Mol Breeding (2017) 37: 107
DOI 10.1007/s11032-017-0708-7
Enhancing Fusarium crown rot resistance by pyramiding
large-effect QTL in common wheat (Triticum aestivum L.)
Zhi Zheng & Shang Gao & Meixue Zhou & Guijun Yan &
Chunji Liu
Received: 12 April 2017 / Accepted: 24 July 2017 / Published online: 9 August 2017
# Springer Science+Business Media B.V. 2017
Abstract Fusarium crown rot (FCR) has become one
of the most damaging cereal diseases in semi-arid regions worldwide. Targeting three large-effect QTL (located on the chromosome arm 3BL, 5DS and 2DL,
respectively), we investigated the feasibility of enhancing FCR resistance by gene pyramiding. Significant
effects were detected for each of the three QTL in both
populations assessed. Lines with any combination of
two resistant alleles gave significantly better resistance
than those with a single resistant allele only and those
without any allele, and lines possessing all three resistant alleles showed the best resistance. These results
demonstrated that gene pyramiding can be an effective
approach in improving FCR resistance. Those lines with
resistant alleles from all three QTL could be valuable
genetic stocks for breeding programs.
Z. Zheng : S. Gao : C. Liu (*)
CSIRO Agriculture and Food, 306 Carmody Road, St Lucia,
Queensland 4067, Australia
e-mail: chunji.liu@csiro.au
Z. Zheng
National Foxtail Millet Improvement Centre, Institute of Millet
Crops, Hebei Academy of Agricultural and Forestry Sciences,
Shijiazhuang, China
Z. Zheng : G. Yan : C. Liu
School of Agriculture and Environment, The University of
Western Australia, Perth, WA 6009, Australia
S. Gao : M. Zhou
University of Tasmania, School of Land and Food and Tasmanian
Institute of Agriculture, Private Bag 54, Hobart, Tasmania 7001,
Australia
Keywords Fusarium crown rot . Host resistance . Wheat
Introduction
Fusarium crown rot (FCR), from infection of crown, basal
stem and root tissues, is a severe and chronic cereal
disease found in many parts of the semi-arid regions of
the world (Chakraborty et al. 2006). It can be caused by
many different species of Fusarium although field surveys
found Fusarium pseudograminearum as the most prevalent pathogen in Queensland and New South Wales of
Australia (Akinsanmi et al. 2004). The disease is found
in every wheat-growing region in Australia and it
caused an estimated $80 million Australian dollars
in lost production in wheat alone (Murray and
Brennan 2009). Results obtained in the Pacific Northwest of USA (Smiley et al. 2005) found the disease
can reduce yields of commercial wheat crops by up to
35%. Its economic importance ranges from not only
yield losses but also the production of mycotoxins in
grains as well as other tissues (Mudge et al. 2006)
which could cause health concerns for humans and
livestock.
The incidence of FCR has increased in Australia as
well as in many other cereal-growing regions worldwide in recent years, most likely due to the high
intensity of cereals in cropping combined with the
wider adoption of minimum tillage for moisture conservation (Smiley et al. 2005; Chakraborty et al. 2006;
Hogg et al. 2010), as FCR pathogens are carried over
in residues (Chakraborty et al. 2006). It has long been
107 Page 2 of 8
recognised that growing resistant varieties should
form an integral part for an effective strategy of combating this disease (Wildermuth and Purss 1971). As
for many other diseases, high-quality resistant
sources would be essential for effective breeding of
FCR resistant varieties.
To facilitate the breeding of FCR resistant varieties, several QTL have been detected on chromosomes 1A, 1D, 2B, 2D, 3B and 5D (Wallwork et al.
2004; Bovill et al. 2010; Li et al. 2010; Ma et al. 2010;
Poole et al. 2012; Zheng et al. 2014). Of the QTL
reported so far, only three QTL (Ma et al. 2010;
Zheng et al. 2014) were consistent with significant
effects in more than two populations. The first one,
designated as Qcrs.cpi-3B, was located on the long
arm of chromosome 3B accounting for up to 49% of
phenotypic variance in five genetic backgrounds (Ma
et al. 2010). This QTL was inherited from a Triticum
spelta accession ‘CSCR6’ which is one of the most
resistant lines among the more than 2500 genotypes
screened by Liu et al. (2010). The second one, designated as Qcrs.cpi-5D, was located on the short arm of
chromosome 5D. This QTL explained up to 31.1% of
the phenotypic variance. The third one was located on
the long arm of chromosome 2D (designated as
Qcrs.cpi-2D) and explained up to 20.2% of the phenotypic variance. Both of the QTL were further validated in other two populations (Zheng et al. 2014).
These two QTL were derived from one of the most
resistant bread wheat varieties EGA Wylie in Australia (Queensland wheat variety guide 2013). This variety was also one of the most resistant ones among
2500 genotypes screened by Liu et al. (2010). The
combination of its elite agronomic characteristics and
its level of resistance makes this variety very attractive in improving FCR resistance.
Gene pyramiding has been used as an effective
approach to achieve multiple and durable resistance,
such as barley FCR resistance (Chen et al. 2015), rice
blast disease resistance (Fukuoka et al. 2015), sunflower downy mildew resistance (Qi et al. 2017) and
wheat stripe rust resistance (Zheng et al. 2017). The
purpose of the present work is to combine these three
large-effect QTL conferring FCR resistance with the
assistance of molecular markers. It was expected that
the lines containing all three QTL would perform the
best FCR resistance among all the lines. The results
obtained through evaluation of two populations segregating for the three QTL are reported in this paper.
Mol Breeding (2017) 37: 107
Materials and methods
Plant materials
Two populations segregating for three major QTL conferring FCR resistance were used in this study. They
include the following:
A) EGA Wylie//Lang/CSCR6 (Pop1) containing 178
F8 lines; and
B) EGA Wylie/3/EGA Wylie//EAG Wylie/Sumai3/4/
CSCR6 (Pop2) containing 181 F8 lines.
The parents CSCR6 and EGA Wylie are two of the
most resistant genotypes identified from a systematic
screening of genetic stocks representing different geographical origins and plant types (Liu et al. 2010).
CSCR6 is an accession of T. spelta and the 3BL locus
targeted in this study was mainly responsible for the
FCR resistance of this genotype (Ma et al. 2010). EGA
Wylie is a commercial variety in Australia and it provides the other two loci (on chromosome arms 5DS and
2DL, respectively) of FCR resistance targeted in this
study (Zheng et al. 2014). Lang was a commercial
variety released in Australia, and Sumai3 was a Chinese
commercial variety. Both of the populations were used
for FCR assessment in this study.
Both populations were specifically developed for this
study. In generating Pop1, a single F1 plant of the
Lang/CSCR6, was crossed with a single plant of EGA
Wylie and then the single seed descent method was used
to process the F2 individuals to F8 lines. In generating
Pop2, a single F1 plant of EGA Wylie/Sumai3 was
backcrossed to the female parent two times. Two single
plants with the best agronomic characteristics were then
selected to cross with a single CSCR6 plant. The single
seed descent method was then used to process the F2
individuals to F8 lines. All of the crossing and progeny
processing were conducted in the glasshouse at the
Queensland Bioscience Precinct (QBP) in Brisbane,
Australia.
Evaluation of resistance to FCR
A highly aggressive F. pseudograminearum isolate,
CS3096, was used in FCR assessment. This isolate
was collected in northern New South Wales, Australia,
and maintained in CSIRO collection (Akinsanmi et al.
2004). The methods used for inoculum preparation,
Mol Breeding (2017) 37: 107
inoculation and FCR assessment were based on that
described by Li et al. (2008). Briefly, inoculum was
prepared using plates of 1/2-strength potato dextrose
agar. The inoculated plates were incubated for 7 days
at room temperature before the mycelium was scraped.
The plates were then incubated for a further 5–7 days
under a combination of cool white and black (UVA)
fluorescent lights with a 12-h photoperiod. The spores
were harvested and the concentration of spore suspension was adjusted to 1 × 106 spores per millilitre in
distilled water. Tween 20 was added (0.1% v/v) to the
spore suspension prior to use for inoculation.
Seeds were germinated in Petri dishes on two layers
of filter paper saturated with water. The germinated
seedlings were immersed in the spore suspension for
1 min and two seedlings were planted into each of the
square punnets of a 56-well tray (Rite Grow Kwik Pots,
Garden City Plastics, Australia) containing steam
sterilised University of California mix C (50% sand
and 50% peat v/v). The punnets were arranged in a
randomised block design and the experiments in evaluating FCR resistance were all conducted in a controlled
environment facility (CEF) at QBP in 2016. Settings for
the CEF were the following: 25/16 (± 1)°C day/night
temperature and 65/ 85(± 5)% day/night relative humidity, and a 14-h photoperiod with 500 μmol m−2 s−1
photon flux density at the level of the plant canopy. To
promote FCR development, water-stress was applied
during the FCR assessment. Inoculated seedlings were
watered only when wilt symptoms appeared.
Two replicated trials were carried out with each of the
populations, designed as FCR-01 and FCR-02 for the
population of Pop1, and FCR-03 and FCR-04 for the
population of Pop2, respectively. Each trial contained
two replicates, each with 14 seedlings. FCR severity
was assessed 35 days after inoculation, using a 0 (no
obvious symptom) to 5 (whole plant severely to
completely necrotic) scale as described by Li et al.
(2008). A disease index (DI) was then calculated for
each line following the formula of DI = (∑nX/5 N) × 100,
where X is the scale value of each plant, n is the number
of plants in the category and N is the total number of
plants assessed for each line.
Evaluation of plant height and heading date
To assess possible effects of plant height and heading
date on FCR resistance, a field trial was conducted at the
CSIRO Research Station at Gatton in Queensland
Page 3 of 8 107
(27°34′S, 152°20′E). The trial was sown on 18
June 2015 using randomised block degisn with three
replicates. For each replicate, 20 seeds for each line were
sown in a single 1.5 m row with a 25 cm row-spacing.
Plant height was recorded from the five tallest tillers in
each row and the average from the five measurements
was used for statistical analyses. Heading date of a line
was recorded weekly for five consecutive weeks following the emergency of the first spike. Three scores were
used for each of the assessment: ‘1’ representing lines
for which stem elongation was not observed, ‘2’
representing lines which stem elongation occurred but
spikes were not emerged and ‘3’ representing those
which reached spike emergence or later stages of development at the time of each assessment. Accumulative
scores from the five assessments were used to represent
the heading date of a given line. Thus, larger scores
indicate earlier spike emergence.
Molecular marker analysis
SSR markers closely linked to each of the three QTL
were used to identify individual lines with or without
resistant allele at each of the three targeted loci. They
included Xgwm181 (forward TCATTGGTAATGAG
GAGAGA and reverse GAACCATTCATGTG
CATGTC) for Qcrs.cpi-3B (Ma et al. 2010). The marker
Xcfd89 was not polymorphic in these two populations,
so Xcfd189 (forward ATGAAATCCTTGCCCTCAGA
and reverse TGAGATCATCGCCAATCAGA) was selected in this QTL region for Qcrs-cpi-5D and Xcfd73
(forward GATAGATCAATGTGGGCCGT reverse
AACTGTTCTGCCATCTGAGC) for Qcrs.cip-2D
(Zheng et al. 2014). As this marker was only polymorphic in the Pop1 population, another marker Xgwm539
(forward CTGCTCTAAGATTCATGCAACC and reverse GAGGCTTGTGCCCTCTGTAG) was selected
in this QTL region for the Pop2 population. PCR reactions for the SSR marker analyses were carried out using
α [33P] dCTP (3000 ci/mmol) following the manufacturer’s protocol (Multiplex-Ready Marker User Handbook, version 2.0). The amplified products were mixed
with an equal volume of loading dye, denatured at 95 °C
for 10 min and 3.8 μl of amplified samples were separated on a 5% polyacrylamide gel containing 8 M urea at
100 W for 2 h. The gels were subsequently dried using a
gel dryer for 50 min at 80 °C and exposed to Kodak XOmat X-ray film for 4–6 days.
107 Page 4 of 8
Mol Breeding (2017) 37: 107
Fig. 1 Frequency distribution for FCR severity obtained from the
two populations. FCR severity of the parents are indicated by
arrow: ‘R1’ and ‘R2’ represent the two resistant genotypes CSCR6
and EGA Wylie, respectively, and ‘S1’ and ‘S2’ represent commercial varieties Lang and Sumai3, respectively
Data analysis
in the ith replication; μ = general mean; ri = effect due to
ith replication; gj = effect due to the jth genotype;
wij = error or genotype by replication interaction, where
genotype was treated as a fixed effect and that of replicate as random. The Duncan’s new multiple range test of
one-way ANOVA analysis (Duncan 1955) was
employed to detect possible differences among the
means. MapQTL 6.0 (Van Ooijen and Kyazma 2009)
was used to detect the percentage of phenotypic
Statistical analyses were performed using GenStat for
Windows, 13th edition (copyright Lawes Agricultural
Trust, Rothamsted Experimental Station, UK) and the
SPSS statistics 17.0 for Windows statistical software
package (SPSS Inc., Chicago, IL). For each trial, the
following model of mixed-effects was used: Yij = μ +
ri + gj + wij, where: Yij = trait value on the jth genotype
Table 1 Effects of each of the three large-effect QTL conferring FCR resistance in the two populations assessed
QTL
Population
Trial
RR
rr
DI %
p value
Qcrs.cpi-3B
Pop1
FCR-01
38.5
52.8
27.0
<0.01
FCR-02
39.2
52.6
25.5
<0.01
FCR-03
37.8
52.1
27.5
<0.01
FCR-04
37.4
52.4
28.5
<0.01
Pop2
Qcrs.cpi-5D
Pop1
Pop2
Qcrs.cpi-2D
Pop1
Pop2
FCR-01
40.6
48.9
17.0
<0.01
FCR-02
40.9
49.1
16.6
<0.01
FCR-03
37.9
49.2
22.9
<0.01
FCR-04
39.2
48.5
19.3
<0.01
FCR-01
41.3
48.2
14.3
<0.01
FCR-02
41.8
48.3
13.4
<0.01
FCR-03
40.3
49.9
19.3
<0.01
FCR-04
41.5
48.9
15.1
<0.01
‘RR’ and ‘rr’ represent the resistant and susceptible alleles for the targeted locus, respectively; DI %, percentage DI value reduced in ‘RR’
genotype calculated as (DI value of ‘rr’ − DI value of ‘RR’)/DI value of ‘rr’.
Mol Breeding (2017) 37: 107
Page 5 of 8 107
Fig. 2 Box plot distributions of
disease indices for FCR severity
among lines possessing various
combinations of the three targeted
QTL. Boxes indicate the 25 and
75 percentiles, respectively; the
median is indicated by the solid
horizontal line. Vertical lines
represent the range; the different
letters above each box denote
statistically significant at p < 0.05
with one-way ANOVA Duncan’s
multiple range test
variation explained by different markers. In order to
determine the effect of plant height or heading date on
different loci conferring FCR resistance, an analysis for
disease resistant was conducted by using plant height or
heading date as a covariate. Logarithm of the odds
(LOD) threshold values applied to declare the presence
of a QTL were estimated by performing the genome
wide permutation tests using at least 1000 permutations
of the original data set for each trait, resulting in a 95%
LOD threshold around 2.90.
Results
lines possessing any combination of two resistant alleles
can reduce DI value between 36 and 38% in the two
populations assessed. As expected, lines possessing all
three QTL showed the best FCR resistance with a DI
value reduced by 60% on average (Fig. 2).
The data in this study showed that those lines containing a single resistant allele reduced FCR severity by
26% across the two populations assessed. The effects of
a given allele of resistance seem to decrease with the
increase in the number of resistant alleles an individual
Table 2 Effects of plant height (PH) and heading date (HD) on
FCR resistance conferred by each of the three loci targeted
Trait
Characterisation of FCR severity
The frequency distribution of DI values in both populations indicated continuous variation with transgressive
segregation (Fig. 1). The averages of DI values for the
two sources of resistance (R1 and R2) were 21.9 and
24.3, respectively, and 70.3 for the commercial variety
Lang and 73.5 for Sumai3.
Significant variation was identified in DI values for
lines containing the same alleles from the three QTL
assessed. However, based on the average DI values for
RILs containing the different resistant alleles, significant
effects were detected for each of them in each of the
populations assessed (Table 1). Comparing with the
lines without any of the resistant alleles, the lines with
one resistant allele can reduce DI value between 21 and
33% in two populations across the four trials conducted.
Lines with any of two resistant alleles performed better
in FCR resistance than those with a single resistant
allele. Comparing those without any resistant alleles,
FCR
PH
HD
FCR-PH
FCR-HD
Pop1
Pop2
Locus
LOD
Locus
LOD
3BL
20.47
3BL
22.65
5DS
5.64
5DS
8.73
2DL
3.55
2DL
6.76
3BL
0.17
3BL
0.10
5DS
1.13
5DS
0.00
2DL
0.11
2DL
0.28
3BL
0.25
3BL
0.20
5DS
0.31
5DS
0.16
2DL
0.07
2DL
0.56
23.21
3BL
20.44
3BL
5DS
6.02
5DS
8.71
2DL
3.52
2DL
6.78
3BL
20.22
3BL
22.97
5DS
5.41
5DS
8.66
2DL
3.48
2DL
5.43
FCR-PH, FCR resistance assessed using PH as a covariate; FCRHD, FCR resistance assessed using HD as a covariate
107 Page 6 of 8
Mol Breeding (2017) 37: 107
Fig. 3 Box plot distributions of
disease indices for FCR severity
among lines with different PH of
the two populations. Boxes
indicate the 25 and 75 percentiles,
respectively; the median is
indicated by the solid horizontal
line. Vertical lines represent the
range; the same letter ‘a’ above
each of the boxes denotes no
significant difference at p < 0.05
with one-way ANOVA Duncan’s
multiple range test
group possessed, when those containing any combination of two resistant alleles reduced FCR severity by
18% per locus on average. However, the effects of those
lines containing all three resistant alleles become large
again, which reduced FCR severity by 20% per locus.
date were not detected in either of the populations. QTL
analysis using heading date as covariate showed that
none of the three FCR loci was significantly affected by
this trait (Table 2, Fig. 4).
Effects of plants height and heading date on FCR
resistance
Discussion
Plant heights differed significantly among the 359 RILs
in the two populations assessed, varying from 64.0 to
149.5 cm with an average of 95.3 cm. However, significant correlations between DI value and plant height
were not detected in either of the populations. QTL
analysis using plant height as covariate also failed to
detect any significant effects of this trait on any of the
three loci (Table 2, Fig. 3).
Similarly, heading date also differed significantly
among the RILs in both of the populations. However,
significant correlations between DI value and heading
Fig. 4 Box plot distributions of
disease indices for FCR severity
among lines with different HD of
the two populations. Boxes
indicate the 25 and 75 percentiles,
respectively; the median is
indicated by the solid horizontal
line. Vertical lines represent the
range; the same letter ‘a’ above
each of the boxes denotes no
significant difference at p < 0.05
with one-way ANOVA Duncan’s
multiple range test
The feasibility of improving FCR resistance by
pyramiding resistant alleles from large-effect QTL was
investigated in the study by targeting three QTL showing consistent resistance in different genetic backgrounds. Two populations segregating for the three loci
were generated and they consisted of a total of 359 lines.
Results from this study showed that the presence of
resistant alleles from each of the three QTL significantly
reduced FCR severity. Lines with resistant alleles from
two of the QTL were on average significantly more
resistant than those with only a single resistant allele,
and lines with resistant alleles from all three of the
Mol Breeding (2017) 37: 107
targeted QTL gave the best resistance. This study demonstrated that gene pyramiding is an effective approach
to improve FCR resistance.
The quality and number of available loci are among
factors affecting the efficiency of gene pyramiding for a
given characteristics. Apart from the three loci targeted
in this study, at least ten additional loci conferring FCR
resistance have been reported in wheat (Liu and
Ogbonnaya 2015). Most of these putative loci, however,
have only been detected in a single population. The
values of these loci need to be assessed in different
genetic backgrounds and different environments. Additional loci conferring FCR resistance should be possible
from those highly resistant genotypes identified from a
study of germplasm screening (Liu et al. 2011).
Results from previous studies showed that both plant
height (Li et al. 2009; Zheng et al. 2014; Chen et al.
2014) and flowering time (Liu et al. 2012) could affect
FCR resistance in cereals. Wide ranges of variations in
FCR severity were detected among lines belonging to
each of the groups with different numbers of resistant
alleles in this study. Surprisingly, significant interactions
between these two characteristics and FCR resistance
were not detected in this study. It seems that some
undetected factors affected FCR assessment. The fact
that markers used in this study were all derived from
QTL mapping could have contributed to these variations. It has been known for a long time that segregating
populations routinely used in QTL mapping do not
provide markers that can be reliably used to tag a locus
(Paterson et al. 1988). Thus, lines with the same marker
profiles may not possess the same resistant alleles.
Acknowledgements Z. Zheng is grateful to the University of
Western Australia and China Scholarship Council (CSC) for his
PhD scholarships. The authors also wish to thank Caritta Eliasson
for her technical supports.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interests.
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