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AGRONOMIC AND QUALITY QTL MAPPING IN SPRING WHEAT

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Wheat (Triticum aestivum L.) flour represents one of the primary sources of calories and proteins for the human diet. The increase in the wheat yield without losing its baking and milling quality is an important breeding objective. The use of QTL analysis is an expedient methodology to help breeders to face this multifaceted challenge. Here, a population of 129 recombinant inbred lines (RILs) developed from a cross between ‘Steele-ND’ cultivar and ‘ND 735’ advanced line was used to evaluate several yield and quality traits and map the genomic regions controlling these traits. The phenotypic data were collected from field experiments conducted at four North Dakota (ND), USA environments. Transgressive segregation was observed for all traits, with RILs outperforming the most adapted parent and commercial cultivars. Using a linkage map of 392 markers, composite interval mapping identified a total of 13 environment-specific QTLs, all explaining large phenotypic variations (R2=16-44%). The genotypic values of these “reserve” alleles were directly used as criteria of selection in breeding programs.
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J. Plant Breed. Genet. 01 (2013) 19-33
19
Available Online at eSci Journals
Journal of Plant Breeding and Genetics
ISSN: 2305-297X (Online), 2308-121X (Print)
http://www.escijournals.net/JPBG
AGRONOMIC AND QUALITY QTL MAPPING IN SPRING WHEAT
aMohamed Mergoum, aVibin E. Harilal*, aSenay Simsek, bMohammed S. Alamri,
cBlaine G. Schatz, aShahryar F. Kianian, aElias Elias, aAjay Kumar, aFilippo M. Bassi
a Department of Plant Sciences, North Dakota State University, USA.
b Nutrition and Food Sciences Department. College of Food and Agricultural Sciences, King Saud University, KSA.
c Carrington Research Extension Center, North Dakota State University, USA.
A B S T R A C T
Wheat (Triticum aestivum L.) flour represents one of the primary sources of calories and proteins for the human diet.
The increase in the wheat yield without losing its baking and milling quality is an important breeding objective. The
use of QTL analysis is an expedient methodology to help breeders to face this multifaceted challenge. Here, a
population of 129 recombinant inbred lines (RILs) developed from a cross between ‘Steele-ND’ cultivar and ‘ND 735’
advanced line was used to evaluate several yield and quality traits and map the genomic regions controlling these
traits. The phenotypic data were collected from field experiments conducted at four North Dakota (ND), USA
environments. Transgressive segregation was observed for all traits, with RILs outperforming the most adapted
parent and commercial cultivars. Using a linkage map of 392 markers, composite interval mapping identified a total of
13 environment-specific QTLs, all explaining large phenotypic variations (R2=16-44%). The genotypic values of these
“reserve” alleles were directly used as criteria of selection in breeding programs.
Keywords: Reserve alleles, quality; grain yield, grain hardness, baking traits, mixogram peak time.
INTRODUCTION
Wheat (Triticum aestivum L.), with a yearly production
of over 650 million tons worldwide (FAO 2010), is
among the most important source of plant calories and
protein in the human diet. The United States
contributes approximately 10% of world production
(FAO 2010), of which 16.9 million tons are hard red
spring wheat (HRSW) (USDA 2012). North Dakota (ND)
is the leading state in the production of HRSW,
accounting for over 7 million tons per year, followed by
Minnesota, South Dakota, and Montana (Regional
Quality Report 2011). The higher protein content and
superior gluten quality of HRSW makes it an excellent
source of flour for baked goods, which in turn
guarantees lucrative returns to the farmers in domestic
and international markets. The challenge before HRSW
breeders is to release high yielding varieties, without
losing their high value baking quality. Most agronomic
and baking/milling traits are quantitative in nature
with a complex array of genetic interactions, making
selections for these traits far from simple. Nearly 85
years ago, Sax (1923) developed the basic concepts of
detecting quantitative trait loci (QTL). Today, with the
advent of molecular markers and the development of
powerful statistical tools, QTL analysis has become a
routine approach and is often applied for the
identification of markers for molecular breeding (Salvi
and Tuberosa 2005). Recombinant inbred lines (RILs)
populations developed from genetically diverse parents
(broad base) or elite lines (narrow base) have proven
successful in mapping important agronomic and quality
traits in wheat (Rousset et al. 2001; Börner et al. 2002;
McCartney et al. 2005, 2006; Quarrie et al. 2005;
Suenaga et al. 2005; Arbelbide and Bernardo 2006;
Breseghello and Sorrells 2006; Kumar et al. 2007;
Maccaferri et al. 2008). Zhang et al. (2010), after
reviewing the distribution of 541 yield and yield related
QTL, reached the conclusion that yield QTLs are
distributed all over the wheat genome. Changes in
almost any trait will inevitably always result in an effect
on the final grain yield (Zhang et al. 2010). QTL analysis
* Corresponding Author:
Email: mohamed.mergoum@ndsu.edu
© 2012 eSci Journals Publishing. All rights reserved.
J. Plant Breed. Genet. 01 (2013) 19-33
20
has also been used to study various wheat quality traits
(Rousset et al. 2001; Arbelbide and Bernardo 2006;
Breseghello and Sorrells 2006; McCartney et al. 2006;
Nelson et al. 2006; Li et al. 2009; Kumar et al. 2009; Sun
et al. 2010) and once again the wide distribution of
QTLs suggest a large and complex interaction of many
genes, each responding to small environmental and
genotypic changes.
Regardless of the abundance and complexity of the
QTLs identified for yield and quality traits, it is essential
that conventional and molecular breeding work
together to succeed in the daunting challenge of
developing high yielding and high quality varieties
(Phillips 2009; Carena 2009). The objectives of this
study were to map QTLs for major agronomic and
quality traits in a typical breeding population involving
two elite genotypes adapted to the growing conditions
of ND, and directly employ the detected QTL for
selection within this population.
MATERIALS AND METHODS
Plant material and experimental design: The present
study used a RIL mapping population comprising of
129 F2-8 lines developed from a cross between HRSW
cultivar (cv.) ‘Steele-ND’ and HRSW breeding line ‘ND
735’ (S x N) as described by Mergoum et al. (2009a).
The S x N RIL population, the parental genotypes
‘Steele-ND’ (Mergoum et al. 2005a) and ‘ND 735’
(Mergoum et al. 2006) and the checks were evaluated
in a randomized complete block design with two
replications for two consecutive years (2008 and 2009)
at Prosper (PR; 47.002º, -97.115º; elevation 284 m) and
Carrington (CR; 47.507º, -99.132º; elevation 475 m),
ND. The four commercially grown HRSW cultivars.
‘Faller’ (Mergoum et al. 2008), ‘Reeder’ (PI200000211),
‘Dapps’ (Mergoum et al. 2005b), and ‘Glenn’ (Mergoum
et al. 2006) were used as experimental checks. Each
genotype was planted in a plot size of seven rows , with
each row 2.44 m long and 15.20 cm apart. The CR
location was irrigated and represents East Central
region of ND, with soil type of Heimdal-Emrick series
(loamy, mixed, superactive, and Calcic/Hapludolls). The
PR location was rainfed and represents the Eastern
region of ND with soil type of Beardon series (fine silty,
mixed, superactive, frigid aeric Calciaquolls).
Agronomic and quality traits evaluation: In field,
phenotypic data were collected for days to heading
(DTH), plant height (PH) (cm), spike density per m2,
and spike length (cm). The DTH was recorded as
number of days between sowing and inflorescence
emersion in at least 50% of plants in each plot and the
PH was recorded in cm for each plot by measuring the
length of ten plants in each plot from soil surface to top
of the spikes excluding the awns. Spike density (SD) per
m2 was calculated as the average number of spikes per
0.5 m in two individual rows and then converted to m2.
Spike length (SL) (cm) was calculated by averaging the
length of ten individual spikes collected at random from
each plot. The harvested grains from each plot were
cleaned with clipper grain cleaner and all remaining
agronomic traits evaluated. A sub-sample of 200 g of
grain was further cleaned in the Cereal Quality Lab at
North Dakota State University (NDSU) on a Carter
dockage tester (Carter-day Co., Minneapolis, MN) to
measure wheat grain quality data.
Grain yield (GY) (Kg ha-1) was determined weighing the
cleaned seeds from each plot. Grain volume weight
(GVW) (Kg m-3) was calculated according to AACC
standard method 55-10 (AACC 2000). The number of
kernels per spike (KPS) was measured on ten randomly
collected spikes from each plot before harvest, hand
threshed, and the seeds of each spike were counted on
an electronic seed counter (Seedburo Equipment Co.,
Chicago, IL). Thousand-kernel weight (TKW) (g) was
calculated by counting the number of kernels in 10 g of
sample using an electronic seed counter and then
converted it into the weight of 1,000 kernels. Kernel
size distribution (KSD) (%) was determined on 100 g of
seeds shaken for 2 minutes on a mechanical shaker. The
kernels that remained on the top of the sieve (Taylor
No.7, 2.92 mm) were classified as ‘large’, in the middle
sieve (Taylor No.9, 2.24 mm) were considered as
‘medium’, and the ones which reached the bottom were
classified as ‘small’. Since the small size kernels are
negligible (less than 1%), only large and medium size
kernels are reported. A total of 100 g of seed was
evaluated to determine the kernel size. Grain protein
content (GPC) (%) was measured according to AACC
standard method 46-30 (AACC 2000), using an Infratec
1226 Cold Grain Analyzer. Kernel hardness (KH) was
measured using the Single Kernel Characterization
System (SKCS) on 300 kernels, and the hardness is
expressed as an index of 20 to 120, with 20 being very
soft and 120 being very hard. Kernel diameter (KD)
(mm) was measured as the average diameter of 300
kernels analyzed in SKCS system. Flour extraction (FE)
was measured after sample cleaning using a Carter-Day
dockage tester, before milling. Samples were tempered
for 16 hrs to 15.5% moisture content, equilibrated to
J. Plant Breed. Genet. 01 (2013) 19-33
21
150 g, and then milled on a Barbender Quadromat
Junior Mill according to the standard procedures of
Cereal Quality Laboratory, Department of Plant
Sciences at North Dakota State University. The
percentage of flour extraction was calculated by
dividing the flour weight by total grain weight milled.
The mixograph peak time (MPT) test (min) was
determined using the National Manufacturing
Mixogram with a ten grams mixing bowl (National
Manufacturing, TMCO Division, Lincoln NE). Mixograph
water absorption was based on GPC in each location
following the formula of Finney (1945). Mixograph data
was collected using Mixsmart software program.
Molecular Analysis: The linkage map as described
previously by Singh et al. (2011) was used in present
study. Briefly, 364 Diversity Array Technology markers
(DArT; Akbari et al. 2006) and 28 simple sequence
repeat (SSR) markers were assigned to 277 unique loci
spanning 14 chromosomes (AABB) and none to the D
genome (for more details about genetic map, see
Mergoum et al. 2013). The total genetic length of the
linkage map was 1,789.3 cM, with an average density of
one marker per 4.57 cM.
Statistical Analysis: The analysis of variance (ANOVA)
on the collected data was performed as general linear
model (PROC GLM) (Statistical Analysis System version
8.2) (SAS Institute 1999). Error homogeneity was
tested using factor of 10 fold test. Genotypes were
considered as having fixed effects and the
environments having random effects; the combined
ANOVA across four environments are provided as
supplementary data. Standard deviations were used to
identify differences between parents, checks and RILs,
for all traits across environments. For every trait, a
difference between genotypes that is larger than one
standard deviation unit in each environment was
considered as significant. Broad sense heritability (H2)
was estimated using the formula:
MSg
MSge
1
where MSg is the mean sum of squares (MS) of the
genotype, MSge is the MS of genotype by environment
(Falconer and Mackay 1996; Tsilo et al. 2011b).
The QTL analysis was conducted for individual
experimental data with composite interval mapping
(CIM) using Windows QTL Cartographer v2.0 software
(Wang et al. 2004) employing a standard model
Zmapqtl 6 with window size of 10 cM and automatic
cofactor selection. The walking speed chosen for the
CIM was 1 cM. The empirical LOD threshold at the 95%
level of confidence was determined by a 1,000-
permutation test.
RESULTS
Agronomic and quality traits segregation: Only five
traits (PH, SL, FE, KH and MPT) of the 15 measured
segregated significantly between the two parental
genotypes ‘ND 735’ and ‘Steel ND’ (Table 1), with the
former having harder kernels (KH) and shorter MPT.
For all other traits, the performances of the parental
lines were not significantly different. The experimental
checks ‘Faller’, ‘Dapps’, ‘Reeder’, and ‘Glenn’ differed
significantly from ‘Steele-ND’, but mainly in an
environment specific manner, with only KH values
more than one standard deviation away in most
environments, and with only ‘Faller’ more than one
standard deviation superior to ‘Steele-ND’ for GY and
GPC across environments (Table 1).
Regardless of the phenotypic similarity between
‘Steele-ND’ and ‘ND 735’, the S x N RILs population had
considerable variation, with several traits showing
transgressive segregation (Table 1, Figure 1, and
Supplementary Material). The combined analysis of
data for all traits that were homogenous across the four
environments showed significant differences (P < 0.05)
between RILs for all traits, and also significant
interaction between RIL and environment for all traits
except DTH, SD, and KH.
The GY mean of RILs (4493.0 Kg ha-1) was lower than
the parental lines ‘Steele-ND’ (4510.8 Kg ha-1), ‘ND 735’
(4507.2 Kg ha-1), and the check cultivar ‘Faller’ (5111.6
Kg ha-1), but higher than check cultivar ‘Glenn’ (4163.6
Kg ha-1). The upper range of GY of RILs exceeded GY of
parents and checks in all experiment, with four lines (S
x N101, S x N033, S x N161, and S x N108) more than
one standard deviation higher than their parental line
‘Steele-ND’ in two environments. The average RILs
value for GPC was commercially appreciable at 15.1 %,
with a maximum of 17.1% in CR09 for the low yielding
line S x N095. The same GPC was achieved by ‘Glenn’ in
the same environment, while the parental lines
averaged 14.8%. The high yielding S x N lines (S x N101,
S x N033, S x N161, and S x N108) had generally lower
than average GPC, with a maximum of 15.8% in CR09
for line S x N161. KH, and MPT had very large range of
variations with RILs averaging 76.4 and 6.4 min,
respectively, with clear evidence of transgressive
segregation.
J. Plant Breed. Genet. 01 (2013) 19-33
22
Table 1. Transgressive segregation in traits for which no QTL was found in the ‘Steele-ND’ x ‘ND 735’ (SxN) RILs
expressed as mean of four environments, their standard deviation (StD), minimum-maximum range, and broad sense
heritability (H2)
Parents
Checks
SxN RILs
H2
Trait
Steele-ND
ND 735
Glenn
Faller
Mean
Range
StD
(%)
DTH (days)
58.8
59.5
56.1
60.3
59.1
48.8-61.3
1.2
31.4
PH (cm)
86.4
93.6
86.3
86.6
90.3
80.0-102.6
3.3
75.3
SL (cm)
7.8
8.6
7.9
7.7
8.1
7.9-8.9
0.3
84.9
SD per m-2
421.1
428.2
387.4
384.0
431.1
326.3-488.2
27.8
40.8
GVW
(Kg m-3)
776.4
786.8
810.6
774.8
782.6
701.5-801.1
11.6
38.6
LSK (%)
58.3
52.9
52.7
70.4
58.6
36.3-74.5
7.2
81.3
FE (%)
61.1
58.7
60.9
65.1
59.5
52.6-63.5
2.1
57.8
GP (%)
15.4
14.9
15.6
14.6
15.2
14.1-17.1
0.6
80.3
DTH, days to heading; PH, plant height; SL, spike length; SD, spike density; GVW, grain volume weight; LSK, large size
kernels; FE, flour extraction; GP, grain protein.
The broad sense heritability for the majority of the
traits studied was high. Among the agronomic traits, GY
reached a heritability of 63.3%, while the other
components were inherited at higher levels with PH, SL,
TKW, KD, LSK, all above 75%. Particularly low was the
heritability of DTH, SD, GVW, and KNS with values
between 31% and 41%. Among the quality traits, GP,
KH, and MPT had heritability values above 80.0%, while
FE was 57.8% only.
Environment specific QTL mapping: QTL mapping
was performed by CIM on ten agronomic and five
quality traits, for each environment independently. The
details of QTL mapping are provided in Table 3 and
Figure 2. A total of 13 QTLs were identified for six
traits: GY, TKW, KD, KH, KPN and MPT. All the QTLs
were identified only in one of the four environments.
Two potential pleiotropic QTLs for GY, TKW, and KPN
were detected on chromosome 5A between markers
Xwpt-4131 and X344239 and on chromosome 6B
between markers Xwpt-9881 and Xwpt-9270. Similarly,
one potential pleiotropic QTL for KH and MPT was
observed on chromosome 7B in the proximity of
marker Xwmc273. Additional three QTLs were
identified for KH, one each on chromosomes 1A
(Xwpt3698-Xwmc312), 5A (Xwmc475-X345412), and 7A
(Xbarc222-Xwpt1076). Two QTLs for MPT, one each on
chromosome 2B (Xwmc382-Xwpt8004), and
chromosome 3B (X343926-Xwpt1081), making the
total to eight genomic regions with the QTLs. All QTLs
had major effects, explaining large phenotypic
variations (PV) with R2 varying from 16% to 44%. In
particular, the strongest QTL identified in this study
was for MPT in CR09 on chromosome 7B which
explained 44% of the PV. The GY QTL on chromosome
5A had LOD of 3.5 and explained 27% of the PV under
PR08 conditions. The QTLs for TKW and KPN located
on chromosome 6B explained 19% and 20% of the PV,
respectively under the heavy rain conditions of PR09.
All the remaining QTLs accounted for more than 20% of
the PV, with the exclusion of GY on 6B that controlled
only 16% of the total PV. The ‘ND 735’ allele (N) on the
5A and 6B chromosomes yield QTLs that increased the
GY of 252.04 kg ha-1 and 243.14 kg ha-1 respectively, as
compared to the ‘Steele-ND’ allele (S) (Table 2). The
TKW was positively influenced by the presence of the N
allele on chromosome 5A and 6B QTLs under the
stressed PR09 conditions. Among the four QTLs
detected for KH, the N allele on chromosome 5A and 1A
reduced the KH, while the S allele on chromosome 7B
and 7A increased the KH. The only QTL detected for KD
mapped to chromosome 3B and explained 23% of PV at
CR08. The S allele at this QTL has an additive effect that
increased the kernel diameter. Two QTLs were mapped
for MPT on chromosomes 2B, and 7B, where the N
allele contributed to extend the MPT by about 2
minutes in grains from CR09 and PR08.
QTL analysis for the selection of the best S x N lines:
Twelve S x N RILs were selected on the basis of having
phenotypic performances similar or superior to their
parents for GY, TKW, KD, KH, MPT, and GPC (Table 2).
Thirteen environment-specific QTLs were identified for
five of these traits (no QTL was identified for GPC). The
J. Plant Breed. Genet. 01 (2013) 19-33
23
allele at each QTL was used as an additional element for
selection among the best performing twelve S x N
progenies. In general, the best performing genotypes
had the N allele at all QTLs, with the exception of the
QTLs for KH and KD, where the S allele is most
advantageous. The best yielding line S x N101 has the N
allele at the 5A and 6B QTLs, that provides superior
yield and TKW in two of the four environments. Also,
the N allele at MPT QTLs at 7B and 2B provides very
long mixograph peak time across all environments.
However, the presence of the N allele at the four KH
QTLs cause a softening of the kernel as compared to
lines harboring the S allele for hard kernels. Also, GPC
for this line is below average in all environments. SN-
0101 has a total of eight positive alleles for the 13
environment specific QTLs considered (62%). SN-0095
is the lowest yielding line and has practically the
opposite alleles at all QTLs, except for KH where it
shares the N allele with S x N101, but the lower GY
provided high GP. S x N141 and S x N161 have nine
positive alleles for the 13 QTLs considered (69%),
which resulted in average yield across all
environments, above average TKW for most
environments, average KD but softer kernels, extended
MPT, and average GPC levels. The remaining lines
follow similar trends, with advantages determined
largely by the specific alleles at each QTL. However,
among the S x N RIL progenies we could not find any
lines with all the desirable alleles at all QTL loci.
DISCUSSION
S x N Parental selection: The parental genotypes of
the RIL population described here are the cultivar
‘Steele-ND’ (Mergoum et al. 2005a), a cultivar that was
commercially grown on over 250 thousands hectares in
North Dakota between 2006 and 2008 (Regional
Quality Report 2011), and the advanced breeding line
‘ND 735’ (Mergoum et al. 2006; Mergoum et al. 2009a).
‘Steele-ND’ is a good yielding cultivar, with very good
milling and baking qualities, moderate resistance to
Fusarium head blight (FHB) (Fusarium graminearum
Schwabe), good leaf rust (Puccinia triticina Eriks.) and
stem rust (P. graminis Pers. f. sp. tritici Eriks. & E. Henn)
resistance, but it is highly susceptible to major leaf
spotting diseases and to all virulent races of tan spot
fungi found in ND (Mergoum et al. 2007; Singh et al.
2011). ‘ND 735’ combines resistance to leaf rust, stem
rust, and major leaf spotting diseases, with adequate
yield and quality performances (Mergoum et al. 2006).
The S x N population segregates for resistance
responses to three major leaf spotting diseases that
affect the Northern Great Plains: tan spot caused by
Pyrenophora tritici-repentis Drechs (Singh et al. 2010),
Stagonospora nodorum blotch caused by Phaeosphaeria
nodorum (Singh et al. 2011), and Septoria tritici blotch
caused by Mycosphaerella graminicola (Harilal et al.
2012). Three major QTLs have been previously
identified as providing resistance to all three pathogens
when harboring the N allele, two located in close
proximity on chromosome 5B and a third on
chromosome 2B (Singh et al. 2010, 2011; Harilal et al.
2012). Additionally, ‘ND 735’ is moderately resistant to
FHB due to the presence of the ‘Sumai 3’ genotype in its
pedigree. It is worth pointing out that the cross of
‘Steele-ND’ x ‘ND 735’ was specifically designed for
breeding of superior HRSW cultivars adapted to the ND
conditions. The progenies of S x N were aimed to
combine good agronomic performances equal or
superior to the best parent, while stacking disease
resistance and quality traits.
Transgressive segregation in a narrow base
population suitable for breeding: Four commercially
grown cultivars were used in our experiment as checks.
These four checks are currently being planted on over a
million hectares in ND and the surrounding regions
(Regional Quality Report 2011). The two parental
genotypes behaved similarly to the checks in all four
environments, with the exclusion of ‘Faller’ which out
yielded all other lines. The cultivar ‘Dapps’ produced
higher GPC, ‘while the cultivars Steele-ND’ and ‘Glenn’
both had the hardest kernels and the longest MPT.
Transgressive segregation was observed for all traits in
this narrow base population, with some S x N lines
outperforming the best checks. It is not surprising that
a population developed from the cross between two
elite genotypes outperforms commercially grown lines,
since this is in perfect accordance with the basic
principle of wheat breeding (Mergoum et al. 2009b).
The heritability for traits was consistent with what was
previously reported for PH, SL (Wu et al. 2012); SD
(Marza et al. 2006); GY, GVW (Kilic and Yagbasanlar
2010); LSK (Toklu and Yagbasanlar 2007); TKW
(Aydin et al. 2010) and KD (Tsilo et al. 2010). Also, the
quality components GPC (Kilic and Yagbasanlar 2010);
KH (Zhang et al. 2009) and MPT (Simons et al. 2012)
J. Plant Breed. Genet. 01 (2013) 19-33
24
Table 2. Top 12 ‘Steele-ND’ x ‘ND 735’ (S x N) RILs as compared to their parental lines and checks for traits identified
by QTL analysis across four environments in North Dakota, USA.
Grain yield (Kg ha-1) 1
QTL 2
Thousand kernels weight (gr) 1
QTL 2
Plant ID
CR08
CR09
PR08
PR09
StD
5A
6B
CR08
CR09
PR08
PR09
StD
5A
6B
Steele-ND
4281
4106
4609
4145
340
S
S
29.7
35.5
28.6
32.1
3.2
S
S
ND735
4139
4007
4638
4195
287
N
N
30.4
32.0
29.3
32.2
2.1
N
N
Faller
4440
5298
5400
4233
800
33.6
37.6
28.3
38.5
4.7
Reeder
4106
4428
3833
4351
633
29.6
31.2
27.0
33.8
2.8
Dapps
3751
4786
4415
3752
555
30.9
35.0
29.8
33.8
2.6
Glenn
3471
4485
4475
3444
753
29.7
33.0
30.0
31.7
1.9
SxN101
5237
5743
5202
4008
899
N
N
32.2
37.5
32.4
33.5
2.6
N
N
SxN141
5249
5062
5441
3039
1245
N
N
33.7
35.4
32.7
32.1
1.9
N
N
SxN033
3956
4695
5011
4620
519
N
N
29.2
32.7
28.4
29.9
2.6
S
N
SxN161
4395
5463
5719
4087
803
N
N
31.7
37.7
30.9
36.0
3.1
N
N
SxN095
3959
4360
3767
2932
707
S
S
35.1
35.5
31.0
32.8
2.4
S
N
SxN001
4046
4015
3999
3465
529
N
S
32.9
31.1
26.8
30.7
2.9
N
S
SxN159
4403
5227
5549
3189
1080
N
N
32.3
36.8
32.8
34.7
2.0
N
N
SxN009
3693
5163
4582
3444
755
-
S
34.4
38.6
31.1
36.2
3.2
-
N
SxN060
3896
5296
3879
3755
745
S
S
29.3
33.5
25.2
30.5
3.4
S
S
SxN097
4702
4143
4225
3439
676
N
S
30.8
36.3
27.1
32.6
3.8
N
S
SxN104
4866
5318
3942
3353
919
S
N
33.3
34.2
30.6
33.8
1.7
N
N
SxN108
4921
5515
4911
4088
627
N
N
28.5
33.3
26.2
28.8
2.9
N
-
Table 2 (continued)
Kernel diameter (mm) 1
QTL 2
Kernel hardness (index) 1
QTL 2
Plant ID
CR08
CR09
PR08
PR09
StD
3B
CR08
CR09
PR08
PR09
StD
5A
7B
1A
7A
Steele-ND
2.7
3.0
2.8
2.9
0.1
S
81.0
76.3
86.8
83.5
4.8
S
S
S
S
ND735
2.7
2.8
2.7
2.8
0.1
N
73.5
69.9
78.9
76.6
3.8
N
N
N
N
Faller
2.8
3.0
2.8
3.1
0.1
73.1
74.0
82.2
74.1
4.3
Reeder
2.6
2.8
2.6
2.8
0.1
74.4
75.2
79.6
73.2
3.8
Dapps
2.7
2.9
2.7
2.9
0.1
77.5
70.2
79.6
75.7
4.6
Glenn
2.7
2.9
2.7
2.9
0.1
81.6
77.9
77.6
82.7
4.2
SxN101
2.8
3.0
2.7
2.9
0.1
N
73.5
68.8
83.4
72.9
7.9
N
N
N
N
SxN141
2.8
2.9
2.7
2.8
0.1
S
69.3
67.8
77.8
72.4
5.0
N
S
N
N
SxN033
2.7
2.9
2.8
2.8
0.1
N
80.9
75.8
80.7
80.7
4.5
N
S
S
N
SxN161
2.7
3.0
2.8
3.0
0.1
S
73.6
69.0
77.5
70.2
4.0
N
S
N
N
SxN095
2.8
3.0
2.7
2.9
0.1
S
69.3
70.7
81.0
74.4
5.6
S
N
N
N
SxN001
2.7
2.8
2.7
2.8
0.1
N
84.1
83.7
88.3
85.7
3.3
N
S
N
N
SxN159
2.7
3.0
2.8
2.9
0.1
S
71.3
69.7
76.6
71.8
3.5
N
N
N
N
SxN009
2.8
3.0
2.8
3.0
0.1
N
75.7
70.2
82.6
74.8
5.0
N
N
N
N
SxN060
2.6
2.9
2.7
2.8
0.1
S
88.2
78.8
92.3
83.1
6.2
S
N
S
S
SxN097
2.6
3.0
2.7
2.8
0.2
S
74.9
70.2
81.6
78.2
4.7
N
N
N
N
SxN104
2.8
2.9
2.8
2.9
0.1
N
71.7
66.6
71.7
72.7
3.2
N
N
N
N
SxN108
2.8
2.9
2.7
2.8
0.1
N
86.5
78.1
82.4
78.3
7.8
S
N
S
.
J. Plant Breed. Genet. 01 (2013) 19-33
25
Table 2 (continued)
Mixograph peak time (min) 1
QTL 2
Grain protein content (%) 1
Disease response 3
Plant ID
CR08
CR09
PR08
PR09
StD
7B
2B
CR08
CR09
PR08
PR09
StD
5B.1
5B.2
2B
Phen
Steele-ND
5.8
4.1
5.5
5.0
1.1
S
S
15.9
15.6
15.3
15.1
0.4
S
S
S
R
ND735
6.8
7.7
8.1
10.1
2.2
N
N
15.7
14.4
15.0
14.6
0.7
N
N
N
r
Faller
6.5
4.3
5.2
5.7
0.9
14.8
14.7
14.7
14.3
0.2
Reeder
5.1
4.3
5.0
5.4
0.5
15.8
14.2
14.7
14.7
0.6
Dapps
5.6
3.4
5.0
5.7
1.1
16.7
17.1
16.2
15.2
0.8
Glenn
6.8
5.1
7.4
9.3
1.7
15.9
15.9
15.8
14.7
0.6
SxN101
9.2
7.6
8.4
11.1
1.4
N
N
14.9
15.0
14.1
14.2
0.4
S
S
N
r/R
SxN141
7.4
6.2
8.0
9.5
2.0
S
N
15.2
15.8
14.9
15.0
0.4
S
S
S
r
SxN033
7.1
5.3
7.7
6.7
1.4
S
N
15.3
14.7
15.3
14.8
0.4
N
N
N
R
SxN161
6.1
3.8
5.3
6.2
1.2
S
S
15.3
15.8
14.6
14.8
0.5
S
S
S
r
SxN095
5.9
4.9
9.3
7.7
2.1
S
S
16.3
17.1
15.6
15.3
0.8
S
S
S
r
SxN001
6.8
5.8
5.7
8.5
1.5
S
N
15.3
14.9
14.9
14.9
0.4
N
N
N
R
SxN159
7.4
4.7
5.4
7.5
1.4
S
N
14.9
15.0
14.4
14.4
0.3
S
S
N
r/R
SxN009
7.5
5.0
7.0
6.5
1.5
N
N
15.6
15.7
14.9
15.0
0.4
N
N
N
R
SxN060
5.1
3.7
5.8
5.1
0.9
N
S
15.9
16.8
15.9
15.0
0.7
N
N
N
R
SxN097
10.7
7.0
10.7
11.5
1.9
N
N
15.1
15.6
14.8
14.0
0.7
S
S
N
r/R
SxN104
4.7
3.0
4.5
5.1
0.9
N
S
16.4
16.6
15.7
15.8
0.4
N
N
S
R/r
SxN108
7.3
4.7
7.0
6.4
1.2
N
S
15.3
15.3
14.5
14.4
0.5
N
N
S
R/r
1 In each environment (CR, Carrington; PR, Prosper; 08, 2008; 09, 2009) data are presented as average between two replications.
2 QTL, the chromosome of appurtenance is indicated and for each genotype the corresponding allele is provided (S for ‘Steele ND’-like allele, N for ‘ND
735’-like allele).
3 As measured in Singh et al. 2010, 2011 and Harilal et al. 2012 for Pyrenophora tritici-repentis, Phaeospheria nodorum, and Mycosphaerella graminicola;
Phen, phenotype; R, resistance; r, susceptibile.
J. Plant Breed. Genet. 01 (2013) 19-33
26
Table 3. Chromosomal location of 13 QTLs identified for 6 traits in a ‘Steele-ND’ x ‘ND 735’ RIL populations across
four environments in North Dakota, USA.
Trait.
Chr.
Peak (cM)
Flanking markers
LOD
R2 (%)
Add.
H2 (%)
Env.
GY
5A
58.5
Xwpt4131 - X344239
3.53
27
-252.0
63.5
PR08
6B
0.0
Xwpt9881 - Xwpt9270
2.79
22
-243.1
63.5
PR08
TKW
5A
58.4
Xwpt4131 - X344239
2.53
16
-3.1
82.8
PR09
6B
2.1
Xwpt9881 - Xwpt9270
3.35
19
-3.4
82.8
PR09
KNS
5A
58.1
Xwpt4131 - X344239
2.81
27
-3.6
33.7
PR09
6B
2.7
Xwpt9881 - Xwpt9270
4.04
20
-3.2
33.7
PR09
KD
3B
17.5
X343926 - Xwpt1081
2.50
23
0.1
80.0
CR08
KH
5A
131.8
Xwmc475 - X345412
2.67
23
3.5
89.0
CR08
7B
5.9
Xwmc723 - Xwpt5463
2.57
23
-3.3
89.0
CR09
1A
136.5
Xwpt3698 - Xwmc312
2.79
25
3.3
89.0
PR09
7A
44.5
Xbarc222 - Xwpt1076
2.56
26
-3.2
89.0
PR09
MPT
7B
15.6
Xwmc723 - Xwpt8106
3.40
44
1.3
85.2
CR09
2B
23.3
Xwmc382 - Xwpt8004
2.80
26
0.9
85.2
PR08
GY, grain yield; TKW, thousand kernels weight; KNS, kernel number per spike; KD, kernel diameter; KH, kernel
hardness; MPT, mixograph peak time; CR, Carrington; PR, Prosper; 08, 2008; 09, 2009; Chr., chromosome; Add.,
additive effect; H2, broad sense heritability; Env., significant environments.
had heritability values similar but higher than what
was previously reported Thus, suggesting a good
response to selection for these traits. This is consistent
with the breeding purpose of this population that
aimed to generate novel lines with improved disease
resistances (Singh et al. 2011), maintaining the
agronomic performance of the parents, while
improving the quality traits. On the other hand, DTH, FE
(Smith et al. 2011) and KNS (Marza et al. 2006) had
heritability values lower than what were previously
reported, indicating that this population would not
respond well to selection for these traits. The
transgressive segregation and high heritability traits
were used to identify the twelve best performing RILs
on the basis of disease resistance, GY, GP, TKW, MPY,
KH, LSK, KD, SL and PH (Table 2).
Identification of QTL for economic traits using elite
by elite population: The progenies of the S x N cross
proved to be an excellent material for breeding
purposes. However, the narrow genetic diversity
between the two elite parents weakened the power of
the QTL analysis. In fact, the two parents segregate for a
limited number of traits, making QTL discovery
challenging. While QTL discovery in broad based
populations often provides good statistical significance
with QTLs showing high LOD values and their
effectiveness across environments (Collard and Mackill
2008). In elite by elite populations, the parents
segregate minimally for most of the valuable traits (i.e.
yield and quality traits), still progenies that outperform
the parents are usually identified and selected for
breeding purposes. This type of segregation is
transgressive in nature, since its origin cannot be easily
attributed to one of the two elite parents. Hence, the
search for QTLs governing this phenotypic
transgressive segregation is then a search for QTLs
harboring those alleles with minor effects that
additively contribute to the genetic gain sought by
breeders through selection. In this study, thirteen QTLs
were identified for six traits, four of which (GY, TKW,
KNS, and KD) did not show any significant PV between
the two parents (Table 2). Although all QTLs were
environment-specific and explained more than 16% of
PV. The four traits for which no phenotypic segregation
was observed among the parents were associated to
just three chromosomal regions. In particular, grain
yield (GY) and its components (TKW and KNS) were
associated to only two QTLs on chromosomes 5A and
6B and in only one location (PR) in both years. This
location is the highest yielding of the two tested
environments. All the S x N lines that carry the N allele
at these two QTLs yielded more than both the parents.
J. Plant Breed. Genet. 01 (2013) 19-33
27
Figure 1. Frequency distribution of grain yield (top) and mixograph peak time (bottom) in the ‘Steele-ND’ x ‘ND 735’
population, across four environments in North Dakota, USA (dark grey) and for the environment for which a major
QTL was found (light grey; PR08, Prosper 2008; CR09, Carrington 2008). The average values of the checks and the
parents (bold) are indicated on the figure.
The chromosome locations of both QTLs (distal of
Xgwm433 on 5A, and close to the telomere of 6B) have
been identified previously for association with yield-
related traits (Huang et al. 2006; Marza et al. 2006; Wu
et al. 2012), employing both narrow and broad base
mapping populations. TKW and KNS appear as the
primary traits controlled by these loci. Thus, making
these regions as ideal targets for molecular breeding
and fine mapping.
The present study also identified a QTL on
chromosome 3B for KD that mapped in close proximity
of a major QTL for test weight in durum (Qtgw.ics-3BS;
Elouafi and Nachit, 2004). However, to the best of our
knowledge, this is the first time that a QTL for KD per se
has been identified on chromosome 3B, and thus, may
represent a novel QTL. Following the wheat standard
nomenclature (McIntosh et al. 2011) we propose to
designate this QTL as QKD.ndsu-3B. This QTL was
identified only in CR08, but the presence of the S allele
increased kernel diameter by 23%, making it a
potentially valuable allele for molecular breeding,
especially when aimed at developing cultivars for the
targeted environment. The KH is an important quality
trait that positively affects flour yield (Bassett et al.
1989). This study found four QTLs for KH on
chromosomes 7A, 5A, 7B, and 1A.
J. Plant Breed. Genet. 01 (2013) 19-33
28
Fig. 2.1
Fig. 2.1
Figure 2. Map location of QTL identified for six traits evaluated in four environments in North Dakota, USA, using a
RIL population derived from ‘Steele-ND’ × ‘ND 735’ cross. The black bars indicate regions of significant LOD.
Abbreviations for traits are KD, kernel diameter; KH, kernel hardness; MPT, mixograph peak time; TKW, thousand-
kernel weight; KPN, kernel per spike; GY, grain yield. Abbreviations for environment are CR, Carrington; PR, Prosper.
J. Plant Breed. Genet. 01 (2013) 19-33
29
The elite parents -used to generate the S x N
populations are minimally polymorphic along their D
chromosomes (Mergoum et al. 2009a), and likely do not
segregate for the Ha locus on chromosome 5DS, known
as the primary regulator of kernel hardness in wheat
(Campell et al. 2001; Nelson et al. 2006; Souza et al.
2012). Sun et al (2010) also reported minor QTLs
affecting the KH apart from the major Ha locus. Among
those, a region of chromosome 7A was common with
what is reported here. The QTL for KH identified in the
S x N populations as distal of Xwmc475 on chromosome
5AL was also recognized in a HRSW elite by elite RIL
populations adapted to the environmental conditions
of Northern Great Plains (QSkhard.mna-5A.2; Tsilo et
al. 2011b). Chromosome 7B does not harbor any known
KH QTLs per se. However, Tsilo et al. (2011a) identified
a flour quality related QTL (QFash.mna-7B) in the same
region proximal of Xwpt8106, in an elite by elite HRSW
RIL population adapted to the same environmental
conditions used in this study. The remaining QTL for
KH on chromosome 1A has not so far been reported.
This novel QTL has temporarily designated as
QKh.ndsu-1A and it could be specific to the genetic
background of the two HRSW parents.. The parental
lines segregate largely for KH, with alleles for harder
kernels on chromosomes 7B and 7A from ‘Steele-ND’
and alleles for soft kernels on 1A and 5A contributed by
‘ND 735’. All of these QTLs are environment-specific
and were expressed in three of the four test sites.
Two QTLs for MPT were mapped to the B genome (2B
and 7B). The QTL on 2B covers the same chromosomal
region identified as harboring an unlabeled QTL for
bread making quality in a French elite by elite RIL
populations (Gross et al. 2007). In the absence of a pre-
established name, we temporarily designate this QTL as
QMpt.ndsu-2B. The QTL for MPT on 7B overlapped with
the QTL for KH (QFash.mna-7B). This is likely due to
the inverse relationship that exists between KH and
MPT, with harder kernels typically having shorter MT
(Table 2) (Ohm and Chung 1999). Complex interactions
exist between the loci controlling quality traits,
probably not just at the genetic level per se but also
because of the specific process of flour and dough
handling (Ohm and Chung 1999). Similarly to the often
observed negative correlation between yield and GPC
(Table 2) (Mergoum et al. 2009b), KH and MPT also
inversely interact to present an additional challenge to
breeders. Harder kernels have a structural advantage
against harvest and threshing damages, but
simultaneously produce flours of lower quality, poorer
gluten components, and shorter dough MT (Ohm et al.
2009). The 7B QTL was the most important for both
traits, with up to 44% increase in MPT and 22% KH
reduction when the N allele was present at the QTL
interval. Specific combinations of soft/long peak and
hard/short peak alleles (S or N) in the KH and MPT QTL
could be tested. Ideal combinations could be identified
by crossing the various S x N RILs and ultimately
combined into a superior genotype with the desirable
balance between hardness and baking quality.
The overall low number of QTLs identified in this study
for the 14 traits analyzed is likely due to the narrow
genetic variability of the parents used to create this
population. However, the alleles at the QTL that have
been identified are transgressive in nature and were
detected under the unique environmental conditions of
North Dakota, one of the most productive wheat
regions in the world. Thus, the markers tagged to these
QTLs provide an excellent selection tool for commercial
molecular breeding within the S x N populations.
Selection for the “reserve” alleles within
population: The QTL that were identified in the course
of this work lack consistency across environments and
we would not recommend the markers underlying
them for marker assisted selection (MAS) in
populations lacking ‘Steele-ND’ or ‘ND 735’ in their
pedigrees (Collard and Mackill 2008). However, these
QTLs are significant for the S x N progenies and can be
directly employed for molecular selection within the
population. To a lesser extent, this can be considered as
an extrapolation of the concept of ‘mapping as you go’
(Podlich et al. 2004), with the mapping limited only to
the selection generation.
Among the S x N lines, 12 RILs were considered as
superior on the basis of their performances, including S
x N101, S x N141, S x N033, S x N161, S x N159 and S x
N108. All these lines carry the favorable N allele at two
of the yield QTLs, but only three had the advantageous
S allele for KD on chromosome 3B. The introduction of
the S allele in the other three lines can help to further
boost their yields. All lines had extended MPT, but only
four had the advantageous N allele in 2B QTL. Only one
line (S x N108) combined good KH and extended MPT.
Similarly, only one line maintained GPC similar to
‘Steele-ND’.
J. Plant Breed. Genet. 01 (2013) 19-33
30
The alleles described above are not active under all
environmental conditions, with QTLs identifiable only
in determined locations by years combinations. From a
breeding prospective, the alleles for agronomic or
quality environment-specific QTLs are not less
important than resistance loci against non-endemic
pathogens, which become valuable only when the
diseases occur. These genes become active and
detectable only in specific environmental conditions,
but at that time they provide additional yield, or better
quality characteristics or resistance in the case of the
disease. Given their ability to boost and help the genes
controlling the trait only under determined ambient
conditions, we defined these as “reserve” alleles.
Because of their eclectic nature, field selection is not
likely to provide the finesse necessary to follow these
“reserve” alleles. On the other hand, it is demonstrated
that an environment-specific QTL analysis provide the
statistical means to identify the advantageous “reserve”
alleles in an elite by elite population. The allele values
at the 13 environment-specific QTLs were included as
criteria in the selection to identify five lines harboring
the maximum number of advantageous “reserve” alleles
(S x N001, S x N033, S x N101, S x N141, S x N161).
Additionally, in Table 2 we reported the alleles carried
by the S x N lines at the three major QTLs controlling
resistance to leaf spotting diseases as established in
Singh et al. (2010 and 2011) and Harilal et al. (2012).
The presence of the resistance N allele at these QTLs
was also added as criteria in the selection. The S x N001
and S x N033 are the only lines harboring the resistant
N allele at all three loci. All five selected lines have been
advanced and used in our HRSW breeding program.
The particular attention is being given on lines S x N033
and S x N001, as they carry alleles for disease
resistance, good yield, and good quality. Also, these five
S x N lines represent a good starting point to design
targeted new crosses for pyramiding the most useful
loci into superior genotypes. Wheat growers and end-
users are certain to appreciate cultivars combining
disease resistance alleles together with these “reserve”
alleles under the specific environmental conditions.
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... Previous studies highlighted that quality parameters, grain yield and its components, and morphological characteristics are all complex traits that are controlled by multiple genes, environments, and genetic-by-environment interactions [6,[22][23][24]26,36,45,54,56,57]. The phenotype ranges of the RILs displayed transgressive segregation for all traits, indicating that the gene alleles of the traits from both parents had a positive contribution to the phenotypic variation of new genetic combinations in recombinant lines, as reported in previous studies [13,18,23,24,26,45,54,58]. Notably, most end-use quality traits were improved by the favorable alleles from TX05A001822 in the present study. ...
... This is consistent with previous studies [6,23,24,57,61]. Additionally, the heritability of MLPT, one of the most significant mixograph parameters, was 0.82; this is similar to the previous studies [17,23,24,58,61]. Among the agronomical and yield component traits, BM, BMYLD, and SPM displayed a low heritability, while other traits were moderately heritable. ...
... In bread wheat, many previous genetic studies demonstrated the presence of a YLD QTL on chromosome 5A [13,36,45,58,[76][77][78]. In this study, Qyld.tamu.5A.532 was physically close to the YLD QTL at 503 Mb and 553 Mb on 5A, as reported by Yang et al. [26], and at 555 Mb on 5A, as reported by Dhakal et al. [36], which had RIL populations derived from TAM 111 that contributed the favorable alleles that increase the YLD. ...
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... The two most extensively utilized methods for determining the genetic basis of complex quantitative traits in agricultural crops are genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping. Extensive research efforts have been made in the last decade to discover QTLs linked with grain micronutrients, protein, and TKW through bi-parental based mapping populations in wheat [24][25][26][27][28][29][30][31][32][33]. The classical method of QTL mapping relies on structured populations like F2, RILs, back-crosses (BCs), and doubled haploids (DH). ...
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A set of 188 recombinant inbred lines (RILs) derived from a cross between a high-yielding Indian bread wheat cultivar HD2932 and a synthetic hexaploid wheat (SHW) Synthetic 46 derived from tetraploid Triticum turgidum (AA, BB 2n = 28) and diploid Triticum tauschii (DD, 2n = 14) was used to identify novel genomic regions associated in the expression of grain iron concentration (GFeC), grain zinc concentration (GZnC), grain protein content (GPC) and thousand kernel weight (TKW). The RIL population was genotyped using SNPs from 35K Axiom® Wheat Breeder’s Array and 34 SSRs and phenotyped in two environments. A total of nine QTLs including five for GPC (QGpc.iari_1B, QGpc.iari_4A, QGpc.iari_4B, QGpc.iari_5D, and QGpc.iari_6B), two for GFeC (QGfec.iari_5B and QGfec.iari_6B), and one each for GZnC (QGznc.iari_7A) and TKW (QTkw.iari_4B) were identified. A total of two stable and co-localized QTLs (QGpc.iari_4B and QTkw.iari_4B) were identified on the 4B chromosome between the flanking region of Xgwm149–AX-94559916. In silico analysis revealed that the key putative candidate genes such as P-loop containing nucleoside triphosphatehydrolase, Nodulin-like protein, NAC domain, Purine permease, Zinc-binding ribosomal protein, Cytochrome P450, Protein phosphatase 2A, Zinc finger CCCH-type, and Kinesin motor domain were located within the identified QTL regions and these putative genes are involved in the regulation of iron homeostasis, zinc transportation, Fe, Zn, and protein remobilization to the developing grain, regulation of grain size and shape, and increased nitrogen use efficiency. The identified novel QTLs, particularly stable and co-localized QTLs are useful for subsequent use in marker-assisted selection (MAS).
... The quantitative inheritance of wheat quality traits and significant effects of environment and genotype-environment interaction (GEI) on the expression of GFeC GZnC and TKW were documented in several studies 10-13 . In the past decade, extensive efforts have been made to identify QTLs associated with GFeC and GZnC [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and TKW 25,26,[29][30][31][32][33] in wheat through bi-parental populations based QTL mapping. However, QTLs identified in such approaches had a low resolution due to the restricted number of crossovers. ...
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... Hundreds of genetic mapping and GWAS studies have been conducted in wheat using population-specific and consensus genetic maps (Somers et al., 2004;Edae et al., 2014;Wang et al., 2014;Grogan et al., 2016;Wen et al., 2017;Babben et al., 2018;Khalid et al., 2019;Mellers et al., 2020;Sallam et al., 2020), which identified numerous genes, QTL, and MTAs in nearly all the 21 hexaploid wheat chromosomes (Mergoum et al., 2013;Guan et al., 2018;Su et al., Crop Science 2018;Mérida-García et al., 2019). In the absence of reliable physical maps for most reported genomic regions, however, direct comparisons of results from independent studies are challenging and unreliable. ...
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... QTL have been identified for grain iron (19,(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38), grain zinc (13,19,(27)(28)(29)(30)(31)(32)(33)(35)(36)(37)(38)(39)(40)(41), grain protein content (19,27,31,35,36,(42)(43)(44)(45)(46)(47)(48)(49)(50), and thousand kernel weight (19,27,29,45,(51)(52)(53)(54). However, most investigations on mapping nutritional quality have exploited low-density maps, which have resulted in large interval QTL that have rarely been exploited in breeding. ...
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Micronutrient and protein malnutrition is recognized among the major global health issues. Genetic biofortification is a cost-effective and sustainable strategy to tackle malnutrition. Genomic regions governing grain iron concentration (GFeC), grain zinc concentration (GZnC), grain protein content (GPC), and thousand kernel weight (TKW) were investigated in a set of 163 recombinant inbred lines (RILs) derived from a cross between cultivated wheat variety WH542 and a synthetic derivative (Triticum dicoccon PI94624/Aegilops tauschii [409]//BCN). The RIL population was genotyped using 100 simple-sequence repeat (SSR) and 736 single nucleotide polymorphism (SNP) markers and phenotyped in six environments. The constructed genetic map had a total genetic length of 7,057 cM. A total of 21 novel quantitative trait loci (QTL) were identified in 13 chromosomes representing all three genomes of wheat. The trait-wise highest number of QTL was identified for GPC (10 QTL), followed by GZnC (six QTL), GFeC (three QTL), and TKW (two QTL). Four novel stable QTL (QGFe.iari-7D.1, QGFe.iari-7D.2, QGPC.iari-7D.2, and QTkw.iari-7D) were identified in two or more environments. Two novel pleiotropic genomic regions falling between Xgwm350–AX-94958668 and Xwmc550–Xgwm350 in chromosome 7D harboring co-localized QTL governing two or more traits were also identified. The identified novel QTL, particularly stable and co-localized QTL, will be validated to estimate their effects on different genetic backgrounds for subsequent use in marker-assisted selection (MAS). Best QTL combinations were identified by the estimation of additive effects of the stable QTL for GFeC, GZnC, and GPC. A total of 11 RILs (eight for GZnC and three for GPC) having favorable QTL combinations identified in this study can be used as potential donors to develop bread wheat varieties with enhanced micronutrients and protein.
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The grain hardness index (HI) is one of the important reference bases for wheat quality and commodity properties; therefore, it is essential and useful to identify loci associated with the HI in wheat breeding. The grain hardness index of the natural population including 150 common wheat genotypes was measured in this study. The phenotypic data diversity of HI based on four environments and the best linear unbiased prediction (BLUP) was analyzed. The results showed that the grain HI of the natural population ranged from 15.00 to 83.00, the variation range was from 5.10% to 24.44%, and the correlation coefficient was 0.872–0.980. BLUP value was used to grade and assign the grain HI to hard wheat, mixed wheat, and soft wheat, and the assigned phenotypes were used for genome-wide association analysis. Two types of grain hardness index phenotypic values were used for genome-wide association analysis (GWAS) using a 55K SNP array. A total of five significant association loci (p < 0.001) were excavated, among which four loci could be detected in three or more environments. They were distributed on chromosomes 1A and 7D, and the phenotypic contribution rate was 7.52% to 10.66%. A total of 48 sites related to grain hardness were detected by the assignment method, among which five were stable genetic sites, distributed on chromosomes 1A(2), 3B(1), 4B(1), and 7D(1), with phenotypic contribution rates ranging from 7.63% to 11.12%. Of the five loci detected by the assignment method, two stable loci were co-located in the phenotypic mapping results of the hardness index. One of the loci was consistent with previous reports and located on chromosome 1A, and one locus was unreported on chromosome 7D. Therefore, it may be a feasible attempt to use the assignment method to conduct genome-wide association analysis of the grain hardness index. In this study, a total of five genetic loci for grain hardness stability were excavated, and two of the loci were located in the two phenotypic values, two of which were not reported.
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Chapter
Marker-assisted selection (MAS) is a selection method for improving agriculturally essential traits in wheat. MAS allows efficient screening of difficult-to-phenotype traits, introgression of genomic complements from donor germplasm into elite breeding lines, and gene/trait pyramiding for quantitative traits. The discovery of a large number of QTL-specific molecular markers for qualitative and quantitative traits has accelerated MAS in recent years. Markers for loci related to disease resistance and agronomic and quality traits are available in wheat. The convenience of detecting and following the inheritance pattern of major genes/QTL at a low cost has improved genotyping and germplasm selection in the breeding programs. The development of high-throughput sequencing, genetic loci probing technologies, precision phenotyping systems, crop molecular physiology, and computational tools further extends the possible uses of MAS in wheat. The pan-genomic resources and the annotated genome sequence increase the effectiveness of MAS in hexaploid wheat by allowing for primer design, in silico validation of primers for specific binding, QTL walking, and genome-anchored fine mapping. This chapter sheds light on the use of MAS in shortening the breeding cycle, improving biotic and abiotic stress resistance, and sustaining the yield potential of wheat.
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Yieldbarriersmustbebroken.� Thediminished� stockofstaplefoods,� highergrainprices,� and� increasesinproductionfailingtokeepupwith� demand,� coupledwith� 80� millionpeoplebeing� addedtotheworldpopulationeveryyear,�sug- geststhatweareonacollisioncoursewith� famineunlessgreaterinvestmentsaremadein� researchanddevelopment,�aswellaseducation.� Geneticimprovementofstapleshasaccounted� formorethanhalfofthepastincreasesinyields.� Fortunately,� arevolutioningeneticknowledge� isco-evolvingwiththeincreaseddemandfor� food,�feed,�fiber,� andfuel.�Utilizinggeneticdiver- sityhasbeenamainstayofpastproduction� improvementsHighthroughputDNAsequenc- ing,� therelatedbioinformatics,� andacascade� ofgenetictechnologiescannowbeemployed� todetectpreviouslyhiddengeneticvariability,� tounderstandgenefunctions,�tomakegreater� useofaccessionsingermplasmbanks,�andto� makebreedingschemesmoreefficacious.� The� involvementofoutstandingscientistswhocan� bringinterdisciplinaryideastothequestionof� howtobreakyieldbarriersmustbepartofthe� strategy.� Educationalprogramsatalllevels,� evenhighschool,�shouldemphasizetheoppor- tunitiesininternationalagriculturetobuilda� cadreofdedicatedscientistsforthefuture.
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