Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain
Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain
Rosa Me? rida-Garc??a 0 1 2
Guozheng Liu 1 2
Sang He 1 2
Victoria Gonzalez-Dugo 0 1 2
Gabriel Dorado 1 2
Sergio Ga? lvez 1 2
Ignacio Sol??s 1 2
Pablo J. Zarco-Tejada 0 1 2
Jochen C. Reif 1 2
Pilar HernandezID 0 1 2
0 Instituto de Agricultura Sostenible (IAS) Consejo Superior de Investigaciones Cient ??ficas (CSIC) , Alameda del Obispo s/n, C o ?rdoba, Spain , 2 Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben , Corrensstra?e 3, Stadt Seeland, Germany , 3 Departamento de Bioqu ??mica y Biolog ??a Molecular , Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3) , Universidad de C o ?rdoba , C o ?rdoba, Spain , 4 Universidad de M a ?laga, Andaluc ??a Tech, ETSI Informa ?tica , Campus de Teatinos s/n, M a ?laga, Spain, 5 ETSIA ( University of Seville) , Ctra de Utrera km1, Seville , Spain
1 Editor: Roberto Papa, Universita? Politecnica delle Marche , ITALY
2 a Current address: BASF Agricultural Solutions Seed GmbH, OT Gatersleben , Am Schwabeplan 8, Gatersleben, Germany ?b Current address: Agriculture Victoria, AgriBio , Centre for AgriBioscience , Bundoora VIC , Australia
Climatic conditions affect the growth, development and final crop production. As wheat is of paramount importance as a staple crop in the human diet, there is a growing need to study its abiotic stress adaptation through the performance of key breeding traits. New and complementary approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), are used for the dissection of different agronomic traits. The present study focused on the dissection of agronomic and quality traits of interest (initial agronomic score, yield, gluten index, sedimentation index, specific weight, whole grain protein and yellow colour) assessed in a panel of 179 durum wheat lines (Triticum durum Desf.), grown under rainfed conditions in different Mediterranean environments in Southern Spain (Andalusia). The findings show a total of 37 marker-trait associations (MTAs) which affect phenotype expression for three quality traits (specific weight, gluten and sedimentation indexes). MTAs could be mapped on the A and B durum wheat subgenomes (on chromosomes 1A, 1B, 2A, 2B and 3A) through the recently available bread wheat reference assembly (IWGSC RefSeqv1). Two of the MTAs found for quality traits (gluten index and SDS) corresponded to the known Glu-B1 and Glu-A1 loci, for which candidate genes corresponding to high molecular weight glutenin subunits could be located. The GS prediction ability values obtained from the breeding materials analyzed showed promising results for traits as grain protein content, sedimentation and gluten indexes, which can be used in plant breeding programs.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Funding: This work was funded by project
P12AGR-0482 to PH from Junta de Andaluc??a
(Andalusian Regional Government), Spain
(Cofunded by FEDER). PH is supported by project
AGL2016-77149-C2-1-P from MINECO (Spanish
Ministry of Economy, Industry and
Competitiveness). The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Durum wheat (Triticum durum) is one of the most important crops in the Mediterranean diet.
It is mainly grown in the Mediterranean basin (Italy, Turkey, Algeria and Spain, providing
50% of the world?s production [
]) and North America (Canada, Mexico and USA). The
genetic dissection of agronomic and quality traits is essential for durum breeding programs.
The identification of QTLs related to quality and yield is important as an entry point for
marker assisted selection (MAS) . Association mapping (AM) is an integrated analysis to
determine genotype-phenotype correlations in a germplasm collection [
] based on the linkage
disequilibrium (LD). AM mapping resolution depends on the number and density of markers
], on the ability to correctly measure the target trait and the traits of the population under
study, and on an efficient field design [
]. It has been used to dissect several agronomic traits
of great importance in bread and durum wheat, such as yield or yield-related traits [
], biotic stress resistance [
] and abiotic stress tolerance [
While MAS uses markers which are significantly linked to qualitative traits, and is
integrated with traditional phenotypic selection and long selection cycles [
], genomic selection
(GS) appears as an alternative approach which considers complex quantitative traits using
genome-wide markers [
]. GS estimates simultaneously all the loci effects across the complete
genome to compute genomic values (GEBVs) of lines for selection by using the sum of the
marker effects which they contain [
], and its potential in plant breeding has already been
]. It has been suggested as a plant breeding methodology that accelerates the
breeding cycle and provides a rapid selection of better genotypes for a low cost [
15, 21, 22
The application of GS in plant breeding programmes is possible due to the availability of
high-throughput molecular markers, which cover the entire genome and facilitate trait value
21, 23, 24
]. Experimental studies based on multi-environment CIMMYT
(International Maize and Wheat Improvement Center) wheat and maize trials showed that genomic
selection models present a considerable prediction ability for genetic values of complex traits
such as grain yield or adaptability to different stresses under markedly different conditions [
Durum wheat is well-adapted to semi-arid and arid environments as the Mediterranean
], despite this is an heterogeneous region with a broad range of soil fertility levels,
temperatures and rainfall. In Mediterranean agricultural environments, high quality durum wheat is
], mainly under rainfed conditions. The main abiotic factors limiting the crop?s
growth and final yield are drought and heat stresses [
]. Mediterranean environments are
characterized by high water deficit and high temperatures during anthesis and grain filling
]. Low rainfall and its erratic distribution, mainly winter-dominated rainfalls,
account for approximately 75% of variations in final yield . These environmental
constraints significantly influence the expression of many important agronomic traits such as
grain yield [
], sedimentation volume and grain protein content , which are main
targets of durum wheat breeding programmes.
Several AM and GS analyses of yield and quality traits in durum wheat, were performed in
limiting environments [
8, 13, 33?35
]. Maccaferri et al. [
], analyzed durum elite lines in
different Mediterranean countries, Mexico and USA, using SSR markers and a broad range of
soil moisture. Recently, Sukumaran et al. [
] assessed CIMMYT durum wheats grown
under three different conditions (yield potential, drought and heat stresses) using DArTseq
The present study was carried out in different areas in Southern Spain (Andalusia), which
produces the 70% of the Spanish durum wheat production (http://www.aetc.es/). This
cropping area presents different macro-environments, which differ in temperature and quantity of
2 / 24
precipitations. These unpredictable conditions result in important abiotic stresses, mainly
drought and/or heat stresses, which strongly affect the final phenological stages, such as
anthesis and grain filling [
]. These erratic variations in rainfall and extreme temperatures in
Southern Spain strongly influence important traits as final yield, protein content and quality
]. To dissect the genetic basis of quality and yield in these particular environments,
a set of CIMMYT elite lines and local varieties presenting a lack of genetic structure was tested,
highlighting the importance of testing the previously selected genotypes in additional local
environments. Genome-wide markers were used to analyse and compare the potential and
limits of the MAS and GS approaches to improve agronomic and quality traits in durum
wheat grown under rainfed Mediterranean agro-climatic conditions.
Material and methods
Plant material and field trials
A panel of 160 experimental CIMMYT elite durum wheat breeding lines and 19 durum wheat
varieties (S1 Table) were grown in a Mediterranean area under rainfed conditions, throughout
three cropping seasons (from 2013 to 2015). All 179 genotypes were tested in field trials in two
locations in the provinces of Seville and Huelva (37? 32? 18" N, 5? 6? 17" O and 37? 27? 28" N,
6? 21? 52" O). The 19 released varieties were grown additionally at three more locations: two in
the province of Cadiz (36? 16? 8" N, 6? 4? 30" O and 36? 42? 12" N, 6? 10? 8" O) and one in the
province of Cordoba (37? 47? 21" N, 4? 36? 28" O). These five locations were diverse in terms of
rainfall, temperatures, altitude, soil type and texture (S2 and S3 Tables) and represent the two
agro-climatic cereal-growing environments present in Southern Spain. Based on the method
proposed by Papadakis [
], the sites in the province of Cadiz are classified as maritime
Mediterranean environments, with high environmental humidity values; while the sites in the
provinces of Seville, Huelva and Cordoba are climatically classified as subtropical Mediterranean
environments, characterised by mild, wet winters with irregular precipitations and hot, dry
summers. The experimental lines assessed were elite genotypes, pre-selected by CIMMYT
based on their yield stability across environments and high quality. The aim of the breeding
strategy was the adaptation to Southern Spain agroclimatic conditions.
The experimental design consisted of one randomized complete block with three
replications of the varieties at the five locations indicated above; and a randomized complete block
design with one plot per experimental line at two of those sites (Seville and Huelva). The trials
were planted in 7.2m2 plots, using a sowing density of 360 seeds/m2 for Seville, Huelva and
one of the sites of Cadiz, while in Cordoba and the second site in Cadiz, the seed density was
adjusted according to the worst estimated nascence of seeds (396 seeds/m2) due to the high
clay soil content. Fields were managed following the standard agricultural practices in each
location (S3 Table) and all trials were performed under non-irrigated conditions.
Seven agronomic traits were evaluated at different stages of development: initial agronomic
score (IAS), specific weight (g, SW), gluten index (%, GI), sedimentation index (cm3, SDS),
whole grain protein (%, WGP), yellow colour (YC) and grain yield (kg/ha, YIELD). IAS was
the only trait which was visually assessed at the field trials, and consists of evaluating the
seedling vigour and amount of soil covered as a value, that for elite material falls within a typical
5?10 range (<5 = very poor; 5 = poor, 6 = fair, 7 = acceptable, 8 = good, 9 = very good and
10 = excellent). For quality assessment, SW and WGP were measured using Near-infrared
spectroscopy (NIRs), following Williams and Norris [
]; SDS was evaluated by UNE
]; GI by ISO 21415:2016 [
]; and YC by using CEN/TS 15465:2008 [
3 / 24
There was no specific permission required for measuring data on the wheat farm trials. The
on-farm field studies did not involve endangered or protected species.
Phenotypic data analyses
Firstly, the correlations among the three replicates of the varieties in the two locations used for
the experimental lines were analysed using the ?cor.test? function in R.
The adjusted entry means for each year for the association mapping study was estimated
based on the following model:
pikn ? m ? gi ? lk ? ?gl?ik ? ?ikn;
where pikn was the trait performance of the ith genotype in the nth replicate of the kth location, ?
was the intercept, gi was the genetic effect of the ith genotype, lk was the effect of the kth
location, (gl)ik was the genotype-by-location interaction effect of the ith genotype in the kth
location, and ?ikn was the corresponding residual. Only ? and gi were treated as fixed effects.
The adjusted means of each genotype over the years was estimated with the following
pij ? m ? gi ? yj ? ?ij;
where pij was the trait performance of the ith genotype in the jth year, ? was the intercept, gi was
the genetic effect of the ith genotype, yj was the effect of the jth year, and ?ij was the
corresponding residual. Only ? and gi were treated as fixed effects. The adjusted means over the years
were used to calculate the phenotypic correlation (Pearson correlation coefficient) across the
To provide an overview of the different sources of the phenotypic variation for both
experimental lines and released varieties and to estimate heritability, we fitted the following model:
pimn ? m ? ?gt?i ? ?gc?i ? em ? ?gte?im ? ?imn;
where pimn was the trait performance of the ith genotype in the nth replication of mth
environment (year-by-location combination), ? was the intercept, (gt)i was the genetic effect of the ith
tester, (gc)i was the genetic effect of the ith candidate, em was the effect of the mth environment,
and ?imn was the corresponding residual. Only ? was treated as a fixed effect. The variance
components for experimental lines and durum wheat varieties were extracted separately by
using the ?dummies? package in R. The significance of variance component estimates was
tested by model comparison with likelihood ratio tests where the halved P values were used
as an approximation [
]. Broad-sense heritability was estimated for released varieties as
. Broad-sense heritability was estimated for experimental lines as
h2 ? sg2cs2 . Here sg2t and sg2c are the genotypic variance for testers and candidates, sg2te was
variance of genotype-by-environment interaction of testers and s?2 was the variance of the
residuals. Nr.Env and Nr.Rep represent the number of environments and number of replicates,
To extract the overall variance components for the tester population, we fitted the following
pimn ? m ? gi ? em ? ?imn;
where pimn was the trait performance of the ith genotype in the nth replication of mth
environment (year-by-location combination), ? was the intercept, gi was the genetic effect of the ith
4 / 24
genotype, em was the effect of the mth environment and ?imn was the corresponding residual.
Only ? was treated as a fixed effect. Broad-sense heritability was estimated for released varieties
as h2 ? sg2?Nr:Esng2vs?2Nr:Rep . The genetic variation extracted under this model was used in genomic
Genotyping and population structure analyses
Plant tissue samples were obtained at the 4-leaf stage and the tissue was immediately frozen
using dry ice. The DNA was isolated using approximately 100mg of frozen leaf and the DNeasy
Plant Mini Kit from Qiagen, following the manufacturer?s instructions. The concentration and
quality of the DNA samples were assessed by electrophoresis in a 0.8% agarose gel using
lambda DNA as the standard. The absence of nucleases in the DNA samples was checked by
performing an incubation at 37?C using a restriction enzyme (Tru1I) from ThermoFisher
before the DartSeq analysis. The results were visualized by electrophoresis in a 0.8% agarose
gel. DartSeqTM genotyping and mapping of the corresponding markers of the wheat genome
sequence from the International Wheat Genome Sequencing Consortium (IWGSC) was
performed at Diversity Arrays (diversityarrays.com), as described by Sukumaran et al. [
All the markers with a minor allele frequency (MAF) below 5% were filtered out and a
missing ratio over 5%. After quality control, 16,383 DArT and 5,649 single-nucleotide
polymorphism (SNP) markers remained. The remaining missing values were imputed following He
et al. [
]. The kindship matrices for the DArT and SNP markers were calculated based on
Roger?s distances (S4 and S5 Tables). The correlation between the two kindship matrices was
calculated using the ?mantel? function of the ?vegan? package in R.
The population structure was assessed applying principal coordinates analyses (PCoA)
based on modified Rogers? distances [
] using the ?prcomp? function in R. The first and
second principal coordinates were used to draw the two-dimensional space graph. In addition, a
heatmap plot was drawn for the modified Roger?s distances in combination with cluster
analysis by R function ?uclust? using the ?complete linkage? method. All further calculations were
made using R.
Genome-wide association analysis and linkage disequilibrium
The following mixed linear model was used for association mapping:
Y ? Wa ? Xb ? Ss ? Zu ? e;
where Y stands for the adjusted entry means of the genotypes per year, a is a vector of group
effects, ? is a vector of year effects, s is a vector of SNP effects, u is a vector of polygene
background effects and e is a vector of residual effects. W, X, S, and Z are incidence matrices
relating Y to a, ?, s, and u, respectively. To check whether the population structure was adequately
controlled by the model, a QQ-plot was drawn, based on the observed P-values and expected
P-values of all markers. The significance of marker-trait associations was tested with the Wald
F statistic. The false discovery rate (FDR) controlling procedure [
] was used to correct for
multiple testing. After the correction, a value of 0.1 was set as threshold. The proportion of the
phenotypic variance explained by a single QTL (R2) was estimated using analysis of variance
(ANOVA) with QTLs reordered according to the P-values, and the effects of detected QTLs
were estimated using a standard multiple regression approach. The genome-wide associations
study (GWAS) was performed using the software ASREML-R. Associated DartSeq and SNP
markers were blasted against the wheat reference assembly RefSeqv1 (IWGSC 2018) with no
indels or mismatches allowed, using an ad hoc Java program, to confirm their physical
5 / 24
mapping location on the A or B genomes. For candidate gene identification, the results were
filtered selecting those hits with best e-value for each marker and the candidate genes were
manually selected based on gene annotations. Differential gene expression analyses were
carried out using RefSeqv1 gene models and two R libraries (Kallisto, version 0.43.0 and STAR
DESeq2, version 1.14.1).
For linkage disequilibrium (LD), the algorithm R2 was used. This value was estimated
between any pair of markers within one chromosome. To determine the genome-wide linkage
disequilibrium, mapped SNP markers were used in the panel of 179 wheat lines. The
calculations were made using Python to establish the average LD decay.
Based on the adjusted entry means over the years, a ridge regression best linear unbiased
prediction (RR-BLUP) was applied. Details of the implementation of the models have been
described earlier [
]. Briefly, the general form of the models is defined as follows:
Y ? 1nm ? ZAa ? ?;
where Y is the adjusted entry means over the years, 1n is the vector of ones, n is the number of
genotypes, a was the additive marker effect, Z is the design matrix for additive effects of the
markers and ? is the residual.
The prediction ability, which was defined as the Pearson?s correlation coefficient between
predicted values and adjusted entry means, was checked by five-fold cross-validation. 1000
cross-validation runs were performed and for each run, four fifths of the genotypes were
randomly sampled as a training population to estimate marker effects, which were then used to
predict the performance of the remaining genotypes. Genomic prediction was applied
separately to SNP and DArT markers.
Phenotypic data analysis
To verify the appropriateness of the assessed breeding trial design (which uses partly
unreplicated trials for the experimental lines) for the subsequent statistical analyses, yield correlations
were analysed among the three replicates of the varieties at the two sites, and found mean
estimates of 0.70 (ranging from 0.42 to 0.97).
Variance components of the total samples are shown in Table 1. Descriptive statistics of
each trait in each location with key quantiles are shown in S6 Table. For the experimental
lines, the agronomic trait showing the highest heritability (h2) was specific weight (SW) with
h2 = 0.71, followed by initial agronomic score (IAS) and whole grain protein (WGP) with h2 =
0.63 and h2 = 0.61, respectively. As expected, the h2 value for YIELD was low (h2 = 0.13). For
released varieties, the traits with the highest heritability values were GI, IAS and SDS, with h2 =
0.88, 0.85 and 0.80, respectively. The heritability of WGP was also higher in the released
varieties (h2 = 0.74) than in the experimental lines (h2 = 0.61). In contrast with the experimental
lines, for released varieties the SW presented low heritability (h2 = 0.30), while the YIELD
showed a high value (h2 = 0.85), probably as consequence of the reduced number of analysed
The phenotypic correlation values presented a wide range. The highest value observed was
r = 0.53 between GI and SDS, followed by SDS?WGP (r = 0.37), SW?YC (r = 0.36) and SW?
YIELD, and also WGP?YC (both r = 0.30). SDS and YIELD showed an intermediate value of
6 / 24
YIELD: yield (Kg/ha); WGP: whole grain protein; SW: specific weight; gluten index, GI; initial agronomic score, IAS; sedimentation index, SDS; and yellow color, YC);
g: genotype variance; g-p: significance test for genotype variance; ge: genotype-by-environment interaction variance; ge-p: significance test for genotype-by-environment
interaction variance. NA: ?ge? couldn?t be calculated due to data without any replications.
a Durum wheat varieties.
b Experimental durum wheat lines.
r = 0.24. The lowest values were found for GI-IAS, GI-SW, IAS-SDS, GI-YC, WGP-YIELD,
IAS-WGP and YC-YIELD (ranging from 0 to 0.07) (Fig 1, S7 Table).
DArT and SNP genotyping, principal coordinates and linkage
A total of 5,711 SNPs and 14,979 DArT markers were mapped across the two constitutive
genomes of durum wheat. In the case of SNP markers, 44% of the markers were located on the
A genome and 56% on the B genome. The highest marker density was found in chromosomes
1B, 2B, 5B and 7A with a total of 558, 550, 512 and 496 markers, respectively. Chromosomes
4B and 5A showed the lowest number of located loci (217 and 231, respectively). For DArT
markers, 41% of the markers were placed on the A genome and 59% on the B genome. The
highest marker density was found in chromosomes 3B, 1B, 2B and 6B with a total of 1,593,
1,439, 1,427 and 1,416 loci, respectively. Chromosomes 4B and 5A contained the lowest
number of loci (500 and 447, respectively) (Table 2).
PCoA was applied to investigate the population structure in the line set (Fig 2A). The first
and second principal coordinates accounted for 13.93% and 6.47% of the molecular variance,
respectively. No significant genetic structure was detected. The heatmap plot for modified
Roger?s distance was used to validate the result (Fig 2B). The PCos and eigenvalues obtained
are shown in S8 and S9 Tables, respectively. As part of chromosome linkage disequilibrium
(LD) assessment, pair-wise focusing on the mapped SNP markers was carried out. The R2
value between marker pairs fell below 0.2 at around 1 to 5cM (Fig 3).
Quantile-quantile plots were used and expected and observed log10 P-values were compared
for the SNP and DArT marker datasets separately (Fig 4, S10 and S11 Tables). The correlation
7 / 24
Fig 1. Phenotypic correlations found among assessed traits. GI: gluten index; IAS: initial agronomic score; SDS:
sedimentation index (SDS); SW: specific weight; WGP: whole grain protein; YC: yellow colour; and YIELD: grain
yield. Above, the range for p-values was indicated using a scale from ?a? to ?e? (a: represents p-values larger than 0.1; b:
represents values between 0.1 and 0.01; c: represents values between 0.01 and 0.001; d: indicates values between 0.001
and 0.0001; e: for values between 0.0001 and 0.00001); below, correlations are shown using a colour scale (highest
correlations in red, lowest correlations in blue).
between the SNP and DArT kindship matrices (S4 and S5 Tables) was 0.938. As we had noted
the absence of a pronounced population structure (Fig 2), we only fixed a group effect for the
kinship model analysis (advanced lines vs. tester varieties), which improved the null model for
most traits (Fig 4).
After analysis of the seven agronomic traits assessed, 37 MTAs were found for three quality
traits (gluten index, GI; specific weight, SW; and sedimentation index, SDS) (Table 3). Twenty
of the markers were found in association with GI, corresponding to 17 DArTs (7 unmapped)
and 3 SNPs, located on chromosomes 1B, 2B, and 3A and accounting for 0.02 to 23.32% of the
phenotypic variation. Ten markers were associated with SDS: 7 DArTs (4 unmapped) and 3
SNPs, all placed on chromosome 1B, which accounted for 0.06 to 16.14% of the phenotypic
variation. Finally, one DArT and six SNPs (three of them unmapped and the rest located on
chromosomes 1A, 2A and 3A) were associated to SW, accounting for 0.58 to 5.79% of the
phenotypic variation (Table 3). The marker effects were within a 0.11?18.49 range (Table 3). Nine
markers (8 associated to GI and 1 to SDS) showed the highest marker effects (7.3?18.49
range). Among the GI MTAs, marker DArT1707, placed on chromosome 1B, presented the
8 / 24
highest additive effect value (18.49), followed by DArT22904 and DArT26318, both unmapped,
with effects of 11.52 and 10.50, respectively. We can also highlight marker effects for DArT
1762 and DArT6596, placed on chromosomes 1B and 3A, with values of 9.85 and 9.52,
respectively. Linked to SDS, the markers DArT26104 (unmapped) and DArT24559, placed on
chromosome 1A, showed effects of 7.37 and 5.46, respectively. Finally, for SW, the marker effects
had a narrower range from 0.1 (DArT2892) to 1.62 (SNP2318). Two major associations were
detected, one for GI (marker DArT26104; R2 = 23.32%) and one for SDS (marker DArT26318;
R2 = 16.14%), based on Flint-Garcia et al [
], who described ?major QTLs? as those
characterized by 10% R2 detected in AM analysis.
BLAST analyses of DArT and SNP sequences on the Enssemble genome browser for the wheat
genome (https://plants.ensembl.org/Triticum_aestivum/Info/Index) showed that two DArT
markers were related to some important proteins with nutrient?s reservoir activity (Fig 5,
Table 4). The marker DArT1744 (located in chromosome 1BL) was associated with GI, and
corresponds to the Glu-B1 locus [
]. It is very closed to two high molecular weight (HMW)
subunit genes: TraesCS1B01G570600LC.1 (3278kb from the marker) encoding a Glu1B y-type
HWM glutenin subunit; and TraesCS1B01G330000.1 (8414kb), encoding a Globulin 1 protein.
The marker DArT24559 (located in chromosome 1AL) was associated to SDS, and
corresponds to the Glu-A1 locus. It is located closed to three HMW subunit genes: TraesCS1A01G3
17500.1 (-3016kb from the marker) encoding a Globulin 1 protein; TraesCS1A01G466400LC.1
(-17452kb) encoding a Glu1Ay; and TraesCS1A01G466500LC.1 (-7321kb) encoding a Glu1Ay
protein. Differential expression analyses highlighted two of these high confidence genes,
TraesCS1B01G330000.1 in chromosome 1BL, and TraesCS1A01G317500.1 in chromosome
1AL (Fig 5), which are differentially expressed under different drought stress conditions (Sl
Genome-wide prediction analysis
Genome-wide prediction ability was calculated and was represented for the seven traits
assessed in the 179 genotypes panel, using 16,383 DArT and 5,649 SNP markers (Fig 6). There
9 / 24
Fig 2. Population structure analysis. a) Principal Coordinates Analysis (PCoA) of the durum wheat panel assessed.
The graph shows first versus second coordinates; b) Heatmap showing pairwise modified Roger?s distance among 179
lines genotyped by 5,649 SNP markers. Average linkage clustering was used to order the lines.
were slight differences between both marker types in their prediction ability for the same trait,
ranging from 0.01 to 0.05 (Table 5). The highest prediction accuracy was found for WGP
(r = 0.482 using DArTs and r = 0.474 with SNPs), followed by SDS (r = 0.371 using SNPs),
while the lowest values were obtained for IAS (r = 0.108 with DArTs and r = 0.093 using
SNPs). Four of the traits showed higher prediction values using DArT markers (GI, IAS, WGP
and YC) and three traits using SNP markers (YIELD, SDS and SW).
Field experiments for the assessment of yield and quality traits under rainfed conditions were
carried out at five sites in Southern Spain. These Mediterranean environments present
10 / 24
Fig 3. Linkage disequilibrium (LD) analysis of the line set. R2: correlation between a pair of loci; cM: centimorgan.
unpredictable water deficit and heat stress during the final stages of wheat development,
affecting the mentioned traits. A strong effect of maximum temperatures on yield was observed at
final stage (S2A Fig), while thermal sum (GDD) presented a moderate to minor effect (S2B
Yield is greatly influenced by both environmental conditions and genotype [
resulting in low plot-based heritabilities under water stress conditions [
]. Previous studies
11 / 24
Fig 4. Quantile-quantile plots for the GWAS model and Manhattan plots for the assessed traits. (GI: gluten index; SDS:
sedimentation index; SW: specific weight; IAS: initial agronomic score; WGP: whole grain protein; YC; yellow colour; and YIELD: grain
yield). Expected and observed P values are shown on QQ-plots. Dotted blue lines represent the null model; red lines show the kinship
model. Manhattan plots illustrate the marker index for each trait and the significance of the association test (as the negative logarithm of
the P value).
performed in durum wheat, showed variations in yield heritability caused by differences in
environmental conditions [
]. In line with this, our results showed low plot-based
heritability for yield (h2 = 0.13) over the different locations and years of assessment. This is in
agreement with Gonzalez-Ribot et al. [
], who obtained a low plot-based heritability for yield
(h2 = 0.24), in unrelated high-yield durum lines grown under water stress in Mediterranean
As previous studies highlighted [
], yield is negatively correlated to protein content
(WGP) (r = -0.29) (S6 Table); and an increment in protein content results in reductions in
final yield . It has been highlighted that there is no genetic basis for this negative
correlation, since strong environmental and physiological interactions are in charge .
12 / 24
SW: specific weight; GI: gluten index; SDS: sedimentation index; R2: percentage of phenotypic variation explained by the marker; cM: centimorgan.
Nevertheless, Groos et al.  showed that this negative correlation could be due to a close
genetic relation or contrary effects produced by environmental conditions in both traits.
Blanco et al.  emphasised that yield and protein content are managed by a complicated
genetic system which is influenced by environmental conditions and agricultural practices. As
13 / 24
Fig 5. Candidate genes and related markers located on chromosomes 1A and 1B.
result of the environmental influence, differences in final YIELD and WGP were observed
between locations and years (S12 Table). Variance component analyses showed that the effect
of genotype-by-environment interactions was far higher for WGP than in the case of YIELD
(Table 1). These results agree with previous studies which reported that protein content is
strongly influenced by environmental conditions [68, 69]. Protein content usually presents
high heritability values [70, 71]. In this study, a moderate to high value was obtained for WGP
heritability (h2 = 0.62) in comparison with previous studies [67, 72] reporting heritabilities in
the 0.54?0.78 range for durum wheat recombinant inbred lines (RILs) grown at several
Gluten strength (GStr) is a highly significant trait in durum wheat , in direct relation to
GI and SDS, which are considered a measure of GStr [
]. Both traits have been described
as highly inheritable  and show a strong correlation [
32, 73, 74
]. In agreement with these
findings, our results showed high heritability values for GI (h2 = 0.88) and SDS (h2 = 0.80),
and also a positive correlation between them (r = 0.53).
The genome-wide association analysis is becoming a popular approach to dissect the
genetic base of complex traits in durum wheat. Previous AM and QTL mapping studies found
QTLs involved in quality traits in most of chromosomes [
34, 72, 75?79
]. In this work, the AM
approach taken over the years and different locations, resulted in 37 significant markers
associated with three important quality traits (gluten index, sedimentation index and specific
weight) in known and novel genomic regions (Table 3). Most of the markers associated with
GI were located on chromosome 1B (0.02?2.06% of phenotypic variation), where major
genomic regions for gluten strength and several genes related to endosperm proteins as gliadin and
glutenin subunits are located [
]. The remaining MTAs for GI were located in
chromosomes 2B (5.49%) and 3A (0.02?1.86%). In line with these results, previous studies carried out
in durum wheat, under similar limiting conditions, found DArT markers in association with
GStr in several chromosomes, including 1B (0.07?0.16% phenotypic variation) and 3A (0.04?
Markers found in association with SDS were all located on chromosome 1B (0.06?16.14%
of phenotypic variation), consistent with previous studies across environments and conditions,
which used different marker types and populations [
78, 79, 85
] (RILS, F2:7, F9 or double
haploids, respectively). Bread wheat MTA studies also found major QTLs associated with SDS in
this chromosome [
14 / 24
15 / 24
High molecular weight
High molecular weight
with MIZ/SP-RING zinc
finger. PHD-finger and
phosphatase 2A 55 kDa
regulatory subunit B
High molecular weight
High molecular weight
High molecular weight
2-binding protein 1-A
kinase family protein
transposon TNT 1?94
ARM repeat superfamily
Gbinding protein A
transposon TNT 1?94
Receptor protein kinase
Fig 6. Genomic selection and heritability. a) Genomic selection accuracies for 179 lines using SNP and DArT markers for the assessed traits. b) Relationship between
prediction ability and heritability. GI: gluten index; IAS: initial agronomic score; SDS: sedimentation index; SW: specific weight; WGP: whole grain protein; YC: yellow
colour; and YIELD: grain yield.
Finally, novel MTAs for SW were found on chromosomes 1A, 2A and 3A (0.58 to 5.79% of
phenotypic variation). Studies in durum and bread wheat, carried out in a wide range of
environments and conditions, placed markers in association with this trait in several other
35, 75, 84
]. A recent study in durum wheat landraces, performed in Northern Spain
under rainfed conditions  found significant DArT markers associated with SW in several
chromosomes, including 3A (0.07?0.09% of variation), but in a different position.
The relationship between durum wheat gluten strength and HMW- glutenins is well
known and controlled by major loci [
]. While we did not observed MTAs for the Gli-B1
locus, consistent with the previous selection carried out for the favourable ?-gliadin 42 allele in
this elite material, we could detect MTAs for the Glu-B1 [
78, 87, 88
] and Glu-A1 [
(markers DArT1744 and DArT24559). By blasting both markers, we have precisely mapped
the Glu-B1 and Glu-A1 loci on the wheat reference genome (IWGSC 2018) and proposed the
corresponding candidate genes among the gene models annotated as HMW subunits
GI: gluten index; IAS: initial agronomic score; SDS: sedimentation index; SW: specific weight; WGP: whole grain
protein; YC: yellow colour; and YIELD: grain yield.
(Table 4). In agreement with our results, several major and meta QTLs for quality under
drought stress reported the Glu-A1 locus [
]. The marker DArT1744 (chromosome 1BL)
associated with GI, was found close to the gene models TraesCS1B01G570600LC.1 and
TraesCS1B01G330000.1, encoding for HMW glutenin subunits (a Glu1B y-type and a Globulin
1 proteins respectively). The locus Glu-B1 was previously located within a meta-QTL
(MQTL6) which contains several QTLs for yield components and gluten strength [
78, 88, 89
The marker DArT24559 (chromosome 1AL), in association with SDS, was located within
78, 88, 89
] in the proximity to the gene models (TraesCS1A01G466400LC.1,
TraesCS1A01G466500LC.1 and TraesCS1A01G3175 00.1), also encoding HMW subunits (Glu1A
ytype and a Globulin 1). These novel markers and candidate genes located on the RefSeqv1
wheat genome reference [
78, 88, 89
] for the known Glu-B1 and Glu-A1 loci are new resources
for durum wheat breeding and support the potential of the GWAs approach.
The use of models focused on genomic prediction in wheat breeding programs reduces
the breeding cycle, giving an increase in genetic gains. Nevertheless, genomic prediction
studies taking into account the genotype-by-environment (GxE) interactions are still
reduced on durum wheat [
]. In this work, we applied the genomic selection (GS)
approach to elite and durum wheat varieties, phenotyped under rainfed conditions (Fig 6A,
Table 5). The highest GS prediction accuracy was found for WGP (r = 0.482 using DArTs
and r = 0.474 using SNPs) which could be considered to fall within a similar range as
previous reported by Fiedler et al.  (r = 0.56) using more lines (1184 breeding durum wheats
(F4:7)) and several conditions; or Bentley et al. [
] (r = 0.66; r = 0.58), who analysed 376
winter wheat varieties, grown in field experiments across different environments for a long
period, using DArT markers.
Prediction accuracy values for YIELD (r = 0.263 with DArTs and 0.314 with SNPs) are
similar to those reported by Sukumaran et al. [
] (from 0.20 to 0.40) applying several prediction
models and basic cross-validation strategies for the assessment of durum wheats grown under
different stresses, as drought and heat conditions. Yield prediction accuracies were lower than
for WGP (r = 0.482 with DArTs and 0.474 with SNPs). These results contrast with those
obtained by Bentley et al. [
] for winter wheats, who showed more similar GS prediction values
for both of these traits, with yield results slightly better than those of protein content.
Differences found between these studies could reside in the fact that both traits are heavily influenced
by environment conditions and genotype-by-environment interactions [
54, 55, 91
Our GS analysis showed promising results which support its use in current plant breeding
programs. The prediction accuracies obtained were fairly similar for the two marker systems
used: DArTs and SNPs (Table 5), despite the fact that the number of DArTs almost tripled that
of the SNPs (16,383 vs 5,649 respectively). These results, leveraged with the corresponding
marker prices, could be useful when selecting future marker systems.
Association mapping and genomic selection approaches were applied using the same
genotyped and phenotyped collection of experimental lines and varieties of durum wheat. The
main aim of AM was to detect specific loci on the wheat genome which were directly related
with phenotypic character variations, while GS uses statistical models to predict genomic
values for the assessed lines.
The AM approach revealed interesting marker-trait associations over the years and in the
different environments for three quality traits (gluten index (GI), sedimentation index (SDS)
and specific weight (SW)), which is of great importance for the final durum wheat product,
and presented a wide range of effects in the phenotype expression. Most associated DartSeq
17 / 24
and SNP markers were mapped to the A and B bread wheat sub-genomes using the available
closely-related bread wheat reference IWGSC RefSeqv1. The application of GS was successful
for most of the traits in the breeding materials analysed and showed promising results,
especially for quality traits such as grain protein content or those in which MTAs were found (SDS,
SW and GI). GS showed promising results which support its use in current plant breeding
programmes. These results can be used in current plant breeding programmes for key quality
traits in durum wheat under Mediterranean rainfed conditions with a limited water supply.
S1 Fig. Analysis of candidate genes found in Glu-A1 and Glu-B1 loci in chromosomes 1A
and 1B. Differentially expression was indicated for each gene: SFS?severe stress field
conditions; MFS?mild field stress conditions; P1h - osmotic stress as polyethylene glycol (PEG)
1hour; P6h - osmotic stress PEG 6hours.
S2 Fig. Relationship between yield and temperature. a) Relation between yield and
maximum temperature mean (?C) by location and year for durum wheat varieties for final stages;
b) Relation between yield and thermal sum from 1st April to 30th June. (YIELD: mean values
by place and year for released lines (Kg/ha); Tmax: maximum temperature (?C); GDD:
Growing Degree Days, thermal sum using 4?C as base temperature).
S1 Table. List of durum wheat lines assessed.
S2 Table. Meteorological information collected from agroclimatic stations (Junta de
FrontController). Mean, maximum and minimum values for temperature (maximum temperature
(Tmax), average temperature (Tmed) and minimum temperature (Tmin)) (?C), rainfall (Pp)
(mm) and evapotranspiration (Eto) (mm/day) for the five locations assessed. Daily values are
also include in separated sheets for each location.
S3 Table. Site location and agronomical details.
S4 Table. Kindship matrix for DArT markers.
S5 Table. Kindship matrix for SNP markers.
S6 Table. Descriptive statistics of each trait in each environment with key quantiles.
S7 Table. Matrix of correlations between assessed traits across years and environments
(yield (Kg/ha), YIELD; yellow colour, YC; whole grain protein (%), WGP; specific weight
(g), SW; sedimentation index (cm3), SDS; initial agronomic score, IAS; and gluten index
S8 Table. Projection of each genotype assessed on all the PCos.
18 / 24
S9 Table. Eigenvalues from the PCo analysis.
S10 Table. BLUP analysis results using DArT markers for assessed traits (gluten index, GI;
initial agronomic score, IAS; sedimentation index, SDS; specific weight, SW; whole grain
protein, WGP; yellow colour, YC; yield, YIELD).
S11 Table. BLUP analysis results using SNP markers for assessed traits (gluten index, GI;
initial agronomic score, IAS; sedimentation index, SDS; specific weight, SW; whole grain
protein, WGP; yellow colour, YC; yield, YIELD).
S12 Table. Accession mean values and standard deviation for assessed traits by year and
location. (IAS, initial agronomic score; WGP, whole grain protein (%); SW, specific weight
(g); YC, yellow colour; GI, gluten index (%); SDS, sedimentation index (cm3); and YIELD,
yield (Kg/ha)). Means were calculated for three replications of the durum varieties at the five
locations, and one plot per genotype for experimental lines at two sites.
The authors gratefully acknowledge Dr. Karim Ammar from CIMMYT (International Maize
and Wheat Improvement Center) for selecting plant materials, providing seed and advice for
this work; and Dr. JA Go?mez (IAS-CSIC) for his help on soil classification.
Conceptualization: Ignacio Sol??s, Jochen C. Reif, Pilar Hernandez.
Formal analysis: Guozheng Liu, Sang He, Sergio Ga?lvez, Jochen C. Reif.
Funding acquisition: Victoria Gonzalez-Dugo, Gabriel Dorado, Pablo J. Zarco-Tejada.
Investigation: Rosa Me?rida-Garc??a, Guozheng Liu, Sang He, Victoria Gonzalez-Dugo, Gabriel
Dorado, Sergio Ga?lvez, Ignacio Sol??s, Pablo J. Zarco-Tejada, Pilar Hernandez.
Project administration: Pilar Hernandez.
Visualization: Rosa Me?rida-Garc??a, Guozheng Liu, Sang He.
Writing ? original draft: Rosa Me?rida-Garc??a, Jochen C. Reif, Pilar Hernandez.
Writing ? review & editing: Rosa Me?rida-Garc??a, Guozheng Liu, Sang He, Victoria
Dugo, Gabriel Dorado, Sergio Ga?lvez, Ignacio Sol??s, Pablo J. Zarco-Tejada.
19 / 24
20 / 24
21 / 24
22 / 24
Groos C, Robert N, Bervas E, Charmet G. Genetic analysis of grain protein-content, grain yield and
thousand-kernel weight in bread wheat. Theor Appl Genet. 2003; 106(6):1032?40. Epub 2003/04/03.
https://doi.org/10.1007/s00122-002-1111-1 PMID: 12671751.
De Vita P, Nicosia OLD, Nigro F, Platani C, Riefolo C, Di Fonzo N, et al. Breeding progress in
morphophysiological, agronomical and qualitative traits of durum wheat cultivars released in Italy during the
20th century. European Journal of Agronomy. 2006; 26(1):39?53. https://doi.org/10.1016/j.eja.2006.08.
Blanco A, Simeone R, Gadaleta A. Detection of QTLs for grain protein content in durum wheat. Theor
Appl Genet. 2006; 112(7):1195?204. Epub 2006/02/03. https://doi.org/10.1007/s00122-006-0221-6
Simmonds NW. The relation between yield and protein in cereal grain. Journal of the Science of Food
and Agriculture. 1995; 67(3):309?15. https://doi.org/10.1002/jsfa.2740670306.
Blanco A, Mangini G, Giancaspro A, Giove S, Colasuonno P, Simeone R, et al. Relationships between
grain protein content and grain yield components through quantitative trait locus analyses in a
recombinant inbred line population derived from two elite durum wheat cultivars. Molecular Breeding. 2011; 30
Rharrabti Y, Villegas D, Royo C, Martos-Nu??ez V, Garcia del Moral L. Durum wheat quality in
Mediterranean environments II. Influence of climatic variables and relationships between quality parameters.
Field Crops Research. 2002; 80:133?40.
Rharrabti Y, Villegas D, Garcia del Moral L, Aparicio N, Elhani S, Royo C. Environmental and genetic
determination of protein content and grain yield in durum wheat under Mediterranean conditions. Plant
Breeding. 2001; 120:381?8.
Pagnotta MA, Mondini L, Codianni P, Fares C. Agronomical, quality, and molecular characterization of
twenty Italian emmer wheat (Triticum dicoccon) accessions. Genetic Resources and Crop Evolution.
2008; 56(3):299?310. https://doi.org/10.1007/s10722-008-9364-4
Reynolds MP, Pask AJD, Mullan DM. Physiological breeding I: interdisciplinary approaches to improve
crop adaptation. ix, 174 pags Mexico, DF (Mexico) CIMMYT. 2012.
Blanco A, Pasqualone A, Troccoli A, Di Fonzo N, Simeone R. Detection of grain protein content QTLs
across environments in tetraploid wheats. Plant Molecular Biology. 2002; 48:615?23. PMID: 11999838
Feillet P, Dexter JE. Quality requeriments of durum wheat for semolina milling and pasta production.
Pages 9511 in Pasta and Noodle Technology Kruger J E, Matsuo R R and Dick J W, eds AACC
International: St Paul, MN. 1996.
Clarke FR, Clarke JM, Ames NA, Knox RE, Ross RJ. Gluten index compared with SDS-sedimentation
volume for early generation selection for gluten strength in durum wheat. Canadian Journal of Plant
Science. 2009; 90:1?11. https://doi.org/10.4141/CJPS09035.
Fiedler JD, Salsman E, Liu Y, Michalak de Jimenez M, Hegstad JB, Chen B, et al. Genome-Wide
Association and Prediction of Grain and Semolina Quality Traits in Durum Wheat Breeding Populations.
Plant Genome. 2017; 10(3). Epub 2018/01/03. https://doi.org/10.3835/plantgenome2017.05.0038
Huang XQ, Cloutier S, Lycar L, Radovanovic N, Humphreys DG, Noll JS, et al. Molecular detection of
QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian
wheats (Triticum aestivum L.). Theor Appl Genet. 2006; 113(4):753?66. Epub 2006/07/14. https://doi.
org/10.1007/s00122-006-0346-7 PMID: 16838135.
Sun C, Zhang F, Yan X, Zhang X, Dong Z, Cui D, et al. Genome-wide association study for 13
agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of
China. Plant Biotechnol J. 2017; 15(8):953?69. Epub 2017/01/06. https://doi.org/10.1111/pbi.12690
PMID: 28055148; PubMed Central PMCID: PMCPMC5506658.
Patil RM, Oak MD, Tamhankar SA, Rao VS. Molecular mapping of QTLs for gluten strength as
measured by sedimentation volume and mixograph in durum wheat (Triticum turgidum L. ssp durum).
Journal of Cereal Science. 2009; 49(3):378?86. https://doi.org/10.1016/j.jcs.2009.01.001
Kumar A, Elias EM, Ghavami F, Xu X, Jain S, Manthey FA, et al. A major QTL for gluten strength in
durum wheat (Triticum turgidum L. var. durum). Journal of Cereal Science. 2013; 57(1):21?9. https://
Kaan F, Branlard G, Chihab B, Borries C. Relations between genes coding for grain storage protein and
two pasta cooking quality criteria among world durum wheat (Triticum durum Desf.) genetic resources.
Journal of Genetic Breeding. 1993; 47:151?6.
Ruiz M, Carrillo JM. Linkage relationships between prolamin genes on chromosome 1a and
chromosome 1B of durum wheat. Theoretical and Applied Genetics. 1993; 87(3):353?60. https://doi.org/10.
1007/BF01184923 PMID: 24190262.
23 / 24
1. Li Y-F , Wu Y , Hernandez-Espinosa N , Pe?a RJ . Heat and drought stress on durum wheat: Responses of genotypes, yield, and quality parameters . Journal of Cereal Science . 2013 ; 57 ( 3 ): 398 - 404 . https:// doi.org/10.1016/j.jcs. 2013 . 01 .005
2. Kabbaj H , Sall AT , Al-Abdallat A , Geleta M , Amri A , Filali-Maltouf A , et al. Genetic Diversity within a Global Panel of Durum Wheat (Triticum durum) Landraces and Modern Germplasm Reveals the History of Alleles Exchange . Front Plant Sci . 2017 ; 8 : 1277 . Epub 2017/08/05. https://doi.org/10.3389/fpls. 2017 . 01277 PMID: 28769970; PubMed Central PMCID : PMCPMC5513985 .
3. Crossa J , Perez P , Hickey J , Burgueno J , Ornella L , Ceron-Rojas J , et al. Genomic prediction in CIMMYT maize and wheat breeding programs . Heredity (Edinb) . 2014 ; 112 ( 1 ): 48 - 60 . Epub 2013/04/11. https://doi.org/10.1038/hdy. 2013 .16 PMID: 23572121; PubMed Central PMCID : PMCPMC3860161 .
4. Zondervan KT , Cardon LR . The complex interplay among factors that influence allelic association . Nat Rev Genet . 2004 ; 5 ( 2 ): 89 - 100 . Epub 2004/01/22. https://doi.org/10.1038/nrg1270 PMID: 14735120 .
5. Flint-Garcia SA , Thornsberry JM , Buckler ESt . Structure of linkage disequilibrium in plants . Annu Rev Plant Biol . 2003 ; 54 : 357 - 74 . Epub 2003/09/25. https://doi.org/10.1146/annurev.arplant. 54 .031902. 134907 PMID: 14502995 .
6. Stich B , Melchinger AE . An introduction to association mapping in plants . CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources . 2010 ; 5 ( 039 ). https://doi.org/10. 1079/PAVSNNR20105039
7. Neumann K , Kobiljski B , Den?i? S , Varshney RK , Bo?rner A. Genome-wide association mapping: a case study in bread wheat (Triticum aestivum L.) . Molecular Breeding . 2010 ; 27 ( 1 ): 37 - 58 . https://doi.org/10. 1007/s11032-010-9411-7
8. Sukumaran S , Reynolds MP , Sansaloni C . Genome-Wide Association Analyses Identify QTL Hotspots for Yield and Component Traits in Durum Wheat Grown under Yield Potential, Drought, and Heat Stress Environments . Front Plant Sci. 2018b; 9:81. Epub 2018 /02/23. https://doi.org/10.3389/fpls. 2018 .00081 PMID: 29467776; PubMed Central PMCID : PMCPMC5808252 .
9. Breseghello F , Sorrells ME . Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars . Genetics. 2006 ; 172 ( 2 ): 1165 - 77 . Epub 2005/08/05. https://doi.org/10.1534/ genetics.105.044586 PMID: 16079235; PubMed Central PMCID : PMCPMC1456215 .
10. Kristensen PS , Jahoor A , Andersen JR , Cericola F , Orabi J , Janss LL , et al. Genome-Wide Association Studies and Comparison of Models and Cross-Validation Strategies for Genomic Prediction of Quality Traits in Advanced Winter Wheat Breeding Lines . Front Plant Sci . 2018 ; 9 : 69 . Epub 2018/02/20. https:// doi.org/10.3389/fpls. 2018 .00069 PMID: 29456546; PubMed Central PMCID : PMCPMC5801407 .
11. Kollers S , Rodemann B , Ling J , Korzun V , Ebmeyer E , Argillier O , et al. Genome-wide association mapping of tan spot resistance (Pyrenophora tritici-repentis) in European winter wheat . Molecular Breeding . 2014 ; 34 ( 2 ): 363 - 71 . https://doi.org/10.1007/s11032-014-0039-x
12. Steiner B , Michel S , Maccaferri M , Lemmens M , Tuberosa R , Buerstmayr H. Exploring and exploiting the genetic variation of Fusarium head blight resistance for genomic-assisted breeding in the elite durum wheat gene pool . Theor Appl Genet . 2018 . https://doi.org/10.1007/s00122-018 -3253-9 PMID: 30506523
13. Maccaferri M , Sanguineti MC , Demontis A , El-Ahmed A , Garcia del Moral L , Maalouf F , et al. Association mapping in durum wheat grown across a broad range of water regimes . J Exp Bot . 2011 ; 62 ( 2 ): 409 - 38 . Epub 2010/11/03. https://doi.org/10.1093/jxb/erq287 PMID: 21041372 .
14. Zhang J , Gizaw SA , Bossolini E , Hegarty J , Howell T , Carter AH , et al. Identification and validation of QTL for grain yield and plant water status under contrasting water treatments in fall-sown spring wheats . Theor Appl Genet . 2018 . Epub 2018/05/17. https://doi.org/10.1007/s00122-018-3111-9 PMID: 29767279 .
15. Crossa J , Perez-Rodriguez P , Cuevas J , Montesinos-Lopez O , Jarquin D , de Los Campos G, et al. Genomic Selection in Plant Breeding: Methods, Models, and Perspectives . Trends Plant Sci . 2017 ; 22 ( 11 ): 961 - 75 . Epub 2017/10/03. https://doi.org/10.1016/j.tplants. 2017 . 08 .011 PMID: 28965742 .
16. Fernando RL , Habier D , Stricker C , Dekkers JCM , Totir LR . Genomic selection . Acta Agriculturae Scandinavica , Section A-Animal Science . 2007 ; 57 ( 4 ): 192 - 5 . https://doi.org/10.1080/09064700801959395
17. Bernardo R . Genomewide selection for rapid introgression of exotic germplasm in maize . Crop Science . 2009 ; 49 : 419 - 25 .
18. Battenfield SD , Guzman C , Gaynor RC , Singh RP , Pena RJ , Dreisigacker S , et al. Genomic Selection for Processing and End-Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program . Plant Genome . 2016 ; 9 ( 2 ). Epub 2016 /11/30. https://doi.org/10.3835/plantgenome2016. 01 .0005 PMID: 27898810 .
19. Haile JK , N'Diaye A , Clarke F , Clarke J , Knox R , Rutkoski J , et al. Genomic selection for grain yield and quality traits in durum wheat . Molecular Breeding . 2018 ; 38 ( 6 ). https://doi.org/10.1007/s11032-018- 0812-3
20. Rapp M , Lein V , Lacoudre F , Lafferty J , Muller E , Vida G , et al. Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection . Theor Appl Genet . 2018 . Epub 2018/03/08. https://doi.org/10.1007/s00122-018-3080-z PMID: 29511784 .
21. Meuwissen THE , Hayes BJ , Goddard ME . Prediction of total genetic value using genome-wide dense marker maps . Genetics . 2001 ; 157 : 1819 - 29 . PMID: 11290733
22. Poland J , Endelman J , Dawson J , Rutkoski J , Wu S , Manes Y , et al. Genomic Selection in Wheat Breeding using Genotyping-by- Sequencing . The Plant Genome Journal . 2012 ; 5 ( 3 ). https://doi.org/10. 3835/plantgenome2012. 06 .0006
23. de los Campos G, Naya H , Gianola D , Crossa J , Legarra A , Manfredi E , et al. Predicting quantitative traits with regression models for dense molecular markers and pedigree . Genetics . 2009 ; 182 ( 1 ): 375 - 85 . Epub 2009/03/19. https://doi.org/10.1534/genetics.109.101501 PMID: 19293140; PubMed Central PMCID : PMCPMC2674834 .
24. Crossa J , Campos Gde L , Perez P , Gianola D , Burgueno J , Araus JL , et al. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers . Genetics . 2010 ; 186 ( 2 ): 713 - 24 . Epub 2010/09/04. https://doi.org/10.1534/genetics.110.118521 PMID: 20813882; PubMed Central PMCID : PMCPMC2954475 .
25. Burgue?o J , de los Campos G, Weigel K , Crossa J. Genomic Prediction of Breeding Values when Modeling Genotype ? Environment Interaction using Pedigree and Dense Molecular Markers . Crop Science. 2012 ; 52 ( 2 ). https://doi.org/10.2135/cropsci2011. 06 .0299
26. Belaid A . Durum wheat in WANA (West Asia and North Africa): production, trade and gains from technological change . In: Royo C , Nachit M Mm Di Fonzo N , Araus J L, eds Durum wheat improvement in the Mediterranean region: new challenges Options Me?diterrane?ennes . 2000 ; 40 ( Zaragoza , Spain: CIHEAM): 35 - 9 .
27. Borghi B , Corbellini M , Minoia C , Palumbo M , Di Fonzo N , Perenzin M. Effects of Mediterranean climate on wheat bread-making quality . European Journal of Agronomy . 1997 ; 6 : 145 - 54 .
28. Rajaram S , Saari EE , Hettel GP . Durum Wheats: Challenges and Opportunities . Wheat Special Report . 1992 ; 9 : 14 - 27 .
29. Loss SP , Siddique KHM . Morphological and Physiological Traits Associated with Wheat Yield Increases in Mediterranean Environments . Advances in Agronomy Volume 52 . Advances in Agronomy1994 . p. 229 - 76 .
30. Blum A , Pnuel Y. Physiological attributes associated with drought resistance of wheat cultivars in a mediterranean environment . Australian Journal of Agricultural Research . 1990 ; 41 ( 5 ): 799 - 810 . https:// doi.org/10.1071/AR9900799.
31. Garc? ?a del Moral LFG , Rharrabti Y , Villegas D , Royo C . Evaluation of grain yield and its components in durum wheat under Mediterranean conditions: An ontogenic approach . Agronomy Journal . 2003 ; 95 ( 2 ): 266 - 74 .
32. Carrillo JM , Rousset M , Qualset CO , Kasarda DD . Use of recombinant inbred lines of wheat for study of associations of high-molecular-weight glutenin subunit alleles to quantitative traits 1. Grain yield and quality prediction tests . Theoretical and Applied Genetics . 1990 ; 79 : 321 - 30 . https://doi.org/10.1007/ BF01186074 PMID: 24226349
33. Sukumaran S , Jarquin D , Crossa J , Reynolds M . Genomic-enabled Prediction Accuracies Increased by Modeling Genotype ? Environment Interaction in Durum Wheat . The Plant Genome. 2018a;0 ( 0 ). https://doi.org/10.3835/plantgenome2017. 12 .0112 PMID: 30025014
34. Asbati A , Elouafi I , Elsaleh A , Mather DE , Nachit M. QTL-mapping of genomic regions controlling gluten strength in durum (Triticum turgidum L. var . durum). In: Royo C. (ed.), Nachit M . (ed.), Di Fonzo N. (ed.), Araus J. L . (ed.). Durum wheat improvement in the Mediterranean region: New challenges. Zaragoza: CIHEAM (Options Me?diterrane?ennes: Se?rie A Se?minaires Me?diterrane?ens ). 2000 ; 40 : 505 - 9 .
35. Patil RM , Tamhankar SA , Oak MD , Raut AL , Honrao BK , Rao VS , et al. Mapping of QTL for agronomic traits and kernel characters in durum wheat (Triticum durum Desf .). Euphytica . 2012 ; 190 ( 1 ): 117 - 29 . https://doi.org/10.1007/s10681-012-0785-y
36. Garrido-Lestache E , Lo? pez-Bellido RJ , Lo? pez -Bellido L. Durum wheat quality under Mediterranean conditions as affected by N rate, timing and splitting, N form and S fertilization . European Journal of Agronomy . 2005 ; 23 ( 3 ): 265 - 78 . https://doi.org/10.1016/j.eja. 2004 . 12 .001
37. Papadakis J . El clima. Con especial referencia a los climas de Ame?rica Latina, Pen??nsula Ibe?rica, ex Colonias Ibe?ricas y sus potencialidades agropecuarias . Editorial Albatros , Buenos Aires, Argentina. 1980 .
38. Williams PC , Norris K. Near Infrared Technology in the Agricultural and Food Industries ( 2nd ed .). American Association of Cereal Chemistry, Inc: St Paul, MN, USA. 2001 .
39. Axford DWE , McDermott EE , Redman DG . Small-scale test for breadmaking quality of wheat . Cereal Foods World. 1978 ; 23 ( 8 ): 477 - 8 .
40. Moonen JE , Aukescheepstra, Graveland A. Use os the SDS-sedimentationn test and SDS-polyacrylamide gel electrophoresis for screening breeders's samples of wheat for bread-making quality . Euphytica . 1981 ; 31 : 677 - 90 .
41. Seabourn BW , Xiao ZS , Tilley T , Herald TJ , Park SH . A rapid, small-scale sedimentation method to predict bread-making quality of hard winter wheat . Crop Science . 2012 ; 52 : 1306 - 15 .
42. Cubadda R , Carcea M , Pasqui LA . Suitability of the gluten index method for assessing gluten strength in durum wheat and semolina . Cereal Foods World. 1992 ; 37 ( 2 ): 866 - 9 .
43. Henstchel V , Kranl K , Hollmann J , Lindhauer MG , Bohm V , Bitsch R . Spectrophotometric determination of yellow pigment content and evaluation of carotenoids by high-performance liquid chromatography in durum wheat grain . Journal of Agricultural and Food Chemistry . 2002 ; 50 ( 23 ): 6663 - 8 . PMID: 12405758
44. Martinez CS , Ribotta PD , Leo?n AE , A?on MC . Colour assessment on bread wheat and triticale fresh pasta . International Journal of Food Properties . 2010 ; 15 ( 5 ). https://doi.org/10.1080/10942912. 2010 . 513215 .
45. Beleggia R , Platani C , Nigro F , Papa R . Yellow pigment determination for single kernels of durum wheat (Triticum durum Desf .). Cereal Chemistry . 2011 ; 88 ( 5 ): 504 - 8 .
46. Anscombe FJ , Tukey JW . The examination and analysis of residuals . Technometrics . 1963 ; 5 ( 2 ): 141 - 60 .
47. He S , Reif JC , Korzun V , Bothe R , Ebmeyer E , Jiang Y. Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe . Theor Appl Genet . 2017 ; 130 : 635 - 47 . https://doi.org/10.1007/s00122-016-2840 -x PMID : 27995275
48. Reif JC , Melchinger AE , Frisch M. Genetical and Mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management . Crop Science . 2005 ; 45 ( 1 ): 1 - 7 . https://doi.org/10.2135/cropsci2005.0001
49. Benjamini Y , Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing . Journal of the Royal Statistical Society B . 1995 ; 57 : 289 - 300 .
50. Zhao Y , Gowda M , Wu?rschum T , Longin CFH , Korzun V , Kollers S , et al. Dissecting the genetic architecture of frost tolerance in Central European winter wheat . Journal of Experimental Botany . 2014 ; 64 ( 14 ): 4453 - 60 . https://doi.org/10.1038/hdy. 2014 .1
51. Pogna NE , Autran JC , Mellini F , Lafiandra D , Feillet P. Chromo - some 1B-encoded Gliadins and Glutenin Subunits . Journal of Cereal Science . 1990 ; 11 ( 1 ): 15 - 34 .
52. Liu Z , Xin M , Qin J , Peng H , Ni Z , Yao Y , et al. Temporal transcriptome profiling reveals expression partitioning of homeologous genes contributing to heat and drought acclimation in wheat (Triticum aestivum L.) . BMC Plant Biol . 2015 ; 15 : 152 . Epub 2015/06/21. https://doi.org/10.1186/s12870-015-0511-8 PMID: 26092253; PubMed Central PMCID : PMCPMC4474349 .
53. Galvez S , Merida-Garcia R , Camino C , Borrill P , Abrouk M , Ramirez-Gonzalez RH , et al. Hotspots in the genomic architecture of field drought responses in wheat as breeding targets . Funct Integr Genomics . 2018 . Epub 2018/11/18. https://doi.org/10.1007/s10142-018-0639-3 PMID: 30446876 .
54. Araus JL , Slafer GA , Reynolds MP , Royo C . Plant Breeding and Drought in C3 Cereals: What Should We Breed For? Annals of Botany . 2002 ; 89 ( 7 ): 925 - 40 . https://doi.org/10.1093/aob/mcf049 PMID: 12102518
55. Maccaferri M , Sanguineti MC , Corneti S , Ortega JL , Salem MB , Bort J , et al. Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability . Genetics . 2007 ; 178 ( 1 ): 489 - 511 . Epub 2008/01/19. https://doi.org/10.1534/genetics.107.077297 PMID: 18202390; PubMed Central PMCID : PMCPMC2206097 .
56. Bidinger FR , Mahalakshmi V , Rao GDP . Assessment of drought resistance in pearl millet [Pennisetum americanum (L.) Leeke]. I. Factors affecting yields under stress . Australian Journal of Agricultural Research . 1987 ; 38 : 37 - 48 . https://doi.org/10.1071/AR9870037
57. Gonzalez-Ribot G , Opazo M , Silva P , Acevedo E. Traits Explaining Durum Wheat (Triticum turgidum L. spp . Durum) Yield in Dry Chilean Mediterranean Environments . Front Plant Sci . 2017 ; 8 : 1781 . Epub 2017/11/07. https://doi.org/10.3389/fpls. 2017 .01781 PMID: 29104578; PubMed Central PMCID : PMCPMC5654942 .
58. Araus JL , Amaro T , Voltas J , Nakkoul H , Nachit MM . Chlorophyll fluorescence as a selection criterion for grain yield in durum wheat under Mediterranean conditions . Field Crops Research . 1997 ; 55 ( 3 ): 209 - 23 . https://doi.org/10.1016/S0378- 4290 ( 97 ) 00079 - 8 .
59. Araus JL , Amaro T , Casadesu?s J , Asbati A , Nachit MM . Relationships between ash content, carbon isotope discrimination and yield in durum wheat . Australian Journal of Plant Physiology . 1998 ; 25 ( 7 ): 835 - 42 . https://doi.org/10.1071/pp98071
60. Mengistu DK , Kidane YG , Catellani M , Frascaroli E , Fadda C , Pe ME , et al. High-density molecular characterization and association mapping in Ethiopian durum wheat landraces reveals high diversity and potential for wheat breeding . Plant Biotechnol J . 2016 ; 14 ( 9 ): 1800 - 12 . Epub 2016/02/09. https:// doi.org/10.1111/pbi.12538 PMID: 26853077; PubMed Central PMCID : PMCPMC5067613 .
61. Kramer T. Environmental and genetic variation for protein content in winter wheat (Triticum aestivum L.) . Euphytica . 1979 ; 28 ( 2 ): 209 - 18 . https://doi.org/10.1007/BF00056577.
62. Pe?a RJ , Amaya A , Rajaram S , Mujeeb-Kazi A . Variation in quality characteristics associated with some spring 1B/1R translocation wheats . Journal of Cereal Science . 1990 ; 12 : 105 - 12 .
82. D'Ovidio R , Masci S. The low-molecular-weight glutenin subunits of wheat gluten . Journal of Cereal Science . 2004 ; 39 ( 3 ): 321 - 39 . https://doi.org/10.1016/j.jcs. 2003 . 12 .002
83. (IWGSC)* IWGSC. Shifting the limits in wheat research and breeding using a fully annotated reference genome . Science . 2018 ; 361 : 661 . doi: 10 .1126/.
84. Giraldo P , Royo C , Gonzalez M , Carrillo JM , Ruiz M. Genetic diversity and association mapping for agromorphological and grain quality traits of a structured collection of durum wheat landraces including subsp. durum, turgidum and diccocon . PLoS One . 2016 . https://doi.org/10.1371/journal.pone. 0166577 PMID: 27846306
85. Conti V , Roncallo PF , Beaufort V , Cervigni GL , Miranda R , Jensen CA , et al. Mapping of main and epistatic effect QTLs associated to grain protein and gluten strength using a RIL population of durum wheat . J Appl Genet . 2011 ; 52 ( 3 ): 287 - 98 . Epub 2011/04/28. https://doi.org/10.1007/s13353-011-0045- 1 PMID: 21523429 .
86. McCartney C , Somers DJ , Lukow O , Ames NA , Noll JS , Cloutier S , et al. QTL analysis of quality traits in the spring wheat cross RL4452x'AC Domain' . Plant Breeding . 2006 ; 125 ( 6 ): 565 - 75 .
87. Rosello M , Royo C , Alvaro F , Villegas D , Nazco R , Soriano JM . Pasta-Making Quality QTLome From Mediterranean Durum Wheat Landraces . Front Plant Sci . 2018 ; 9 : 1512 . Epub 2018/11/22. https://doi. org/10.3389/fpls. 2018 .01512 PMID: 30459781; PubMed Central PMCID : PMCPMC6232839 .
88. Maccaferri M , Cane AC , Sanguineti MC , Salvi S , Colalongo MC , Massi A , et al. A consenss framework map of durum wheat (Triticum durum Desf.) suitable for linkage disequilibrium analysis and genomewide association mapping . BMC Genomics . 2014 ; 15 : 873 . https://doi.org/10.1186/ 1471 -2164-15-873 PMID: 25293821
89. Acu?a-Galindo MA , Manson RE , Subramanian NK , Hays DB . Meta-Analysis of wheat QTL regions associated with adaptation to drought and heat stress . Crop Science . 2015 ; 55 : 477 - 92 . https://doi.org/ 10.2135/cropsci2013. 11 .0793
90. Bentley AR , Scutari M , Gosman N , Faure S , Bedford F , Howell P , et al. Applying association mapping and genomic selection to the dissection of key traits in elite European wheat . Theor Appl Genet . 2014 ; 127 ( 12 ): 2619 - 33 . Epub 2014/10/03. https://doi.org/10.1007/s00122-014-2403-y PMID: 25273129 .
91. Piepho HP . Optimal marker density for interval mapping in a backcross population . Heredity . 2000 ; 84 : 437 - 40 . PMID: 10849067