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The successful application of a marker-assisted wheat
breeding strategy
Haydn Kuchel ÆRebecca Fox ÆJason Reinheimer ÆLee Mosionek Æ
Nicholas Willey ÆHarbans Bariana ÆStephen Jefferies
Received: 19 February 2007 / Accepted: 5 March 2007 / Published online: 30 March 2007
Springer Science+Business Media B.V. 2007
Abstract A number of useful marker-trait asso-
ciations have been reported for wheat. However
the number of publications detailing the inte-
grated and pragmatic use of molecular markers in
wheat breeding is limited. A previous report by
some of these authors showed how marker-
assisted selection could increase the genetic gain
and economic efficiency of a specific breeding
strategy. Here, we present a practical validation
of that study. The target of this breeding strategy
was to produce wheat lines derived from an elite
Australian cultivar ‘Stylet’, with superior dough
properties and durable rust resistance donated
from ‘Annuello’. Molecular markers were used to
screen a BC
1
F
1
population produced from a cross
between the recurrent parent ‘Stylet’ and the
donor parent ‘Annuello’ for the presence of rust
resistance genes Lr34/Yr18 and Lr46/Yr29. Fol-
lowing this, marker-assisted selection was applied
to haploid plants, prior to chromosome doubling
with cochicine, for the rust resistance genes Lr24/
Sr24,Lr34/Yr18, height reducing genes, and for
the grain protein genes Glu-D1 and Glu-A3.In
general, results from this study agreed with those
of the simulation study. Genetic improvement for
rust resistance was greatest when marker selec-
tion was applied on BC
1
F
1
individuals. Introgres-
sion of both the Lr34/Yr18 and Lr46/Yr29 loci
into the susceptible recurrent parent background
resulted in substantial improvement in leaf rust
and stripe rust resistance levels. Selection for
favourable glutenin alleles significantly improved
dough resistance and dough extensibility. Mar-
ker-assisted selection for improved grain yield,
through the selection of recurrent parent genome
using anonymous markers, only marginally im-
proved grain yield at one of the five sites used for
grain yield assessment. In summary, the integra-
tion of marker-assisted selection for specific
target genes, particularly at the early stages of a
breeding programme, is likely to substantially
increase genetic improvement in wheat.
H. Kuchel (&)J. Reinheimer L. Mosionek
S. Jefferies
Australian Grain Technologies, Plant Breeding Unit,
Perkins Building, Roseworthy Campus, Roseworthy,
SA 5371, Australia
e-mail: haydn.kuchel@ausgraintech.com
H. Kuchel R. Fox J. Reinheimer S. Jefferies
School of Agriculture, Food and Wine, University of
Adelaide, Waite Campus, Glen Osmond, SA 5064,
Australia
H. Kuchel R. Fox S. Jefferies
Molecular Plant Breeding Cooperative Research
Centre, University of Adelaide, Waite Campus, Glen
Osmond, SA 5064, Australia
N. Willey H. Bariana
The University of Sydney Plant Breeding Institute,
Cobbitty, PMB11, Camden, NSW 2570, Australia
123
Mol Breeding (2007) 20:295–308
DOI 10.1007/s11032-007-9092-z
Keywords Dough quality Glutenin
Marker-assisted selection Plant breeding
Rust resistance Triticum aestivum
Abbreviations
DH Doubled haploid
HMW High molecular weight
LMW Low molecular weight
MAS Marker-assisted selection
Introduction
The application of molecular markers for the
selection of superior genotypes in plant breeding
programmes has been suggested to be most
beneficial for traits that are difficult to select
phenotypically, are subject to high environmental
error, or are expensive to assess (Koebner and
Summers 2003). An additional benefit of marker-
assisted selection (MAS) is that it can be per-
formed on DNA extracted from leaf tissue and
consequently provides a non-destructive, seed
quantity independent alternative to phenotypic
based selection allowing it to be used at any point
in a breeding programme. Issues relating to the
most effective use of MAS in breeding have been
the subject of numerous studies (Howes et al.
1998; Knapp 1998; Charmet et al. 1999; Frisch
et al. 1999; Bonnett et al. 2005). However, reports
detailing the successful implementation of molec-
ular markers in pragmatic breeding programmes
have been limited (For some examples see Yu
et al. 2000; Yousef and Juvik 2001; Jefferies et al.
2003; Zhou et al. 2003; Eglinton et al. 2006; Fan
et al. 2006) and are often restricted to examples
where selection has focussed on just one or two
traits at a time. Computer simulation has been
used to model the potential of MAS in breeding
programmes (Hospital et al. 1997; Frisch et al.
1999; Ribaut et al. 2002), however the validation
of results from simulation studies in real breeding
populations is generally viewed as cost prohibi-
tive and consequently rarely undertaken.
Recently, a report was published detailing a
computer simulation based analysis of a specific
wheat breeding strategy designed to employ MAS
to select for multiple genes through a limited
backcross conversion (Kuchel et al. 2005). The
point at which MAS was applied in the backcross
conversion was varied in the simulation study and
the relative efficiencies of each strategy were
compared genetically and economically. The
authors showed that the most appropriate of the
molecular marker strategies tested in that partic-
ular backcross conversion resulted in improved
genetic gain over the phenotypic based alterna-
tive at 40% less cost. Here we present the
successful integration of MAS into a routine
wheat breeding strategy aimed at the rapid
elimination of defects in an elite breeder’s line.
This paper addresses two main issues
(1) the practical validation of a computer sim-
ulation of a MAS strategy, and
(2) confirmation of the individual gene effects
of loci manipulated in the breeding strategy
and consequently the effectiveness of MAS.
A critical review of this breeding strategy is
provided and considerations for future marker-
assisted breeding are discussed.
Materials and methods
Breeding scenario
A detailed background to the aims of this
breeding strategy are presented in Kuchel et al.
(2005). Briefly, a limited backcross defect elim-
ination strategy was commenced in 2002 to
improve the rust resistance and end-use quality
(dough resistance and extensibility) of a high
yielding, broadly adapted elite southern Austra-
lian cultivar ‘Stylet’ through the introgression of
adult plant rust resistance genes and by altering
its glutenin allele profile. While ‘Stylet’ is
believed to carry some unknown minor rust
resistance genes (H. Kuchel, unpublished data)
its resistance relied predominantly on the gene
cluster Lr37/Sr38/Yr17 which have been over-
come by recent pathotype changes in Australia.
Another southern Australian wheat variety,
‘Annuello’, was chosen as the donor of rust
resistance genes Lr34/Yr18,Lr46/Yr29 and Lr24/
Sr24 (R. Eastwood, personal communication)
296 Mol Breeding (2007) 20:295–308
123
and also the donor of a glutenin allele for
improved end-use quality.
An overview of the breeding strategy
Four marker-assisted breeding strategies were
outlined and assessed in the simulation studies
undertaken by Kuchel et al. (2005). Due to
resource limitations and logistical considerations,
all four strategies could not be carried out for this
validation study. Rather, a modified version
(Table 1) of the MAS2 strategy (Kuchel et al.
2005) was employed (Fig. 1). A population of 72
BC
1
F
1
plants was produced from the cross
‘Annuello/Stylet-c//Stylet-e’, where Stylet-c and
Stylet-e were selections from a ‘Stylet’ bulk
heterogeneous at the Glu-A3 locus (Glu-A3c
and Glu-A3e segregating). The relative grain
yield of the two ‘Stylet’ glutenin selections was
unknown, and as such both were used in the
construction of the population to ensure repre-
sentation of the ‘Stylet’ bulk. The BC
1
F
1
individ-
uals were screened with molecular markers
(Table 2) linked to two of the targeted rust
resistance genes, Lr34/Yr18 and Lr46/Yr29.
BC
1
F
1
individuals carrying the ‘Annuello’ marker
allele at both of these loci were then used to
generate the doubled haploid (DH) population.
Prior to colchicine treatment, DNA was extracted
from haploid plants and the markers (Table 1) for
Lr34/Yr18, Lr24/Sr24, Rht-B1, Rht-D1 and Rht8
were used to identify plants that were thought to
possess a semi-dwarf genotype (Rht-B1b/Rht-
D1a/Rht8a/b, Rht-B1a/Rht-D1b/Rht8a/b or Rht-
B1a/Rht-D1a/Rht8b), and carried either Lr34/
Yr18 or Lr24/Sr24. Selection was not limited to
haploids carrying the ‘Annuello’ allele at both of
the rust resistance loci for two reasons; (1) in
combination with Lr37, either Lr34 or Lr24
would provide sufficient levels of leaf rust pro-
tection for release within the target environment,
and (2) it was not known if minor stem rust genes,
additional to Sr38 and Sr24, may be possessed by
‘Stylet’ and ‘Annuello’. These haploid plants were
selected for chromosome doubling and ultimately
DH production. Markers linked to the two
glutenin loci Glu-D1 and Glu-A3 were also used
to identify improved end-use quality haploid
plants, however all lines were retained for marker
validation purposes regardless of the glutenin
alleles they carried. During the MAS phase of this
breeding strategy, Rht8 was thought to be carried
by ‘Stylet’, however subsequent experimentation
in alternative populations showed that this was
not the case (H. Kuchel, unpublished data).
Therefore, plant height selection is not consid-
ered further in this report.
In order to determine the effectiveness of
marker-assisted recurrent parent background
selection as described by strategy MAS3 in
Kuchel et al. (2005), 52 microsatellite markers
were screened over a random set of 100 DHs
possessing the favourable glutenin alleles. DHs
(242) possessing the favourable compliment of
plant height, rust resistance and glutenin alleles
were then multiplied in 1.5 m rows over summer
and tested in 2004 for their grain yield and rust
resistance. Following phenotypic selection, 40
lines remained and were entered into an ad-
vanced trialling system.
Table 1 A comparison of the best MAS strategy (MAS2) identified through simulation (Kuchel et al. 2005) and the
validation strategy adopted in this study
Simulation (MAS2) Validation (MAS)
Cross used for study Annuello/Stylet-c//Stylet-e Annuello/Stylet-c//Stylet-e
BC
1
F
1
population size 200 72
MAS of BC
1
F
1
individuals
Lr34/Yr18 &Lr46/Yr29 Lr34/Yr18 &Lr46/Yr29
Number of haploids
generated
4,000 2,000
MAS of Haploids/DHs Rht-B1, Rht-D1, Rht8, Lr24/Sr24, Lr34/Yr18,
Glu-A3, Glu-B3
Rht-B1, Rht-D1, Rht8, Lr24/Sr24, Lr34/Yr18,
Glu-A3, Glu-B3
Differences between the strategies are shaded
Mol Breeding (2007) 20:295–308 297
123
Phenotypic analysis
Grain yield analysis
Grain harvested from the irrigated 2003/04 sum-
mer multiplication at Roseworthy, South Austra-
lia, was used to plant winter grown single
replicate (with replicated grid checks) grain yield
plots (3.2 m ·1.3 m) under normal field condi-
tions at the South Australian sites Booleroo,
Buckleboo, Roseworthy and Pinnaroo during
2004. All DHs were included in the Roseworthy
grain yield experiment and distributed to the
remaining sites depending on seed availability. A
single replicate grain yield trial containing all
DHs was also grown again at Roseworthy during
the 2005 season.
Rust resistance assessment
The ‘Annuello/2*Stylet’ DHs, as well as the two
parents, were included in field based nurseries at
Cobbitty, NSW (one replicate) and Horsham,
Victoria (three replicates) in 2004 and Cobbitty
(one replicate) in 2005, in order to assess adult
plant rust resistance. Stem rust, leaf rust and
stripe rust infections were assessed at Cobbitty
and stripe rust only at Horsham. Stripe rust
infection was also assessed on field plots at
Roseworthy in 2005 where a natural infection of
the 134 E 16 A+ pathotype took place. All field
based rust infection was rated on a 1–9 scale
where 9 was the most severe infection and 1
indicated no observable rust. Seedling based
resistance tests were performed in 2005 at Cob-
bitty for leaf rust and stem rust (Table 3).
Seedling rust response was scored on a 0–4 scale
according to Bariana and McIntosh (1993) and
was later converted to the previously mentioned
1–9 scale for data analysis.
End-use quality assessment
Grain samples from 162 DHs (unselected for
glutenin alleles) grown in field plots at Buckleboo
and Booleroo during 2004 were used for dough
Fig. 1 The MAS breeding strategy utilised to introgress
the rust resistance and end-use quality traits from
‘Annuello’ to ‘Stylet’
Table 2 Recombination frequencies between target genes, and the molecular markers used for MAS
Gene/Locus Marker Recombination frequency References
Lr34/Yr18 Xgwm295 0.08 Suenaga et al. (2003)
Lr46/Yr29 Xgwm140
a
0.30 M. William (personal communication)
Lr46/Yr29 Xwmc44
a
0.05 Suenaga et al. (2003) and Rosewarne et al. (2006)
Lr24/Sr24 Xgwm3
a
0.12 M. Pallota (personal communication)
Lr24/Sr24 Sr24#50
a
0.00 Mago et al. (2005)
Lr37/Sr38/Yr17 Xcfd36 0.00 M. Hayden (personal communication)
Rht-B1 BF-MR1 0.00 Ellis et al. (2002)
Rht-D1 DF-MR2 0.00 Ellis et al. (2002)
Rht8 Xgwm261 0.01 Korzun et al. (1998)
Glu-D1 P1+P2 0.00 Ahmad (2000)
Glu-A3 Xpsp2999 0.00 Devos et al. (1995)
a
Xgwm140 was used for selection of Lr46/Yr29 and Xgwm003 for Lr24/Sr24 prior to the publication of closer markers
Xwmc44 and Sr24#50 respectively. Retrospective classification of gene effects and allele frequencies for this study were
performed using the closer and more recently published markers Xwmc44 and Sr24#50
298 Mol Breeding (2007) 20:295–308
123
rheology quality assessment and subsequent
marker validation. A composite sample (300 g),
formed from a bulk of grain from the two sites,
was milled using a Quadrumat
Junior mill
(Brabender, Germany). The dough performance
characteristics; dough resistance (R
max
), and
dough extensibility (Extensibility), were assessed
on 50 g flour at a 13.5% moisture basis using an
Extensograph (Brabender, Germany) following a
modified small scale version of the AACC
method 54–10.
Marker assisted selection
Two sets of molecular markers were used in this
breeding strategy. The first group of markers
were associated with target genes (Table 2) whilst
the second set of microsatellite markers were
used to select generically for recurrent parent
genome. Markers (52) for recurrent parent back-
ground selection were chosen for representation
of each chromosome and were sourced from the
WMC (Somers and Isaac 2004) and GWM
(Roder et al. 1995,1998) sets of markers. The
commercial service provider, Australian Genome
Research Facility (with the financial assistance of
the Commonwealth Government of Australia),
extracted DNA from haploid plants and
performed the PCR fragment analysis for the
recurrent parent background selection. Molecular
markers being employed for selection of alleles at
specific loci in this cross were amplified by PCR
using DNA extracted from leaves. Amplified
PCR products were separated by electrophoresis
using either agarose or polyacrylamide gels. Gels
were stained in ethidium bromide and the DNA
variants visualised under UV light.
Statistical analysis
Best linear unbiased predictors (BLUPs) for grain
yield were determined for each environment
using the REML directive within GENSTAT 8
(Payne et al. 2002). A spatial model incorporating
row and column effects was fitted to the data
along with any other significant (P< 0.05) spatial
terms, such as seeding or harvest direction
(Gilmour et al. 1997) where appropriate. The
same analysis was performed on rust infection
data recorded at Horsham in 2004 and Rosewor-
thy in 2005. The remaining phenotypic data,
collected from unreplicated trials, was utilised in
raw form.
The relationship between molecular marker
alleles linked with rust resistance genes and rust
severity and the association between the propor-
tion of ‘Stylet’ genome (based on identification of
‘Stylet’ marker alleles) and grain yield was
Table 3 The rust infection assessments made on the ‘Annuello/2*Stylet’ population
Environment Site Year Trait Name Rust type Races present Infection severity Virulence
CB04 Cobbitty 2004 CB04_Lr Leaf 104-1,2,3,(6),(7),11,13 High Lr24
CB04_Sr Stem 98-1,2,3,(5),6 High
CB04_Yr Stripe 134 E 16 A+ Moderate
CB05 Cobbitty 2005 CB05_Lr Leaf 104-1,2,3,(6),(7),11,13 Moderate Lr24
104-1,2,3,(6),(7),11,+Lr37 Moderate Lr37
CB05_Yr1 Stripe 134 E 16 A+ Moderate
CB05_Yr2 Stripe 134 E 16 A+ Moderate
104 E137 A+ Yr17+ Moderate Yr17
CB05_Sr Stem 98-1,2,3,(5),6 Moderate Lr24
34-2,7,+Sr38 Low Sr38
HR04 Horsham 2004 HR04_Yr Stripe 134 E 16 A+ High
RS05 Roseworthy 2005 RS05_Yr Stripe 134 E 16 A+ High
SEED05 Cobbitty 2005 SEED05_Lr1 Leaf 104-1,2,3,(6),(7),11,13 – Lr24
SEED05_Sr1 Stem 34-1,2,3,4,5,6,7 –
SEED05_Sr2 Stem 34-1,2,3,4,5,6,7 –
SEED05_Sr3 Stem 34-1,2,7,+Sr38 –Sr38
The resistance genes segregating in the ‘Annuello/2*Stylet’ population that were ineffective against the rust pathotypes are
indicated in the virulence column
Mol Breeding (2007) 20:295–308 299
123
determined using general linear regression within
GENSTAT 8 (Payne et al. 2002). Two analyses of
association with rust infection were performed.
The first included the complete set of DHs, whilst
the second aimed at determining the effectiveness
of selection for markers linked with Lr34/Yr18,
Lr46/Yr29 and Lr24/Sr24 without the confound-
ing influence of Lr37Yr17/Sr38. Consequently,
the second analysis was restricted to DH lines
lacking Lr37/Sr38/Yr17. The relationship between
marker alleles at the glutenin loci, and both R
max
and Extensibility were determined using the
REML directive within GENSTAT 8 (Payne
et al. 2002).
Results
The effect of selection on the frequencies
of alleles at target loci
Genes Lr34/Yr18,Lr46/Yr29,Lr24/Sr24,Glu-D1
and Glu-A3 were selected with molecular mark-
ers to improve disease resistance and end-use
quality attributes of the wheat cultivar ‘Stylet’.
The final frequency of desirable alleles at each of
these loci was determined by screening the DHs
surviving the MAS and phenotypic selection
regime and was compared to allele frequencies
expected in an unselected population of fixed
lines from this cross (Fig. 2). Increases were
observed for all loci, with the most dramatic rise
observed for Lr34/Yr18 which rose from an
expected frequency of 0.25 to 0.60 with marker
selection and then to 0.75 with the addition of
phenotypic selection. The shifts in allele fre-
quency for Lr46/Yr29 and Lr24/Sr24 were not as
large. With marker based selection only, the
frequency of Lr46/Yr29 in the population in-
creased by 0.02–0.27 but reached 0.34 following
phenotypic selection for rust resistance. Most of
the improvement in the Lr24/Sr24 rust resistance
allele frequency was achieved with marker selec-
tion, with the allele frequency increasing by
0.13–0.38 and only a further 0.02–0.40 with
phenotypic selection. Both glutenin alleles were
fixed (1.0) through marker based selection among
the haploid plants.
Validation of selection for rust resistance
genes
The effects of the rust resistance genes on rust
infection were calculated using the complete set
of DH lines, and then on a restricted set of DHs
not carrying the Lr37/Sr38/Yr17 resistance gene
(Table 4). The primary target of this strategy,
Lr34/Yr18 (using marker Xgwm295 for analysis)
was shown to be associated with all field based
leaf and stripe rust infection severity scores.
Across all the field environments, the ‘Annuello’
Lr34/Yr18 allele was associated with an average
reduction in leaf rust infection of 1.6 units in the
complete set of DHs and 2.6 units in the restricted
set where lines carrying the ‘Stylet’ allele at the
Lr37/Sr38/Yr17 locus were excluded from the
analysis. Across all the field environments, the
‘Annuello’ allele was associated with an average
Fig. 2 The effect of
selection on the frequency
of desirable alleles at the
targeted loci following
MAS and then followed
by phenotypic selection.
Markers linked to the
genes of interest
(Table 2) were used to
assess allele frequencies
at the target loci
300 Mol Breeding (2007) 20:295–308
123
reduction in field stripe rust infection severity of
1.5 and 2.4 units in the complete and restricted
sets respectively. The ‘Annuello’ Lr34/Yr18 allele
was also associated with lower leaf rust infection
in the SEED05_Lr1 assay and lower levels of
stem rust infection in the field in 2005 and
SEED05_Sr1. The effects of Lr46/Yr29 (using
marker Xwmc44 for analysis) on infection levels
were less pronounced. Lines carrying the
‘Annuello’ allele had on average 0.9 units lower
field based stripe rust infection when the lines
carrying the Lr37/Sr38/Yr17 resistance allele were
excluded from the analysis. However an associa-
tion between this gene and field based leaf rust
infection was only observed at CB04. Like Lr34/
Yr18,Lr46/Yr29 was associated with the level of
leaf rust infection in the seedling tests. Interest-
ingly, the ‘Anneullo’ Lr46/Yr29 allele was found
to be associated with a higher stem rust infection
on both adult plants in the field and on seedlings.
The ‘Annuello’ allele at the Lr24/Sr24 locus
(using marker Sr24#50 for analysis) was associ-
ated with lower stem rust infection in each of the
field and seedling based resistance assays. How-
ever, in the case of leaf rust, DHs carrying the
resistant ‘Annuello’ allele at this locus had lower
infection in the field, but had higher infection as
seedlings.
Validation of selection at glutenin loci
The glutenin alleles targeted with molecular
markers in this breeding strategy, showed a very
strong association with the dough properties R
max
and Extensibility (Table 5). DH lines carrying the
‘Stylet’ molecular marker allele for Glu-D1d had
Table 4 The effects of MAS for the desirable alleles at the Lr24/Sr24,Lr34/Yr18, and Lr46/Yr29 loci on leaf rust, stripe rust
and stem rust infection of the genes under selection in the ‘Annuello/2*Stylet’ breeding strategy
Trait Lr37/Sr38/Yr17
resistant DHs
included?
%Lr37/Sr38/Yr17
Xcfd36
Lr24/Sr24
Sr24#50
Lr34/Yr18
Xgwm295
Lr46/Yr29
Xwmc44
effect ± sem sig effect ± sem sig effect ± sem sig effect ± sem sig
CB04_Lr Yes 56.1 –3.4 ± 0.18 *** –0.4 ± 0.18 * –1.4 ± 0.18 *** –0.4 ± 0.19 0.05
CB05_Lr Yes 34.0 –1.5 ± 0.20 *** –1.6 ± 0.20 *** –1.8 ± 0.20 *** ns
SEED05_Lr1 Yes 19.2 –1.3 ± 0.15 *** 0.4 ± 0.15 * –0.5 ± 0.15 ** ns
CB04_Lr No 26.7 N/A ns –2.4 ± 0.33 *** ns
CB05_Lr No 39.2 N/A -1.8 ± 0.33 *** –2.7 ± 0.35 *** ns
SEED05_Lr1 No 6.6 N/A ns –0.5 ± 0.18 ** –0.4 ± 0.20 0.05
CB04_Yr Yes 39.0 –0.9 ± 0.09 *** ns –0.8 ± 0.09 *** –0.4 ± 0.09 ***
CB05_Yr1 Yes 47.6 –2.1 ± 0.15 *** ns –1.6 ± 0.15 *** –0.6 ± 0.17 ***
CB05_Yr2 Yes 38.6 –1.1 ± 0.19 *** ns –2.4 ± 0.19 *** –1.1 ± 0.21 ***
HR04_Yr Yes 48.7 –2.4 ± 0.16 *** ns –1.7 ± 0.16 *** –0.3 ± 0.18 0.06
RS05_Yr Yes 60.0 –2.6 ± 0.12 *** ns –1.0 ± 0.12 *** –0.3 ± 0.13 *
CB04_Yr No 48.7 N/A ns –1.5 ± 0.14 *** –0.7 ± 0.16 ***
CB05_Yr1 No 41.9 N/A ns –2.6 ± 0.27 *** –0.9 ± 0.30 **
CB05_Yr2 No 45.1 N/A ns –3.1 ± 0.31 *** –1.1 ± 0.34 **
HR04_Yr No 42.4 N/A ns –3.0 ± 0.30 *** –0.8 ± 0.33 *
RS05_Yr No 36.0 N/A ns –2.0 ± 0.24 *** –0.8 ± 0.26 **
CB04_Sr Yes 39.7 –1.3 ± 0.09 *** –0.6 ± 0.09 *** ns 0.3 ± 0.09 **
CB05_Sr Yes 23.7 –1.2 ± 0.17 *** –1.2 ± 0.17 *** –0.9 ± 0.17 *** ns
SEED05_Sr1 Yes 60.1 –3.2 ± 0.16 *** –1.8 ± 0.16 *** –0.5 ± 0.16 ** 0.4 ± 0.17 *
SEED05_Sr2 Yes 65.3 –3.2 ± 0.15 *** –2.6 ± 0.15 *** ns 0.4 ± 0.16 *
SEED05_Sr3 Yes 31.8 ns –1.7 ± 0.13 *** ns ns
CB04_Sr No 22.9 N/A –1.2 ± 0.18 *** ns ns
CB05_Sr No 25.4 N/A –1.9 ± 0.31 *** –1.2 ± 0.32 *** ns
SEED05_Sr1 No 41.0 N/A –2.7 ± 0.27 *** –0.5 ± 0.27 0.06 ns
SEED05_Sr2 No 60.0 N/A –3.8 ± 0.26 *** ns ns
SEED05_Sr3 No 32.2 N/A –1.6 ± 0.21 *** ns 0.5 ± 0.24 *
The effects of the Lr37/Sr38/Yr17 locus is also presented. Two separate analyses were performed, one including all DHs and
the other restricted to DHs carrying the Lr37/Sr38/Yr17 ‘Annuello’ (susceptible) allele
Mol Breeding (2007) 20:295–308 301
123
an R
max
92 BU higher than the lines carrying the
marker allele linked to Glu-D1a. For Glu-A3, the
marker allele linked to the ‘Annuello’ ballele was
associated with the most resistant dough followed
by the markers alleles for Glu-A3c and then Glu-
A3e. Lines carrying the Glu-A3e allele also
produced the least extensible dough, 1.3 cm
shorter than the DHs carrying the Glu-A3b allele.
Validation of selection for recurrent parent
background genome
For each DH, the proportion of genome inherited
from ‘Stylet’ was estimated based on the inher-
itance of alleles at the 52 marker loci chosen to
provide genome wide coverage. Across the DHs
tested, an average 74.3% of the genome was
inherited from ‘Stylet’, while the minimum
observed was 53.3% and the maximum 96.5%.
Linear regression between the proportion of
genome inherited from ‘Stylet’, and the grain
yield achieved by the DHs at each of the five
environments only showed a significant associa-
tion at Buckleboo (P= 0.038). The proportion of
variance in grain yield explained by this genetic
association was 5.1% and each percentage rise in
genome inherited from ‘Stylet’ was associated
with a 3.3 ± 1.6 kg Ha
–1
higher grain yield. No
significant association between the proportion of
‘Stylet’ genome and grain yield at the other sites
existed.
The elite forty lines
Following MAS and phenotypic selection for
dough properties, rust resistance and grain yield
(top 40%), forty DH lines were retained. Of these
40 DH lines, only five ranked lower for grain yield
than ‘Annuello’ across the five environments
(Fig. 3). Most of the selected DH lines were
positioned between ‘Stylet’ and ‘Annuello’ for
grain yield, whereas five DHs ranked higher than
‘Stylet’ for grain yield. On average, these 40 DH
lines were not significantly different from ‘Stylet’
for agronomically important traits such as relative
maturity and plant height. One of the final forty
lines achieved grain yields across the five envi-
ronments 21% higher than ‘Annuello’ and only
6% lower than ‘Stylet’. Its dough resistance was
73 BU higher than the superior quality parent
‘Annuello’ (343 BU vs. 270 BU), whereas its
extensibility was similar to ‘Annuello’. In terms of
rust resistance, this line was resistant to all
pathotypes tested of leaf rust and stem rust both
as an adult and seedling and was resistant to the
pathotypes of stripe rust tested at the adult plant
stage. The genome wide marker screen showed
that 85% of this line’s genome was derived from
‘Stylet’, the fifth greatest level for the population.
Discussion
The impact of MAS for improved rust
resistance
One of the primary aims of this breeding strategy
was to develop ‘Stylet’ like derivatives with
resistance to rust pathotypes possessing virulence
for the ‘VPM1’ derived resistance genes Lr37,
Sr38 and Yr17. However, as is often encountered
during phenotypic selection, the appropriate
pathotype disease epidemics were not encoun-
tered during the life of this validation study.
Consequently, the effectiveness of selection for
the rust resistance genes from ‘Annuello’ in a
Table 5 Dough performance of the glutenin loci under selection in the ‘Annuello/2*Stylet’ breeding strategy
Allele R
max
(BU) Extensibility (cm)
mean ± sem sig mean ± sem sig
Glu-D1a 171 ± 7.5 *** ns
Glu-D1d 263 ± 4.4
Glu-A3b 248 ± 9.3
***
20.5 ± 0.25
***Glu-A3c 211 ± 5.0 20.2 ± 0.13
Glu-A3e 191 ± 6.9 19.2 ± 0.18
302 Mol Breeding (2007) 20:295–308
123
‘Stylet’ background could not be assessed directly
on a phenotypic basis. Rather, the association
between marker alleles at the rust resistance loci
and the severity of rust infection was determined
by general linear regression.
In general, effects of the individual rust resis-
tance loci on rust infection, were lower than
assumed for the computer based simulation
(Kuchel et al. 2005). Based on the stripe rust
infection severity results from the set of DH lines
where those carrying Yr17 were excluded, the
combined introgression of Yr18 and Yr29 from
‘Annuello’ would have resulted in average sever-
ity of infection score 3.1 units lower, suggesting
that selection with molecular markers was effec-
tive. However, the effects of selection for their
linked leaf rust resistance genes Lr34 and Lr46 on
the severity of leaf rust infection was less marked.
It is interesting to note however, that although
being characterised as adult plant resistance
genes, both Lr34 and Lr46 showed some associ-
ation with reduced leaf rust infection scores in the
seedling assays. Although it can be argued that
the positive relationship between Lr46/Yr29 and
the level of stripe rust resistance warrant use in
MAS strategies, the results presented here con-
firm that Lr34/Yr18 should have been the primary
target for MAS in this cross.
Of the three rust resistance loci targeted by
MAS, Lr34/Yr18 showed the greatest response
to selection. Although all the frequency of
‘Annuello’ (resistant) alleles for all three rust
resistance loci started at the same level (25%),
the final number of lines carrying the Lr34/Yr18
‘Annuello’ allele was almost twice that of Lr46/
Yr29 and Lr24/Sr24. The final frequency of
‘Annuello’ Lr34/Yr18 alleles resulting from this
breeding strategy (75%) was similar to the resul-
tant allele frequency in the computer based
simulation study (82.5%) (Kuchel et al. 2005).
However the final frequencies observed for Lr46/
Yr29 (34%) and Lr24/Sr24 (40%) were substan-
tially lower than was predicted (53 and 78%
respectively) by simulation. In the simulation
study, heavy rust epidemics virulent on the ‘VPM’
derived rust resistance genes Lr37, Sr38 and Yr17
were assumed to be encountered by the ‘Annu-
ello/2*Stylet’ population, however this was not
the case in this validation study. This lack of
selection against appropriate pathotypes
undoubtedly reduced the effectiveness of selec-
tion for the rust resistance genes segregating in
this population and had the largest effect on the
frequency of Sr24, assumed to be the only stem
rust resistance gene other than Sr38, present in
this population. Results from the simulation study
suggested that the phenotypic selection for Sr24
would be so great in the presence of the Sr38
virulent race that MAS would have no effect on
its final frequency in the population (Kuchel et al.
2005). This was not the case in this validation
study. Results presented here highlight the impor-
tance of MAS where phenotypic selection cannot
be performed at its optimum level, and where
other resistance genes interfere in the phenotypic
selection of target genes. The relatively poor
Fig. 3 The variation in
grain yield (average of
five environments)
observed in the final forty
selected ‘Annuello/
2*Stylet’ DH population
Mol Breeding (2007) 20:295–308 303
123
response to selection observed for Lr46/Yr29,
compared with the results from the simulation
study, could be due to a number of reasons. As
with Lr24/Sr24, a lack of Lr37 and Yr17 virulent
rust infections may have reduced the effective-
ness of phenotypic selection for both leaf and
stripe rust resistance conferred by Lr46/Yr29. The
difference in effectiveness of phenotypic selection
between the results from simulation study and
those from the validation study may also be due
to an excessively high gene effect assumed for the
simulation experiment. However, an examination
of Fig. 2shows that the addition of phenotypic
selection in this study resulted in an increase in
the frequency of the ‘Annuello’ Lr46/Yr29 allele
in the population. In contrast, MAS for Lr46/Yr29
with the loosely linked marker Xgwm140 appears
to have been largely unsuccessful. Most likely, the
estimated recombination interval of 0.30 between
Lr46/Yr29 and Xgwm140 assumed for the simu-
lation study was an underestimate for this popu-
lation. Although it was concluded from the
simulated results that selection with loosely
linked markers could be worthwhile, results
presented here question the wisdom of such
selection. In summary, MAS for minor and major
rust resistance genes can result in appreciably
higher relative genetic gain, particularly
where the linkage between the trait and marker
is close.
Although not the primary objective of this
study, some intriguing associations were detected
between marker alleles at the rust resistance loci
and the level of rust infection. The leaf and stripe
rust resistance gene complexes, Lr34/Yr18 and
Lr46/Yr29 were both found to be associated with
stem rust resistance. The level of adult plant and
seedling resistance to stem rust infection were
greater in DH lines carrying the ‘Annuello’ Lr34/
Yr18 allele, while lines carrying the ‘Annuello’
Lr46/Yr29 allele were actually more susceptible
to stem rust infection at both the adult plant and
seedling stage. Although these effects were not
observed under every test, it seems likely that
Lr34/Yr18 may also impart some level of resis-
tance to stem rust through either pleiotropy, or
linkage with additional resistance genes. Further
investigation should be undertaken to confirm
these observations.
MAS for improved dough properties
The importance of the HMW and LMW glutenins
on dough properties in wheat have been widely
demonstrated (Payne et al. 1987; Gupta et al.
1989). The gene estimates of Eagles et al. (2002)
were used as the basis for the assumptions
underpinning the ‘Annuello/2*Stylet’ simulation
study, which showed substantial improvement in
dough quality following the application of MAS
for the desirable alleles at the Glu-D1 and Glu-
A3 loci (Kuchel et al. 2005). Results presented in
this study also show that MAS was very effective
in improving dough quality. All of the DHs
entering the first year of grain yield testing carried
the desired glutenin alleles Glu-D1d and Glu-A3b
or Glu-A3c. The superiority of the Glu-D1d and
Glu-A3b alleles over the Glu-D1a and Glu-A3e
alleles was confirmed by regression analysis using
the full set of ‘Anuello/2*Stylet’ lines (Fig. 1).
One could conceivably argue that protein based
glutenin analysis could be used to achieve the
same outcome, and although true, this does not
take into account the lower cost associated with
DNA based analysis of targeted glutenin loci
when already undertaking MAS for other genes.
Nor does it recognise the flexibility associated
with DNA analysis which can be performed on
plant tissue rather than seeds, and consequently
during the DH production process. Given the
successful use of DNA based markers on fixed
lines, it is likely that MAS for improved dough
properties could be effectively incorporated into
future breeding strategies at a much earlier stage.
For this study, it seems likely that even greater
genetic improvement would have been possible in
this population if the desirable glutenin alleles
were also selected on BC
1
F
1
individuals prior to
producing DHs.
MAS for recurrent parent background
or phenotypic selection for grain yield?
The application of marker-assisted recurrent par-
ent background selection was examined in this
study. Selection against donor parent genome has
been suggested as a means to increase the rate of
genetic gain achieved within a backcrossing
strategy (Stam and Zeven 1981; Visscher 1999;
304 Mol Breeding (2007) 20:295–308
123
Ribaut et al. 2002; Frisch and Melchinger 2005).
However, other than a weak positive relationship
established between the proportion of recurrent
parent genome and grain yield at one of the five
sites used for grain yield analysis, this was shown
not to be a successful strategy in this study.
Failure to establish a relationship between the
proportion of recurrent parent genome and grain
yield may have been due to the presence of
alternative ‘high grain yield’ genes carried by
‘Annuello’, or perhaps a large level of epistatic
gene action contributing to the superior grain
yield level of the recurrent parent ‘Stylet’. In the
simulation study of Kuchel et al. (2005), where
MAS for recurrent parent genome resulted in
superior grain yield, a purely additive gene action
was assumed for the grain yield genotype-envi-
ronment system. The grain yield distribution of
the DHs selected from this study (Fig. 2) lends
support to the conclusion that substantial non-
additive gene action could be responsible for the
superior level of grain yield conferred by ‘Stylet’.
Rather than clustering toward the grain yield
level of the recurrent parent, as would have been
expected, the DH lines were positioned centrally
with respect to the two parents, even after
selection for high grain yield. In a backcrossing
strategy such as this, where the donor parent has
relatively good adaptation to the target environ-
ment, marker-assisted recurrent parent back-
ground selection may be seen as excessively
conservative. Rather than miss out on potential
gains in grain yield arising through transgressive
segregation, it may be more beneficial to target
specific genes known to be associated with supe-
rior grain yield. However due to the complex
nature of grain yield, its genetic basis is poorly
understood and therefore selection for specific
genes for grain yield is at this time impractical.
Until we have an improved understanding of the
genetic basis to grain yield, phenotypic selection
will remain the method of choice.
Suggestions for future incorporation of MAS
in pragmatic wheat breeding
One of the aims of this study was to validate the
results of a computer simulation (Kuchel et al.
2005), and to investigate the benefits of integrat-
ing MAS into a practical breeding strategy. The
scale and cost of the backcross introgression
strategy studied by simulation, and later by
practical validation, would prohibit most breeding
programmes from adopting this approach across
all breeding populations. Instead, some clear
conclusions and suggestions for the wheat breed-
ing community can be drawn from the results
presented here and in Kuchel et al. (2005).
Kuchel et al. (2005) showed that MAS on fixed
lines could reduce overall resource expenditure,
but that genetic gain was only moderately
improved relative to phenotypic selection on its
own. In this practical test of MAS on the same
population, it has been shown that MAS on DHs
was particularly beneficial to genetic gain where
phenotypic selection was not possible. Both this
study, and the simulation of a similar breeding
strategy by Kuchel et al. (2005) showed a high
response to selection when MAS was performed
on BC
1
F
1
individuals. Allele enrichment at early
generations (F
2
) was also the focus of strategies
developed for wheat by Howes et al. (1998) and
Bonnett et al. (2005). Bonnett et al. (2005) sug-
gested that the most effective means of genetic
improvement in wheat would be achieved by
enriching F
2
populations for favourable alleles
and then selecting homozygotes at later genera-
tions when the lines have reached fixation. Howes
et al. (1998) developed similar conclusions but
also showed that as the number of target loci
became large (>12), a second round of crossing
(between F2 carriers) may be required to keep
population sizes down. Results from this study
and those of Howes et al. (1998), Bonnett et al.
(2005) and Kuchel et al. (2005) agree that the
maximum genetic gain, at the lowest cost, will be
achieved when molecular markers, closely linked
to target genes, are used to enrich target loci
within early generation segregating populations.
Positive outcomes of the validation study
presented here suggest that a considerably more
aggressive (Fig. 4) breeding strategy could have
been attempted with further improvements in
genetic gain and cost efficiency. The key factors
to improved efficiency and genetic gain include
the use of large early generation populations and
an effective selection strategy. For this cross, this
could be achieved by increasing the BC
1
F
1
Mol Breeding (2007) 20:295–308 305
123
population from 72 to approximately 2,500 indi-
viduals and screening the population with mark-
ers linked to all target loci rather than just the
Lr34/Yr18 and Lr46/Yr29 loci. All target genes to
be retained from ‘Stylet’ would be fixed at this
point, and the allele frequency of genes to be
introgressed from ‘Annuello’ increased from an
expected average frequency of 25% without MAS
to 50% with MAS. Haploid seedlings could then
be screened with markers linked to the target
genes to be introgressed from ‘Annuello’, saving
money that would otherwise be spent on chro-
mosome doubling, subsequent seed increase and
phenotypic assessment of lines lacking the desired
traits. Given the results obtained in this study,
and that of Kuchel et al. (2005), marker based
selection for an increased proportion of ‘Stylet’
genome would not be recommended. Based on
the cost profile outlined by Kuchel et al. (2005), it
would cost approximately $AUD 34,600 to get to
the stage of DH seed increase using this strategy,
in comparison to approximately $AUD 77,500 for
MAS2 (Kuchel et al. 2005). In addition to costing
less than MAS2, around 64 DHs would be
expected to carry the complete target molecular
ideotype. This compares to the two DH lines
identified in this validation study that achieved
the same target genetic ideotype.
Conclusions
The aim of this study was to improve the
disease resistance and grain quality of an elite
recurrent parent (‘Stylet’) through marker as-
sisted gene introgression and in doing so vali-
date the results and conclusions of the
simulation study undertaken by Kuchel et al.
(2005). We have shown in a pragmatic breeding
strategy that selection with molecular markers
has resulted in the production of a number of
DH lines with improved rust resistance and
quality. One line achieved grain yields similar to
that of the recurrent parent ‘Stylet’, had dough
properties superior to the donor parent ‘Annu-
ello’, and was resistant to all commercially
important pathotypes of stem rust, leaf rust
and stripe rust prevalent in Australia. Results
presented here led to the conclusion that
although specific practical outcomes may differ
from simulated predictions, computer aided
simulation can be an effective tool to assist in
designing high impact breeding strategies. These
results also confirmed that MAS of donor
alleles within BC
1
F
1
populations was more
effective than MAS on fixed lines, although
where phenotypic selection is not possible,
MAS on fixed lines was still useful. If this
breeding strategy were to be repeated, or a
similar crossing strategy undertaken, it is sug-
gested that a much larger BC
1
F
1
population be
generated and selection for all target loci be
undertaken to capitalise on the benefits offered
by MAS.
While the primary aim of this study was to
validate the outcomes of the simulation study
conducted by Kuchel et al (2005), this study has
also demonstrated the breeding value of the
adult plant rust resistance gene complexes Lr34/
Yr18 and Lr46/Yr29 in Australian germplasm
and that MAS selection in a segregating
population for specific glutenin alleles can
result in improved dough resistance and exten-
sibility.
Acknowledgements The authors would like to
acknowledge the staff at Australian Grain Technologies,
the University of Adelaide and the University of Sydney
for there assistance collecting field, end-use quality,
molecular marker and rust resistance data. Gratitude is
also extended to the Molecular Plant Breeding
Cooperative Research Centre and the Grains Research
and Development Corporation for their financial
assistance. The advice and direction of Prof. P.
Langridge is gratefully acknowledged.
Fig. 4 Proposed breeding strategy incorporating the con-
clusions drawn from this study and those from Kuchel
et al. (2005)
306 Mol Breeding (2007) 20:295–308
123
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