| Common wheat (Triticum aestivum L.) is one of the most important staple crops world-wide. The grain yield and other important agronomic traits and 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 element in crop genetic improvement. The work will not only prepare gene resources for molecular marker assisted selection and molecular breeding by design, but also lay a foundation for fine mapping and clone some important genes. In the present study, 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 33 traits including plant height, heading date, lodging resistance, adult-plant resistance to powdery mildew, grain yield, spike related traits, chlorophyll contents, leaf morphology (leaf angle, length, width and area of the top three leaves), flour whiteness, flour color (a*, b* and L*), polyphenol oxidase, wet gluten content, gluten index and kernel hardness were analyzed.The results were as the following:1.A population of 168 DH lines was produced by hybridization with maize pollen grains of wheat F1 hybrid plants from the cross between two Chinese common wheat varieties Huapei 3 (female parent)×Yumai 57 (male parent), and used for the construction of the genetic map and QTL mapping. We tested the polymorphism of 2002 available markers (1623 SSRs and 379 EST-SSRs) between the two parental varieties. Of these, 270 SSR and 17 EST-SSR markers revealed polymorphism between the parents of the mapping population. The result showed that the mapping population was suitable for map construction.2.A genetic linkage map containing 305 SSR markers, including 283 SSR and 22 EST-SSR loci using MAPMAKER/Exp version 3.0 software, was finally developed. The map covered a total length of 2141.7 cM with an average distance of 7.02 cM between adjacent markers in the map, which resulted in 24 linkage groups comprising 3 to 24 loci with a mean of 12.71 loci per linkage group. Each of the linkage groups could be assigned to one of the 21 chromosomes based on the information from previous mapping studies. This work found some large bins of co-segregating markers near the centromeric regions, which are known to have suppressed recombination. These unresolved regions may provide access to a high density of markers and thereby increase the chance of finding polymorphism among different germplasm materials. It will be interesting to see if the markers in the centromeric regions found in our study would be useful in detecting and tagging QTLs in further research. A further agreement between our work and similar studies is the observation of segregation distortion of certain markers.3. 77 loci (24.4%) showed segregation distortion. Of these loci, 44 markers (57.1%) showed distortion in favor of the female (Huapei 3) alleles, whereas 33 (42.9%) in favor of the male (Yumai 57) alleles. The distortion loci were not evenly distributed among the A, B and D genomes, with 12, 51 and 14 loci mapped on the A, B and D genomes, respectively. The segregation distortion loci were mainly clustered on chromosomes 1A (5), 1B (12), 3B (21) and 6B (13).4.Based on the genetic map established from the DH population, the QTLs were detected using the software QTLNetwork version 2.0 with the composite interval mapping of the mixture linear model. QTLs for 33 traits including grain yield and related agronomic traits and quality traits were analyzed in 2 cropping seasons and 3 environments. A total of 123 additive QTLs and 89 pairs of epistatic effects were detected and distributed on 21 chromosomes. Among them, 31 additive QTLs were major genes, while 92 additive QTLs were minor genes.4.1 QTLs for grain yield and spike related traits. Three additive QTLs were detected for grain yield on chromosomes 2D, 4A and 5D, which could account for 14.07%, 4.52% and 10.32% of the phenotypic variance, respectively. Three pairs of epistatic QTLs were identified for grain yield, explaining 2.25%, 4.03% and 6.51% of the phenotypic variance, respectively. Twenty-four additive QTLs were resolved for spike related traits (spike length, spike grain, spikelets per spike, compactness, fertile spikelets per spike, thousand grain weight and grain diameter) on chromosomes 1B, 2B, 2D, 3A, 3B, 4A, 4B, 4D, 5D, 6A, 6B, 7A and 7D. Each QTL could explain ranging from 1.48～15.63% of the phenotypic variance. Ten pairs of epistatic QTLs were detected for spike related traits, which could explain ranging from 3.33~7.42% of the phenotypic variance.4.2 QTLs for plant height and related agronomic traits. Four additive QTLs were detected for plant height on chromosomes 3A, 4B, 4D and 7D, which could account for 8.50%, 14.51%,20.22% and 2.54% of the phenotypic variance, respectively. Five pairs of epistatic QTLs were identified for plant height, explaining ranging from 2.62% to 6.56% of the phenotypic variance. Two additive QTLs were detected for heading date on chromosomes 1B and 5D, which could account for 3.49% and 53.19% of the phenotypic variance, respectively. Two pairs of epistatic QTLs were identified for heading date, explaining 2.45% and 3.44% of the phenotypic variance, respectively. Two additive QTLs were detected for adult-plant resistance to powdery mildew on chromosomes 4D and 5D, which could account for 20.0% and 1.3% of the phenotypic variance, respectively. Two pairs of epistatic QTLs were identified for adult-plant resistance to powdery mildew, explaining 3.6% and 1.3% of the phenotypic variance, respectively. Five additive QTLs were detected for lodging resistance on chromosomes 1B, 2B, 3A, 4B and 4D, which could account for 4.2%, 2.5%,4.3%, 2.1% and 3.0% of the phenotypic variance, respectively. Six pairs of epistatic QTL were identified for lodging resistance, explaining ranging from 1.0% to 3.9% of the phenotypic variance. Five additive QTLs were detected for internode length below the spike on chromosomes 3A, 4B, 4D, 5A and 7D, which could account for 2.6%, 1.8%,8.2 %, 3.1% and 12.9% of the phenotypic variance, respectively. Two pairs of epistatic QTLs were identified for internode length below the spike, explaining 8.1% and 4.7% of the phenotypic variance, respectively. Among them, a highly significant QTL with F-value 148.96, designated as qHd5D, was observed within the Xbarc320-Xwmc215 interval on chromosome 5DL, accounting for 53.19% of the phenotypic variance and reducing days-to-heading by 2.77 days. The qHd5D closely links with a PCR marker Xwmc215 with a distance of 2.0 cM, which can be used in molecular marker-assisted selection in wheat breeding programs. The qHd5D seems likely to be coincidental with the well-characterised vernalization sensitivity gene Vrn-D1, which lays foundation for fine mapping the qHd5D through backcrossing procedure.4.3 QTLs for physiology traits. Eight additive QTLs were detected for chlorophyll contents on chromosomes 1B, 2D, 4A(2), 5A, 5D(2) and 7A, which could account for ranging from 0.84~23.29% of the phenotypic variance. Three pairs of epistatic QTLs were identified for chlorophyll a content, explaining 3.97%, 1.62% and 1.86% of the phenotypic variance, respectively. Thirty-one additive QTLs were detected for leaf morphology (flag leaf angle, length, width and area of the top three leaves), which could account for ranging from 1.17~21.91% of the phenotypic variance. Twenty-two pairs of epistatic QTLs were identified for leaf morphology, explaining ranging from 0.61~7.98% of the phenotypic variance.4.4 QTLs for some quality traits. Thirty-nine additive QTLs were detected for eight quality traits (flour whiteness, a* value, b* value, L* value, polyphenol oxidase, wet gluten content, gluten index and kernel hardness, which could account for ranging from 0.51~25.64% of the phenotypic variance. Thirty-three pairs of epistatic QTLs were identified for eight quality traits, explaining ranging from 0.60~9.14% of the phenotypic variance.5. For the first time, the map positions of 22 SSR markers were determined, and we noticed that the positions of 20 SSR markers in our map differed from the ones reported previously, thus enriching the maker resources for future wheat genetic and breeding studies. The present study firstly detected QTLs for flag leaf angle, leaf morphology (length, width and area) of the second and third leaf from the top, wet gluten content and gluten index. The present study firstly reveal the genetic detection of the high phenotypic correlations between plant physiology traits (leaf morphology and chlorophyll contents) and grain yield, quality, early maturity and resistance of disease and pest for wheat in the same population.
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