
set No.

LG cM
GenBank UniGene
positiona accession cluster ID of
set members

PitaIFG_2361_1

1

10 102

H75090PtIFG_2361_2

1

7

81

H75090

PitaIFG_1A7_6
PtIFG_1A7_A

2
2

10 112
2 33

AI812330
AI812330

PtSIFG_0206

2

1

98

Pta.1807
Pta.2426

DR160521

PtIFG_2530_A

3

4

43

H75202

PtIFG_2588_1

3

3

86

H75218

PtIFG_653_d

4

1

2

H75097

PtIFG_653_2

4

12 89

H75097

PtIFG_653_3

4

11 81

H75097

PtIFG_3012_2
PtIFG_3012_3

5
5

12 87
2 3

H75261
H75262

Pta.1533

PtIFG_2393_1

61

H75095

Pta.18383

102

Pta.11556

a

11 37

CO170994

PtIFG_149_A

7

7

128

H75140PtIFG_149_2

7

8

132

H75141

estPtIFG_149_a

7

7

0

H75140PtIFG_2220_A

8

5

90

DT626617 Pta.19510

PtIFG_2220_B
PtIFG_2145_76

8
9

4
3

124
9

DT626617
H75056
Pta.2729

PtIFG_2145_1

9

3

115

H75056

PtIFG_2145_28

9

10 31

H75056

PtIFG_2145_3

9

4

0

H75056

PtIFG_1916_1

10

11 48

H75030PtIFG_1916_2

10

7

60

H75030PtIFG_1916_4

10

8

63

H75030PtIFG_1636_2
PtIFG_1636_3

11
11

4
3

111
68

H75187
H7518711

355H75187

PthCAB

121

19

X13407

PtIFG_2006_C

12

3

120

H75042

PtIFG_2718_1

13

3

31

H75230PtIFG_2718_2

13

4

78

H75230PtIFG_2718_3

13

3

74

H75230PtIFG_2574_c
PtIFG_2574_2

14
14

5
5

99
107

H75217
H75216

estPtIFG_1934_a 143

120DR095068

PtIFG_2564_A

15

2

33

H75212

PtIFG_2564_B

15

4

52

H75213

PsyGPD165

57

L26923

PtIFG_1165_a

16

2

112

H75179PtIFG_2068_A

16

3

112

H75130PtIFG_2538_B
PtIFG_2538_5

16
16

2
8

23
131

H75204
H75205

estPtIFG_1950_a 16659H75126

PtIFG_1918_A

17

6

42

H75102

PtIFG_1918_f

17

10 37

H75102

PtIFG_1918_h

17

2

75

H75102

PtIFG_1918_3

17

3

54

H75111

PtIFG_975_3

17

3

109

H75119Pta.6038

Pta.11545

Map position in LG from round-3 JOINMAP integrated mapping calculations.

Pta.1755

estPtIFG_2615_a 6

PtIFG_1636_54

Pta.11535

Pta.1913

Pta.2960

Pta.478

Pta.4922
Pta.4966

Pta.575

Pta.594
Pta.598

Primer pairs to identified SSRs were designed from nonredundant sequences using the STS_Pipeline 1.3 package,
which is the STS_Pipeline1.2 package [50] that we modified by porting to the Redhat Linux 9.0 operating system
and upgrading the primer design engine to Primer3. Similar SSR primer analysis functions are available on-line with
BatchPrimer3 [47]. Input parameters used for primer
design included minimum primer size = 18 nt, maximum
primer size = 24 nt, optimal size = 20 nt, minimum GC
content = 20%, maximum GC content = 80%, minimum
Tm = 56° C, maximum Tm = 64° C, optimal Tm = 60° C,
3’ end complementarities = 3 nt, any complementarities =8 nt, minimum amplicon size = 100 bp, and maximum
amplicon size = 500 bp.
For each sequence, three primer pairs having the three
best PRIMER_PAIR_QUALITY tag values were reported.
The STS_Pipeline reports were screened with custom
PERL scripts to select the one primer pair per sequencethat flanked the intended SSR target and had an optimalbalance of primer quality score and amplicon size. For
example, when considering dinucleotide motif SSRs, theprimer pair with the best quality score was selected if it
had an amplicon size from 100 bp to 299 bp, though if it
did not, then the pair with the shortest amplicon greaterthan 299 bp was selected. Similar selection criteria wereapplied to the longer SSR motifs, except that the preference was for the primer pair with the best quality score
and with an amplicon size greater than 299 bp. If those
two criteria were not met, then the pair that produced
the longest amplicon size less than 299 bp was selected.
We used this size distributed selection strategy toincrease the number of potential marker loci that could
be grouped in multiplexed genotyping assays.
Marker evaluation and genotyping

Pta.84

Pta.9520

The resulting PCR primer pairs were evaluated for ampliconmarker quality with DNA samples obtained from 14 unrelated P. taeda cloned individuals originating from acrossthe natural geographic range, including the four parents ofour two reference mapping pedigrees. The geographic provenance of each population sample is listed in Additional
file 3. We selected markers for subsequent linkage mappingEcht et al. BMC Genetics 2011, 12:17
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and population genetic analysis if they consistently amplified
what appeared to be single loci in the expected ampliconsize ranges. Gene diversity and related allele frequency statistics for the population samples were obtained using the software package GENALEX [51].
We detected and scored SSR alleles by sizing PCRamplicons with capillary electrophoresis. All PCR forward primer oligonucleotides were tailed on their 5’ endwith the M13forward(-29) sequence CACGACGTTGTAAAACGAC. All reverse primer oligonucleotides weretailed on their 5’ end with GTTTCTT, forcing a nontemplated dA addition to the amplicons [52]. Fluorescent dye (6-FAM, VIC, NED or PET) was incorporated
in amplicons by including a 5’ dye-labelled M13 forward(-29) primer in the PCR [53]. PCR composition in a 6 μl
reaction volume was as follows: 10 ng pine DNA template, 40 nM forward primer, 160 nM reverse primer,
160 nM dye-labelled primer, 66 μM dNTPs, buffer (1.5mM MgCl2, 10 mM Tris-Cl, 50 mM KCl, 0.1% Triton
X-100, pH 9.0) and approximately 1 Unit Taq polymerase mixed with anti-Taq polymerase antibody. Equivalent results were obtained by substituting a suitable hotstart Taq polymerase and omitting the antibody. We
used a hot-start PCR thermocycling protocol: 2 min at
94 °C; followed by 20 cycles of 30 s at 94 °C, 30 s at
x °C, and 30 s at 72 °C, where x = 65 °C - 0.5 °C
per cycle; followed by 15 cycles of 30 s at 92 °C, 30 s at
55 °C, 1 min at 72 °C; followed by 15 min at 72 °C.
Completed PCR reactions were refrigerated until analyzed. PCR amplicons and ABI PRISM GS 600 LIZ
internal size standards (Life Technologies Corporation,
Carlsbad, CA, USA) were separated by capillary electrophoresis using ABI PRISM 3100 or 3130xl Genetic Analyzers (Life Technologies Corporation, Carlsbad, CA,
USA). Markers were multiplexed four to a capillary
channel, one of each different dye label, such that marker allele size ranges did not overlap. We used this conservative multiplex strategy to avoid allele detection
complications that can arise when occasional instancesof excessive concentrations of a marker yield spectral
overlap ("bleed-through”) with similarly size markers ofdifferent dye labels. Allele sizing by the third order
least-squares algorithm and allele size binning were performed with ABI PRISM GENEMAPPER 3.7 software(Life Technologies Corporation, Carlsbad, CA, USA),
followed by manual inspection and editing of the automated allele assignments as needed. A second person
independently verified all allele assignments. We standardized SSR marker allele bins among capillary electrophoresis runs with the aid of control genotype samples
from the P. taeda clones 7-56 and B-145-L Control genotypes for all SSR markers are listed in Additional file 4,
Table S7.

Page 10 of 16

Non-SSR marker segregation data for base and qtl
pedigrees were provided by G. Brown [12,11]. We converted non-SSR marker data from a numerical genotype
code format to an ABCDMN format prior to analysis.
The PERL script genojoin.pl http://www.esd.ornl.gov/
PGG/scripts.htm was used to properly format the converted genotype data to a format suitable for linkage
analysis JOINMAP (see Linkage analysis and mapping
section below). SSR genotypes in exported GENEMAPPER tables were analyzed for coding errors and converted to an ABCDMN format by the PERL script
genomapper.pl http://www.esd.ornl.gov/PGG/scripts.
htm. The corrected and reformatted SSR genotype
tables subsequently were converted to a JOINMAP format using the genojoin.pl script.
Marker nomenclature

SSR markers developed in the course of this study were
given the prefix “PtSIFG” if they were derived fromP. taeda ESTs, “PpSIFG” if from P. pinaster ESTs,“PtRIP” if from P. taeda genomic libraries, and “NZPR” iffrom P. radiata genomic libraries. RFLP and ESTP markers were named as previously published [20,54], as wereexisting microsatellite markers of the SsrPt and PtTX series [27,26]. Known aliases are listed in Additional file 2.
Mapping populations

Mapping populations were from two unrelated P. taeda
outbred pedigrees constructed by the Weyerhaeuser
Company, referred to as base and qtl [11]. For the basepedigree (cross: 20-1010 × 11-1060, described in [9]), we
mapped markers that had been genotyped in two sets offull-sib progeny: one set of 95 progeny that prior studies
used for mapping RFLP and ESTP markers [9,14] andone set of 75 progeny that we used to genotype SSR markers. There were 72 progeny in common between these
sets, providing 98 base progeny samples for mapping all
markers. We genotyped six PtTX SSR markers in bothprogeny sets and used the consolidated segregation datafor verifying sample identities between the sets and formapping. For the qtl pedigree (cross: 6-1031 × 8-1070),
we mapped markers that had been genotyped in four setsof full-sib progeny: the full set of 172 progeny and a subset of 48 progeny that prior studies used for mapping
RFLP markers [11,55], a subset of 95 that prior studies
used for mapping ESTP markers [13,14], and a subset of
85 progeny that we used to genotype SSR markers. We
genotyped eleven PtTX and eight NZPR SSR markers in
the latter two progeny sets and used the consolidated
segregation data for verifying sample identities between
the sets and for mapping. From scion material that we
obtained from Weyerhaeuser Company, we established aclone archive of the base (n = 75) and qtl (n = 85)

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progeny sets used for SSR genotyping, which we named
Base2 and Qtl2, respectively. Needle or DNA samples
from these trees can be obtained from author CDN.
By comparing the multi-locus SSR genotypes of eachsample that was in common between the overlapping
progeny sets of each pedigree, we found and subsequently resolved progeny sample code discrepancies
between the archival data and our newly generated data.
For each mapping pedigree we then merged the correctly aligned genotype segregation matrices from thevarious progeny sets into an interleaved matrix that weused for subsequent linkage analyses and mapping(Additional file 4, Tables S1 - S4).
Linkage analysis and mapping

We performed linkage analyses and consensus map integration using JOINMAP 3.0 [56,57]. We established the
12 linkage groups (LGs) reported in published P. taeda
maps by inspecting LOD grouping of markers constructed at 0.5 LOD intervals from LOD 3 to LOD 7.
The maximum recombination parameter for establishinglinkage was set to 0.5, which imposed no restrictions on
the LOD groupings. LG number identifiers wereassigned based on previously reported marker locations
on individual P. taeda linkage groups [14]. The JOINMAP mating type parameter CP (cross-pollination) wasused for the allogamous outcrosses of both mapping
pedigrees. Mapping parameters were set for a “jump”
threshold of 5, a ripple value of 1, and the Kosambi
mapping function. JOINMAP uses a reiterative process
through three rounds of mapping calculations. During
the first two rounds, JOINMAP excludes markers thatexceed the assigned jump threshold (the normalized difference in the goodness-of-fit values for a marker
ordered in a stepwise process), while during the thirdround they are placed into their most likely positions in
the LG regardless of the jump threshold [57]. We
retained markers placed on the round-3 maps for subsequent mapping sessions, except as noted below for the
final integrated mapping session from which we reportresults for the round-2 and round-3 integrated maps.
We first constructed maps separately for the base andqtl pedigrees to assess the quality of the segregation
data for individual markers and identify unsuitable or
redundant markers. We excluded loci if they segregatednull alleles, if they had excessive segregation distortion(P < 0.005 for c2tests), if they altered locus orders, or ifthey appeared to be a redundant marker for a locus. We
did not employ a Bonferroni adjustment of P for tests ofsegregation distortion because we empirically determined that markers within the adopted limit of P could
be mapped with confidence if they had not been
excluded by other criteria. We assigned two markers as
redundant if they mapped within 3 cM of each other

Page 11 of 16

and either derived from the same cDNA clone, had thesame UniGene ID, or had at least a 99% BLASTn
sequence identity over more than 185 bp. The latter criterion was applied only in instances where one markerof the pair was derived from a pine EST sequence otherthan P. taeda or from a non-EST (genomic) sequence.
The 3 cM distance limit was determined empiricallyfrom distances that we observed between non-cosegregating map positions of markers known to be for thesame locus. This included SSR markers independently
genotyped with the same primers in different labs or different mapping progeny subsets and RFLP and ESTP
markers derived from the same gene. Adoption of thislimit was supported by a report that ESTP, RFLP, SNP,
and isozyme markers for the same single copy cad(cinnamyl alcohol dehydrogenase) gene mapped within
2.4 cM of one another in P. taeda [16]. Further, simulations show that a 3% genotype error rate can nearly
double the map distance between loci separated by
2 cM and obscure their correct map order [58]. We
therefore assumed that there may be some degree of
undetected genotyping errors for marker loci that otherwise would be expected to cosegregate and we assigned
such loci a redundant status if we found evidence thatthey covered the same gene and mapped less than 3 cM
apart. The one marker of a redundant pair that we
excluded was the one with the poorer goodness-of-fit,
that is, the one with the larger c 2 contribution to theordered group. We repeated mapping sessions to assessmarker suitability until all unsuitable markers were
excluded.
We did not exclude markers based on missing data
except for extreme instances of markers with >70%
missing data, which we found only in the qtl pedigree
segregation data. Markers with lower percentages of
missing data did not warrant a priori exclusion because
they were rejected by our other exclusion criteria during
the reiterative mapping sessions. We were aware ofpotential negative effects on map order and distancesthat can arise from missing data [58], however, we usedthe efficiency of the mapping process to include markers
that could be placed accurately and exclude those that
could not. The rationale of this approach was to map
SSR markers in the context of prior cDNA-based P.
taeda maps by incorporating the same ESTP and RFLP
marker segregation data used to construct those maps
[14,16,20]. Because sets of data were consolidated fromdifferent mapping population cohorts, described abovein the Mapping population section, the analysed matrixof segregating genotypes contained blocks of missing
genotypes (i.e., ranging from 1% to 40% for RFLP
markers and 45% to 67% for ESTP markers in the
172-sample qtl mapping population). We therefore replicated in our mapping analyses the same levels ofEcht et al. BMC Genetics 2011, 12:17
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missing data for ESTP and RFLP loci that prior studies[14,16,20] used to construct P. taeda maps.
Markers retained from the individual pedigree mapping
sessions were used for integrating the pedigree maps by
applying the JOINMAP function ‘Combine Groups for
Map Integration’ to individual linkage groups. We used
an initial session of map integration to identify and subsequently exclude markers that caused inconsistency inthe order of groups of loci, expanded the LG length bymore than 5 cM, or appeared to be a redundant marker
for a locus. Retained markers were used in a final sessionof map integration. Markers added during the round-3
JOINMAP map calculations are considered ancillarymarkers. Details of round-3 mapping data are listed in
Additional file 2 and used for reporting summary map
statistics, while round-2 map data are listed in Additional
File 5 and depicted in Figure 1. We charted linkage maps
using the MAPCHART v2.1 program [59].
We obtained an estimated genome length by summing
adjusted map lengths for the 12 linkage groups according to method 4 of Chakravarti et al. [60]. In thatmethod the length of each linkage group is adjusted bymultiplying the observed cM length by the factor (m +1)/(m - 1), where m is the number of markers in thelinkage group. We used this method because, when evaluating larger numbers of markers and segregating progeny, it performs better than an alternative maximum
likelihood method based on pair-wise recombination
values or a method-of-moments estimator based onpair-wise LOD scores [60]. Genome coverage was estimated as the proportion of marker coverage c, giventhat at least one marker is located within a specified
mapping interval d (that is, adjacent markers should be
at most 2d apart) in a genome of length L with mmapped markers, such that c = 1 - e -2dm/L [10,61].
These estimates for genome length and genome coverage assume a random distribution of mapped markers;
therefore, we tested for random marker distribution in
the integrated map. We parsed the concatenated map
into 10 cM intervals and counted the number of intervals that contained an x number of markers, for x fromzero to 11. To compare these results to a random distribution, we used a Poisson distribution function, P(x) =μxe-μ/x!, where P(x) was the probability of x number ofmarkers per interval and μ was the average number ofmarkers per interval in the integrated map. We obtained
the distribution of expected number of intervals containing x markers by multiplying P(x) by the total number of 10 cM intervals on the integrated map for eachcorresponding value of x, from zero to 11. We thenused the Kolmogorov-Smirnov (K-S) test for two populations to determine whether the distribution ofobserved counts was likely the same as the distributionof expected random counts [62].

Page 12 of 16

Marker curation

We searched NCBI’s UniGene database [63] for eachmarker’s GenBank accession number and when a match
was found we assigned the Pta (P. taeda) UniGene cluster ID to the marker (Additional file 8). If a marker’s
sequence was from a conifer species other than
P. taeda, or was a P. taeda transcribed sequence notfound in a Pta UniGene cluster, then we used it as thequery in a BLASTn search to find in GenBank a homologous P. taeda EST target. We conducted searcheswith the NCBI BLAST engine http://blast.ncbi.nlm.nih.
gov/Blast.cgi and selected target sequences that had thehighest sequence identity alignment above 85% andspanned at least 220 nucleotides of the query sequence.
If a selected target sequence was also a member of a Pta
UniGene cluster, then that cluster ID was assigned tothe marker. In the few cases of ESTP markers that had
no available cDNA or EST sequence, we used the marker’s primer pair sequences in a BLASTn search of GenBank’s P. taeda ESTs. If both primers aligned exactly
and in the expected orientation, then the accession
number of the target EST was assigned to the marker
and UniGene cluster assignment proceeded as described.
Summary statistics of BLASTn alignments for individual
markers are in the Notes column of Additional file 8.
We assigned RefSeq proteins [64] to markers as themain means to obtain GO term annotations. Using the
RefSeq protein information provided by NCBI for each
P. taeda (Pta) UniGene cluster, we assigned to the marker the RefSeq that had the highest reported amino acid
identity and a functional GO term annotation (Additional file 8). Generally, Arabidopsis thaliana or Oryza
sativa RefSeq proteins were selected. If no RefSeq proteins were listed for a UniGene cluster, but a functionally annotated Pinus mRNA (complete or partial cds)
was in the cluster, then that mRNA’s GenBank protein
was assigned as the reference protein (8 cases). If we
could not assign a UniGene cluster to a marker, thenwe based reference protein assignment on the results ofa BLASTx search of the RefSeq protein database withthe marker’s EST sequence (16 cases). All reference protein assignments were contingent on a greater than 45%
amino acid sequence identity that spanned more than
45% of the marker’s translated amino acid sequencelength, as reported by the NCBI BLAST search engine
http://blast.ncbi.nlm.nih.gov/Blast.cgi. Details of assignments based on these later conditions are in the Notes
column of Additional file 8.
For most markers, we assigned GO terms [65] basedon the assigned RefSeq protein’s GO annotation available either from NCBI records or from The Arabidopsis
Information Resource http://www.arabidopsis.org/. If amarker’s assigned reference protein was a Pinus GenBank protein (that is, not a RefSeq protein), then weEcht et al. BMC Genetics 2011, 12:17
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Page 13 of 16

adopted the GO term of the most homologous anddescriptively annotated RefSeq protein that was listed
for the marker’s UniGene cluster (5 cases). If no RefSeq
was listed, then we assigned a marker’s GO term denovo from the AmiGO database [66] by searching on
the functional annotation of the marker’s assigned Pinus
reference protein (3 cases). We assigned to a marker the“molecular_function” GO term as a stub if its reference
protein had an unknown function or if it had neither areference protein nor a UniGene ID assignment.
GO terms were obtained from the GO database versionof October 2009. We assigned only one GO term for eachgene marker. Our intent was to follow a conservative
assignment of known biochemical function based onamino acid sequence homology or direct experimental
demonstration. With this approach, we avoided inferringfrom taxonomically distant species any specific biologicalrole of a gene that was not corroborated by direct metabolic, developmental, or cytological studies in pine. Given
that intent, our preference of GO assignments was for themolecular_function ontology. If a molecular_function
term was not available for a marker’s assigned RefSeq protein, then the RefSeq protein’s available biological_processontology term was used (9 cases), or we used the RefSeq
protein’s cellular_compartment ontology term (1 case).

Benjamin Figueroa kindly provided GO terms standardized through common lineages of parent GO terms tospecific hierarchical levels through use of a custom PERL
script running on the Dendrome TreeGenes database [67].
The standardized terms for each assignment are in Additional file 8, and are summarized in Figure 3.
PineMap implementation on ConiferGDB

PineMap [34] was developed as an online tool to display
the genetic map and marker database created by this project. In its current implementation, PineMap is based on
customization and function extension of CMap, an opensource and PERL-based computational tool for displaying
both genetic and physical maps [68]. Twenty-two categories of marker information, taken from Additional files
2 and 8, were inserted into the database. We modified
CMap to suit the specific requirements of this project,such as including marker annotations. Using CMap as acore base system, we developed a wrapper system around
it using AJAX technology based on HTML, CSS, JavaScript and PERL, that allowed us to design and implement custom pages, such as free-text searches andadvanced searches. A separate web interface was created
for administrative functions. This administrative pageenables approved users with a username and password to

CMap-AssociatedPages• Free text search• Advanced searchComparative Map
Viewer (CMap)• Map Set metadata• Data downloads

Administrative
Tools• Visualization engine• Add, remove or
update markers• Hyperlinked marker
information• Update Map Set
details

CGDB
PineMap
Figure 4 Components of PineMap. Diagrammatic representation of the main functional components of the PineMap user interface at
ConiferGDB.

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add, delete and update individual marker data, as well as
edit the metadata of a given map set. More modifications
were incorporated to facilitate downloading of marker
data and annotation for markers localised on each chromosome, which was not available in the original CMap
software. A schematic diagram of the major PineMap system components is shown in Figure 4.

Additional material
Additional file 1: Table of all submitted markers’ GenBank anddbSTS accession numbers. This tab-delimited text file can be viewed
with any web browser, word processor, or spreadsheet program.
Additional file 2: Table of data for 517 P. taeda marker loci. Data
include: marker ID, map position and linkage data, databaseaccession IDs, forward and reverse primer sequences, marker type,allele detection method, SSR type, expected and observed
amplicon lengths, marker citations, aliases, and supplemental notes
This HTML data table conforms to the XHTML 1.1 standard of the World
Wide Web Consortium (W3C), as determined at http://validator.w3.org,
and can be viewed with any web browser, as well as with Microsoft
Excel or Word.
Additional file 3: Provenances for 14 population samples andpopulation genetic parameters for 185 P. taeda SSR marker loci.
Data include: GenBank ID, allele frequency statistics, and assigned
linkage group This tab-delimited text file can be viewed with any webbrowser, word processor, or spreadsheet program.
Additional file 4: Marker genotype segregation codes in JoinMap
format for the base and qtl pedigrees (Tables S1 - S4), reasons for
excluding certain markers (Tables S5, S6), and size-estimated SSR
allele genotypes of the four mapping pedigree parents and twocontrol calibration standards (Table S7) This tab-delimited text file canbe viewed with any web browser, word processor, or spreadsheet
program.
Additional file 5: Map data used in Figure 1, formatted for
MAPCHART input. This tab-delimited text file can be viewed with any
web browser, word processor, or spreadsheet program. Changing the fileextension from .txt to .mct will allow the file to be opened by
MAPCHART to generate the map graphic on a Windows platform.
Additional file 6: Comparative P. taeda genetic maps: Round-2 mapaligned with map of Krutovsky et al. 2004
Additional file 7: Comparative P. taeda genetic maps: Map of
Krutovsky et al. 2004 aligned with map of Eckert et al. 2009.
Additional file 8: Table of DNA sequence annotations for reported
markers. Data include: marker and GenBank accession number,clone ID, species of origin, UniGene cluster ID, assigned GO termand GO lineage, assigned reference protein, marker type, P. taeda
map status, and supplemental notes This HTML data table conforms
to the XHTML 1.1 standard of the World Wide Web Consortium (W3C), as
determined at http://validator.w3.org, and can be viewed with any webbrowser, as well as with Microsoft Excel or Word.

Abbreviations
AFLP: amplified fragment length polymorphism; BLAST: basic local alignment
search tool; cM: centiMorgan; EST: expressed sequence tag; EST-SSR: an SSR
obtained from a cDNA sequence located in EST; ESTP: expressed sequence
tag polymorphism; GO: Gene Ontology; LG: linkage group; LOD: log10 of thelikelihood odds ratio; NCBI: National Center for Biotechnology Information;
PCR: polymerase chain reaction; QTL: quantitative trait locus; SNP: single
nucleotide polymorphism; SSR: simple sequence repeat; RAPD: random
amplified polymorphic DNA; RFLP: restriction fragment length
polymorphism.

Page 14 of 16

Acknowledgements
The Agenda 2020 Program and International Paper Co. partially funded thisresearch. We thank the Weyerhaeuser Corp. for use of the base and qtl
pedigrees. We thank the following people for their assistance, without which
this work would not have been possible: Ron Burns, Chuck Burdine, Dennis
Deemer, Sedley Josserand (U.S. Forest Service), Andrea Langman, Lisa
McMillan, Lorelle Phillips, Phillip Wilcox, Minsoon Yoon (The New ZealandForest Research Institute), Terry Burke and John Anthony (Weyerhaeuser Co.).
MM Cordonnier-Pratt, Lee Pratt (University of Georgia, USA), David Chagné
and Christophe Plomion (INRA, France) generously shared their pine EST
sequences and contig assemblies. Steven DiFazio provided help
implementing his PERL scripts that we used for marker genotype coding
and analysis. Benjamin Figueroa provided us with standardized hierarchicalGO terms. Tom Kubisiak and Ross Whetten provided helpful comments that
improved the manuscript prior to submission. We are especially thankful tothree anonymous reviewers whose comments improved the clarity andcomprehensiveness of the manuscript.
Author details
1Southern Institute of Forest Genetics, Southern Research Station, USDA
Forest Service, Saucier, MS 39574, USA. 2Department of Plant & Soil Sciences,
Mississippi State University, Mississippi State, Mississippi 39762, USA. 3Current
address: Department of Plant Pathology and Plant-Microbe Biology, Cornell
University, Ithaca, New York 14853, USA. 4Department of Ecosystem Science& Management, Texas A&M University, College Station, Texas 77843-2138,
USA. 5Crop Genetics Research Unit, USDA Agricultural Research Service,
Stoneville, Mississippi, USA. 6Department of Botany, Miami University, Oxford,
Ohio, 45056, USA.
Authors’ contributions
CSE conceived and coordinated the study, analyzed the data, annotated themarkers and drafted the manuscript with contributions from all co-authors
on subsequent drafts. SS mined SSR sequences from pine EST databases,
wrote PERL scripts used in primer selection, and evaluated SSR primer pairs
developed from Pinus pinaster ESTs. KVK compiled the RFLP and ESTP
sequence and primer data. KW implemented PineMap at ConiferGDB andparticipated in designing the database and web interfaces. JEE constructed
and selected SSR-enriched genomic libraries for Pinus taeda. CL designed,
initiated, and managed the development of the PineMap database system.
CDN co-conceived some of the individual SSR development projects andestablished the mapping population cohorts at SIFG. All authors read andapproved the final manuscript.
Received: 27 April 2010 Accepted: 26 January 2011
Published: 26 January 2011
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doi:10.1186/1471-2156-12-17
Cite this article as: Echt et al.: An annotated genetic map of loblolly
pine based on microsatellite and cDNA markers. BMC Genetics 201112:17.

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