Molecular genetic mapping of quantitative trait loci associated with loaf volume in hexaploid wheat (Triticum aestivum)

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Journal of Cereal Science 47 (2008) 587–598 www.elsevier.com/locate/jcs

Molecular genetic mapping of quantitative trait loci associated with loaf volume in hexaploid wheat (Triticum aestivum) M. Elangovana, R. Raia, B.B. Dholakiaa, M.D. Lagua, R. Tiwarib, R.K. Guptab, V.S. Raoc, M.S. Ro¨derd, V.S. Guptaa, a

Plant Molecular Biology Unit, Biochemical Sciences Division, National Chemical Laboratory, Pune 411008, India b Directorate of Wheat Research, Karnal 132001, India c Genetics and Plant Breeding Unit, Plant Sciences Division, Agharkar Research Institute, Pune 411004, India d Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D 06466 Gatersleben, Germany Received 16 April 2007; received in revised form 2 June 2007; accepted 2 July 2007

Abstract Major efforts in wheat research are being made to improve the yield and quality of wheat. Loaf volume (Lv) is the main quality parameter deciding the bread making potential of wheat. To genetically dissect quantitative trait loci (QTLs) for Lv, a Recombinant Inbred Line (RIL) population (F8) was developed from a cross between two Indian wheat varieties ‘‘HI 977’’ and ‘‘HD 2329’’. A total of 914 SSR and 100 ISSR primers were used for molecular analysis and the genetic map comprising 19 chromosomes was constructed with 202 SSR markers and 2 HMW glutenin subunit loci: Glu-B1 and Glu-D1. The phenotypic data were collected from six environments including three different agro-climatic zones for 2 consecutive years. Dissection of Lv through AMMI model revealed significant G  E variance for the trait. QTL analysis was performed using composite interval mapping. A total of 30 QTLs for Lv were detected and significant QTLs were identified on 6B and 6D chromosomes; 1B, 1D, 2A, 3A, 5B and 5D also contributed genetically to Lv. Association between 6B and 6D QTLs and variable expression of gliadins on group 6 chromosomes were discussed. QTLs detected in this study were compared with other QTL analysis in wheat. r 2007 Elsevier Ltd. All rights reserved. Keywords: Breadmaking; Loaf volume; Wheat quality; QTL; SSR

1. Introduction Bread making quality (BMQ) of wheat is considered to be a complex trait influenced by interactions of many biochemical traits such as seed protein quality and content (Payne et al., 1987), starch quality and content (Gray and Bemiller, 2003) and oil content (Helmerich and Koehler, 2005) supplemented by various physico-chemical traits such as moisture content, water retention capacity, vacuole formation, grain hardness and texture (Huang et al., 2006). Considerable studies have been accomplished in BMQ during the last two decades. Payne et al. (1987) and GarciaOlmedo et al. (1982) evaluated some of the alleles at the high-molecular-weight (HMW) glutenin subunit loci Corresponding author. Tel.: +91 20 25902247; fax: +91 20 25902648.

E-mail address: [email protected] (V.S. Gupta). 0733-5210/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jcs.2007.07.003

(Glu-A1, Glu-B1 and Glu-D1) and scored their importance to wheat quality. Later, the combined effects of alleles at both the HMW and low-molecular-weight (LMW) glutenin loci on dough strength were investigated (Eagles et al., 2002; Gupta et al., 1989; Nieto-Taladriz et al., 1994). The HMW and LMW glutenins through disulfide bonds interact to make a gluten network capable of holding the gas evolved during fermentation of the dough (Shewry and Tatham, 1997). A large number of factors have been suggested to affect BMQ such as, lipids (Pomeranz and Chung, 1978), pentosans (D’Appolonia et al., 1970), hydrolytic enzymes and LMW ‘soluble’ proteins within the albumin and globulin fractions (Pogna et al., 1990; Zawistowska et al., 1986). Some of these proteins are enzymes, involved in metabolic processes, while others are amylase and protease inhibitors playing protective roles in plants (Bietz, 1988). Along with quantitative trait loci

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(QTLs) affecting grain protein content (Dholakia et al., 2001; Groos et al., 2003; Prasad et al., 2003; Turner et al., 2004), few studies reported additional genetic loci that influence dough rheology and baking quality (Groos et al., 2004; Law et al., 2005; Perretant et al., 2000). In order to understand BMQ of wheat many direct and indirect methods are being exploited. The indirect methods include mixograph, farinograph, sedimentation volume and extensograph (Bushuk, 1985). Loaf volume (Lv) is usually considered as one of the most important and direct measures of BMQ (Weegels et al., 1996). However, Lv estimation of bread demands large sample material, cost and labor. Visco-elastic property of wheat proteins is therefore, studied to dissect the genetic effect of loci governing this trait (Ma et al., 2005). Diagnostic markers are available for BMQ influencing parameters like HMWand LMW-glutenin subunits, PinA, PinB, secalins, grain protein content (Gale, 2005), however, studies on Lv, a main direct estimate of BMQ are limited. Moreover, QTL studies on Lv have suggested that the HMW- and LMWglutenin subunits cannot be used as indirect measure of Lv (Rousset et al., 2001). Loci on chromosome 3A (Law et al., 2005) and chromosome 2A (Kuchel et al., 2006) were found to control Lv directly. Identifying such loci for Lv would help in implementation of and early progeny selection for BMQ using marker assisted breeding. The Recombinant Inbred Lines (RILs) population is commonly used to dissect the QTLs associated with quality traits in wheat (Campbell et al., 1999; Kuchel et al., 2006). In order to study Lv, we analyzed a RIL population that was developed from a cross between HI 977 and HD 2329 using SSR markers. The phenotypic traits were analyzed by growing the RIL population in three different agroclimatic conditions in India for 2 consecutive years (2003–2004; 2004–2005). The goals of this study were: (1) to construct framework map using SSR markers (2) to identify Lv QTLs using composite interval mapping (3) to study the Q  E interaction of Lv across different locations and (4) to study the relevance of Glu-1 loci in determining the Lv. 2. Materials and methods 2.1. Plant material RIL population of F8 generation comprising 105 lines was derived from a cross between HI 977 and HD 2329. The pedigree for HI 977 was [Gallo/AUST II 61.157 /2/ Ciano 67/NO66 /3/ Yaqui50-Enano/3*Kalyansona] and for HD 2329 was [HD 2252/UP 262]. The cultivar HI 977 has been known for its good BMQ having Glu-A1 (2*), Glu-B1 (17+18) and Glu-D1 (5+10) and HD 2329 for poor BMQ with Glu-A1 (2*), Glu-B1 (7+9) and Glu-D1 (2+12). The population was developed at Directorate of Wheat Research (DWR), Karnal, India by single seed descent from F2 generation onwards, bulked plantwise at F8 generation and grown at three different agro-climatic regions (Karnal—North Western plain zone, Kota—

Central zone and Pune—Peninsular zone) for 2 consecutive years, in an augmented block design (Table 3). The RILs were not replicated within a location (Hessler et al., 2002) and the design comprised an Augmented Randomized Complete Block (RCB) design having 8 blocks with 20 lines and 5 replicating checks, in each block. The lines were grown in 2 rows with 2 m  0.23 m spacing in between the lines. The meteorological data were collected including temperature, humidity and rainfall (Gupta et al., 2002). The data analysis was performed with IRRISTAT (IRRI, Philippines) using ‘‘Single site analysis module’’. The analysis of variance (ANOVA) revealed significant difference among the genotypes of the population in each location for Lv. The G  E interaction of RILs with the environments was deciphered by using AMMI (Additive Main effects and Multiplicative Interaction) model with IRRISTAT (IRRI, 2002) software through ‘‘Cross site analysis module’’. Two year’s data at three sites were treated as six environments in the analysis. The sum of squares was first partitioned into genotype, environment, and G  E interaction, then, the sum of squares for G  E interaction term was further partitioned by principal components analysis using the AMMI model (Crossa et al.,1990; Gauch,1992) using the formula Y ij ¼ u þ gi þ ej þ

n X

lk ajk gik þ Rij ,

k¼1

where Yij is the value of the ith genotype in the jth environment, is the grand mean, gi is the mean of the ith genotype minus grand mean, ej is the mean of the jth environment minus the grand mean, lk is the singular value for the principal component analysis axis k, aik and gjk are the principal component scores for principal component analysis axis k of the ith genotype and jth environment, respectively, and Rij is the residual. Standard error differences pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi between two check means were calculated by 2MSE=b; where ‘MSE’ is error mean sum of square (checks) and ‘b’ number of blocks. Broad sense heritability (H2) using the formula H2 ¼ 1M2/M1; where M1 is the mean sum of square due to genotypes and M2 is the mean sum of square due to genotype  environment (G  E). The adjusted means were calculated for each treatment by interpolation of block effects and used for QTL analysis. 2.2. Lv analysis The grain samples were tempered (AACC Method 26-10, American Association of Cereal Chemists, 2000) and milled using a Brabender Senior Quadrumat Mill (AACC Method 26-21A) with a 70% extraction rate. The bread making performance of the flour was determined using the straight dough (AACC Method No. 10-10 B), with the remixing procedure of Irvine and McMullan (1960) with minor modification. The bread formula for each loaf included 100 g flour (14% moisture), 60 ml water, 5 g sugar,

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2 ml clarified butter, 2.0 g salt and 2.5 g yeast. The following baking schedule was adopted: mixing all ingredients except clarified butter for 1 min, fermentation for 1 h 40 min at 3071 1C and 88% r.h., kneaded again with clarified butter for 40 s, degassing, proofing for 50 min at 3271 1C and 90% r.h., and baking for 12 min at 22071 1C using rotatory oven. Loaves were then placed on a wire grid for about 2 h and Lv was measured by the rapeseed displacement method (Dhingra and Jood, 2004). 2.3. HMW glutenin subunits score HMW glutenin subunits were extracted from the different RILs as described by Fullington et al. (1983). Thirty milligram flour sample was treated with a solution containing 1 ml of 2% (w/v) SDS, 5% (v/v) 2-mercaptoethanol and 0.062 M Tris (pH 6.8). The mixture was sonicated for 3 min, shaken and left at room temperature for 30 min, then glycerol was added to the mixture. The samples were separated on denaturing PAGE with SDS and stained with coomassie blue R.250 as described by Lawrence and Shepherd (1980). The Glu-B1 and Glu-D1 alleles were scored and included in the mapping data. 2.4. Molecular marker analysis The DNA was isolated from the leaf tissue of parental genotypes and RILs of 14-day-old seedlings using CTAB extraction protocol (Stein et al., 2001). In all, 914 SSR primers (gwm, barc, psp, cfd, cfa, wmc) and 100 ISSR primers (UBC, British Columbia, Canada) were used in this study. The amplification was carried out using SSR primers following the protocol of Ro¨der et al. (1998) and that with ISSR primers according to Ammiraju et al. (2001). All SSR primers except gwm were resolved on 6% polyacrylamide gel and silver stained (Sanguinetti et al., 1994) and ISSR primers were screened on 2% agarose gel, followed by ethidium bromide staining. The gwm primers were resolved following the protocol of Ro¨der et al. (1998), using Automated Laser Fluoresence sequencer (ALF express) and the software ‘‘Fragment Analyzer v 1.02’’ (Amersham Biosciences, UK) was used to estimate the fragment size with the help of internal and external standards. 2.5. Framework map construction and QTL analysis Mapmaker v. 3.0 (Lander et al., 1987) was used to analyze the data of 217 loci including 2 loci for HMWglutenin subunits, for framework map construction. The recombination frequency (0.4) and LOD value 3.0 were used as threshold limits for linkage group construction. The unskewed markers were grouped at LOD 3.0 and the first order command was used to order the markers in a linkage group. Additional markers (skewed markers) were placed into the linkage group using ‘‘try’’ command. The order of markers and stability of the linkage groups were

589

fine-tuned using ‘‘ripple’’ command. The QTL analysis was performed and the main effect QTLs (M-QTL) were identified using QTL Cartographer v. 2.5 (Basten et al., 1994, 2000). LOD score of 2 was used for declaring the presence of a putative QTL. The threshold LOD scores for detection of QTLs were calculated based on 1000 permutations (Doerge and Churchill, 1996). The Model 6 of the CIM was used with forward regression and backward elimination module of QTL Cartographer for scanning intervals of 2 cM between the markers and putative QTLs with a window size of 10 cM. Five markers were used as the background control for forward–backward stepwise regression. The position, genetic effects and percentage of phenotypic variation of the QTLs were estimated at the significant LOD peak in the region under consideration. 3. Results 3.1. Phenotypic analysis of Lv Values of Lv for the two parents, along with standard error deviation (S.E.) and the RILs grown at three locations, for two consecutive years are depicted in Table 1. HI 977 was superior to HD 2329 for Lv in all the environments and the mean Lv ranged from 524.90 cm3 (KotLv2) to 562.30 cm3 (PunLv1). The maximum and minimum values for Lv in all the environments for the RILs showed transgressive segregation, revealing presence of favorable and unfavorable alleles. The ANOVA for Lv with AMMI model is presented in Table 2. Contribution to the sum of squares due to Genotype, G  E interaction and Environment were calculated as percentage of total sum of squares (Tarakanovas and Ruzgas, 2006). The major components of total sum of squares due to Lv were genotype (38%) and G  E interaction (47%). The AMMI model (Table 2) deciphered the G  E interaction into 4 principal components, the first interaction principal component axes (AMMI component 1) score accounted for a large portion of the sum of squares with G  E interaction (22%), and the second component (AMMI component II) score accounted for 13%. The meteorological parameters (Table 3) showed that Kota had lower humidity compared to Karnal and Pune; and Karnal had low temperature compared to Kota and Pune during wheat growing season. Also the average monthly rainfall was the highest in Pune (36.2 mm) followed by Kota (11.5 mm) and Karnal (5.7 mm). Rank correlation for all the possible combinations of Lv is presented in Table 4. The correlation among the traits recorded at the same location for 2 years, different locations for each year and different locations for 2 years was significantly small in all the cases, except for KarLv1 Vs KotLv1 (0.862). Between the 2 years data, comparatively less correlation was observed in the year 2004–2005. KarLv2 did not show any correlation with KotLv2 and

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Table 1 Distribution of Lv in parents and RIL population Trait

HI 977 (cm3)

Description

HD 2329 (cm3)

S.E. (cm3)

Average (cm3)

Range (cm3) Min

Max

KarLv1

Karnal loaf volume (2003–2004)

570.00

500.00

8.66

534.20

480.00

590.00

KarLv2

Karnal loaf volume (2004–2005)

576.25

493.75

12.12

541.42

477.13

629.13

KotLv1

Kota loaf volume (2003–2004)

593.75

504.38

3.46

547.97

487.38

614.38

KotLv2

Kota loaf volume (2004–2005)

571.88

501.88

6.24

524.90

448.63

603.63

PunLv 1

Pune loaf volume (2003–2004)

607.50

540.63

6.90

562.30

502.13

607.13

PunLv 2

Pune loaf volume (2004–2005)

581.88

525.63

7.75

552.29

485.75

601.75

S.E. represents the standard error for the parents. Average for each trait was calculated from overall population. Range represents minimum and maximum of the RILs.

Table 2 Analysis of variance for Lv with AMMI model Source

Table 4 Spearman Rank correlation between Lv recorded at different environments

Lv D.F.

Sum of squares (S.S)

Mean sum of square (M.S)

Explained (%)

Traits

KarLv1

KarLv2

KotLv1

KotLv2

PunLV1

KarLv2 KotLv1 KotLv2 PunLv1 PunLv2

0.585*** 0.862*** 0.506*** 0.559*** 0.273**

0.477*** 0.083ns 0.082ns 0.135ns

0.513*** 0.544*** 0.213*

0.473*** 0.241*

0.532***

Genotypes Environment Genotype  Environment (G  E) AMMI component 1 AMMI component 2 AMMI component 3 AMMI component 4 G  E residual

104 5 520

198 394 75 628 247 452

1907.64** 15125.6** 475.86**

38.0 15.0 47.0

108 106 104 102 100

114 578 65 326 39 949 19 530 8070

1060.91** 616.27** 384.12** 191.46**

22.0 12.5 7.6 3.7 1.5

Total

629

521 474

The AMMI components were denoted as AMMI 1, AMMI 2, etc. ANOVA was calculated from the values of RILs across all six environments, significance of AMMI components were indicated with asterisk symbol (*). **Po0.01.

Table 3 Meteorological parameters of different locations Environment

Karnal Kota Pune

Temperature (1C)

Humidity (%)

Min.

Max.

Min.

Max.

10.2 14.8 15.0

27.7 29.4 32.6

39.3 22.7 39.0

86.0 62.8 84.8

Monthly rainfall (mm) 5.7 11.5 36.2

Meteorological data from different locations represent mean temperature, mean relative humidity and mean rainfall during the growing seasons.

PunLv2. Though correlation was less, the broad sense heritability of Lv (0.75) was higher and revealed that major part of this trait is under genetic control.

ns—non-significant. ***Po0.001. **Po0.01. *Po0.05.

3.2. Framework map construction The genotypic data of HI 977  HD 2329 population was generated using 212 SSRs, three ISSRs and two HMW glutenin loci (Glu-B1 and Glu-D1). Linkage groups were assigned to chromosomes when the groups had two or more SSR loci that had been assigned to a particular wheat chromosome in previously published maps (Ro¨der et al., 1998; Somers et al., 2004). Of the 217 markers used for population screening, 202 SSRs, with two HMW glutenin loci formed 19 linkage groups. At a LOD score of 3, nine markers (4.1%) were unlinked and four markers mapped in the same loci, hence were not considered for the linkage analysis. The framework map is depicted in Fig. 1. The length of A genome of the map was 909 cM, while that of B and D genome was 1100.7 and 1152 cM, respectively. The overall arrangement of the markers was same as the published microsatellite map of Ro¨der et al. (1998)) and wheat consensus map of Somers et al. (2004). The distribution of markers was quite even except for a few linkage groups viz., 1A (51.8 cM), 5A (50.4 cM) and 6D (51.2 cM) having a gap of more than 50 cM. The chromosome 1B and 4D had maximum of 23 and

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Fig. 1. Linkage map of HI 977  HD 2329. Mapped markers are indicated on the right and their corresponding cumulative genetic distances (cM) are indicated on the left.

minimum of 4 markers, respectively. The chromosome 2B had maximum linkage distance (300.3 cM), followed by 5D (297.0 cM), 1B (276.1 cM), 6B (252.2 cM) and 1A (248.9 cM). The total map length was 3161.8 cM and the mean interval between loci was 15 cM. The Marker loci were subjected to w2-test at Po0.05% and identified that 41.17% (84) loci were skewed, compared to 58.83% (120 loci) fitting in the ratio of 1:1. 3.3. QTL identification for Lv A total of 30 QTLs were identified for Lv on 12 chromosomes (Table 5), which explained 5.85–44.69% of phenotypic variation. The frequency of QTLs was the highest for group B (11) followed by group A (10) and group D (9). However, Group D QTLs were represented in more than one location compared to other genomes. Group 5 chromosomes had the maximum number of QTLs (8) followed by group 6 (7), group 2 (6), group 1 (5) and group 3 (3). The chromosome 6B had maximum of four QTLs followed by three QTLs on 2A, 2B, 3A, 5A and 5D. The dominance value was zero for all the QTLs suggesting the absence of epistatic QTLs. Among 30 QTLs, 21 had positive additive effect and 9 had negative additive effect, which implied the important role of the inferior parent HD 2329 in carrying favorable alleles of Lv. The highest positive additive effect was observed for QLv.ncl3A.2, which explained 18.5% variation for Lv and the

lowest negative additive variance for QLv.ncl-6D.3, governing PunLv1 with 44.69% contribution. Of the total 30 QTLs, 15 were detected on chromosomes 1D, 2A, 2B, 3A, 5A, 5D, 6B and 6D explaining 5.93–22.5% phenotypic variation of Lv for Karnal location. Similarly 9 QTLs were identified on chromosomes 1A, 1B, 2A, 2B, 5A, 5B, 5D and 7B, which explained 6.5–36.71% variance of Lv for Kota location. Also 9 unique QTLs were detected for Pune location on chromosomes 1B, 2B, 3A, 5A, 5D, 6B and 6D, explaining 5.8–44.6% variance due to Lv. Contribution of the positive allele from poor parent was realized through chromosome 1A QTL QLv.ncl-1A.1 for trait KotLv2 having negative additive effect. Two QTLs were detected on Chromosome 1B, for the trait KotLv2 and PunLv2. Chromosome 1D had two QTLs for the trait KarLv1, which explained 10.8% and 7.3% genetic variance. The chromosome 2A, had 3 QTLs governing KarLv2 among which, two had positive additive effect and the third one had negative additive effect. Chromosome 2B had three QTLs each governing KarLv2, KotLv2 and PunLv2, with correlation 10.4%, 10.2% and 5.8%, respectively. Three QTLs were detected on chromosome 3A, two for KarLv2 and one for PunLv1, having 19.1%, 18.5% and 16.7% contribution, respectively. The traits KotLv2, KarLv1 and PunLv1, each had one QTL on chromosome 5A, while KotLv2 had two QTLs on chromosome 5B. Chromosome 5D had three QTLs for PunLv1, KarLv1, KotLv1 and KarLv2. The QTL

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592 Table 5 Composite interval mapping for Lv QTL

Chromosome

QLv.ncl-1A.1 QLv.ncl-1B.1 QLv.ncl-1B.2 QLv.ncl-1D.1 QLv.ncl-1D.2 QLv.ncl-2A.1

1A 1B 1B 1D 1D 2A

QLv.ncl-2A.2 QLv.ncl-2A.3 QLv.ncl-2B.1 QLv.ncl-2B.2 QLv.ncl-2B.3 QLv.ncl-3A.1 QLv.ncl-3A.2 QLv.ncl-3A.3 QLv.ncl-5A.1 QLv.ncl-5A.2 QLv.ncl-5A.3 QLv.ncl-5B.1 QLv.ncl-5B.2 QLv.ncl-5D.1 QLv.ncl-5D.2

2A 2A 2B 2B 2B 3A 3A 3A 5A 5A 5A 5B 5B 5D 5D

QLv.ncl-5D.3 QLv.ncl-6B.1 QLv.ncl-6B.2 QLv.ncl-6B.3 QLv.ncl-6B.4 QLv.ncl-6D.1

5D 6B 6B 6B 6B 6D

QLv.ncl-6D.2 QLv.ncl-6D.3 QLv.ncl-7D.1

6D 6D 7D

Trait

KotLv2 PunLv2 KotLv2 KarLv1 KarLv1 KarLv2 KotLv2 KarLv2 KarLv2 KotLv2 KarLv2 PunLv2 KarLv2 KarLv2 PunLv1 KotLv2 KarLv1 PunLv1 KotLv2 KotLv2 PunLv1 KarLv1 KotLv1 KarLv2 KarLv2 PunLv1 KarLv2 KarLv1 KarLv2 PunLv2 PunLv1 PunLv1 KotLv2

Marker Right

Left

Xbarc148 Xgwm1078 Xgwm1028 Xgwm4063 Xgwm903 Xgwm830 Xgwm830 Xgwm1045 Xgwm1115 Xgwm1128 Xgwm501 Xgwm1300B Xgwm369B Xgwm757 Xgwm1071B Xgwm415 Xgwm156 Xcfd20 Xbarc88 Xgwm443C Xbarc58 Xgwm190 Xgwm190 Xgwm736B Xbarc178 Xgwm921 Xgwm1199C Xgwm1233 Xcfd13 Xcfd13 Xgwm459 Xgwm732B Xgwm350

Xgwm691A Xgwm1130 Xwmc406 Xgwm337 Xcfd83 Xgwm249A Xgwm249A Xcfa2263 Xgwm761 Xgwm429 Xgwm1300B Xgwm526A Xgwm369A Xgwm638 Xgwm1038 Xgwm4879 Xcfd20 Xbarc142 Xgwm67 Xgwm234B Xgwm1559 Xgwm4265B Xgwm4265B Xgwm1016 Xgwm132C Xgwm132B Xgwm1199A Xbarc198 Xgwm1009C Xgwm1009C Xgwm1103B Xgwm732A Xgwm885

LOD

Position

Additive

R2  100

3.19 2.14 3.32 4.40 2.81 3.00 2.05 2.07 2.94 2.24 2.86 2.30 2.49 2.34 2.17 2.68 2.17 2.13 6.22 2.05 2.31 2.67 2.01 2.95 2.62 2.77 3.67 2.22 3.47 7.07 3.30 3.11 3.53

83.81 46.71 67.31 72.61 79.11 24.01 22.01 111.41 61.21 54.61 64.11 205.51 14.01 46.41 109.11 70.41 92.31 135.11 66.41 27.41 28.01 83.61 77.61 275.41 40.01 58.01 112.01 168.61 12.01 4.01 98.71 142.61 99.61

12.31 6.24 29.07 9.02 7.54 37.11 29.20 11.00 11.67 10.78 11.99 6.17 38.24 39.20 10.81 10.08 6.24 10.71 31.36 14.18 7.75 6.89 9.04 34.94 35.33 17.02 29.60 10.68 37.21 14.93 16.51 17.11 14.51

13.50 6.07 29.18 10.82 7.33 20.14 30.33 9.89 9.21 10.20 10.48 5.85 19.14 18.49 16.72 6.50 5.93 17.85 36.71 14.47 9.62 6.99 9.59 22.49 20.55 44.11 22.33 17.93 20.22 36.06 44.20 44.69 15.03

Right and Left represent flanking markers to the corresponding QTL. a Nomenclature for QTLs in wheat: the Q for QTLs should be followed by a trait designator, a laboratory designator, a hyphen (-) and the symbol for the chromosome in which the QTL is located. b Positive value is associated with an increasing effect from HI 977alleles and negative value is associated with an increasing effect from HD 2329 alleles KarLv1—Karnal loaf volume 2003–2004, KarLv2—Karnal loaf volume 2004–2005, KotLv1—Kota loaf volume 2003–2004, KotLv2—Kota loaf volume 2004–2005, PunLv1—Pune loaf volume 2003–2004 and PunLv2—Pune loaf volume 2004–2005.

(QLv.ncl-5D.2) governed both KarLv1 and KotLv1, with contribution of 6.99% and 9.59%, respectively. Four QTLs were identified on chromosome 6B of which two were for KarLv2, one for PunLv1 with positive additive effect and one for KarLv1 with negative additive effect. Three QTLs were identified on chromosome 6D, QLv.ncl-6D.1 for KarLv2 and PunLv2 and two QTLs were detected on the same chromosome for PunLv1. A single QTL was observed on chromosome7D for KotLv2 explaining 15.03% genetic variance. The QTL QLv.ncl3A.1 on chromosome 3A appeared to be a putative common QTL for KarLv1 and KarLv2, as the LOD value for KarLv1 was below the threshold level (2.0). Similarly a putative common QTL for KotLv1 and KotLv2 was detected on chromosome 5D (QLv.ncl-5D.2). PunLv1 and

PunLv2 had one putative common QTL on chromosome 6D (QLv.ncl-6D.1). 4. Discussion 4.1. Lv shows significant influence of environment The population means for Lv in all the environments posed a normal distribution (Table 1), without skewing towards either of the parents, suggesting absence of epistatic effects between the QTLs (Blanco et al., 2006). The RIL population developed for the present study showed both positive and negative transgressive segregants, suggesting the possibility of finding positive alleles in the parents with poor Lv (HD 2329) while negative alleles

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in the parent with better Lv (HI 977). Low rank correlation among the traits at the same location and lower rank correlation in the year 2003–2004 as compared to 2004–2005 suggested influence of environment on these traits. Similar influence of environment on genotypes was reported by Fufa et al. (2005) and Tarakanovas and Ruzgas (2006). In our study significant effect of G  E to total variation (47%) was observed for Lv (Table 2), which was three times compared to variation due to environment (15%). Studies on predictive assessment revealed that AMMI with only two interaction principal component axes was the best predictive model (Zobel et al., 1988). In our study, the AMMI component 1, which is the major component of G  E, explained only 22%, followed by AMMI component 2 (12%), indicating the poor fit of this model to help in selection of a single stable genotype for Lv. But this G  E interaction was not due to rainfall (Table 3), as the experiments were carried out under wellirrigated condition. We also estimated the effect of rainfall by grouping the data set based on rainfall data and performed AMMI analysis (data not shown) and identified that though the variation due to rainfall exist, still, it cannot be considered as only important factor contributing to G  E variation. Also, molecular markers can be effectively deployed to aid in selection of traits influenced by high G  E. Moreover, correlation studies revealed that, KarLv2 did not show any correlation with PunLv2 and KotLv2. Also, PunLv2 depicted low correlation with all the other locations for both the years further confirmed the role of environmental variation in the trait values. Therefore, pooled QTL analysis was not performed and all the data sets were analyzed separately. Such an analysis necessitates identification of QTLs across different environments and molecular markers for these QTLs can be exploited in breeding program to develop region-specific genotypes. 4.2. Features of framework map The map length reported in this study (3161.8 cM) was comparatively less than the previously published maps (3551 cM) by Nelson et al. (1995a–c), Van Deynze et al. (1995), and Marino et al. (1996) and the map (4110 cM) by Chalmers et al. (2001). This could be due to formation of only 19 linkage groups instead of 21 groups for the present map. However, even with 19 linkage groups, it was more than the consensus map (2569 cM) of Somers et al. (2004), which might be due to linkage gap and low marker density compared to the consensus map. In our present map skewed markers represented 41.17% of the total markers. Framework maps with skewed markers have been constructed in wheat by Suenaga et al. (2005) and Nachit et al. (2001). Also, molecular markers representing skewed segregation have been reported earlier in several Triticeae species (Blanco et al., 1998; Heun et al., 1991; Liu and Tsunewaki, 1991). These distortions could be due to chromosomal rearrangements (Tanksley, 1984); alleles

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inducing gametic or zygotic selection (Nakagahra, 1986), reproductive differences between the two parents (Foolad et al., 1995), lethal genes (Blanco et al., 1998), sterility induced by the distant genetic parental background or selective survival of RILs caused by the single-seed descend method (Nachit et al., 2001). Distorted loci may lead to spurious linkages and a reduced estimate of recombination value (Kammholz et al., 2001). The unskewed markers were used to construct linkage groups and then skewed markers were introduced, thereby the possibility of spurious linkage by distorted loci in this study was eliminated. Also the order of loci, were checked using ‘ripple’ command and compared with those in reported maps. The present map showed mean interval of 15 cM between the two loci and uniform distribution of markers indicating usefulness of the map for QTL analysis (Campbell et al.,1999; He et al., 2001; Suenaga et al., 2005).

4.3. QTL analysis for Lv In deciphering the Lv QTLs, we used a small population of 105 RILs; however, Price (2006) postulated that QTL positions identified using small populations were nearly same as that of large mapping population. The gene isolation for important traits such as wheat frost tolerance (cbf3, 74 lines) (Vagujfalvi et al., 2003), wheat grain protein (GPC, 74 lines) (Distelfeld et al., 2004), barley photo period response (Ppd H1, 94 lines) (Turner et al., 2005) have been achieved using population less than 100 individuals. However, sampling affects the confidence interval and maximum LOD may not be found at true QTL position (Darvasi et al., 1993). Lv is due to the ability of dough to hold the gas produced during fermentation within its evenly distributed discrete cells and maintain the firmness after baking (Simmons, 1989). Law et al. (2005) predicted a QTL on chromosome 3A, for Lv, but did not come to a conclusive location of that QTL. Significant QTLs governing Lv (Table 5) were identified on chromosomes 2A, 5D and 6D, which explained 7–36% phenotypic variance. Three QTLs detected on chromosomes 2A (QLv.ncl-2A.1), 5D (QLv.ncl5D.2) and 6D (QLv.ncl-6D.1) were found to be represented in more than one location. Especially QLv.ncl-6D.1 detected in both KarLv2 and PunLv2, while the LOD value was less than 2 for KotLv2 and PunLv1. Though, CIM analysis failed to identify a single major consistent QTLs across all the locations, which could be due to the fact that most of the QTLs detected with higher LOD score in one environment were not detected in the other environments. This proves that in an individual environment, QTLs may often escape detection at the threshold value of LOD score due to Q  E interactions that lead to variable expression of QTLs (Kulwal et al., 2005). Though consistency of QTLs is affected due to larger G  E effect across environments, development of an ideotype for a specific environment could be well achieved through the

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environment-specific QTLs (Asins, 2002; Dholakia et al., 2001). In this study we have identified significant number of QTLs on group 6 chromosomes controlling Lv, of which the QTL QLv.ncl-6D.1 was a prominent one (Fig. 2). 4.4. Effect of glutenin and gliadin loci on Lv The HMW loci of chromosome 1B and 1D were also placed in the framework map (Fig. 1). The relationship of Glu-B1 and Glu-D1 loci with BMQ is well established (Garcia-Olmedo et al., 1982; Payne et al., 1987). In our study the relationship between Lv and glutenin loci could not be achieved, and it supports similar results of Kuchel et al. (2006). However, Skerritt et al. (2003) showed a relationship between glutenin alleles and dough rheology but reported a lack of association between the glutenin alleles and BMQ. According to Rousset et al. (2001) BMQ is under complex control and the Glu-1 loci are only a

component of genetic control of these characters. Similarly, Hamer et al. (1992) questioned the accuracy of using HMW glutenin subunits to predict baking quality changes in the dough rheology traits. Our study is supported by recent findings on Lv, revealing the importance of loci other than HMW in governing BMQ potential in wheat (Kuchel et al., 2006; Law et al., 2005). The group 6 chromosomes were considered important for BMQ, due to their association with gliadins (the monomeric, hydrophobic proteins). The a, b and some g gliadins are encoded by tightly clustered genes at a single locus on each of the short arm of group 6 chromosomes, namely Gli-A2, Gli-B2 and Gli-D2 (Branlard et al., 2001). A correlation between gliadin surface hydrophobicity and Lv has been reported (Ornebro et al., 2003). Wanous et al. (2003) reported the influence of group 6 chromosomal arms on expression of HMW glutenin, through competition for aminoacid in protein synthesis. Recently Kawaura et al. (2005) suggested the importance of 6BS and 6DS

Fig. 2. Chromosomal locations of QTL for Lv in RIL population of HI 977  HD 2329. The dark interval represents the location of QTL having LOD value more than 2 and gray region represents QTL with LOD value less than 2. 1-KarLv1, 2 -KarLv2, 3-KotLv1, 4-KotLv2, 5-PunLv1, 6-PunLv2.

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Fig. 2. (Continued)

having higher a and b gliadin gene expression compared to 6AS and showed variable expression at 10 and 20 days after anthesis in Chinese spring. Also expression of a/b gliadin and LMW glutenin multigenes are independently regulated irrespective of their phylogenetic relationship in response to wheat seed maturation (Duan and Schuler, 2005; Kirch et al., 2005). We identified a consistent QTL (QLv.ncl-6D.1) for Lv on chromosome 6D, for KarLv2 and PunLv2, explaining 20% and 36% phenotypic variation. This QTL (Fig. 2) did not affect KotLv2, provided evidence for G  E interaction. In our study, we identified a QTL QLv.ncl-2A.1 on short arm of chromosome 2A, while Kuchel et al. (2006), identified a locus on the long arm of 2A for Lv. Zanetti et al. (2001) identified a strong QTL controlling sedimentation volume on chromosome 2A. Probably the QTL QLv.ncl2A.1 could be a new locus governing Lv. Law et al. (2005) also mapped a QTL associated with Lv to chromosome 3AL and they proposed a single gene (Lvl 1) responsible for Lv. In our study, we identified a putative QTL on chromosome 3AS contributing to Lv, which may be different locus for Lv.

4.5. Implications of Q  E on BMQ The Q  E interaction in the present case might be due to difference in day temperature at different locations. It has been reported that the BMQ enhanced up to 30 1C (Randall and Moss, 1990) and decreased at further temperature rise (above 35 1C) in a genotype-dependent manner (Blumenthal et al., 1993; Stone and Nicolas, 1995). D’ovidio and Masci, (2004) have also reported that the heat stress can affect the aggregation behavior of LMW glutenins. Furthermore, heat stress of wheat genotypes carrying Glu-D1 locus leads to change in glutenin particle size, resulting in abnormal dough characteristics (Don et al., 2003). Recently, Ma et al. (2005) have deciphered the environmental effect of LMW and its interaction with HMW in deciding the dough quality. In the present study QTL mapping for BMQ has been the major objective, due to its higher Q  E interactions. Particularly the markers could be useful in transferring the QTLs, which have poor heritability and high environmental interaction. These QTLs may represent minor proteins in wheat grain that modify gluten functionality

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(e.g. by affecting the degree of cross-linking and thus size distribution of the polymer), or transcription factors that control glutenin expression level (Ma et al., 2005). Asins (2002) suggested identification of many QTLs regardless of their effect and environmental sensitivity, as it would help in genetic dissection of this complex trait, apart from the postulated role of QTL analysis in MAS. Our study stresses the importance of location-specific combination of QTL for better phenotype and also gives clues to the breeding community regarding combining QTLs that are best expressed in specific environment. Towards this effort, the QTLs presented in our study will contribute in dissecting the genetic architecture of Lv that leads to BMQ potential of wheat. Acknowledgments M.E. acknowledges the Council of Scientific and Industrial Research (CSIR) for financial support through Senior Research Fellowship and also acknowledges DAAD-IAESTE (International Association for Exchange of Students for Technical Experience) for support to carry out part of research work at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Germany. Some of the SSR primers were kindly provided by the company Trait Genetics GmbH, IPK, Germany. The financial assistance for this project from Department of Biotechnology (DBT), New Delhi to NCL, DWR, ARI is gratefully acknowledged. References AACC, 2000. Approved Methods of American Association of Cereal Chemists, 10th ed. The Association, St. Paul, MN. Ammiraju, J.S.S., Dholakia, B.B., Santra, D.K., Singh, H., Lagu, M.D., Tamhankar, S.A., Dhaliwal, H.S., Rao, V.S., Gupta, V.S., Ranjekar, P.K., 2001. Identification of inter simple sequence repeat (ISSR) markers associated with seed size in wheat. Theoretical and Applied Genetics 102, 726–732. Asins, M.J., 2002. Present and future of quantitative trait locus analysis in plant breeding. Plant Breeding 121, 281–291. Basten, C.J., Weir, B.S., Zeng, Z.B., 1994. Zmap-a QTL cartographer. In: Proceedings of the Fifth World Congress on Genetics Applied to Livestock Production: Computing Strategies and Software, vol. 22, pp. 65–66. Basten, C.J., Weir, B.S., Zeng, B.S., 2000. QTL Cartographer. Version 2.5 A Reference Manual and Tutorial for QTL Mapping. Department of Statistics, North Carolina State University, Raleigh, NC. Bietz, J.A., 1988. Genetic and biochemical studies of nonenzymatics endosperms proteins. In: Heyne, E.G. (Ed.), Wheat and Wheat Improvement, second ed. Agron. Mono. No. 13. Am. Soc. Agron., Crop Sci. Soc. Am. and Soil Sci. Soc. Am, Madison, WI, pp. 215–242. Blanco, A., Bellomo, M.P., Cenci, A., De Giovanni, C., D’Ovidio, R., Iacono, E., Laddomada, B., Pagnotta, M.A., Porceddu, E., Sciancalepore, A., 1998. A genetic linkage map of durum wheat. Theoretical and Applied Genetics 97, 721–728. Blanco, A., Simeone, R., Gadaleta, A., 2006. Detection of QTLs for grain protein content in durum wheat. Theoretical and Applied Genetics 112, 1195–1204. Blumenthal, C.S., Barlow, E.W.R., Wrigley, C.W., 1993. Growth environment and wheat quality: the effect of heat stress on dough properties and gluten proteins. Journal of Cereal Science 18, 3–21.

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