QTL Mapping For Quality Traits In Wheat (Triticum Aestivum L.)

Wheat (Triticum aestivum L.) is the most widely grown food crop in the world, and is increasing in production. It ranks first in world crop production and is the national staple food. The quality traits of wheat are generally controlled by multiple genes, and inherited as QTL. With the development of molecular genetics and genomics, location, seperation and expoiting QTL have become an important and essential component in crop genetic improvement. In the present study, we report a new genetic linkage map developed from an F1-derived doubled haploid (DH) population of 168 lines generated from the cross between two elite Chinese common wheat varieties Huapei 3 and Yumai 57. QTL analyses were performed using the software of QTLNetwork 2.0 based on the mixed linear model approach. QTL for 10 traits including grain protein content, flour protein content, RVA profile parameters, Zeleny sedimentation value, Farinograph parameters, falling number, crude starch, crude fat, white water noodle score parameters and noodle TPA parameters were analyzed. The work will not only provide gene resources for molecular marker assisted selection and molecular breeding by design, but also lay a foundation for fine mapping and cloning some important genes. The results were summarized as follows:1. QTL mapping for grain protein content and flour protein contentBased on the genetic map established from the DH population, the QTL were detected with the composite interval mapping of the mixture linear model. QTL for grain protein content and flour protein content were analyzed in 2 cropping seasons and 3 environments. For grain protein content, 4 additive QTL, 2 pairs of epistatic effect and 2 QTL×environment effect were detected, explaining a total of 51.52% of the phenotypic variance. For flour protein content, 4 additive QTL, 5 pairs of epistatic effect and 2 QTL×environment effect were detected, accounting for 45.8% of the phenotypic variance in total. Of the 15 QTL, three additive QTL and one pair of epistatic QTL were identified both in grain protein content and flour protein content. Two main-effect QTL, QGpc2B/QGpc4A, were detected, explaining 14.12% of the phenotypic variance, and QFpc3A explained 15.11% of the phenotypic variance.2. QTL mapping for RVA profile parametersMapping analysis showed 17 additive QTL, 18 pairs of epistatic effect and 1 QTL×environment effect for 6 RVA profile parameters (pasting temperature, peak viscosity, breakdown, setback, final viscosity, trough viscosity) with a single QTL explaining 0.91–21.34% of phenotypic variance. Seven main-effect QTL were detected, QBd-4A, QFv-6A, QSb-4A, QPv-1D /QPv-6D.2, QTv-1D /QTv-6D, QBd-5D.2 /QBd-6D.2, and QPt-3A /QPt-7D, explaining 21.34%, 11.56%, 15.52%, 11.76, 14.55%, 14.95%, and 10.99% of the phenotypic variance, respectively. Two QTL clusters for RVA profile parameters were located on chromosomes 2A and 4A, respectively.3. QTL mapping for Zeleny sedimentation valueFour additive QTL, 4 pairs of epistatic effect and 2 QTL×environment effect for Zeleny sedimentation value were found, with each explaining 0.64- 14.39% of phenotypic variance, the model explained 46.11% of the phenotypic variance. One main-effect QTL was detected, Qzsv-1B, explaining 14.39% of the phenotypic variance. This study provided a QTL within the Xwmc93 and GluD1 interval, designated Qzsv-1D, and the information proved the relationship between the starch sedimentation value and high molecular weight glutenin subunits at the genetic level.4. QTL mapping for Farinograph parametersMapping analysis indicated 25 additive QTL, 16 pairs of epistatic effect and 4 QTL×environment effect for 5 RVA profile parameters (Flour water absorption, Dough stability time, Dough development time, Breakdown time, Mixing tolerance index), with a single QTL explaining 1.00% - 26.56% of phenotypic variance. Five main-effect QTL, QFwa-4B, QDst-1D, QMti-1B, QMti-1D and QBdt-1D, were detected, explaining 12.36%, 26.56%, 15.66%, 14.15, and 19.63% of the phenotypic variance, respectively. It is worth noting that 4 significant additive QTL, 2 pairs of epistatic QTL and 1 additive×environment (AE) interactions for Farinograph parameters were identified at GluD1 locus on chromosome 1D, the information proved the relationship between the Farinograph parameters and high molecular weight glutenin subunits from the genetic level.5. QTL mapping for falling numberTwo additive QTL and 3 pairs of epistatic effect for Zeleny sedimentation value were detected, with each explaining 2.83 - 10.65% of phenotypic variance, and 33.64% of the phenotypic variance in a simultaneous fit. One main-effect QTL Qfn-6A explained 10.65% of the phenotypic variance.6. QTL mapping for crude starchThree additive QTL and 3 pairs of epistatic effect for Zeleny sedimentation value were identified, with a single QTL explaining 2.25 - 16.12% of phenotypic variance, and they explained 33.64% of the phenotypic variance in total. One main-effect QTL, Qcs-2B, explained 16.12% of the phenotypic variance.7. QTL mapping for crude fatNo QTL was detected for crude fat using the software QTLNetwork version 2.0, and the possible reasons for this result were discussed.8. QTL mapping for white water noodle score parametersTwo additive QTL, 4 pairs of epistatic effect and 1 QTL×environment effect for 7 white water noodle score parameters (Color, Appearence, Firmness, Stickiness, Smoothness, Taste and Totel score) were found, with each explaining 1.44 - 16.39% of phenotypic variance. Two main-effect QTL were detected, Qst-3A/ Qst-5D and Qse-3A/ Qse-5D explaining 12.02% and 16.39% of the phenotypic variance, respectively. It is worth noting that one significant additive QTL for Appearence was identified at the GluD1 locus on chromosome 1D, and this information proved the relationship between the Appearence and high molecular weight glutenin subunits at the genetic level.9. QTL mapping for Noodle TPA parametersTen additive QTL, 3 pairs of epistatic effect and 1 QTL×environment effect for 7 noodle TPA parameters (Hardness, Adhesiveness, Springiness, Cohesiveness, Gumminess, Chewiness and Resilience) were identified, with each explaining 2.13 - 11.61% of phenotypic variance. Two main-effect QTL, Qche-1B and Qche-1D, were detected, explaining 11.61% and 10.28% of the phenotypic variance, respectively. It is worth noting that one significant additive QTL for Chewiness was identified at GluD1 locus on chromosome 1D, and this information proved the relationship between the Chewiness and high molecular weight glutenin subunits at the genetic level.