Abstract Development of wheat varieties with high yield and good quality has been a major objective in wheat breeding. A BC 1 F  2-3  population was used to detect QTLs for wheat quality related traits： SDS sedimentation value （Ssd）， grain protein content （GPC）， grain hardness （GH） and 11 mixograph parameters， as well as five agronomic traits： spike length （SL）， spikelet number per spike （SPN）， grain number per spike （GN）， thousand grain weight （TGW）， and plant height （PH）. A total of 44 putative QTLs were detected in the present study， 31 for quality parameters and 13 for important agronomic traits， including three important major QTLs. One major QTL for Ssd QSsd.saas 1B.1， linked to barc137， explained on average 21.1% of the phenotypic variation in three environments. The allele increasing Ssd at this locus also significantly increased GN. The second locus on chromosome 1B with the linked marker Barc61 was a major locus for mixograph parameters. It explained 21.3%-32.5%， 24.3%-30.6%， 30.6%-37% and 20.1%-22.7% of phenotypic variation for mixing tolerance （MT）， weakening slope （WS）， midline peak time （MPTi） and midline time x=8 value （MTxW）， respectively. The third major QTL， explaining above 40% of plant height variation， close to Rht B1 on the short arm of chromosome 4BS， co located with QTL for quality and yield related traits.
　　Key words QTL; Wheat; Quality related traits; Yield related traits
　　 Bread wheat （Triticum aestivum L.） is one of the most important crops in the world. Improving grain yield and quality is a major focus of most wheat breeding programs around the world. Grain yield in wheat is determined concurrently by a number of plant and grain characteristics， such as grain number per spike （GN） and thousand grain weight （TGW）， which are complex quantitative traits controlled by multiple genes and highly influenced by environmental conditions[1].
　　The end use quality of wheat is also a complex character influenced by both genetic factors and environmental conditions[2-3]. SDS sedimentation volume （Ssd）， grain protein content （GPC）， grain hardness （GH） and mixograph parameters are important grain quality parameters in bread wheat. The Ssd， a parameter of gluten strength of flour， is well correlated with bread making quality and is an parameter widely used to evaluate flour quality in durum and bread wheat. Grain hardness affects milling yield， along with the size and shape of the flour particles. Protein content， one of the major factors affecting bread making quality[4]， is controlled by a complex genetic system and is strongly influenced by several environmental factors. Mixograph parameters are used to determine the rheological properties of dough. High molecular weight glutenin subunits （HMW GS）， low molecular weight glutenin subunits （LMW GS） and gliadins are now generally acknowledged to be the major contributors to bread making quality in wheat[5-6]. Overall， the gluten fraction in wheat could accounts for up to 1/3 of the variation in bread making quality[7]. It still leaves a substantial amount of variation unaccounted for and determined by non gluten factors. 　　Yield and quality traits are quantitative and controlled by multiple genes. The genetic basis of these traits is not well elucidated. Quantitative trait locus （QTL） analysis can detect the genetic factors controlling yield and quality related traits. Some QTLs for yield or yield related traits were reported previously[8-10]. The quality related traits for QTL analysis in bread wheat included sedimentation value[10-14]， grain protein content[10，13，15]， kernel hardness[16-18]， mixograph parameters[10，19]， and bread making quality scores[20]， and so on. Large numbers of QTL analyses for quality traits have been performed， but their influence on yield have seldom been analyzed. Nevertheless， a negative correlation existing between protein content and grain yield has been shown[8，12，21]. In high yield and high quality breeding programs， it is useful to understand the influence of quality loci on yield and the influence of yield loci on quality.
　　In the present study， 14 quality parameters and five agronomic related traits were investigated in a population consisting of 194 BC 1 F  2∶3  lines derived from two Chinese commercial varieties. Neither the recurrent parent ‘Yanzhan 1’ nor the donor parent ‘Gao 38’ contain good quality glutenin subunits. But Gao 38 has a high Ssd， so this variety is expected to carry alleles affecting the quality traits controlled by other loci. The objectives of the present study were： ① to detect new QTLs for wheat quality; ② to detect the QTLs for agronomic traits; ③ to analyze the influence of quality QTLs on yield related traits.
　　Materials and Methods
　　Plant materials and field trials
　　A population of 194 BC 1 F 2 materials from a single BC 1 F 1 plant derived from the cross between two Chinese commercial  varieties  ‘Gao 38’ and ‘Yanzhan 1’， and backcrossing by  Yanzhan 1.
　　All the BC 1 F 2 materials and the two parents were planted in Luoyang， Henan， China， in 2005-2006， and the BC 1 F 3 family lines and the two parents were planted in Zhengzhou and Luoyang， Henan， China， in 2006-2007. The field trials were conducted in randomized complete blocks with three replicates. Each plot consisted of three 4 m rows spaced 25 cm apart， with 100 plants in each row. Field management was in accordance with local practice and no pesticides were used.
　　Evaluation of quality and yield related traits
　　SDS sedimentation volumes （Ssd）， grain protein content （GPC）， hardness （GH） and mixograph parameters： midline peak time （MPTi， min）， midline peak value （MPV， %）， midline peak width （MPW， %）， midline left of peak value （MLV， %）， midline left of peak width （MLW， %）， midline right of peak value （MRV， %）， midline right of peak width （MRW， %）， midline time x=8 value （MTxV， %）， midline time x=8 width （MTxW， %）， mixing tolerance （MT， min） and weakening slope （WS， %） were determined using methods according to Li  et al. [22]. The measurements were calibrated using calibration  samples. 　　Spike length （SL， excluding awns）， spikelet number per spike （SPN） and grain number per spike （GN） were measured. Thousand grain weight （TGW） was calculated according to the grain weigh per plant and total grain number per plant. All agronomic traits were calculated based on the mean of 10 plants for each BC 1 F 3 family line.
　　Statistical analysis of data
　　The significance of differences among the genotypes of the population and among the environments was determined by analysis of variance （ANOVA） using general linear model （GLM） procedure of SPSS15.0 software. Pearsons correlation coefficients between traits were also calculated using SPSS15.0. The correlations based on the mean data across the three environments.
　　Microsatellite marker analyses
　　DNA was extracted following the hydroxybenzene chloroform improved methods for SSR marker analyses[23]. Microsatellite polymorphism was determined using parental DNA and DNA pool of ILs. Each pool was composed of 8 plants selected randomly. PCR analysis of the SSR markers was performed according to Roder  et al. [24].
　　A total of 1449 SSR markers and 6 storage protein loci （Glu A1， Glu B1， Glu D1， Glu A3， Glu B3 and Glu D3） were screened for polymorphism among the different populations. The 6 glutenin loci were detected by SDS PAGE[25]. The Primer of Rht B1 gene and the PCR procedure was referenced Wu et al.[26].
　　Linkage and QTL analysis
　　Linkage analysis of the molecular and morphological trait data was performed with MAPMAKER/EXP 3.0b[27]. QTL detection was determined by composite interval mapping （CIM） using QTL MAPPER2.0. The threshold for the detection of the QTL was fixed at a LOD （log of the odds） value of 2.5 and P<0.01. Markers detecting significant effects within regions of <20 cM were considered to be associated with the same QTL[28]. The proportion of phenotypic variance explained by segregation of each marker was determined by the R2 value. For each QTL， R2 was determined for the single marker closest to the identified QTL. All the QTLs were named as "QTL+trait+research department+chromosome" according to the international nomenclature for quantitative trait loci in wheat and related species[29]. In this paper， SAAS is the abbreviation of the research department： Shandong Academy of Agricultural Sciences.
　　Results and Analysis
　　Analysis of phenotypic data
　　All quality and yield related traits measured in this study showed a pronounced segregation （Table 1）. All traits had a wide range of variability in the population. Ssd， GPC， 11 mixograph parameters and five agronomic traits tended to have more variability in the population than was present between the parents， indicating transgressive segregation for these traits. The mean square （MS） for the genotypes and environments was calculated in the study （Table 2） and significant differences among the BC 1 F 2 families were observed for Ssd， GPC， GH and five agronomic traits. The differences were also significant between environments （except GN）， indicating that these traits （except GN） were influenced by genotype and environmental factors simultaneously. The MS for the genotypes and environments for mixograph parameters were not calculated as there was only one set of data for each  parameter. 　　Correlations among the quality and yield related traits
　　The correlations between quality traits， and between quality traits and agronomic traits were summarized in Table 3. GPC showed a strong positive correlation with GH， MPW， MPV， MTxV， MRW， MRV and MLW at P<0.01， and a positive correlation with MTxW and MLV at P<0.05. Ssd had a strong positive correlation with nine mixograph parameters except MPV and WS， but showed no significant correlation with GPC and GH. WS was negatively correlated to Ssd and all mixograph parameters except MPV and MRV. TGW had a significant negative correlation with most quality parameters， such as GPC， Ssd， MTxW， MPW， MT， MTxV， MRW， MRV， MLW and MLV. GN was positively correlated with Ssd， MTxW， MPTi and MT， and showed negative correlations with MPV and WS.
　　The linkage map construction
　　We analyzed 1449 SSR markers and six glutenin loci （Glu A1， Glu B1， Glu D1， Glu A3， Glu B3 and Glu D3） in the present study， and among them， 151 loci， including 148 SSR loci and three glutenin loci （Glu B1， Glu D1 and Glu B3） were polymorphic between the two parents. Among these， 74 loci were polymorphic in the descendant population. Sixty markers were located on 13 chromosomes （1B， 1D， 2A， 2B， 2D， 3A， 4B， 5B， 5D， 6B， 6D， 7A and 7B）. The polymorphic markers distribution was asymmetric， with two （2A， 2B， 3A， 6B， 6D and 7B） to 16 （1B） markers per chromosome. The others markers were not linked to each other and could not be located on the map.
　　QTL detection for quality parameters
　　The glutenin subunits at Glu A1， Glu B1， Glu D1， Glu A3， Glu B3 and Glu D3 loci of donor parent Gao 38 were n， 7+8， 2+12， Glu A3b， Glu B3b and Glu D3a， respectively. The glutenin subunits of the recurrent parent Yanzhan 1 were n， 14+15， 4+12， Glu A3b， Glu B3d and Glu D3a respectively. Both the donor parent and recurrent parent did not contain high quality glutenin subunits， but the donor parent and some progenies had high Ssd and higher mixograph parameters. So there must be other new loci affecting quality in this population.
　　All the QTLs for 14 quality parameters detected in the population are shown in Table 4， and their map position in Fig. 1. A total of 44 QTLs， 31 for quality related traits and 13 for agronomic traits were detected. For Ssd， five QTLs were observed， and  among  them， a major QTL near the centromere of chromosome arm 1BS， was named QSsd.saas 1B.1. It was detected in all three environments， explaining 18.2%-23.3% of the phenotypic variation. Barc137 was the marker most strongly associated with this QTL and the positive allele was from Gao 38. In order to prove the relation between the QTL and Glu B1 locus， we selected all the material in the population with same allele at Glu B1 locus to  analyze  the effect of barc137 locus on Ssd. The result showed that the effect of barc137 locus on Ssd was strongly significant with probabilities of 0.003， 6E 05 and 9.7E 05 in the three environments. At the same time， we selected all the material in the population with the same allele at the barc137 locus to analyze the effect of the Glu B1 locus， and the result showed that alleles 7+8 and 14+15 had no significant effect on Ssd. Therefore the QTL QSsd.saas  1B.1  was a new locus for Ssd. 　　 Another QTL， QSsd.saas 1B.2， located on short arm of chromosome 1B， close to Glu B3 locus， was detected in all three environments， explaining 7.3%-14.7% of the phenotypic variations. The QTLs QSsd.saas 4B was detected in two environments.
　　A total of four QTLs for GPC were detected. One （QGpc.saas 4B） was located in the marker interval wms375 wmc692  on the long  arm of chromosome 4B， and detected in two  environments.  It accounted for 10.3%-21.7% of phenotypic variance. The locus is a pleiotropic QTL which was associated not only with GPC， but also with Ssd and GH. At this locus， the Gao 38 allele increased GPC， Ssd and GH. The three additional QTLs for GPC were located on chromosome 5D， 5B and 6B.
　　For GH two QTLs were detected： QGh.saas 4B （12.6%-14.2%） and a QTL in the interval barc137 cfd65a on chromosome 1B in Zhengzhou 2006-2007， co locating with QSsd.saas 1B.1.
　　For 11 mixograph parameters， 20 QTLs were detected and distributed on chromosome 1B， 1D， 2D and 7B. Among of them， 13 were located on chromosome 1B and four on chromosome 1D. Seven QTLs were located in the marker interval barc61 cfd65b on chromosome 1B， with large effects on MT， WS， MTxW， MPTi， MRW， MLV and MTxV. The locus explained 21.3%-32.5%， 24.3%-30.6%， 20.1%-22.7%， 30.6%-37.0%， 9.7%-11.5%， 8.4%-10.2% and 15.2%-16.6% of the phenotypic variation for MT， WS， MTxW， MPTi， MRW， MLV and MTxV， respectively. The positive allele was from Gao 38. It indicated that the locus was important for bread making quality. In addition， the Glu B3 locus was also important for this character， influencing MT and WS significantly.
　　QTL detection for agronomic traits
　　Overall 15 QTLs were detected for six agronomic traits in the present study （Table 4， Fig.1）. A major QTL （QPh.saas.4B） linked to barc1096 for plant height was detected in all three environments and explained 42.4%， 43.8% and 36.7% of phenotypic variation in Luoyang 2007， Zhengzhou 2007 and Luoyang 2006， respectively. The allele from Gao 38 decreased the plant height by 8.6 to 17 cm compared with the allele from Yanzhan 1. The QTL on chromosome 4B detected for PH was located on the short arm. The locus showed a large effect on PH， explaining more than 40% of the phenotypic variation （reduced plant height by about 16.1 cm）. So in order to evaluate the linkage between the QTLs on chromosome 4B and the RhtB1 gene， we designed a pair of primers Rht B1cp for RhtB1[26] and analyzed the population and two parents using these primers. The RhtB1 was located on the short arm of chromosome 4B above the barc1096， at a distance of 6.3 cM. So we considered that the effects of the QTLs on 4B meight be caused by RhtB1， but it will need further investigation to be  validated. 　　For TGW， four QTLs were detected. The QTL QTgw.saas 4B was identified in all three environments， explaining  30.7% ，  18.4%  and 9.3% of the phenotypic variation in Luoyang2006， Luoyang2007 and Zhengzhou2007， respectively. At this QTL， the allele from Yanzhan 1 increased TGW. The other three QTLs for TGW were located on chromosome 1B near to Glu B3 locus， located in the marker interval cfd8 gdm43 on chromosome 5D， and located in the marker interval cfd72 cfd65c on chromosome 1D， which explained 11.0%， 9.8% and 7.1% of the phenotypic variation， respectively.
　　For GN， two QTLs were detected. The QTL QGn.saas 1B.1 is， located on chromosome 1B and was detected in two environments， with LOD>3.5， explaining 12.0%-13.3% of phenotypic variation. The Gao 38 allele at this locus increased GN. QGn.saas 1B.2 was detected in Luoyang2006 and accounted for 9.7% of the phenotypic variation.
　　For SPN， four QTLs， two on chromosomes 5D， one on 4B and one on 6D， were detected. They were responsible for 5.9%-13.2% of the phenotypic variation. The QTL on chromosome 6D was detected in two environments.
　　For SL， two QTLs， QSl.saas 5D.1 and QSl.saas 5D.2， were detected， co located with the QTLs （QSpn.saas 5D.1 and QSpn.saas 5D.2） for SPN， respectively. They explained 9.8%-12.9% of the phenotypic variation. The Gao 38 alleles increased SL at both loci.
　　The influence of QTLs for quality traits on yield related traits
　　The significance of the influence of quality trait QTLs on yield related traits was analyzed by t test using the most linked markers （Table 5）. The allele at QSsd.saas 1B.1 from donor parent Gao 38 showed a strong positive effect on Ssd （Table 4）， and the allele increased GN， significantly in all environments at P<0.005， compared with the allele from recurrent parent. The Gao 38 allele at locus barc61 cfd65b on chromosome 1B revealed a strong positive effect on mixograph parameters and GN. The pleiotropic QTL on chromosome 4B （wms375 wmc692） was not only affecting quality traits but also yield related traits. The allele from Gao 38 at this locus increased GPC and GH but strongly decreased TGW by 5.1 to 10.4 g. The Gao 38 allele at psp3000 locus significantly decreased TGW in one environment. The pleiotropy of the above loci can be explained by the co location or near location of their QTL. The other quality trait QTL had no significant influence on agronomic traits.
　　Discussion
　　A population of 194 BC1F2 materials from a single BC1F1 plant was selected， because the BC1F1 plant was with high gluten quality. BC1F2 population from single BC1F1 had lower polymorphism， and reduced the background interference， so the results were more precise. 　　QTLs for quality parameters
　　HMW GSs and LMW GSs are now generally acknowledged to be the major contributors to bread making quality in wheat[5， 6]， and in the present study， our results also support this conclusion. The QSsd.saas 1B.2， QWs.saas 1B.2 and QMt.saas 1B.2 were clearly influenced by the Glu B3 locus. At this locus， Gao 38 allele increased Ssd， MT and decreased WS， so Glu B3b was better than Glu B3d for bread making quality. Glu B1 interacts with other loci to affect GH， Glu B3 interacts with other loci to affect GPC， GH and MRW， Glu D1 interacts with other loci to affect GH， WS， MRV and MTxV.
　　The QTL QSsd.saas 1B.1， linked to barc137， is a new major QTL for Ssd， explaining on average 21.1% of the phenotypic variation in three environments. This locus nears the centromere of chromosome 1BS， while the Glu B3 and Gli 1 loci were located on the bottom of the chromosome 1BS， so we can conclude that the QSsd.saas 1B.1， linked to barc137， is a new locus for Ssd. The allele increasing Ssd at this locus also significantly increased the grain number per spike. In addition， the locus， barc61 cfd65b on chromosome 1B was important for bread making quality， influencing nine mixograph parameters with large effects.
　　For sedimentation volume， the QTL QSsd.saas 2D detected in this study was close to the QTL detected on chromosome 2D for SDS sedimentation volume by Huang et al.[10]. Huang et al.[10] found another QTL on 5D chromosome; Blanco et al.[11] detected five QTLs on chromosome arms 3AS， 3BL， 5AL， 6AL and 7BS; Zanetti et al.[16] found three major QTL on chromosomes 2A， 5A and 5D; Kunert et al.[14] detected two QTLs on 5D and 6D chromosomes; however none were associated with the five QTLs for Ssd detected in this study. We confirmed that the major QTL （QSsd.saas 1B.1） on chromosome 1B for Ssd was a novel QTL and was not the Glu B1 locus.
　　Variation of grain hardness is mainly controlled by the Ha locus on chromosome arm 5DS. Puroindoline a （Pina D1）， puroindoline b （Pinb D1） and grain softness related protein （Gsp D1） at Ha locus have been associated with grain hardness[30]. There were other loci also influencing grain hardness. Zanetti et al.[16] found a major QTL for GH on chromosome 2A， Narasimhamoorthy  et al. [31] reported a QTL for GH located on chromosome 3BL， Perretant et al.[17] detected two minor QTLs for GH on chromosomes 1A and 6D and Kunert et al.[14] detected a minor effect QTL for grain hardness on chromosome 2D. These were not detected in the present study but two QTLs for GH were detected on chromosome 1B and 4B， explaining 8.5% and 12.6% of phenotypic variation. 　　Co located QTLs/ pleiotropic QTLs
　　In the present study， five QTL clusters were found with QTLs for different parameters distributed in the same or close regions （Fig.1）. This phenomenon was partially caused by the correlations among these traits. For example the marker wms375 on chromosome 4B was shared by QTLs for Ssd， GPC， GH and TGW; Psp3000 on chromosome 1B was shared by QTLs for Ssd and TGW; The QTL for SL on 5D coincides with the QTL for SPN. The pleiotropic phenomenon may be caused ① by two tightly linked genes modulating the expression of separate traits; ② by two tightly linked genes influencing two or more traits simultaneously; ③ by a single gene affecting two or more traits at the same time.
　　The relation between quality and yield， and the use of these results in future breeding
　　Large numbers of QTL analyses have been performed for quality related traits， but few people reported the influence of quality QTLs on agronomic traits. The results of the present study reveals the influence of some loci having opposite effects on quality and yield traits while some loci have a synergic positive effect on both quality and yield traits. Based on the present study， the new major QTL （barc137 cfd65a） on chromosome 1B for Ssd was an important locus for wheat breeding. The Gao 38 allele at this locus can not only significantly increase Ssd and GH， but also increase GN and has no negative effect on other yield related traits. This locus can improve quality and yield and is therefore favorable for wheat breeding. The linked marker barc137 can be used in marker assisted selection （MAS） by breeders. While the effects of the QTL （wms375 wmc692） on chromosome 4B showed opposite effect on quality and yield traits， Gao 38 allele increased GPC， Ssd and GH， but decreased TGW. It could be used in marker assisted selection and alleles carefully selected according to the objective of the breeding program. If the aim is to cultivate high quality materials， Gao 38 allele at this locus should be selected. While if the aim is to cultivate high yield materials， Yanzhan 1 allele should be selected.
　　References
　　[1] KEARSEY MJ， POONI HS. The genetical analysis of quantitative traits[M]. London： Chapman and Hall， 1996.
　　[2] ROUSSET M， CARRILLO JM， QUALSET CO， et al. Use of recombinant inbred lines of wheat for study of associations of high molecular weight glutenin subunit alleles to quantitative traits.2 Milling and bread baking quality[J]. Theoretical and Applied Genetics， 1992， 83： 403-412. 　　[3] PETERSON CJ， GRAYBOSCH RA， SHELTON DR， et al. Baking quality of hard winter wheat： response of cultivars to environment in the great plains[J]. Euphytica， 1998， 100： 157-162.
　　[4] KHAN IA， PROCUNIER JD， HUMPHREYS DG， et al. Development of PCR based markers for a high grain protein content gene from Triticum turgidum ssp. dicoccoides transferred to bread wheat[J]. Crop Science， 2000， 40： 518-524.
　　[5] PAYNE PI， JACKSON EA， HOLT LM， et al. Wheat storage proteins： their genetics and their potential for manipulation by plant breeding[J]. Philosophical Transactions Royal Society London， Series B， 1984， 304： 359-371.
　　[6] PAYNE PI， NIGHTINGALE MA， KATTIGER AF. The relationship between HWM glutenin subunit composition and the bread making quality of British grown wheat varieties[J]. J Sci Food Agr， 1987， 40： 51-65.
　　[7] BLACKMAN JA， PAYNE PI. Wheat Breeding， its Scientific Basis[M]. Cambridge， Great Britain： Chapman and Hall Ltd， University Press， 1987： 455-485.
　　[8] BORNER A， SCHUMANN E， FURSTE A， et al. Mapping of quantitative trait loci determining agronomic important characters in hexaploid wheat （Triticum aestivum L.）[J]. Theor Appl Genet， 2002， 105： 921-936.
　　[9] MARZA F， BAI GH， CARVER BF， et al. Quantitative trait loci for yield and related traits in the wheat population Nin7840×Clark[J]. Theoretical and Applied Genetics， 2006， 112， 688-698.
　　[10] HUANG XQ， CLOUTIER S， LYCAR L， RADOVANOVIC N， et al. Molecular detection of QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian wheats （Triticum aestivum L）[J]. Theoretical and Applied Genetics， 2006， 113： 753-766.
　　[11] BLANCO A， BELLOMO MP， LOTTI C， et al. Genetic mapping of sedimentation volume across environments using recombinant inbred lines of durum wheat[J]. Plant Breeding， 1998， 117： 413-417.
　　[12] BLANCO A， SIMEONE R， GADALETA A. Detection of QTLs for grain protein content in durum wheat[J]. Theor Appl Genet， 2006， 112： 1195-1204.
　　[14] KUNERT A， NAC AA， DEDECK O， et al. AB QTL analysis in winter wheat： I. Synthetic hexaploid wheat （T.trugidum ssp. dicoccoides×T.tauschii）as a source of favourable alleles for milling and baking quality traits[J]. Theoretical and Applied Genetics， 2007， 115： 683-695.
　　[13] ZANETTI S， KELLER M， WINZELER M ，et al. QTL for quality parameters for bread making in a segregating wheat by spelt population[C]// In： Slinkard AE（ed） Proc 9 th  Int Wheat Genet Symp， Vol 1. Canada： University Extension Press， 1998， 1： 273-276. 　　[14] KUNERT A， NAC AA， DEDECK O， et al. AB QTL analysis in winter wheat： I. Synthetic hexaploid wheat （T.trugidum ssp. dicoccoides×T.tauschii）as a source of favourable alleles for milling and baking quality traits[J]. Theoretical and Applied Genetics， 2007， 115： 683-695.
　　[15] BLANCO A， BELLOMO MP， LOTTI C， et al. Quantitative trait loci influencing grain protein content in tetraploid wheats[J]. Plant Breeding， 1996， 115： 310-316.
　　[16] ZANETTI S， WINZELER M， FEUILLET C， et al. Genetic analysis of bread making quality in wheat and spelt[J]. Plant Breeding， 2001， 120： 13-19.
　　[17] PERRETANT MR， CADALEN T， CHARMET G， et al. QTL analysis of bread making quality in wheat using a doubled haploid population[J]. Theoretical and Applied Genetics， 2000， 100： 1167-1175.
　　[18] TURNER AS， BRADBURNE RP， FISH L， et al. New quantitative trait loci influencing grain texture and protein content in bread wheat[J]. Journal of Cereal Science， 2004， 40： 51-60.
　　[19] PRASHANT R， MANI E， RAIB R， ET AL. Genotype×environment interactions and QTL clusters underlying dough rheology traits in Triticum aestivum L. Journal of Cereal Science， 2015， 64： 82-91
　　[20] GROOS C， BERVAS E， CHANLIAUD E， et al. Genetic analysis of bread making quality scores in bread wheat using a recombinant inbred line population[J]. Theoretical and Applied Genetics， 2007， 115：  313-323.
　　[21] BLANCO A， PASQUALONE A， TROCCOLI A， et al. Detection of grain protein content QTLs across environments in tetraploid wheat[J]. Plant Molecular Biology， 2002， 48： 615-623.
　　[22] LI YL， ZHOU RH， WANG J， et al. Novel and favorable QTL allele clusters for end use quality revealed by introgression lines derived from synthetic wheat[J]. Mol Breeding， 2012， 29： 627-643.
　　[23] SHARP PJ， CHAO S， GALE MD. The isolation， Characterization and application in the Triticeae of a set of wheat RFLP probes identifying each homologous chromosome arm[J]. Theoretical and Applied Genetics， 1989， 78： 342-348.
　　[24] RODER MS， KORZUN V， WENDEHAKE K， et al. A microsatellite map of wheat[J]. Genetics， 1998， 149： 2007-2023.
　　[25] SINGH NK， SHEPHERD KW， CORNISH GB. A simplified SDS PAGE procedure for separating LMW subunits of glutenin. Journal of cereal science， 1991， 14： 203-208.
　　[26] WU J， KONG X， WAN J， et al. Dominant and pleiotropic effects of a GAI gene in wheat results from a lack of interaction between DELLA and GID1 1[C][W][OA][J]. Plant Physiology， 2011， 157（4）： 2120-2130.
　　[27] LANDER E， GREEN P， ABRAHAMSON J， et al. MAPMAKER： an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations[J]. Genomics， 1987， 1： 174-181.
　　[28] PILLEN K， ZACHARIAS A， LEON J. Advanced backcross QTL analysis in barley （Hordeum vulgare L.）[J]. Theoretical and Applied Genetics， 2003， 107： 340-352.
　　[29] MCINTOSH RA， HART GE， GALE MD. Catalogue of gene symbols for wheat： 1994 supplement[J]. Wheat Inform. Serv. 1994， 79： 47-56.
　　[30] TRANQUILLI G， LIJAVETZKY D， MUZZI G， et al. Genetic and physical characterization of grain texture related loci in diploid wheat[J]. Molecular and General Genetics， 1999， 262： 846-850.
　　[31] NARASIMHAMOORTHY B， GILL BS， FRITZ AK， et al. Advance backcross QTL analysis of a hard winter wheat × synthetic wheat population[J]. Theoretical and Applied Genetics， 2006， 112： 787-796.