Genotype × environment interactions and QTL clusters underlying dough rheology traits in Triticum aestivum L.

Journal of Cereal Science 64 (2015) 82e91

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Journal of Cereal Science journal homepage: www.elsevier.com/locate/jcs

Genotype
environment interactions and QTL clusters underlying dough rheology traits in Triticum aestivum L. Ramya Prashant b, Elangovan Mani a, b, Richa Rai b, R.K. Gupta c, Ratan Tiwari c, € der e, Narendra Kadoo b, Vidya Gupta b, * Bhushan Dholakia b, Manoj Oak d, Marion Ro a

Advanta Biotech Center, IKP Knowledge Park, Hyderabad 500078, India Biochemical Sciences Division, CSIR-National Chemical Laboratory, Pune 411008, India ICAR-Indian Institute of Wheat and Barley Research, Karnal 132001, India d Plant Sciences Division, Agharkar Research Institute, Pune 411004, India e Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 January 2015 Received in revised form 18 April 2015 Accepted 11 May 2015 Available online 16 May 2015

Genetic dissection of dough rheology traits (DRT) in hexaploid wheat was carried out using nine mixograph characters evaluated in two consecutive years in three agro-climatic zones in India in an RIL population (HI977
HD2329). Pearson correlations determined in each year-location indicated 15 stable trait inter-relationships among them but inconsistent correlations with loaf volume (LV) were observed. Using AMMI analysis we derived patterns in G
E interactions (GEI) indicating 6e47% contribution for the DRT. Composite interval mapping using a linkage map of 202 SSR markers identified 144 DRT QTLs of which, 96 were detected in single- and the rest in two to five year-locations. Sixteen QTL clusters located on ten chromosomes were identified and only three of them on chromosomes 1B, 5B and 6D involved LV QTLs. For each trait, majority of the DRT QTLs detected in single as well as multiple environments showed location-specificity and suggested that owing to GEI, breeding for wheat dough quality might need careful selection of QTLs targeted for individual agro-climatic zones. The inconsistent correlations of DRT and LV and differential locations of their QTLs in this population corroborated that using dough rheological traits alone to predict LV might pose challenges during wheat improvement. © 2015 Elsevier Ltd. All rights reserved.

Keywords: AMMI analysis Dough rheology Mixograph QTL

1. Introduction The global bakery industry is growing by 6% every year (http:// www.researchandmarkets.com), which has created need for developing wheat varieties adapted to local environments with not only optimum yields but also with better end-use qualities. Understanding the genetic basis of the quantitative traits governing wheat end-use, especially the dough and bread quality can help develop such superior varieties. The rheological properties of the flour-water mix influence dough development and stability, which in turn influence the quality of the baked product (Li et al., 2013). The farinograph, alveograph and mixograph have been used to analyse quantitative determinants of dough rheology. Among these, the mixograph could reveal the maximum number of parameters that could be subjected to quantitative genetic analyses

* Corresponding author. E-mail address: [email protected] (V. Gupta). http://dx.doi.org/10.1016/j.jcs.2015.05.002 0733-5210/© 2015 Elsevier Ltd. All rights reserved.

(Martinant et al., 1998). Previously, molecular marker analyses of mixograph dough rheology traits (MDRTs) detected QTLs co-locating with the Glu-B1 and Glu-D1 loci (Kerfal et al., 2010; Ma et al., 2005; Mann et al., 2009; McCartney et al., 2006; Simons et al., 2012; Tsilo et al., 2011; Zhang et al., 2008). In these studies, very few QTLs located in chromosomal regions other than the above were detected possibly since either the number of environments or the MDRT analysed in each environment were few. Though Huang et al. (2006) evaluated eight MDRTs in four year-location combinations, only six chromosomal regions on 1B, 1D, 3B, 4D and 5D could be identified using the average data across these environments for QTL analyses. In contrast, Li et al. (2012) analysed 11 MDRTs using introgression lines with the aim of identifying novel QTLs for enduse quality from synthetic hexaploid wheat derived from Triticum carthlicum
Aegilops tauschii, and detected 91 QTLs distributed on 13 wheat chromosomes. However, the stability of these QTLs could not be assessed since the measurements were based on mixed samples from two locations from the same year. Bordes et al. (2011)

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identified loci for dough consistency, elasticity, strength and mixing time on all wheat chromosomes except 2D, 4B and 4D by association mapping. However, since the traits were analysed in only two environments, the stability of these loci needs further validation. Hence detailed QTL analyses of MDRTs with data collected over many environments is necessary to efficiently identify loci controlling these traits that can be considered for marker assisted selection. In the previous studies, majority of the QTLs for dough and bread quality parameters were detected in single or few of the tested environments which suggested the impact of genotype
environment interactions (GEI) on these traits (Bordes et al., 2011; Mann et al., 2009; Simons et al., 2012; Tsilo et al., 2011). The analyses of variance (ANOVA) in previous reports indicated substantial unexplained variance in addition to the G and E effects that in part could be due to the underlying GEI (Ma et al., 2005; Tsilo et al., 2011). However, very few studies have evaluated GEI effects in the mapping populations used for QTL analyses of these traits in detail. GEI effects underlying grain protein content (GPC) were revealed using additive main effects and multiplicative interactions (AMMI) and factorial regression analyses by Groos et al. (2003) for the parental genotypes of a doubled haploid population evaluated in six locations in the same year. In our previous studies, AMMI analysis of a hexaploid wheat recombinant inbred line (RIL) mapping population evaluated in six year-location environments could reveal patterns in GEI for loaf volume (LV), sodium dodecyl sulphate sedimentation volume (SV), GPC, kernel weight and hectolitre weight (Elangovan et al., 2008, 2011). Mixograph traits that reflected optimum dough development and stability have shown correlations among themselves and with grain, flour and baking quality traits in earlier reports (Groos et al., 2007; Mann et al., 2009; Simons et al., 2012; Tsilo et al., 2011; Zhang et al., 2008). Further, detection of QTL clusters for those characters suggested common or closely linked underlying loci. However, if any of the dough quality tests have to be considered for indirect selection for wheat end-use quality, they should exhibit consistent and significant correlations in multiple environments. The environmental influence on such correlations could not be evaluated conclusively in the above studies since averages of trait data recorded across environments were used for calculating correlations or they were calculated individually in as few as one to three environments. Hence we present here, a comprehensive GEI and QTL analysis in a hexaploid wheat RIL population derived from the cross HI977
HD2329 for nine MDRTs recorded in three agro-climatic zones in India for two consecutive years. Loaf volume, SV and GPC that had been recorded in the same environments for this mapping population were used to calculate phenotypic correlations and QTL colocations for grain, flour, dough and bread quality traits. The stability of the correlations across years and locations and the patterns in GEI derived from AMMI analysis for these traits were compared.

development and analysis of LV, SV and GPC are described in detail in Elangovan et al. (2008, 2011). The parents and the population were grown at three locations situated in agro-climatically diverse wheat-growing regions in India for two consecutive years (2004 and 2005) in the Rabi season (October to April). The three locations were e Karnal (North Western Plains Zone - NWPZ), Kota (Central Zone) and Pune (Peninsular Zone e PZ). Comparison of these locations in terms of placement within agro-climatic zones, climatic and soil conditions are presented in Supplementary Fig. 1. The RILs were grown in an Augmented Randomized Complete Block Design using five replicating checks that included the two parental genotypes. The grains were bulk harvested from the plots of 2
2 m rows for each RIL, parents and checks; threshed, cleaned and phenotypic analyses were carried out. Dough rheological analyses were performed (AACC method 5440A; AACC Chemists, 2004) using a computerized 10 g Mixograph (National Manufacturing Company, U.S.A.). The wheat grains were tempered to 14% moisture content, milled and the whole grain flour was rested overnight. Distilled water (6.2 mL) was added to 10 g of flour and subjected to mixograph analysis for 8 min at 88 rpm. During the analysis, every 11th sample was repeated to ensure reproducibility. Data analysis was performed using MixSmart v. 3.40 (AEW Consulting, USA) to construct curves with two envelopes and one midline and record 44 parameters. Of these, nine parameters that efficiently described the dough mixing properties like optimum dough development, break down and change in consistency with minimum redundancy were selected for further analyses (Martinant et al., 1998). These were- (1) Midline peak time (MPT): the time in minutes to reach maximum resistance offered by the dough for mixing; represents near-optimum dough development time (2) Envelope peak integral (EPI) - area under the envelope curve from the starting point to envelope peak time (3) Midline right integral (MRI) e area under the midline curve from the start to one minute after the peak time (4) Midline curve tail integral (MTI) e area under the midline curve from the start to the end of the mixing process. These areas under the curves denote the energy used during the mixing process (5) Midline right value (MRV): the height of the curve at 1 min after MPT at which the dough is subjected to over mixing after achieving maximum resistance (6) Midline curve tail value (MTV) height of the curve at total breakdown or loss of strength during mixing. The curve heights illustrate the changes in consistency of the dough. The widths of the curves depict the elasticity of the dough e (7) Midline right width (MRW) e exhibits dough tolerance during over mixing measured as the width of the curve at 1 min after MPT (8) Midline curve tail width (MTW) - width of the peak at the end of the 8 min mixing period. (9) Weakening slope (WS) indicates the rate of dough breakdown while mixing and calculated as the difference of curve height at peak time and curve height at 8 min.

2. Materials and methods

ANOVA for phenotypic data were performed using IRRISTAT v. 5.0 (International Rice Research Institute, Philippines) using the single site module for balanced data. The adjusted values for each treatment based on the variation in checks grown in each block were derived and used for further analyses. The Cross-site analysis module of IRRISTAT was used for AMMI analysis (Nachit et al., 1992) to derive patterns in the variance unexplained by the ANOVA in terms of significant interaction principal component axes (IPCA). The contributions of G, E and GEI as well as of individual IPCA to total variance and of IPCA to G
E effects were calculated using the total sum of squares and individual sums of squares of the respective components (Tarakanovas and Ruzgas, 2006). The IPCA1 values and the means for the RILs and the

2.1. Plant material and phenotypic analyses The parental genotypes and F7:8 RIL population of 105 lines developed from the hexaploid wheat cross HI977 (HI, Glu-A1 (2*), Glu-B1 (17þ18) and Glu-D1 (5þ10), Superior loaf volume) x HD2329 (HD, Glu-A1 (2*), Glu-B1 (7þ9) and Glu-D1 (2þ12), Low loaf volume) were analysed for various end-use quality traits. Both these genotypes do not possess the 1B/1R translocation (Tiwari et al., 2002). The pedigree of the parents are as follows - HI977 [Gallo/AUST II 61.157/2/Ciano 67/NO66/3/Yaqui50-Enano/ 3*Kalyansona] and HD2329 [HD 2252/UP 262]. Population

2.2. Statistical analyses

84

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environments were used for plotting AMMI1 biplots, which helped visualize the extent of adaptability of the RILs to the environments. The IPCA values of the first two significant axes were used for constructing AMMI2 biplots to graphically represent the extent of participation of both RILs and environments in GEI (Yan and Tinker, 2006). Heritability (H2) was estimated as H2 ¼ M1/ (M1þM2) based on ANOVA, where M1 and M2 are mean squares for genotype and residual error, respectively (Li et al., 2012). QGene 4.3 (Joehanes and Nelson, 2008) was used for assessing the normality of the trait distributions by KolmogoroveSmirnov tests and to calculate Pearson correlation coefficients (r) for pairs of traits at each year-location combination (environment). LV, SV and GPC recorded for the same environments (Elangovan et al., 2008, 2011) were also included in the correlation analysis to assess trait interrelationships. 2.3. QTL analyses A framework linkage map of HI
HD population developed previously by us (Elangovan et al., 2008) with 202 simple sequence repeat markers (SSRs), and two HMW glutenin loci (Glu-B1 and Glu-D1) representing 19 chromosomes (3161.8 cM with mean intervals of 15 cM) was employed for QTL mapping in this study. Its overall arrangement of the markers was similar to the microsat€der (2007). For each trait, phenotypic ellite map of Ganal and Ro data for the individual environments were analysed separately and LV, SV and GPC were analysed together with the MDRTs to compare the QTLs for both. The positions and effects of QTLs were detected by Composite interval mapping (CIM) and the tests of pleiotropy versus close linkage was performed by Multi-trait CIM (Mt-CIM) (Jiang and Zeng, 1995), both implemented in QTL Cartographer v. 2.5 (Wang et al., 2007) with parameters described by Patil et al. (2009). QTLs were declared at LOD 3.0 and those in identical, overlapping or adjacent marker intervals in a linkage group were treated as the same. Those QTLs with their closest markers placed within 10 cM from each other and their support intervals showing partial or complete overlap were considered to be present in a QTL cluster. The additive effects (AE) and the IPCA1 and IPCA2 values (I1 and I2, respectively) computed from the AMMI analysis were also included in QTL mapping (Van Eeuwijk et al., 2007) in order to evaluate the presence of additive and environmentally modulated loci. 3. Results 3.1. Phenotypic analysis of quality traits The MDRTs means and ranges for the RILs as well as the parental means in the six environments are presented in Supplementary Table 1. Considering the six environments, nine traits namely, EPI, MRI, MTI, MTW, MPT, MRV, WS, LV and SV showed significant differences between the parental genotypes (P  0.05), with HI showing higher trait value for all except WS for which, HD indicated faster rate of breakdown of dough resistance to mixing after achieving MPT. However, in the individual environments we noted that HD displayed higher trait value for MRW and MTW compared to HI in Kota05 and Pune04, respectively. The RILs indicated wide phenotypic range and bi-directional transgressive segregants for all the MDRTs suggesting the presence of several underlying loci with contribution of favourable alleles from either of the parents. The frequency distributions displayed continuous phenotypic variation and 66 of 72 trait-environment data were normally distributed based on KolmogoroveSmirnov test of normality (P > 0.05) (data not shown).

3.2. Phenotypic correlations among the mixograph traits, LV, SV and GPC Among the possible 396 pair-wise trait combinations considering all the six environments, 203 (51.26%) were significant with 26.1% at P  0.05, 26.6% at P  0.01 and 47.3% at P  0.001 (Supplementary Table 2). Significant correlations in similar numbers were observed in Karnal (30%) Kota (36%) and Pune (34%) as well as between the two years (53.4% in 2005). The highest positive correlation was observed between MPT and MRI at Kota05 (r ¼ 0.959), while MTV-WS at Pune04 showed the highest negative correlation (r ¼ 0.879). Notably, many of the trait correlations were consistent across environments (Table 1). MTV showed highly significant positive correlations with EPI, MRI, MTI and MRV in all the six environments. Similarly, MTI with MTW and MRV; and MRI with MPT also showed positive correlations in all the year-locations. EPI, MTI, MTW and MTV displayed consistent positive correlations with each other in four to six environments. Stable negative correlations were observed mainly in trait combinations involving WS viz., WS-MRI, WS-MPT and WS-MTV in four to six environments. In addition to this, MPT and MRW were negatively correlated in four environments. In contrast, the correlations of LV, SV and GPC among themselves as well as with the MDRTs were highly sensitive to environmental effects. LV with MRI and MPT and SV with EPI, MRI and MPT indicated positive correlations in three environments. The correlations of GPC with the other traits were strongly influenced by the environment since they were significant with only four of the MDRTs only in either one or two environments. Additionally, correlations of GPC with MTW in 2004 and 2005 at Kota were positive and were negative in Karnal and Pune in 2005. Interestingly, correlations that were either positive or negative in different environments were observed for 13 other trait pairs which suggested that they were highly prone to environmental influence (Table 1). 3.3. G
E interactions of mixograph traits Analysis of variance indicated that G
E interaction (18.28e57.97%) and E main-effects (22.64e74.20%) contributed more to the MDRT phenotypic variation compared to G maineffects (6.24e25.63%). Maximum contributions from G were for WS, while from E and G
E for MTI and EPI, respectively (Table 2, Supplementary Table 3). Majority of these traits showed moderate heritability ranging from 0.56 (MTW) to 0.74 (MPT) (Supplementary Table 4). In comparison, LV indicated 38.09% contribution from G and heritability of 0.80, the highest among the traits analysed in the HI
HD population. AMMI analysis could derive patterns in the G
E component in terms of one to three significant IPCA for the MDRTs which could account for 29.72% (MRV) to 85.88% (WS) of the variation unexplained by ANOVA. In effect, their contribution amounted to 5.74% (MRV) to 47% (EPI) of the trait variation (Table 2; Supplementary Table 3). The ratios of contributions to total variance in terms of G/G
E (0.08e1.13) and G/IPCA (0.32e1.23) clearly indicated the importance of the GEI underlying the MDRTs (Table 2). AMMI1 biplots indicated stable (low as well as high) performance RILs, those favourably influenced/adapted to specific environments and RILs influenced unfavourably by specific environments in the HI977
HD2329 population (Supplementary Fig. 2). Hence, the AMMI1 biplots suggested the presence of QTL complements that responded differentially to the environments and hinted at the presence of stable, location-specific as well as single environment QTLs for the traits. For six MDRTs as well as LV, SV and GPC where the first two IPCA axes contributed significantly

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Table 1 The number of year-location combinations in which the correlations among the mixograph traits, LV, SV and GPC were significant. Trait

EPI

EPI MRI MTI MPT MRW MTW MRV MTV WS LV SV GPC

5þ 5þ 4þ 3þ 5þ 3þ 6þ 2:2 1þ 3þ ns

MRI

MTI

MPT

MRW

MTW

MRV

MTV

WS

LV

SV

GPC

5þ

5þ 4þ

4þ 6þ 2:1

3þ 1:4 4þ 4

5þ 3þ 6þ 1þ 5þ

3þ 4:1 6þ 3 4þ 5þ

6þ 6þ 6þ 5þ 2þ 4þ 6þ

2:2 5 2:1 6 2þ 1:1 2þ 4

1þ 3þ 1þ 3þ 1:1 1 1þ 2:1 1:2

3þ 3þ 1þ 3þ 1:1 1þ 1:1 2:1 2þ 1

ns ns 1þ ns ns 2:2 2þ ns 1þ 1:1 2þ

4þ 6þ 1:4 3þ 4:1 6þ 5 3þ 3þ ns

2:1 4þ 6þ 6þ 6þ 2:1 1þ 1þ 1þ

4 1þ 3 5þ 6 3þ 3þ ns

5þ 4þ 2þ 2þ 1:1 1:1 ns

5þ 4þ 1:1 1 1þ 2:2

6þ 2þ 1þ 1:1 2þ

4 2:1 2:1 ns

1:2 2þ 1þ

1 1:1

2þ

Numerical values indicate the numbers of year-location combinations in which correlations were significant at P  0.05, 0.01 or 0.001 (r values with levels of significance given in Supplementary Table 2). Positive or negative correlations are indicated by þ or  signs. The ratios indicate the number of years either positive or negative correlations, respectively were observed for a particular trait combination. ns: non-significant.

to the G
E component, AMMI2 biplots were constructed using their IPCA scores. In these biplots, though a majority of the RILs clustered near the origin, deviation away from the origin was observed for at least 20% of them which suggested their high GEI (Supplementary Fig. 3). 3.4. Composite interval mapping 3.4.1. QTL distribution, additive effects and contributions to phenotypic variance Considering all the 12 traits, 239 QTL peaks with LOD 3.0 were detected by CIM. These corresponded to 158 QTLs distributed on all the linkage groups (Supplementary Tables 5e7; Fig. 1, Supplementary Fig. 4). In all, 144 QTLs for MDRTs and 14 QTLs for LV, SV and GPC together were detected. The number of QTLs identified for each of the MDRT ranged from four (EPI) to 26 (WS). The B genome showed 63 MDRT QTLs (43%), followed by the A and the D genome with 44 (31%) and 37 QTLs (26%), respectively (Supplementary Table 5). Among the homeologous groups, the Group 6 chromosomes indicated 48 MDRT QTLs (33%) with chromosomes 6B and 6D harbouring 23 and 20 QTLs, respectively. For LV, SV and GPC in comparison, chromosome 1B and 2B together indicated seven of the 14 QTLs. Among the 158 QTLs, 108 QTLs indicated favourable allele contribution from the superior parent HI, while HD contributed the same for 43 QTLs (Supplementary Tables 6 and 7). Interestingly, 16 of the 22 QTLs for MTV were contributed from HD. For six MRI, and single MTV and MRW QTLs expressed in more than one

environment, additive effect was negative in one of the environments, while being positive in the other(s). This could possibly suggest the presence of more than one QTL in those regions, which might be resolved by increasing the marker density. For the MDRTs, the phenotypic variance explained (PVE, R2
100) by the QTLs in the individual environments ranged from 9 to 46%. Though QTLs for LV, SV and GPC showed similar range of PVE, only two QTLs namely QLv.ncl-1B.1 (34%) and QLv.ncl-5B.1 (45%) displayed more than 20% PVE. In all, 71 MDRT QTLs showed PVE 30% in at least one environment. Furthermore, 30 QTLs showed PVE 40% of which, 21 QTLs were those detected in 2 environments. Three MRW QTLs and five WS QTLs were of special mention since they were detected in 3 environments and in each environment, the PVE ranged from 20 to 46% (Supplementary Tables 6 and 7). In all, 112 QTLs were detected together for the main-effects and the IPCA scores (Supplementary Fig. 5, Supplementary Table 8). For AE, 56 QTLs and for I1 and I2 - 43 and 13 QTLs respectively were identified. Among the MDRTs, MPT, WS and MTV that showed maximum contributions from G main-effects (Table 2) indicated 22, 13 and six QTLs for AE, respectively. In contrast, 19 QTLs for I1 and I2 together were indicated for MTV that had displayed maximum contribution from IPCA to the trait variation. In all, 47 out of 158 QTLs were coincident with either AE or I1/I2 QTLs for the same traits (Fig. 1). This was possibly because even though QTLs for the respective traits were present on the same chromosomes, there were shifts in the support intervals or only the scores of the first two significant IPCA were included in the QTL analyses. In 15 chromosomal regions, the AE and I1/I2 QTLs indicated overlap

Table 2 Sources of variation for mixograph traits revealed by Additive main effects and multiplicative interaction analysis and their comparison with the same for LV, SV and GPC. Trait

EPI MRI MTI MPT MRV MTV MRW MTW WS LV SV GPC a b c

Contribution total variance (%) G

E

G
E

16.98 13.51 06.24 19.69 07.06 13.73 17.30 15.10 25.63 38.09 18.40 09.11

24.44 55.65 74.20 47.37 72.13 43.65 26.02 29.48 22.64 14.30 38.88 62.38

57.97 29.73 18.28 32.31 19.31 41.75 55.88 55.45 51.60 47.61 42.71 28.51

No. of IPCAa 3 2 1 3 1 2 1 3 3 4 4 2

Contributions of b

IPCA to G
E (%)

IPCA to TSS (%)

G, E and IPCA to TSS (%)

81.09 59.10 36.53 78.63 29.72 74.71 31.23 84.63 85.88 96.75 93.94 56.06

47.00 17.57 6.68 25.41 5.74 31.19 17.45 46.93 44.31 46.06 40.12 15.99

88.42 86.73 87.12 92.47 84.93 88.57 60.77 91.51 92.58 98.45 97.40 87.48

IPCA: Interaction principal component axi(e)s. TSS: Total sum of squares. Ratios of the contributions of the individual components to TSS.

G/G
Ec

G/IPCAc

0.69 0.24 0.08 0.42 0.10 0.31 0.66 0.51 1.13 2.66 0.47 0.15

0.36 0.77 0.93 0.77 1.23 0.44 0.99 0.32 0.58 0.83 0.46 0.57

86

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Fig. 1. Linkage groups for HI977
HD2329 population harbouring QTL clusters for four or more traits. Only pleiotropic loci for three traits are indicated. QTL cluster numbers are indicated against the arrowheads. Horizontal lines indicate QTLs whose support intervals overlap with those for additive effects, IPCA1 and IPCA2 values computed from AMMI analysis.

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Table 3 Summary of the features of the QTL clusters for mixograph traits, LV, SV and GPC in HI977
HD2329 population. Chr

QTL cluster

No. of QTLs

Traits

Range of R2

Consistent QTLsa

1A 1B

1A-1 1B-1 1B-2

4 6 6

MRW, MTV, MTW, WS MPT, MRW, MTV, MTW, WS, LV MPT, MRI, MRW, MTV, MTW, WS

0.25e0.41 0.20e0.39 0.16e0.46

1D

1D-1

4

EPI, MPT, MRI, WS

0.11e0.29

2A

2A-1

6

MPT, MRI, MRV, MRW, MTV, WS

0.16e0.41

3A 5A 5B 5D

3A-1 5A-1 5B-1 5B-2 5D-1

6 5 5 6 6

MPT, MRI, MRW, MTV, MTW, WS MRV, MTI, MTV, MTW, GPC MPT, MRV, MTV, MTW, WS MPT, MRI, MRW, MTW, WS, LV MPT, MRI, MRW, MTV, MTW, WS

0.16e0.44 0.12e0.19 0.30e0.35 0.19e0.45 0.16e0.46

6B

6B-1

7

MPT, MRI, MRV, MRW, MTV, MTW, WS

0.21e0.46

6B-2 6B-3 6D-1 6D-2

6 5 8 6

MPT, MPT, MPT, MPT,

0.16e0.39 0.16e0.34 0.18e0.36 0.20e0.46

6D-3

6

MPT, MRV, MRW, MTV, MTW, WS

e QMrw.ncl-1B.1(3) QMrw.ncl-1B.2 (3) QMtv.ncl-1B.2 (3) QWs.ncl-1B.3 (5) QMpt.ncl-1D.1(4) QMri.ncl-1D.1(3) QMrw.ncl-2A.1(4) QWs.ncl-2A.1(4) QWs.ncl-3A.4 (3) e e e QMrw.ncl-5D.1(3) QMtv.ncl-5D.1(3) QWs.ncl-5D.1(4) QMrw.ncl-6B.1(4) QWs.ncl-6B.1(4) QMrw.ncl-6B.2 (3) QMrw.ncl-6B.3(3) QMrw.ncl-6D.1(3) QMrw.ncl-6D.2 (4) QMtv.ncl-6D.2 (3) QWs.ncl-6D.2 (3) QMrw.ncl-6D.3 (3) QWs.ncl-6D.3(3)

6D

MRI, MRI, MRI, MRI,

MRW, MTV, MTW, WS MRW, MTV, WS MRV MRW, MTV, MTW, WS, LV MRW, MTV, MTW, WS

0.28e0.46

Chr: Chromosome. a QTLs detected in 3 year-location combinations (environments). The number of environments in which the respective QTL was detected is given in brackets.

(Supplementary Fig. 5). However, six of them showed additive effects with opposing signs and suggested that increasing the marker density could help resolve the overlapping QTLs. Among the AMMI component QTLs, 38% of the AE QTLs and notably, 61% and 54% respectively of the QTLs for I1 and I2 contributed 30% PVE (Supplementary Table 8). 3.4.2. QTL stability and environmental specificity We identified 23 consistent MDRT QTLs that were observed in three to five environments (Table 4, Supplementary Tables 6 and 7). In contrast, 96 were in single environments; while 25 were detected in two environments (Table 4). Among the QTLs that were consistent, 10 MRW QTLs were expressed in 3 environments followed by seven for WS. Notably, QWs.ncl-1B.3 was displayed in five environments while three QTLs each for MRW and WS; and one

for MPT were detected in four environments. MTV showed the highest number of QTLs detected in consistent pattern with 11 QTLs in two environments and three QTLs in 3 environments. Furthermore, 11 out of the 23 QTLs detected in 3 environments were coincident with AE QTLs for the respective traits (Supplementary Fig. 5). When the QTLs detected in single environments were considered, 21 were for MPT and more than ten each for MRI, MRV, MTW and WS. In case of LV, SV and GPC, apart from QGPC.ncl-6A.1 which was detected in two environments, all the other QTLs were identified in single environments. We questioned if there were patterns in location-specificity in case of the 96 MDRT QTLs expressed in single environments. Interestingly, nearly 51% of such QTLs were specific to Karnal and the rest were detected in Kota and Pune in equal proportions (Table 4). Preferential expression in specific locations was noted

Table 4 Number of environment-specific and consistent QTLs for mixograph traits, LV, SV and GPC in HI977
HD2329 population. QTLs detected in two env

QTLs detected in 3 env

Karnal

Kota

Pune

Total

4 13 6 21 11 8 8 12 13

e 3 e 0 e 11 2 3 6

e 2 e 1 e 3 10 e 7

e 2 e 1 e 5 e e 5þ1a

e e e e e e 9 e 1a

e e e e e e 0 e e

0 2 0 1 0 5 9 0 6

23

96

25

23

13þ1a

9þ1a

0

23

1 2 1 27

4 5 4 109

e e 1 26

e e e 23

e e e 13

e e e 9

e e e 0

0 0 0 23

Trait

Total no. of QTLs

QTLs detected in single years Karnal

Kota

Pune

Total

EPI MRI MTI MPT MRV MTV MRW MTW WS

4 18 6 22 11 22 20 15 26

2 6 3 19 1 5 2 4 7

1 3 3 e 10* e 4 e 3

1 4 e 2 e 3 2 8 3

Total (MDRT)

144

49

24

LV SV GPC Total

4 5 5 158

e 2 2 53

3* 1 1 29

*All the QTLs were detected in Kota 2005; env: environment. a QWs.ncl-1B.3 was detected in consecutive years in both Karnal and Kota.

QTLs detected in consecutive years within a location

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when QTLs for individual traits were examined. MPT showed 19 out of 21 single environment QTLs expressed in Karnal and the rest in Pune. Notably, 10 out of 11 MRV QTLs detected in single environments were in Kota05 and eight out of 12 MTW single environment QTLs displayed preference to Pune. We further examined location specificity for the 23 QTLs observed in both the years in at least one of the locations (Table 4). Interestingly, only QWs.ncl-1B.3 was detected in both Karnal and Kota in consecutive years, 13 of such QTLs were specific to Karnal, nine for Kota (all MRW QTLs) and none for Pune (Supplementary Table 9). The support intervals of 12 of these QTLs showed overlap with those of I1 or I2 QTLs (Supplementary Fig. 5).

environments. Majority of the pleiotropic regions influenced MDRT combinations while two chromosomal regions on 1B and 6A involved SV and GPC, respectively with two other MDRTs. In addition, an 8 cM region on chromosome 1B harboured a pleiotropic region for LV and MRW. Since trait data from the six environments were used separately for the analyses, it was possible to assess if the pleiotropic loci were observed over years and locations. Eleven loci were detected repeatedly in two or three years and seven of these were pleiotropic for the same trait combinations (Supplementary Table 10). MPT indicated nine and five pleiotropic regions with MRI and WS, which were consistently correlated in six and five environments, respectively.

3.4.3. QTL clusters and putative pleiotropic regions Sixteen QTL clusters for four to eight traits were identified on 10 chromosomes (Table 3, Fig. 1, Supplementary Table 6). In majority of the clusters, the closest markers of the QTLs were situated within 2e10 cM regions. Chromosomes 6B and 6D indicated three QTL clusters each. A 12 cM region on chromosome 3A between the markers Xgwm480 and Xgwm1038 was the smallest to harbour a QTL cluster and involved QTLs for six traits. QTL clusters on chromosomes 1B and 6B indicated 14 peaks each, involving six and seven traits, respectively. Majority of the QTL clusters (12 of 16) involved at least one consistent QTL expressed in three or more environments. Consistent QTLs for MRI and MPT were located within a QTL cluster on Chromosomes 1D, for MRW, MTV and WS on 5D; while 2A, 6B and 6D together showed four QTL clusters that included consistent QTLs for MRW and WS. The co-location of QTLs for MDRT with LV, SV or GPC QTLs was also examined. A QTL cluster involving QGpc.ncl-5A.1 also included an MTV QTL detected in two environments and QTLs for MRV, MTI and MTW. In case of LV; QLv.ncl-1B.1, QLv.ncl-5B.1 co-located with five and QLv.ncl-6D.1 with seven MDRT QTLs. The MDRTs that exhibited stable correlations also indicated QTLs that showed consistent co-location (Table 1; Fig. 1). WS which was negatively correlated with MPT in six environments and with MRI in five environments co-located with their QTLs in 14 and 11 clusters, respectively. Similarly, MPT was positively correlated with MRI in six and with MTV in five environments and indicated 12 and 14 QTL co-locations with them, respectively. Though EPI and MTI were involved in stable correlations with other MDRTs, only 10 QTLs were detected for them in total, of which six indicated colocation with other MDRTs. The other trait pairs that were correlated in five or six environments showed QTL co-location in five to 12 chromosomal regions. Among the QTL clusters, all except two each on chromosomes 5B and 6B coincided with clusters of QTLs detected for the AMMI component traits (Supplementary Fig. 5). Multi-trait CIM detected 29 genomic regions on 13 chromosomes harbouring putative pleiotropic regions for two to three trait combinations (Table 5, Supplementary Table 10). Except EPI and MRV, all the other traits were under the influence of at least one pleiotropic region. Eighteen genomic regions indicated pleiotropic influence on MTV and WS that showed negative correlations in four

4. Discussion

Table 5 Chromosomal regions with putative pleiotropic effects on three of the quality traits in HI977
HD2329 population. Chromosome

Region (cM)

Nearest marker

Traits

Environment

1B 1D 2B 3A 6A 6B 6B

219e233 162e186 79e95 103e107 78e95 33e42 159e166

Xwmc419 Xgwm642 Xgwm148 Xgwm1071B Xgwm1150 Xgwm132C Xgwm1233

MPT-WS-SV MRI-MPT-WS MPT-MTV-WS MRI-MPT-WS MRW-MPT-GPC MTW-MTV-WS MRW-MTV-WS

Pune 2005 Pune 2004 Pune 2004 Karnal 2005 Kota 2005 Pune 2004 Karnal 2004

4.1. Trait inter-relationships and their stability The comparisons among correlations could be performed efficiently in our study since all the 12 traits were assessed in all the six environments and correlations were calculated in individual environments (Table 1). We compared these with correlations calculated using average data across the six environments (Supplementary Table 11). Among the 20 trait pairs whose correlations were non-significant when the average trait data were used, except GPC-EPI, all indicated significant correlations in one to five environments when analysed separately (Table 1, Supplementary Table 2). Furthermore, it could be noted that in Karnal, higher number of trait pairs showed correlations in consecutive years compared to the other locations and in year 2005, more number of trait pairs showed significant correlations in all the three locations compared to 2004 (Supplementary Tables 12 and 13). We could also note that even though LV with MRI and MPT and SV with EPI were significant in only three environments, they were so in all the three locations in the same year. Hence, our analysis provided clear information about the stability of the trait relationships which would otherwise have been masked if average trait data alone had been used for calculating correlations. The most distinct of the MDRTs, MPT showed stable positive correlations with EPI and MRI indicating that higher energy inputs would be needed when peak resistance is achieved at longer time (Table 1). This was possibly because of stronger gluten as corroborated by the stable and positive relationship of MPT with SV. In addition, MRI showed positive correlation with LV suggesting that optimum dough development time could be crucial for superior loaf volume. Similarly, Simons et al. (2012) showed highly significant correlation of Midline peak integral (MPI) with MPT (0.97, P < 0.01) and with LV (0.58, P < 0.01) but based on average phenotypic data from six environments. However, the correlations of MPT with MTI and MTW; and of MTI and MTW with LV and SV in our study were not consistent, which suggested that dough tolerance might influence LV far less than MPT and could be more prone to environmental effects. Li et al. (2013) reported positive correlations of MTW and MTV with MPT and SV but it was not possible to assess their influence on LV since it was not included in their study and the stability of the correlations could not be gauged since the average trait values across three environments were used for calculating them. MPT in our study indicated negative correlations with WS in all the six environments suggesting that dough that takes longer to develop is slow to break down. The consistent negative correlations of MPT with MRW suggested that the dough that has taken longer time to develop is possibly less elastic and might contribute to poor LV because of its rigidity. However, this could not be confirmed since the direction of correlation between LV and MRW was not consistent in different environments. MTV was positively

R. Prashant et al. / Journal of Cereal Science 64 (2015) 82e91

correlated with MPT in five environments which suggested that stronger dough at the end of the mixing process is still more resistant to breakdown and has a stable consistency. Our analyses emphasized that any conclusions about relationships among wheat end-use quality traits should be drawn from careful assessments in diverse environments. Though the MDRTs showed significant correlations among each other that were stable over the environments, their correlations with LV were highly variable in our analysis indicating that they could not be efficient predictors of LV in HI
HD population. However, the MDRTs with their stable interrelationships can help evaluate dough properties comprehensively, which might crucially influence the efficiency of wheat processing for end-use. 4.2. Contributions of G
E interactions to mixograph traits AMMI analysis enabled us to compare the contributions of GEI to the variation among the 12 quality traits in the HI
HD population. Previously, significant contribution from G
E in terms of IPCA were detected for GPC (Groos et al., 2003; Hristov et al., 2010), SV and LV (Elangovan et al., 2008, 2011; Hristov et al., 2010). For the MDRTs, ANOVA indicated significant contributions from the G
E component for MPT (Li et al., 2013; Tsilo et al., 2011) and MPI (Tsilo et al., 2011). However, multivariate analyses to separate it from the residual were not performed in those studies. In our study, the effects of the true GEI and the unexplained variation could be clearly distinguished in case of MRW. Here, though ANOVA indicated 56% contribution from the G
E, AMMI analysis could delegate 31% of it to a single IPCA and the rest was the error. Hence, it was possible to assess that nearly 61% of total variation for this trait was due to G (17%); E (26%) and the IPCA (17.5%). We could assess that G and E main-effects and the IPCA contributions together could explain up to 93% (WS) of the total variation in the MDRTs (Table 2). The substantial contributions from the E and G
E components to the MDRTs in the present study were reflected in their moderate heritabilities (Supplementary Table 4). In comparison, Tsilo et al. (2011) indicated heritabilities of 0.8e0.94 for MDRTs and 0.6 for LV evaluated in three locations in a single year. In a durum wheat RIL population, Patil et al. (2009) analysed 10 MDRTS in two environments and reported wide heritability range of 0.17 (mixograph total energy) to 0.96 (MTW). The variable levels of heritabilities for the MDRTs observed in different populations and prominent contributions from G
E and E main effects in the present population suggested that selection of breeding material based on these traits would be highly challenging. In the present study, similar numbers of QTLs for AE and IPCA scores for the 12 traits together and the high PVE of a majority of the I1/I2 QTLs indicated the importance of G
E interactions for these traits (Supplementary Table 8). Furthermore, for MTV and MTW, more number of QTLs were detected for I1 and I2 compared to AE. In contrast, 22 main-effect QTLs were detected for MPT, which also displayed relatively high heritability (0.74) and contribution from G effects (19.69%) in the ANOVA (Supplementary Tables 3 and 4) suggesting that this trait could have a potential in selection for dough quality during breeding. 4.3. Location specificity of the QTLs in HI
HD population In the present study, QTLs detected in single years and those in consecutive years in Karnal, Kota or Pune indicated preference to specific locations for each of the MDRTs and made the GEI underlying them evident (Table 4). These could be due to differential accumulation of the gluten protein fractions in different environments which resulted in dough property changes (Zhang et al.,

89

2009). Previously, HI, HD and 16 of the RILs grown in Kota and Pune (2004) were analysed for gliadin fraction levels by RP-HPLC (Elangovan et al., 2010), which indicated that aþb and g gliadins differentially accumulated in those locations. We examined the results of Mann et al. (2009) reported in two locations in one year which showed that 32 out of 40 QTLs for various glutenin and gliadin fractions and 26 out of 38 baking quality trait QTLs were detected in single environments and showed distinct preference to specific locations. Patil et al. (2009) examined MDRTs in two locations in only single years and identified 22 QTLs that were expressed in single environments and 60% of them showed preference to one of the locations. In the present study, even though MRW indicated 56% contribution from the G
E component, surprisingly 10 QTLs were detected in 3e4 environments. However, it was interesting to note that nine of them were detected in consecutive years in Kota (Supplementary Table 9). Similarly, all the three stable QTLs for MTV detected in three environments indicated expression in consecutive years specifically in Karnal and in Pune04. In addition, even though 11 MTV QTLs were detected in two environments, ten were detected in one year each in Karnal and Pune. Hence, when closely observed, hidden location-specificity was detected even in the stable QTLs. Both AMMI1 and AMMI2 biplots indicated the significant participation of Pune 2004 and Pune 2005 environments in GEI in the HI
HD population (Supplementary Figs. 2 and 3). The vectors of Pune 2004 and Pune 2005 were situated in the second and third quadrants of the AMMI1 biplots and showed high IPCA1 values for five of the traits, which suggested favourable influence of Pune environment on them (Supplementary Fig. 2). For WS, Pune 2005 was in the fourth quadrant and suggested its favourable influence on dough quality. However, though we detected 67 of the 239 QTL peaks in Pune, none of the QTLs that were detected in consecutive years in the same location were for Pune (Table 4). It is possible that the QTLs adapted to Pune in HI
HD population might be in chromosomal regions not covered by the linkage map such as chromosomes 4A and 7A, and distal regions of chromosomes 3B, 3D, 4B and 5B. Previous studies detected MDRTs QTLs on 4A (Li et al., 2012), 7A (Patil et al., 2009) and 4B (Li et al., 2012; Mann et al., 2009; Patil et al., 2009; Simons et al., 2012). Enriching the present linkage map of the HI
HD population could help identify more QTLs that are specifically adapted to the favourable environment of Pune. Both the biplots indicated significant participation of Karnal and Kota in GEI and AMMI1 biplots showed Karnal and Kota in the first and fourth quadrants for five and ten traits respectively (Supplementary Figs. 2 and 3). Predictably, many location-specific QTLs for Karnal and Kota were detected in the HI
HD population and many of them were co-incident with I1 or I2 QTLs for the respective traits (Supplementary Fig. 5). 4.4. QTL clusters for mixograph traits, LV, SV and GPC QTL clusters and pleiotropic QTLs were detected in HI
HD population that suggested common or closely linked loci controlling the correlated traits (Table 3, Fig. 1; Supplementary Table 6). Previous reports indicated co-location of mixograph, GPC, SV and various other end-use quality QTLs on different chromosomes (Li et al., 2012; Mann et al., 2009; McCartney et al., 2006; Nelson et al., 2006; Patil et al., 2009; Tsilo et al., 2011; Simons et al., 2012). We compared the markers associated with the QTL clusters in the present population with those reported previously. For 10 out of 16 QTL clusters in the HI
HD population, concurrent chromosomal regions in the previous reports were associated with dough rheology and/or either LV or its related traits (Supplementary Table 14). The QTL clusters 5B-1, 5B-2, 5D-1 and

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R. Prashant et al. / Journal of Cereal Science 64 (2015) 82e91

6B-1 were not associated with MDRTs in the earlier studies and hence might represent new chromosomal regions controlling them. Similarly, the clusters 6B-3 and 6D-2 identified in the present population were not reported in relation to either baking or dough traits previously and hence represented new chromosomal regions associated with these traits. In the present study, chromosome 2B indicated two smaller clusters having three and four QTLs each and the first region corresponded to a cluster on the short arm of 2BS as reported by Li et al. (2012) for seven MDRTs and SV. The second cluster on 2B with MRI, MPT, SV and GPC QTLs appeared to be a new chromosomal region controlling MDRTs since it was not reported in earlier studies. In HI
HD population, the distal short arm region of chromosome 6A had MRW, MPT and MTV QTLs in a cluster which also associated with MDRTs in McCartney et al. (2006) and with GPC and SV in Li et al. (2012). Increasing the marker density of the chromosomal regions harbouring the QTL clusters and pleiotropic loci is necessary to characterize them. 5. Conclusions  This study demonstrated that multi-location and year trials and environment-wise assessment of correlations are necessary to draw concrete conclusions about the relationships among wheat end-use quality traits.  Mixograph traits were correlated among each other consistently and hence are useful to assess dough properties, which can be independent criteria to select wheat breeding material at all stages.  Our study showed that correlations of LV with MDRTs were not consistent and corroborated that prediction of LV based on dough rheology traits would not be reliable. MPT was the only MDRT that showed reasonably good heritability (0.74), moderately consistent correlations (three environments) with LV and a large number (22) of AE QTLs. Using mixograph traits, LV, SV and GPC as discrete criteria to evaluate the end-use quality at the final stages of wheat varietal development might help determine their economic value more comprehensively.  Our results suggested that while selecting QTLs for wheat quality breeding, their PVE, stability across environments and preferential expression in specific agro-climatic zones all need to be considered.  Considering the significant GEI underlying the mixograph traits, low heritability and large number of QTLs with locationspecificity, focussing on individual agro-climatic zones might be more effective while developing wheat varieties with favourable dough properties. Acknowledgements We thank the Department of Biotechnology, Government of India for financial support (Project Code: GAP244826) and the Council of Scientific and Industrial Research, Government of India for research fellowships to RP and EM. Our grateful thanks to the two anonymous Reviewers whose inputs helped us improve the manuscript. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jcs.2015.05.002. References American Association of Cereal Chemists, 2004. Methods 54e40A. In: Approved

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