Conditional And Unconditional QTL Mapping For Main Agronomic Traits In Wheat (triticum Aestivum L.)

Common wheat (Triticum aestivum L.) is one of the most important staple crops world-wide provides 35%%-50% of the dietary calories consumed by more than 2 billion people.The agronomic traits of wheat are generally controlled by multiple genes, and inherited as QTL(quantitative trait loci). With the development of molecular genetics and genomics, the analysis of QTL play more and more important role in the crop genetic improvement. The location, seperation and expoiting of important QTL will not only provide gene resources to the marker-assisted selection breeding, but lay a foundation for fine mapping and molecular clone. In this paper, we report a new genetic linkage map developed from an F1-derived doubled haploid (DH) population of 168 lines, which was 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. QTLs for 9 traits including plant height, grain yield, spike related traits, The results were as the following:1. Based on the mixed linear model approach, the QTL were detected using the software of QTLNetwork 2.0. We detected 7 additive QTLs for single kernel weight, 8 QTLs for kernel length and 5 QTLs for hardness of wheat kernel.2. Conditional analysis of plant height was used by QGAstation software, and then, the conditional QTLs were detected using the software QTLNetwork version 2.0. A total of 21 additive QTLs for plant height were detected including 10 conditional QTLs and 18 non-conditional QTLs (7 of those were both conditional and non-conditional QTLs. Among the non-conditional QTLs for plant height, Qph5D.1and Qph4D.1 earning higher values can be detected from 4 and 3 period, respectively. The two non-conditional QTLs were identified the major QTLs for plant height and contributed more during the early and later periods of height formation. Three conditional QTLs: Qph.4B, Qph5D.1 and Qph5D.2 can be detected from 2 samlling period and contributed more significantly. The Qph5D-2 is the major affected markedly during the later growth stage of plant height.3. Conditional analysis of yield related traits was used by QGAstation software. Then, the conditional QTLs were detected using the software QTLNetwork version 2.0. A total of 12 additionsl QTLs for kernel weight, 14 QTLs for grain number, 14 QTLs for kernel weight were detected. When the spike number per plant, grain number, or 1000-kernel weight were predetermined, a total of 14, 15 and 18 additional QTLs were detected, respectively. We classified the QTLs according to the differences of conditional and non-conditional QTLs. The yield per plant and grain number per spike was controlled by the same QTL (Qyd1A.2) that was on the same location, but Qyd1A.2 was not detected from analysis when was conditioned on the panicle weight, indicating that the Qyd1A.2 influenced yield per plant by controlling the grain number per spike, which showed pleiotropisms. Qyd3B was detected when was conditioned on the 1000-kernel weight, this suggests that the Qyd3B influenced yield per plant was not caused by controlling the1000-kernel weight. Qyd2B.2 was a site that regulate the yield per plant, the effect of yield per plant has not much changed when the effects of spike number per plant, 1000-kernel weight and grain number per spike were ruled out. The results described above demonstrate that Qyd2B.2 controlled the above three traits at the same time, all three elements of yield components play an important role in yield per plant.4. Conditional analysis of grain related traits was used by QGAstation software. Then, the conditional QTLs were detected using the software QTLNetwork version 2.0. A total of 14 QTLs for kernel weight, 15 QTLs for panicle length, 16 QTLs for grain number were detected. When the panicle length, grain number or the kernel weight were predetermined, a total of 22, 19 and 11 additional QTLs were detected, respectively. Qskw4A.2 which account for 43.66% of the phenotypic variance as an important major QTL could be detected for grain weight in the unconditional analysis only. Qskw2D.2,Qskw4A.1 and Qskw6A.2 could be detected for grain weight by both conditional and unconditional QTL mapping methods which account for 16.79%,26.12% and 12.6% of the phenotypic variance. As major QTL, the three QTLs could be used for molecular assisted selection.