Construction of dense linkage maps ‘‘on the fly’’ using earlygeneration wheat breeding populations

J. T. Eckard • J. L. Gonzalez-Hernandez •

S. Chao • P. St Amand • G. Bai

Received: 4 March 2014 / Accepted: 15 May 2014

� Springer Science+Business Media Dordrecht 2014

Abstract In plant species, construction of frame-

work linkage maps to facilitate quantitative trait loci

mapping and molecular breeding has been confined to

experimental mapping populations. However, devel-

opment and evaluation of these populations is

detached from breeding efforts for cultivar develop-

ment. In this study, we demonstrate that dense and

reliable linkage maps can be constructed using extant

breeding populations derived from a large number of

crosses, thus eliminating the need for extraneous

population development. Using 565 segregating F1

progeny from 28 four-way cross breeding populations,

a linkage map of the hexaploid wheat genome

consisting of 3,785 single nucleotide polymorphism

(SNP) loci and 22 simple sequence repeat loci was

developed. Map estimation was facilitated by appli-

cation of mapping algorithms for general pedigrees

implemented in the software package CRI-MAP. The

developed linkage maps showed high rank-order

concordance with a SNP consensus map developed

from seven mapping studies. Therefore, the linkage

mapping methodology presented here represents a

resource efficient approach for plant breeding pro-

grams that enables development of dense linkage

maps ‘‘on the fly’’ to support molecular breeding

efforts.

Keywords Linkage mapping �Consensus map �Pedigree analysis �Wheat breeding �High-throughput genotyping

Introduction

Genetic linkage maps, consisting of linked marker loci

ordered along chromosomes, provide the essential

framework for identifying genomic regions involved

in trait expression and detection of marker-trait

associations to enable molecular breeding. For plant

species, linkage mapping has largely been confined to

an experimental paradigm, in which a purpose-built

population derived from a cross between two inbred

lines is used for map construction. These experimental

mapping populations have been attractive tools for

genetic mapping due to their simplicity of develop-

ment, power for quantitative trait loci (QTL) detection

and applicability of available mapping algorithms

and user-friendly software implementations (Doerge

Electronic supplementary material The online version ofthis article (doi:10.1007/s11032-014-0116-1) contains supple-mentary material, which is available to authorized users.

J. T. Eckard � J. L. Gonzalez-Hernandez (&)

Department of Plant Science, South Dakota State

University, Brookings, SD, USA

e-mail: jose.gonzalez@sdstate.edu

S. Chao

USDA, Agricultural Research Service, Fargo, ND, USA

P. St Amand � G. Bai

USDA, Agricultural Research Service, Manhattan, KS,

USA

123

Mol Breeding

DOI 10.1007/s11032-014-0116-1

2002). Despite these attractive attributes, experimen-

tal mapping populations have important limitations for

the development of genetic linkage maps owing to

their narrow genetic base and detachment from

applied breeding efforts (Crepieux et al. 2004).

The increasing availability of high-throughput

marker genotyping platforms (Kilian et al. 2003;

Akbari et al. 2006; Akhunov et al. 2011; Allen et al.

2011; Deschamps et al. 2012) provides a means for the

development of dense linkage maps (Bowers et al.

2012). However, the narrow genetic base of conven-

tional mapping populations means that only a small

fraction of these marker loci are polymorphic and thus

informative for mapping in any given population.

Development of a linkage map with dense marker

coverage has therefore required integration of maps

from several experimental populations to form a

consensus map (Wu et al. 2008). However, the process

of developing and genotyping several large popula-

tions that have no direct contribution to cultivar

development is inefficient from the perspective of an

applied breeding program. Furthermore, conventional

mapping algorithms can only facilitate joint likelihood

estimation of the linkage map if each of the constituent

populations is of the same structure (Wu et al. 2008).

Therefore, consensus mapping is computationally

inefficient since it often relies on interpolation of

disparate estimates from each constituent population.

To overcome the limitations of conventional map-

ping populations, researchers have proposed using

broad-based populations derived from multi-parent

crosses. For example, the maize nested association

mapping (NAM) population consists of 5,000 recom-

binant inbred lines derived by crossing 25 diverse

founders to a single common founder (Yu et al. 2008).

Early generation four-way cross populations have

been used for genetic mapping in cotton and wheat,

facilitating the development of higher density genetic

maps relative to biparental populations (Trebbi et al.

2008; Qin et al. 2008). This concept of developing ‘‘n-

way’’ intercross mapping populations has been

extended to recombinant inbred lines, with such

populations referred to multi-parent advanced gener-

ation intercross (MAGIC) populations (Cavanagh

et al. 2008). A MAGIC population of over 1,500 RILs

from a four-way cross among elite wheat cultivars was

used to map 1,162 simple sequence repeat (SSR),

single nucleotide polymorphism (SNP) and DArT loci

(Huang et al. 2012) and later used to map 4,300 SNPs

(Cavanagh et al. 2013). MAGIC populations have also

been developed for rice using eight-way crosses

among elite indica and japonica lines (Bandillo et al.

2013). These rice populations enabled the identifica-

tion 17,387 polymorphic SNP loci using genotype-by-

sequencing methods (Bandillo et al. 2013).

The preceding results indicate that multi-parent

mapping populations, also referred to as ‘‘second

generation’’ (Rakshit et al. 2012) or ‘‘next generation’’

(Morrell et al. 2012) mapping populations, provide a

powerful resource for the construction of dense

linkage maps. A major motivation for utilizing these

multi-parent mapping populations is that they more

closely resemble the broad genetic base and multi-

allelic/multi-genic inheritance of breeding popula-

tions and thus provide more direct inference for QTL

mapping applications (Holland 2007). However, con-

siderable time and resources are required for the

development and evaluation of these complex exper-

imental populations, which detracts from applied

breeding efforts for cultivar development. For exam-

ple, development of an n-way cross for deriving a

MAGIC population requires n/2 generations of inter-

crossing to generate the base population, followed by

6–7 generations of inbreeding to develop the RILs

(Rakshit et al. 2012).

Plant breeders are continually developing a large

number of segregating populations through carefully

planned crosses among numerous elite parents.

Primary segregating populations from these crosses

consist of relatively small sibships that have yet to be

subjected to intense selection by breeders. Collec-

tively, these early generation breeding populations

represent a substantial pool of informative genetic

recombinations that can be used for the development

of dense genetic maps. Linkage maps developed using

a large number of early generation breeding popula-

tions should therefore provide comparable marker

density and reliability to those developed using

consensus mapping or multi-parent mapping popula-

tions. Thus, utilizing existing populations available in

plant breeding programs for the purpose of genetic

linkage mapping should provide an efficient alterna-

tive to development of experimental mapping popu-

lations. In combination with high-throughput and

nondestructive genotyping technologies, this strategy

would allow breeders to develop dense linkage maps

‘‘on the fly’’ to support their molecular breeding

efforts.

Mol Breeding

123

Development of linkage maps using plant breeding

populations requires mapping algorithms that can

handle partially informative marker data, ambiguous

linkage phases and simultaneous analysis of numerous

small sibships of arbitrary population structure. Due to

the prevailing experimental paradigm, such general-

ized mapping algorithms are not incorporated into

software used for linkage mapping in plant popula-

tions (Cheema and Dicks 2009). However, these

mapping algorithms are commonly used for linkage

analysis in humans and animals populations, where

purpose-built populations are not available. Therefore,

mapping algorithms used for multi-point linkage

analysis in general pedigrees (Lander and Green

1987; Weaver et al. 1992) can be adopted for the

purpose of constructing linkage maps in plant breed-

ing populations.

In this study, we apply multi-point linkage analysis

of general pedigrees to develop a dense genetic

linkage map from a 9,000 SNP array using early

generation wheat breeding populations. Our objec-

tives are to (1) confirm that dense linkage maps can be

obtained using primary segregating populations from

breeding programs and (2) assess the accuracy of such

derived linkage maps by evaluating their concordance

with a recently released SNP consensus map of the

wheat genome.

Materials and methods

Plant materials

Segregating F1 populations were developed from 28

four-way crosses among 10 winter wheat founder lines

(Table 1). These wheat breeding populations were

developed for the purpose of pyramiding resistance

loci for Fusarium head blight. Founders included two

backcross derived lines Wesley-Fhb1-BC06 and Wes-

ley-Fhb1-BC56 (Wesley/2*ND2928), an experimental

line AL-107-6106 (Alsen/NE00403//NE02583-107),

hard winter wheat cultivars Lyman (KS93U134/Arap-

ahoe), Overland (Millennium sib//Seward/Archer),

NE06545 (KS92-946-B-I5-1/Alliance), NI08708

(CO980829/Wesley), McGill (NE92458/Ike), and soft

winter wheat cultivars Ernie (Pike/MO9965) and

Freedom (GR876/OH217). A total of 565 four-way

F1 plants were derived from the 28 four-way crosses,

with an average of 20 four-way F1 plants per cross

(Table 1). Founder lines and four-way F1 plants were

vernalized and then transplanted as individual plants in

4 9 4 inch pots in a greenhouse.

DNA extraction

Approximately 2 g of healthy leaf tissue was collected

from each founder and four-way F1 plant. For founder

lines, tissue samples from multiple plants were pooled

into a single sample. Leaf tissue was transferred to

liquid nitrogen immediately upon collection and

subsequently stored at -80� C to prevent degradation.

DNA was isolated from the leaf tissue using a

midiprep phenol/chloroform extraction protocol

adapted from Karakousis and Langridge (2003).

Briefly, leaf tissue was flash frozen in liquid nitrogen

and then ground to a fine powder using a mortar and

pestle. Ground leaf tissue was then mixed with 5 mL

of DNA extraction buffer (1 % n-lauroylsarcosine,

100 mM Tris-base, 100 mM NaCl, 10 mM EDTA,

2 % polyvinyl–polypyrrolidone, pH 8.5) and 5 mL of

phenol/chloroform/isoamyl alcohol 25:24:1 saturated

with 10 mM Tris (pH 8.0) for nucleic acid separation.

After mixing and centrifugation, the supernatant was

transferred to a 10:1 solution of isopropanol and

sodium acetate for overnight precipitation of nucleic

acids. Precipitated nucleic acid was pelletized by

centrifugation and washed with 70 % molecular grade

ethanol to remove salts. After drying, the pellet was

suspended in 10 mM Tris buffer (pH 8.0) containing

40 lg/mL of RNase A.

Genotyping

Founder lines and all 565 four-way F1 plants were

genotyped at 26 polymorphic simple SSR marker loci.

SSR genotyping was conducted at the USDA-ARS

Hard Winter Wheat Genetics Research Unit, Manhat-

tan, KS. PCR was conducted in 14 lL PCR, consisting

of 40 ng of template DNA, 0.1 lM of each primer,

0.2 mM of each dNTP, 19 ammonium sulfide PCR

buffer, 2.5 mM MgCl2 and 0.6 unit of Taq polymer-

ase. A touchdown PCR program described by Zhang

et al. (2012) was used for amplification. Differentially

labeled primers were used for fluorescence detection

of SSR amplicons from multiplex PCR as described by

Zhang et al. (2012). PCR products were separated and

detected using an ABI Prism 3730 Genetic Analyzer,

and allele calls were made from resulting fluorescence

Mol Breeding

123

peaks using GeneMarker version 1.6 (SoftGenetics,

LLC). SSR marker genotypes were scored after visual

assessment of fluorescence profiles to correct errone-

ous and ambiguous allele calls.

A subset of 18 populations consisting of 372 four-

way F1 plants (Table 1) were genotyped for approx-

imately 9,000 SNP marker loci. SNP genotyping was

conducted using an Infinium 9,000 SNP iSelect

Beadchip assay developed for wheat (Cavanagh

et al. 2013). The assay was performed using the

Illumina BeadStation and iScan instruments at the

USDA-ARS Biosciences Research Laboratory, Fargo,

ND. GenomeStudio version 2011.1 (Illumina) was

used for cluster analysis and SNP genotype calling.

The minimum ‘‘GenTrain’’ score (a measure of the

reliability of SNP calling based on cluster distribution)

was reduced to 0.05 in GenomeStudio to facilitate

delineation of compressed but unambiguous SNP

clusters. Genotype clusters were then visually

assessed for each SNP and manually revised to

improve genotype calling. SNP loci represented by

more than three genotypic clusters, and those SNP loci

with[20 % deviation from the expected heterozygote

frequency under Mendelian segregation were

excluded from the analysis to avoid complications of

polyploid inheritance.

Mendelian inheritance errors for both SSR and SNP

loci were detected using the ‘‘prepare’’ function of

Table 1 Summary of breeding populations used for linkage map development, including the pedigrees, number of progeny and

number of plants used for SNP genotyping

Population Pedigree Four-way F1 plants SNP genotyped

01 Wesley-Fhb1-BC56/NE06545//Ernie/Overland 20 19

03 Ernie/Wesley-Fhb1-BC06//Ernie/NE06545 26 –

05 Ernie/Wesley-Fhb1-BC06//Lyman/AL-107-6106 22 16

06 Ernie/Wesley-Fhb1-BC56//Ernie/Lyman 40 37

09 Ernie/Wesley-Fhb1-BC56//NI08708/Lyman 40 38

10 Ernie/Lyman//Ernie/Wesley-Fhb1-BC06 12 –

14 Ernie/Overland//Freedom/Wesley-Fhb1-BC56 5 –

16 Ernie/Overland//Overland/Wesley-Fhb1-BC56 24 23

17 Ernie/Overland//NI08708/Wesley-Fhb1-BC06 33 30

20 Ernie/NE06545//McGill/Wesley-Fhb1-BC56 28 –

23 Ernie/McGill//Lyman/Wesley-Fhb1-BC06 12 9

26 Freedom/Wesley-Fhb1-BC06//Ernie/Overland 12 9

27 Freedom/Wesley-Fhb1-BC06//Lyman/AL-107-6106 7 7

28 Freedom/Wesley-Fhb1-BC06//Overland/Wesley-Fhb1-BC56 11 9

30 Freedom/Wesley-Fhb1-BC56//Ernie/NE06545 4 –

35 Freedom/Ernie//Overland/Wesley-Fhb1-BC56 34 30

36 Freedom/Ernie//NI08708/Wesley-Fhb1-BC06 29 –

40 Freedom/Overland//Lyman/AL-107-6106 8 8

41 Freedom/NI08708//Wesley-Fhb1-BC56/NE06545 7 –

45 AL-107-6106/Overland//Lyman/Wesley-Fhb1-BC06 11 10

48 AL-107-6106/Overland//NI08708/Lyman 14 14

54 Lyman/Wesley-Fhb1-BC56//Ernie/Lyman 37 35

57 Lyman/Wesley-Fhb1-BC56//NI08708/Lyman 31 –

64 Overland/Wesley-Fhb1-BC56//Ernie/Lyman 44 41

65 Overland/Wesley-Fhb1-BC56//Ernie/NE06545 5 –

67 Overland/McGill//Lyman/Wesley-Fhb1-BC06 12 9

71 NI08708/Wesley-Fhb1-BC06//Ernie/NE06545 8 –

76 NI08708/Lyman//Overland/Wesley-Fhb1-BC56 29 28

Total population 565 372

Mol Breeding

123

CRI-MAP version 2.504 (Green et al. 1990). Genetic

impurities in the founders were diagnosed as outliers

(i.e., samples with a low ‘‘GenTrain’’ score) within the

respective homozygote cluster and by unexpected

segregation patterns. For those cases where genotyp-

ing errors could not be rectified, including cases where

a founder line conferred more than one allele at a locus

within the same population, the genotypic data were

replaced with missing values.

Linkage mapping

Linkage analysis was performed using the software

package CRI-MAP version 2.504 (Green et al. 1990).

CRI-MAP provides an interactive environment for

multi-point maximum-likelihood estimation of link-

age maps in general pedigrees. For the purpose of

constructing the required pedigree data, each four-way

cross was considered as a separate pedigree. The

assumption of independence among pedigrees was

made without any loss of generality, since the founder

lines were phase known. The sex designation of

founders and single-cross F1 hybrids was generally

assigned corresponding to the actual crossing scheme,

while four-way F1 plants were arbitrarily designated as

females. All linkage analysis in CRI-MAP was

conducted on an IBM 93755 M2 server with 24

processor cores and 128 GB of RAM.

Maximum-likelihood estimates of pairwise recom-

bination fractions among marker loci were obtained

using the ‘‘twopoint’’ option of CRI-MAP. Pairwise

recombination fractions and associated LOD scores

were exported to JoinMap version 4.0 (van Ooijen

2006) for identification of linkage groups. Marker loci

were hierarchically clustered in JoinMap based on

independence test LOD scores (van Ooijen 2006).

Linkage groups were designated as the hierarchical

nodes beyond which no significant disaggregation

occurred. This point was reached at LOD scores

ranging from 10.0 to 35.0 for different linkage groups.

Cross linkage statistics computed by JoinMap were

used to combine fragmented linkage groups and

unassigned marker loci. Chromosomal assignment of

the linkage groups was enabled by cross referencing

loci from each linkage group with available SSR and

expressed sequence tag (EST) mapping data.

Pairwise recombination fractions were then used to

cluster marker loci with recombination fractions

\0.001 into genetic bins. From each genetic bin, the

locus with the greatest number of informative, phase-

known meioses was identified and considered to be the

‘‘primary’’ locus representing the genetic bin. These

primary loci were used to estimate a linkage map of

uniquely ordered loci, prior to incorporating the

remaining loci to derive the final linkage map. This

strategy reduced the number of possible marker orders

that had to be initially interrogated, thus increasing the

efficiency of the mapping algorithm.

Linkage maps were constructed using the CRI-

MAP ‘‘build’’ option. For each chromosome, several

pairs of highly informative primary loci with a

recombination fraction of 0.30–0.40 were selected to

initialize the map order. A framework linkage map

was then constructed from each selected pair of loci by

sequentially incorporating the remaining primary loci

in decreasing order of informativeness. For this first

round of map development, only those loci that

mapped to an interval with a likelihood ratio 1,000:1

compared to all other intervals were retained in the

map. The resulting set of linkage maps was compared,

and the most complete map with the highest likelihood

was retained for further development. The stringent

likelihood threshold and interrogation of multiple

initial map orders provided a reliable framework of

highly informative markers for subsequent rounds of

map development.

Successive rounds of map development were

performed to incorporate the primary loci that could

not be uniquely ordered in the first round. The

likelihood threshold for incorporation of loci was

reduced between each round of map development.

Specifically, the successive rounds of map develop-

ment were conducted using likelihood thresholds of

100:1, 10:1 and 2:1. Between each round of map

development, the CRI-MAP ‘‘flips4’’ option was used

to test the 24 possible permutations for each set of 4

consecutive loci in the current map order. Any local

rearrangements that increased the likelihood were

used to revise the current map order prior next round of

map development. Any loci that mapped to end of the

chromosome with a distance of[30 cM to the nearest

locus were removed from the map and were assumed

to either be misclassified to the chromosome or belong

to unlinked regions of the same chromosome.

After determining the most likely order of the

primary loci, the CRI-MAP ‘‘chrompic’’ function was

used to detect loci and individuals resulting in unlikely

recombination patterns. Double recombination events

Mol Breeding

123

between a set of 3 consecutive loci were considered to

be the result of genotyping errors, and these data were

replaced with missing values. Double recombinations

between loci separated by uninformative regions were

retained. Unlikely crossover events that were pre-

valent at a locus within a specific pedigree were

considered to be the result of genotyping errors on the

founder lines, and the data for the entire pedigree were

replaced with missing values for the locus in question

when the errors could not be rectified.

Finally, the remaining loci within genetic bins were

incorporated into the linkage map. Unmapped loci

having a zero estimated recombination fraction with a

primary locus were incorporated as a ‘‘haplotyped

system,’’ with the primary locus. CRI-MAP only

considers the primary locus in each ‘‘haplotyped

system’’ when evaluating marker orders, whereas all

loci were used in likelihood calculations (Green et al.

1990). Genetic distances were not forced to zero

between markers within ‘‘haplotyped systems.’’

Unmapped loci with nonzero estimated recombination

fractions with the primary locus were incorporated

into the map order to the right of the primary locus, in

decreasing order of informativeness. The CRI-MAP

‘‘flips4’’ option was then iteratively used to permutate

the local marker orders until no higher likelihood map

order could be obtained. The final linkage maps were

charted using MapChart version 2.2 (Voorrips 2002).

Concordance analysis

A consensus map of the wheat genome has been

developed for the iSelect 9,000 SNP assay through the

Triticeae Coordinated Agricultural Project (TCAP) as

described by Cavanagh et al. (2013). The TCAP

consensus map incorporates a four-way MAGIC

population and 6 biparental mapping populations with

a combined population size of 2,486 fixed lines. The

TCAP consensus map was used as the standard for

evaluating the accuracy of linkage maps developed in

this study. For each chromosome, Spearman’s rank-

order correlation coefficient was computed as a

measure of concordance for locus ordering between

the TCAP consensus map and the linkage map

developed in this study. For each linkage map, relative

genetic distances were computed as the locus position

in cM divided by the total map length in cM. The

relative genetic distances estimated from the breeding

populations in this study were plotted against those

from the TCAP consensus map to visually compare

patterns of recombination and locus ordering, as well

as diagnose causes of poor concordance.

Results

Of the approximately 9,000 SNPs assayed, 3,977 were

polymorphic and produced clusters that facilitated

reliable scoring of SNP genotypes. Additionally, 22 of

the 26 SSR loci amplified a product that could be

reliably scored, resulting in a total of 3,999 informa-

tive loci for subsequent linkage analysis. The average

number of informative meioses per SNP locus was 320

and ranged from 20 to 604 (Fig. 1). Hierarchical

clustering of these loci in JoinMap resulted in the

identification of 31 linkage groups. Cross linkage

statistics provided by JoinMap combined with previ-

ous SSR and EST mapping data enabled the combi-

nation of these linkage groups and unassigned marker

loci into 21 groups, putatively representing the 21

wheat chromosomes. Evaluation of recombination

fractions identified a total of 1,269 unique genetic

bins, with an average bin size of 5 loci. Therefore,

67 % of the interrogated loci were found to be co-

localized, resulting from tight linkage among the

marker loci as well as markers that interrogated the

same locus. Comparatively, only 38 % of these SNP

Fig. 1 Distribution of the number of informative meioses for

polymorphic SNP loci. Primary loci are the most informative

loci from each genetic bin used for initial map development,

whereas secondary loci are the remaining loci in the genetic bins

Mol Breeding

123

markers were colocalized on the TCAP consensus

map. Primary loci from each of the genetic bins

provided approximately 437,000 of the uniquely

informative data points, from which over 18,000

recombination events could be observed (Table 2).

Linkage maps estimated from the breeding popu-

lations are summarized in Table 2 and depicted in

Fig. 2a–g. The estimated linkage maps included 3,875

loci and covered a total genetic distance of 3,080 cM,

with an average interval of 2.5 cM between genetic

bins. Marker coverage was relatively poor for the D

genome. After curating the data to remove double-

crossover events among consecutive marker trios, a

total of 184 singletons remained within partially

informative regions of 20 cM or less. This number

of observed singletons represents a double-crossover

rate of 0.04 % within a span of 20 cM, which is

consistent with the expected maximum recombination

rate (0.22 = 0.04). Therefore, the majority singletons

remaining in the data were assumed to result from

genuine recombination events.

Poor marker coverage on the D genome resulted in

multiple linkage groups per chromosome for both the

TCAP consensus map and the map estimated in this

study. Therefore, only the A and B genomes were used

for analysis of concordance with the consensus map.

Linkage maps of the A and B genomes developed from

the breeding populations in this study exhibited high

concordance with the TCAP consensus maps (Fig. 3a–

c). Excluding chromosomes 2B and 6B, the average

rank-order correlation with the consensus maps was

0.98, indicating a high level of agreement regarding the

locus ordering between the two sets of linkage maps.

Chromosome 6B had the lowest rank-order correlation

with the consensus map (0.52), due to a large centro-

meric inversion of the locus order (Fig. 3b). The linkage

map for chromosome 2B had a region of highly

suppressed centromeric recombination compared to

Table 2 Summary of the estimated genetic maps for each chromosome

Chromosomes Loci mapped Centimorgans Recombination

Total Genetic

bins

Total Mean

interval

Informative

meioses

Observed

crossovers

Singletons

(\20 cM)

1A 350 88 174.3 2.0 29,086 1,074 19

1B 193 59 160.5 2.8 20,150 919 18

1D 110 30 92.1 3.2 10,532 464 2

2A 214 92 225.3 2.5 30,299 1,450 13

2B 364 72 142.8 2.0 25,932 917 12

2D 50 25 103.2 4.3 9,613 596 7

3A 231 76 171.6 2.3 27,134 1,107 8

3B 288 116 156.9 1.4 40,886 1,245 17

3D 30 11 78.3 7.8 2,762 205 2

4A 104 56 151.8 2.8 20,438 994 7

4B 104 47 140.6 3.1 18,667 901 18

4D 9 7 39.9 6.6 2,325 58 1

5A 296 104 255.5 2.5 41,824 1,750 15

5B 335 113 212.5 1.9 32,417 1,284 12

5D 39 22 190.1 9.1 7,337 747 1

6A 242 72 176.8 2.5 25,239 926 13

6B 338 85 133.7 1.6 29,899 840 7

6Da 44 16 45.4 3.0 5,618 104 0

7A 316 105 206.6 1.9 32,964 1,263 4

7B 189 58 186.8 3.3 20,273 1,161 7

7Da 29 15 36.2 2.7 3,748 189 1

Overall 3,875 1,269 3,080.9 2.5 437,143 18,194 184

a The chromosome was represented by multiple linkage groups

Mol Breeding

123

4643, 47540.0

6644, 1602, 318113.8137618.47559, 8622, 515020.86645, 1375, 506522.2160322.6318023.0644424.5432130.45702, 7385, 6110, 1566, (9)33.8559334.576436.54506, 1934, 4579, 5509, (5)41.5779649.021752.5644156.01388, 1387, 737757.64164, 4163, 4644, 51357.87151, 337560.42655, 2656, 710, 7421, (8)65.8334766.0339866.1158066.2639, 6887, 7879, 7922, (7)66.4750566.62651, 1991, 807166.9243867.31450, 1582, 1583, 7115, (8)67.41811, 2057, 2247, 2289, (48)67.81078, 2630, 3473, 356, (22)68.73144, 49869.22981, 2982, 3532, 3533, (24)69.67021, 4326, 432875.0459, 4126, 268, 5839, (8)75.7130775.8531075.9649776.1757776.24117, 3134, 3891, 4116, (7)76.41594, 3475, 3613, 3820, (11)76.77956, 2584, 5138, 6609, (7)81.66708, 5174, 670783.916285.0560, 163, 6553, 7871, (13)85.22540, 4511, 60585.793186.1340687.42487, 2488, 2490, 2484, (9)87.960190.4231491.7634192.133993.73405, 7145, 3859, 5493, (6)94.77144, 662496.9530, 53197.38212100.01081, 4537, 6933105.1577, 578, 5832106.17754109.06042, 3434, 3435, 3804, (5)110.53060, 8334112.15047, 5046112.2691114.35822116.16081116.91225, 3146, 2404, 2405, (5)117.11619, 6253, 735, 2818, (20)118.21587118.37428118.47591118.5672118.92994, 4978, 5491150.93377, 4021, 3378151.64897, 4898159.23089159.71560, 8051, 2035, 5318, (10)161.23590161.62015162.05097, 4944, 5407, 1710, (5)162.64518, 8523166.94271, 7290167.71645, 6916, 1644170.03764170.55405, 4121, 4123, 4120, (5)172.13661 3977172.93215, 3799173.75734, 5806174.3

1A

47150.0

424015.9

5301, 40634.6148037.26836, 537037.5733138.04349, 678740.11883, 63, 6442.3228243.1119148.91949, 3443, 7480, 1578, (9)49.1704849.97117, 2578, 257750.8373856.16728, 7280, 6703, 4389, (8)57.1131, 15, 6290, 353, (6)57.8861958.66063, 5348, 8081, 8082, (9)60.7128, 7721, 2517, 3502, (26)61.27017, 4556, 455762.1330762.6431662.755462.96107, 491, 3945, 269, (14)63.15076, 286164.685465.3540, 51566.14681, 4680, 5962, 5229, (26)66.6131368.55382, 538369.93120, 448870.6664672.62411, 7037, 255, 2308, (8)74.75186, 36875.141578.75749, 591580.4850782.14999, 413989.65445, 3095, 5448, 3096, (5)102.47422105.26663, 7141, 4031105.8695109.33097, 5447, 5446110.97619111.28543, 696112.11020, 8542118.51092122.78461, 3998128.9919, 920129.47992, 8332130.63660132.23893133.9

1791144.6724145.77892147.25758148.5

1504, 4694, 545, 4935158.46512, 6511, 2928, 198160.22077, 6647160.5

1B

59960.0

40721.6244922.181723.45372, 6500, 139723.74716, 7533, 71328.0

157258.5667559.1362, 5019, 5018, 5020, (6)60.3742560.61221, 830, 64260.81192, 119363.7569864.3344664.4348166.8715469.3523280.3523480.52340, 2021, 5235, 2341, (6)80.66186, 1736, 3014, 3058, (26)81.04886, 7838, 488584.2138685.66955, 4938, 7675, 3465, (9)86.14960, 2043, 4024, 5514, (21)87.0800288.05182, 7023, 8485, 5541, (5)88.9784889.1474, 342089.353289.77702, 3548, 3547, 354992.1

1DA

Fig. 2 a Estimated genetic maps for homeologous group 1

chromosomes. Genetic bins are followed by the number of

constituent markers in parentheses if[4. See supplemental data

for a complete list. b Estimated genetic maps for homeologous

group 2 chromosomes. Genetic bins are followed by the number

of constituent markers in parentheses if [4. See supplemental

data for a complete list. c Estimated genetic maps for

homeologous group 3 chromosomes. Genetic bins are followed

by the number of constituent markers in parentheses if[4. See

supplemental data for a complete list. d Estimated genetic maps

for homeologous group 4 chromosomes. Genetic bins are

followed by the number of constituent markers in parentheses if

[4. See supplemental data for a complete list. e Estimated

genetic maps for homeologous group 5 chromosomes. Genetic

bins are followed by the number of constituent markers in

parentheses if [4. See supplemental data for a complete list.

f Estimated genetic maps for homeologous group 6 chromo-

somes. Genetic bins are followed by the number of constituent

markers in parentheses if [4. See supplemental data for a

complete list. g Estimated genetic maps for homeologous group

7 chromosomes. Genetic bins are followed by the number of

constituent markers in parentheses if[4. See supplemental data

for a complete list

Mol Breeding

123

6745, 24260.04989, 6922, 15112.115632.373702.62427, 2425, 24282.9barc2127.86391, 53419.7151210.2183012.9499013.43468, 3469, 338215.9

68125.6

3047, 8274, 422, 423, (5)35.0

gwm35943.7

323551.52696, 2059, 741054.1508754.2wmc59856.9252658.22798, 328067.06478, 6477, 672767.9wmc52277.56566, 2067, 2731, 6565, (6)78.36564, 63078.75022, 5893, 5023, 90184.13193, 319484.95495, 5824, 56285.16332, 2531, 3569, 4026, (8)85.67248, 1597, 4410, 5585, (10)86.75378, 534, 2758, 53387.6294887.8384287.95273, 2733, 3368, 5092, (8)88.53839, 675390.13151, 1174, 488, 794792.1280792.924098.024198.36270103.13199, 5574, 5640, 6090, (7)104.42612106.22092, 200, 4562113.13597113.44732113.74733, 1275114.72640116.45856, 5855118.03629119.42835, 3988, 6503, 7540, (18)120.88157122.1970, 2157, 7864122.74373123.0

1960, 8385141.01539145.4684, 3752145.96931146.15066146.35959, 2938148.18040, 8041, 5685, 5686153.85872, 4336, 7727, 3897154.32051, 3920157.3756160.64475, 227161.75840, 5007, 7761, 5271164.38036, 2602170.02601170.46798, 6797171.11348173.73596174.16600, 2555174.81349175.36839, 7142175.85588, 7143, 319176.06620, 1350, 1347, 1351176.35161186.25759189.85894191.24461194.84463, 6963, 4491195.04492, 4493195.14664, 5879, 3687, 7327, (6)195.23809196.15082, 2983196.42777, 3688199.54459202.84460203.27885203.4

7412220.77156223.06841, 5072, 4229, 6840225.3

2A49530.01413, 62632.16262, 76332.447239.72973, 4808, 7656, 248210.0485513.47544, 3589, 592113.82407, 2846, 793624.4513725.274935.6372340.55554, 2110, 2088, 2111, (6)44.0wmc15446.35736, 2440, 2442, 572149.62443, 2441, 712049.92116, 2115, 5410, 4284, (7)54.61931, 5737, 1929, 604854.888854.9383855.0780055.1211755.26740, 4420, 4421, 6739, (8)58.7136059.1438, 121067.9gwm42977.71407, 689378.2602678.46462, 1204, 429, 32882.06427, 5811, 7076, 4388, (5)82.36209, 2766, 535, 543682.55939, 631782.87825, 5506, 2237, 5397, (6)83.23924, 489484.07263, 3908, 7661, 1763, (5)84.24720, 464284.42671, 432384.616985.3170, 1102, 1127, 1128, (25)85.77951, 8244, 6875, 4983, (12)86.21981, 4303, 561, 3452, (5)86.62253, 2236, 2184, 5254, (15)87.3546487.4572387.56093, 671, 3236, 6076, (103)87.631087.9837, 808588.32676, 2678, 3395, 5850, (8)88.87909, 827089.1470, 4256, 4956, 652, (29)89.6138990.14356, 4357, 4890, 4948, (8)90.53148, 1707, 5051, 1291, (6)95.1287295.4307595.72502, 3233, 795596.03938, 5675, 3937, 502496.25809, 6778, 1599, 2052, (9)96.77113, 7112, 508197.03198.85738118.87671123.31535124.86656125.13474125.9746129.22377, 3982, 8504, 5093130.03252132.73206, 1668, 4118, 3773, (7)133.8988135.93315136.12094, 7335, 5694, 6852136.35991, 5989142.8

2B

16010.047110.34746, 45300.94354, 11071.22160, 77903.618554.16302, 63018.1

590520.8

76030.492731.7

645246.77418, 7273, 1975, 449647.0324847.2

6374 563, 20952.4

14456.51858, 652058.2

272261.8

2961, 5637, 856265.5

733275.3107277.1478977.5

525285.6

5919, 1083, 5205, 5630, (15)90.3

8365103.2

2D

B

Fig. 2 continued

Mol Breeding

123

the consensus map, which resulted in shuffling of locus

orders and a reduced rank-order correlation with the

consensus map (0.72).

The majority of the linkage maps estimated from

the breeding populations showed regions with reduced

recombination relative to the TCAP consensus maps.

These lower estimates of recombination were typi-

cally centromeric, which resulted in ‘‘s-shaped’’ trends

when plotted against the consensus map positions

(Fig. 3a–c). However, the differences in estimated

5443, 77710.0447, 5427, 4927, 5430, (8)0.278611.76387, 2993, 82802.52737, 27383.140667.5

8127, 85113.5

8105, 8106, 393917.9480420.2425722.0

199647.4634048.73448, 5642, 373949.088549.84334, 4335, 4333, 755253.3641354.1539956.5515156.6702256.944357.3197257.75763, 8526, 6334, 1812, (9)58.9687759.02763, 2095, 33559.35387, 5444, 9059.5161460.56306, 63760.672061.2143, 2156, 1922, 4912, (5)61.4379461.86187, 531662.12153, 215462.4202362.5701262.7625662.83071, 6923, 7010, 3069, (5)62.9516463.61487, 3156, 4397, 462, (13)63.9731964.82617, 234, 108565.54923, 2750, 2886, 3930, (13)66.53836, 6170, 678366.7407566.88580, 711467.58465, 133, 743, 512467.85315, 7541, 2801, 5994, (14)69.33250, 411069.53999, 1701, 1536, 1700, (22)69.65286, 7159, 5285, 1678, (11)70.0319870.5183173.5837474.7816278.9177880.4925, 4926, 4851, 1260, (5)80.8405381.55419, 5596, 8455, 1892, (6)86.9716987.4189187.91611, 85589.36996, 6997, 129491.47696, 3524, 545695.55980, 5213, 5982, 5212, (5)101.9524, 799, 523, 2029, (5)107.46396120.37297, 1207, 2372134.77812, 1367149.795, 94152.33559, 3560158.33493159.65111159.95112161.54258, 7835, 2949162.18000, 7999, 2396, 6716, (5)166.33949170.84407, 1457, 602, 2870171.6

3Agwm389, 1950.066513.6barc1335.854269.8529910.62908, 75812.0gwm49316.0288, 289, 249320.61187, 71635.5830339.7663, 280, 4625, 5618, (5)46.7734246.8398352.86632, 3425, 342656.54235 2662, 2663, 4054, 378759.45325, 3117, 3716, 347, (5)59.9390461.46192, 3150, 3149, 691961.6584362.3748, 74763.56482, 3731, 3732, 726764.51458, 4838, 3390, 145964.92409, 2408, 484365.12074, 2119, 7748, 1416, (7)66.14040, 4310, 7860, 4039, (5)66.24755, 7294, 6165, 6243, (14)66.4380, 376, 5351, 729, (6)69.3210, 377, 563869.66017, 9170.6gwm285, 299071.73021, 6814, 6655, 249472.0262272.1120672.31383, 1607, 7247, 4193, (10)73.06793, 2329, 6677, 4452, (15)73.1300073.4537, 5613, 629774.02782, 3710, 3711, 5101, (10)75.3443975.5751075.7577075.95880, 5677, 249277.4629, 628, 638178.0579278.25775, 4653, 5710, 4218, (8)81.8586983.23245, 461383.6408584.9735385.2barc164, 4156, 707885.6571185.8450786.2615787.16221, 6222, 1148, 713187.33305, 3306, 649387.46254, 1467, 7225, 1196, (8)87.56223, 5805, 6158, 330487.7301888.2472188.3649288.56104, 610588.77946, 343989.8212491.71598, 2399, 2510, 133192.21332, 2361, 3833, 383592.36552, 7870, 813692.48137, 1333, 133492.52204, 3244, 3834, 445792.63046, 3402, 360192.72720, 2721, 2661, 722192.9455394.92862, 7889, 2620, 3455, (11)96.5216796.6168296.7206396.84906, 654296.9154197.0168397.17561, 859497.25646102.7714, 5941, 6375, 6299, (6)102.92951, 3592, 1095, 3591, (6)104.31731, 3170, 6002, 4498, (10)115.33332, 3331117.63667118.45013, 5014, 5015, 4778, (5)119.14600125.01094133.1barc84133.37542134.6784139.5787, 785, 8628139.9786140.25594, 8058141.0939141.56930149.04312, 4311152.66273, 8185156.9

3B

3250.0

5469, 7468, 5695, 1321, (5)26.3767228.8464731.1229331.5

648556.2

7526, 408160.66777, 4683, 179660.98059, 1715, 7274, 8578, (7)65.0153, 1624, 4877, 3012, (7)65.6

523078.3

3DC

Fig. 2 continued

Mol Breeding

123

recombination frequencies did not greatly affect the

relative ordering of the loci.

Discussion

The application of mapping algorithms developed for

general pedigrees to existing breeding populations

facilitated the development of a 3,875 locus linkage

map of the wheat genome without the need for

extraneous population development. High-through-

put SNP genotyping of 18 crosses comprised of a

total of 372 four-way F1 individuals enabled the

mapping of over 43 % of the interrogated loci. These

results indicate that a collection of breeding popu-

lations, derived from crosses among numerous

parents, provide a highly polymorphic and informa-

tive genetic resource for the development of linkage

maps. Comparatively, Cavanagh et al. (2013) were

able to map roughly 46 % of the SNP loci from the

same 9,000 SNP assay using a four-way cross

MAGIC population consisting of 1,579 recombinant

inbred lines. Biparental mapping populations used

for consensus map individually enabled mapping of

66960.046510.515052.45353, 4858, 7365, 4084, (5)6.0

4859, 7364, 1836, 1835, (5)9.4

3449, 4030, 719713.6

669020.4776521.5

386427.655829.9656330.2

7322 306845.44527, 596846.33061, 369846.9

485, 3188, 5152, 8168, (5)62.1250564.5276167.1820969.6

152176.9

271984.56906, 290485.44243, 2384, 238386.5705890.51692, 169190.95200, 172092.38432100.23981, 6883, 4199102.16884102.26882102.46885102.62207, 3728, 7535104.22582, 2581, 5422104.77442105.25349110.8482, 6501114.42045116.0402, 1792117.17699, 2585, 2901, 3191, (9)117.41727117.87521121.33584122.46100, 1728, 1992, 4252, (5)123.44261, 4260, 3902, 4480, (6)124.02533127.74232128.05363128.61137132.21170132.67394134.22764139.23993140.33161145.67632147.3285, 8389, 54148.61900151.8

4A

64570.0

573917.4

8108, 2298, 810723.8

176833.87268, 131634.3726636.0361538.3817839.2212639.8456944.4329046.0wmc710, 104550.1466250.9

231365.8gwm495, 2963, 3400, 3874, (7)72.6332573.525878.0856478.2586378.4324081.67284, 776781.8768881.9339282.21910, 741182.3648082.486, 8163, 3241, 373682.54347, 4348, 100682.65195, 100782.81344, 2532, 7437, 7168, (8)83.02666, 3846, 4070, 4330, (11)83.3140583.51028, 907, 3074, 7752, (5)84.8756689.05365, 3041, 3042, 3038, (5)91.73040, 646192.03279, 411597.8325398.91861101.05885, 7202, 3697, 8197102.62031, 6465105.34490, 5408, 6397, 2595, (5)107.9wmc125108.5564113.45358113.83780, 3779, 3781, 6719116.37555, 3770128.0

6230140.7

4B

7520.0

1633, 404428.9430, 16129.9311631.6

355537.4381538.941039.9

4DD

Fig. 2 continued

Mol Breeding

123

2642, 26450.02641 2646, 2558, 69881.1

75099.9

294714.75002, 500315.22837, 2838, 749117.1778918.8648, 64920.6308321.22839, 284022.62802, 167023.56641, 289726.23323, 515429.9756832.8

700943.7

2857, 2113, 2856, 2858, (10)47.1

235261.5

3705, 3702, 3704, 3703, (7)70.2674, 675, 7014, 704371.6

4394, 791182.2480583.1481384.3504086.04276, 222386.3444788.6139890.558991.57579, 6681, 1439, 274392.5148696.32014, 6459100.45842, 5623, 6637100.64392, 4391, 4390, 5945102.44205, 1343, 7833, 7834104.45624, 4980106.26961106.7

3996, 5668, 12118.17665122.56456122.82364, 2365123.72363130.21669, 3370133.76573, 3369140.86522, 6523143.26949, 6515147.23413147.64669, 4670, 4668, 4667, (5)150.74914, 1685, 1686151.57742151.84237, 3283, 3645, 3646152.1739, 3647152.51090, 2445, 6747, 7436, (6)4047157.1

6126, 8308, 703160.72172, 3201, 5726, 6899, (21)738165.6

6036, 850, 5567166.72101, 2926167.3gwm156, 122, 121168.45528, 5529, 3873169.95884170.16881172.65033, 5034, 5032173.91630, 5184179.15688, 5539, 5689, 6123, (7)182.6291183.91253, 1988184.2114186.45735186.85107, 7960, 5105187.41950, 7961, 7690, 7925, (7)189.77130, 7129, 1943192.45380193.34736195.43349196.74424197.46412, 2463, 2814, 3212, (37)197.77777 3099, 3100, 7565, 1301197.8barc56, 5431, 3445, 1465, (6)198.07109, 5496, 2480200.05395, 4454, 2120, 2467200.26415, 1546205.33263207.36287, 3190, 8154, 8155208.43530, 7061210.1825212.0cfa2190215.05728, 4465218.53365, 3811, 2378, 1062220.74069, 5615, 5614221.06859224.05368224.84970227.51568230.21569230.43197, 4325, 7801, 6463, (6)235.46227235.64932236.16226, 7360236.77361, 5053237.44445, 4446250.73566, 3567, 5924, 5923250.9481, 2142, 480, 2143, (7)255.5

5A6579, 2682, 2681, 6578, (5)0.04903, 2931, 5802, 4329, (5)8.8232118.33359, 3360, 335822.23658, 106123.51786, 22, 2335.73185, 580438.47903, 165838.74635, 5454, 4634, 7708, (5)39.73740.893646.119746.7156448.53984, 3972, 6393, 321450.24954, 4762, 2659, 476360.47585, 6779, 1433, 6147, (5)62.1825062.22388, 7965, 796662.8125963.54185, 350064.23265, 1441, 1443, 787267.17791, 7668, 7963, 250073.3380074.37910, 7989, 4820, 5552, (5)79.4839581.4671882.01774, 2565, 3226, 3432, (9)84.1410384.48508, 8375, 4211, 7735, (9)85.0344486.57024, 6894, 482999.5689599.76905107.73642, 3641, 3640108.26721, 6112113.81408, 4003119.92373, 2335, 2336, 8187, (6)121.17485, 7514, 4641, 7776124.6952124.77471124.87470124.96125125.05671125.13009125.23008125.31444125.47844125.57513125.67484125.76766125.86366125.96235126.05795126.15487126.25482126.33719126.42698126.52697126.61446126.75488126.8987126.93164, 3165127.12694, 4632, 3964, 337, (10)127.3265127.64074, 5283, 2453, 2454, (8)128.36383, 3025, 1374, 822, (7)128.42536, 2934, 2432, 1402129.66291, 4622129.85217130.11380130.55218, 7562, 2180, 5620131.06638, 3436, 5485, 5486, (8)131.46992, 7123, 7944, 4533, (5)134.11471 2162134.5721, 301, 1584, 5289, (5)135.92791, 5845, 6980, 5743136.83633137.14422138.3302, 123138.57127, 6689, 5279, 1777, (7)138.95784139.08211139.22003139.55633140.62257141.0396, 1705, 1706, 2071, (20)141.35764, 2742, 5497141.56846, 7613, 3682, 4526143.77953145.61057147.06030147.22320148.56065148.6620, 6429, 1461, 279, (17)148.81626148.91779, 6521, 6908, 5494, (8)149.04377, 4378, 4379, 1176, (9)149.21342, 1965, 1084149.57608, 7609149.97300, 4281, 4282, 5108, (5)150.36773, 7227, 6526153.0303153.52597, 3706, 4862, 3106156.24300, 4414, 8005, 2596, (6)161.32609, 2610163.91144, 7209, 7211, 1143, (7)173.54355, 7223178.9421, 332, 3514179.27153, 1709, 5176184.76251, 3606, 3607185.56402, 4416, 4415, 4790, (7)193.21719, 7183, 7254, 8126212.5

5B

83600.062890.3

6052, 5012, 4550, 626820.3

643941.3

168160.8

408775.3

427483.6277185.1

678103.2

5970133.0

7177154.36409156.67071162.96191, 6190, 6189164.37095, 6872, 7147166.86059, 6060, 6061, 700167.7699, 701168.07914, 7915, 2276169.02919169.37383169.5

4113, 4561185.4

1431, 1428, 1427, 1429190.1

5DE

Fig. 2 continued

Mol Breeding

123

21–41 % of the SNP loci with population sizes

ranging from 96 to 250 fixed lines (Cavanagh et al.

2013). Given the time and resources required for the

development of such purpose-built mapping popula-

tions, it seems clear that exploiting existing segre-

gating populations in breeding programs provides a

cost-efficient alternative for development of dense

linkage maps. Furthermore, it should be noted that

the breeding populations used in this study were

highly interrelated by common founders and the two

Wesley-Fhb1 backcross lines were nearly isogenic.

A collection of breeding populations derived from a

larger number of founders or from a more diverse set

of founders would therefore be informative for more

loci and thus allow for more extensive linkage

mapping than possible in this study.

70070.0

6937, 7913, 27326.2816027.57612, 6711, 241327.8263528.327229.6

1205, 660137.0

860852.64961, 680, 6630, 5152.9

152261.0

128269.3133670.1133570.28510, 6013, 61270.4540170.9223573.7

593087.32018, 2017, 108687.8233, 6806, 680789.0320793.43670, 3700, 3866, 4738, (11)95.1126298.25801, 3527, 8110, 6820, (9)98.76928, 6560, 6559, 547099.12188, 2186, 2187, 2421, (15)99.21423, 1285, 1606, 2033, (7)41699.3

4059100.06986100.52812, 3463, 6962, 3483, (7)102.27052103.3664104.26724, 8585104.5224, 1514, 4929, 653, (34)104.76938105.06550, 3024, 2241, 3023, (18)108.5259, 260115.76775, 4950, 20, 19, (10)116.23954119.15021121.7

2539, 2538145.94809146.25398147.4214154.06116, 5704, 504, 503156.0929, 5964156.7928, 8438, 8222, 8386157.06182, 442160.01000160.62580163.37386168.51510, 2639, 7894, 2054169.13067, 2055169.73918, 5655, 6537, 6316, (10)170.42527171.15172172.15746, 5767, 6305, 6304172.42632, 5747, 2603, 5768, (6)172.73246173.13204, 7497, 3205, 7495, (6)176.42795, 4699, 3247, 3203, (5)176.51391176.8

6A

9690.02479, 8477, 14944.99218.12495, 125510.34633, 578010.45942, 1901, 5857, 4610, (6)19.94290, 7725, 401128.05236.61764, 4760, 732041.5476141.9725747.9724048.31721, 328948.87808, 2439, 2888, 5888, (12)49.0505549.46494, 1905, 5056, 828449.6206, 164050.8768951.23501, 293751.87979, 2417, 4824, 1655, (16)52.2379755.04486, 4485, 5625, 1017, (11)55.72039, 6660, 4383, 217355.91434, 323456.118, 5045, 5044, 365056.22090, 3869, 459956.6683, 7954, 2927, 5197, (13)57.3369957.62933, 167958.3750658.51531, 4202, 5170, 45758.98037, 7487, 6904, 657, (6)59.14501, 4502, 4503, 755, (13)61.2762861.63459, 5346, 387, 4440, (5)62.01151, 1743, 4086, 6855, (13)63.1853963.27783, 451563.3316763.46293, 3878, 3963, 2653, (5)63.545063.62342, 5266, 526764.0546865.63923, 6466, 6467, 778665.9367666.0789766.15043, 3677, 5042, 7974, (11)66.3792966.53967, 283, 3636, 1472, (20)69.71838, 5966, 502971.92134, 7574, 550472.14924, 5242, 617, 1840, (62)72.26101, 379672.62652, 846472.74065, 245172.86860, 3971, 740172.9451, 812973.02219, 7810, 3301, 7618, (6)76.77084, 206281.44484, 4436, 443581.680081.87111, 4564, 3801, 572282.0553082.7495984.36428, 221, 3327, 5148, (8)84.51629, 3221, 162892.2404, 40593.42330, 2331, 7056, 3464, (5)95.65732106.35666107.37479109.82212, 5709110.57116, 8072113.34868, 1484, 1485121.13268127.75605127.94245, 8441, 823, 3947, (6)133.03225, 4246, 7098, 4244133.7

6B

2030.0

1927, 1926, 6274, 559120.65931, 491820.9140621.14056, 5354, 4455, 192521.36181, 600, 4315, 59923.36939, 2965, 2966, 3291, (7)26.498632.5138432.6

6D1

5386, 22030.03624, 1317, 1643, 14110.428084.56196.378167.076167.9

6D2

4000, 2338, 66730.06625, 1895, 1896, 55763.534614.69844.9

6D3

F

Fig. 2 continued

Mol Breeding

123

Correct ordering of marker loci during linkage

mapping is a critical factor determining the accuracy

and power of subsequent QTL mapping applications

(Collard et al. 2009). Therefore, an assortment of

breeding populations must not only enable the map-

ping of a large number of marker loci, but also the

accurate ordering of those loci from large genotypic

data sets. Evaluation of rank-order correlations

45580.03978, 6519, 53374.070535.466425.973066.552456.6698910.76626, 173512.0803215.1556, 55718.2

709334.2

834, 93048.3608853.895458.66475, 461463.7175166.5839072.1473, 47272.61805, 2513, 7301, 7201, (5)74.03760, 718777.27192, 175979.18161, 712179.93832, 3831, 6472, 647380.84426, 680281.62507, 2506, 273585.165585.48492, 6331, 371585.76310, 275, 7205, 3674, (6)87.6741988.07460, 4032, 7282, 3903, (7)88.23754, 7731, 2820, 363, (5)92.233495.9318796.4602997.31842, 6569, 3318, 4168, (22)98.25220, 3, 4082, 1871, (28)99.01554, 759, 127, 5867, (14)99.6394199.83843, 497100.06207, 5844, 6208, 208, (6)100.14637, 1491101.81802, 4063, 1834, 1832, (6)101.94639, 7293, 8098, 2302102.02301, 3062, 3403102.13404, 484102.25119, 2082, 3090102.35465, 6629, 7976102.47682, 6866, 7430, 8113, (6)102.57718103.85434, 3668, 8073, 1761105.07917, 635, 476, 1946, (36)105.91110, 1111, 810, 6940106.47432, 7140, 4109, 7549, (5)107.0593, 1581107.86004, 2009, 2012, 2011, (5)108.06562, 7933108.98076, 8077, 4846109.1614, 5489, 2775112.07046117.75790117.86535, 4910, 4911118.85912, 7409, 5913119.66670122.71032, 1031124.63128, 5167126.17406, 7407, 2270126.34621, 4620126.98297128.37028, 1363, 7709135.57325137.93367, 7884141.32723144.24993, 4991, 4994, 6785148.98393153.86115, 7185172.84594, 4595180.44124, 2403, 2402185.97184187.5761, 866, 1223, 865187.97964, 2929191.14364, 4176, 1271, 4433, (7)193.34137, 4173, 1518, 4177, (11)193.5737, 795, 179, 6833, (6)200.56576, 7005, 5904204.08312204.87592205.06736, 7706205.4502, 501206.6

7A

58190.0

1525, 15264.1

1181, 78311.9

108924.7

gwm40034.8497737.9210538.83958, 2832, 556539.34968, 6901, 1314, 4967, (15)39.52894, 289340.6724240.9396041.13572, 3508, 3506, 3507, (7)41.351841.77326, 451642.73438, 306, 3437, 3807, (8)48.88021, 802249.17233, 723256.91108, 6414, 4873, 253460.13663, 5661, 6788, 5210, (9)60.42353, 4727, 6212, 3114, (22)61.31642, 227161.7gwm4662.13063, 830062.4gwm297, 3163, 632, 698, (5)63.9gwm33366.73691, 3112, 415168.8117, 400571.544971.9636, 3986, 6400, 6857, (7)72.71420, 1963, 4190, 4191, (16)73.14857, 855074.4436, 43785.4615, 4701, 8570, 1345, (6)88.41339, 3928, 3423, 7329, (10)89.5134089.8

4305, 7403, 7402110.8

5129116.94309119.8130123.72193, 4750, 6246, 4749, (5)124.5432125.7431, 5564, 2191125.9

4803, 3387, 7907135.85001136.24802139.45837140.45881142.87261142.9

3513, 3603, 2825, 7780, (5)148.8

5597153.3

3416, 3415165.21651, 1649165.5182, 180166.12149, 3675166.51044166.8

8448178.9

1897186.8

7B

63200.02545, 13730.7

12477.0

5391, 23515.1662316.07828, 782717.92523, 2521, 7610, 2522, (5)20.11323, 3851, 6822, 26622.2

227331.35249, 1257, 5557, 60431.468831.873232.3153734.1

7D1

827, 74590.080752.1

7D2

G

Fig. 2 continued

Mol Breeding

123

Fig. 3 a–c Comparison of genetic maps estimated for A and B genomes with the TCAP consensus maps

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123

Fig. 3 continued

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123

between the linkage maps developed in this study and

the extensive TCAP consensus map ([0.97 for most

chromosomes) revealed that marker orders were

largely conserved. Local rearrangements of tightly

linked markers (\5 cM) relative to the consensus map

are of little practical concern, since the marker density

could be thinned for most QTL mapping applications

(Collard et al. 2009). There were a few significant

disparities, most notably the large inversion of locus

order on chromosome 6B, that resulted in poor rank-

order correlations with the consensus maps. Such

disparities can result from true inversions, genotyping

errors or convergence of the mapping algorithm to a

local optimum during the map building process. Local

optima can occur with any linkage mapping approach,

since mapping algorithms can interrogate only a subset

of potential locus orders, and thus may inadvertently

discard the true map order if there is more statistical

support for an alternative order at any stage in the

mapping process (Green et al. 1990). Therefore, the

discrepancies in locus orders observed with the TCAP

consensus order are not necessarily indicative of issues

specific to the mapping algorithms or populations

employed in this study, but more likely reflect the

effects of genotyping errors and initial map orders on

convergence. Overall, the high degree of concordance

with the consensus maps suggests that an amalgama-

tion of relatively small breeding populations can

provide reliable ordering of marker loci when those

orders are jointly estimated over all populations.

While the ordering of loci in this study was

generally consistent with the TCAP consensus map,

there were notable differences in the estimated genetic

distances between loci for many of the chromosomes.

There were several large centromeric regions on the

consensus maps that exhibited low rates of recombi-

nation in the current study (e.g., chromosomes 1B, 2B

and 3A). Furthermore, considerably fewer marker loci

could be mapped to unique genetic positions in this

study compared to the consensus map. There are

several possible explanations for these disparities in

estimated genetic distances. First, the population of

372 four-way F1 individuals (565 for SSR markers)

provided far fewer recombination events than the

2,294 recombinant inbred lines and 194 doubled

haploid lines employed for development of the

consensus map. Due to the smaller sample of recom-

bination events in this study, fewer recombinations

would be expected to be observed between tightly

linked loci, thus resulting in the underestimation of

genetic distances and the co-localization of tightly

linked loci. Therefore, simply utilizing a larger

number of breeding populations should reduce many

of the discrepancies in the estimated recombination

rates. Second, depending on allele frequencies and

crossing schemes, markers can have a wide range of

informativeness when interrogated over a large num-

ber of relatively small breeding populations (Fig. 1).

Markers providing few informative meioses would

present few opportunities to observe recombination

Fig. 3 continued

Mol Breeding

123

and would thus tend to co-localize with nearby marker

loci. Lastly, in certain situations, a consensus mapping

approach can overestimate genetic distances. For

example, if duplicate loci are not jointly mapped in

the same population, then those loci could map to

unique positions when interpolating genetic distances

among populations. The larger amount of data

collected over the consensus mapping populations

could also result in a greater number of genotyping

errors, which can result in overestimated genetic

distances (Goldstein et al. 1997).

The finding that marker loci can be accurately

ordered by using a collection of existing breeding

populations has important implications for molecular

breeding efforts. Breeding programs can use this

mapping approach to support molecular breeding

efforts without expending resources on the develop-

ment of purpose-built mapping populations. Newly

developed marker resources can be directly applied to

molecular breeding efforts by de novo linkage map-

ping in breeding populations rather than awaiting

results of consensus mapping studies. This is an

important consideration given the increasing interest

in statistical methods that integrate QTL mapping

efforts into plant breeding populations (Jansen et al.

2003; Crepieux et al. 2005; Rosyara et al. 2009;

Wurschum 2012). The divide between linkage map-

ping studies and marker assisted selection has been

cited as a primary reason for the limited impact of

marker assisted selection in crop breeding (Beavis

1998). The ability to develop reliable linkage maps

directly in breeding populations represents an impor-

tant step toward integrating these molecular breeding

activates. The approach to linkage map development

presented in this study should allow breeders to make

the leap from having no prior genotypic information to

conducting marker assisted selection within the same

cohort of breeding populations, thus supporting a

completely integrated molecular breeding approach.

Mapping algorithms for general pedigrees enable

the construction of linkage maps using any number of

disjoint populations of arbitrary size, structure and

complexity. The four-way crosses used in this study

resembled three generation pedigrees (grandparent,

parent and offspring) commonly studied in human and

animal populations, with the exception that the

founders are completely inbred and thus generally

provided phase-known haplotypes. However, the

exclusive use of four-way crosses among inbred lines

in this study was completely arbitrary. The methods

presented here can be equivalently applied to inbred

and outbreed mapping populations as well as to any

assortment of crossing schemes. In fact, the mapping

algorithms used in thus study are routinely used for

linkage map development in human and animal

populations. Mapping algorithms typically used for

plant populations have diverged from these general-

ized mapping algorithms as a result of the more

simplistic population structures afforded by the avail-

ability of inbred founders and large single-cross

populations. However, it has become apparent that

these simplified populations, although analytically

convenient, do not provide an accurate depiction of the

complex genetic systems that exist within breeding

programs. Therefore, this research serves as a

reminder that the plant research community is not

confined to the prevailing experimental paradigm for

linkage analysis in plant species. Rather, a more

generalized framework can be adopted to exploit the

extant resources in breeding programs and facilitate

the next generation of molecular breeding activities.

Conclusions

Early generation plant breeding populations provide

an existing source for genetic recombinations that can

be used for the development of dense linkage maps.

Application of a 9,000 SNP assay and 26 SSR markers

to early generation breeding populations derived from

28 four-way crosses resulted in the development of a

3,875 locus linkage map of the wheat genome, with an

average inter-marker distance of 2.5 cM. The devel-

oped linkage map had high rank-order concordance

with an extensive TCAP consensus map developed

from the same SNP assay, indicating that marker

ordering was quite accurate. The mapping algorithms

adopted from general pedigree analysis can handle any

number of related or unrelated populations of arbitrary

size, structure and complexity. Therefore, in combi-

nation with high-throughput genotyping platforms, the

mapping approach used in this study should allow

breeders to develop dense linkage maps ‘‘on the fly’’ to

support their molecular breeding efforts.

Acknowledgments The authors acknowledge support from

the US Wheat and Barley Scab Initiative under ARS Agreement

No: 59-0200-3-005 to J.L.G.H. and by the South Dakota

Agricultural Experimental Station.

Mol Breeding

123

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