Genetic Map Construction And QTL Mappings Of Important Agronomic Traits In Common Wheat

In the present study, a new genetic linkage map was constructed based on the diversity ofsimple sequence repeats (SSR) in173RIL lines derived from the cross of two Chinese elitecommon wheat varieties Jimai21and954072. QTL analyses were performed using thesoftware of QTLNetwork2.0based on the mixed linear model approach. QTL of26important agronomic traits including tillering，plant height, heading date, leaf morphology(length, width and area of the top three leaves), grain and yield related traits wereanalyzed.The results were as the following:1. A population of173RIL lines derived from the cross of two Chinese common wheatvarieties Jimai21(female parent)×954072(male parent) was used for the construction of thegenetic linkage map and QTL mapping. Totally,the polymorphism of1651available SSRmarkers between the two parental varieties were tested, among them152markers revealedpolymorphism between the parents and the mapping population. The result showed that themapping population was suitable for genetic linkage map construction.1.1A genetic linkage map containing128SSR markers which were assigned to20chromosomes, using MAPMAKER/Exp version3.0and Map Chart2.2software, was finallydeveloped. The map covered a total length of2130.4cM with an average distance of16.77cM between adjacent markers, which resulted in26linkage groups comprising2to13lociwith a mean of4.88loci per linkage group.1.2Of these loci,41loci (32%) showed segregation distortion, among them26markers(63.4%) showed distortion in favor of the female (Jimai21) alleles, and15(36.6%) in favorof the male (954072) alleles. The distortion loci evenly distributed to the A, B and D genomes,with15,16and10loci mapped on the A, B and D genomes, respectively. The furtherresearch indicated that11SDR (Segregation Distortion Regions), and most of the SDR inclined to the female (Jimai21).2. Based on the software QTL Network version2.0with the composite intervalmapping of the mixture linear model,QTL for26traits including tiller, heading date, leafmorphology and thousand grain weight were analyzed. A total of63additive QTL and23pairs of epistatic QTL were detected and distributed to20chromosomes.2.1QTL for tillering and its related traits. One additive QTL and One pair ofepistatic QTL were detected for maximum tillering of pre-winter in the two environments,which could account for11.93%and9.28%of the phenotypic variance, respectively. Twoadditive QTL were detected for ear number per plant in the two environments, whichcould account for10.46%to34.44%of the phenotypic variance. Two additive QTL weredetected for fertile tillers in the two environments, which could account for8.95%and13.13%of the phenotypic variance, respectively. Two additive QTL and3pairs ofepistatic QTL were detected for maximum tillering in spring in the three environments,which could account for phenotypic variance from7.16%to13.97%.2.2QTL for plant height and its related agronomic traits.Three additive QTL and1pair of epistatic QTL were detected for plant height in the two environments, which couldaccount for phenotypic variance from5.44%to31.85%. Three additive QTL weredetected for internode length below the spike in two environments, which could accountfor phenotypic variance from7.15%to20.91%. Three additive QTL were detected forinternode length below the flag leaf in two environments,which could account forphenotypic variance from8.74%to12.47%. Two additive QTL and2pairs of epistaticQTL were detected for heading date in three environments, which could account forphenotypic variance from7.62%to12.27%.2.3QTL for leaf morphology traits.Thirteen additive QTL and9pairs of epistaticQTL were detected for leaf morphology (length, width and area of the top three leaves) inthree environments. Six additive QTL and4pairs of epistatic QTL were detected for flagleaf in three environments, which could account for7.19%to13.87%of the phenotypicvariance,respectively. Nine additive QTL and2pairs of epistatic QTL were detected forsecond leaf in three environments, which could account for6.26%to12.79%of thephenotypic variance,respectively. Ten additive QTL and3pairs of epistatic QTL weredetected for third leaf in three environments, which could account for4.57%to12.79%ofthe phenotypic variance, respectively.2.4QTLs for yield and its related traits. Two additive QTL were detected for spikeweight in two environments,which could account for phenotypic variance from10.18%to 11.66%. One additive QTL and2pairs of epistatic QTL were detected for spike length inthe two environments, which could account for7.72%to10.45%of the phenotypicvariance. Three additive QTL and1pair of epistatic QTL were detected for kernel numberper plant in two environments,which could account for7.91%to16.14%of phenotypicvariance. Two additive QTL and1pair of epistatic QTL were detected for grain weightper spike in the two environments, which could account for phenotypic variance from9.91%to19.62%. Two additive QTL and1pair of epistatic QTL were detected foraverage grain numbers per spike in the two environments, which could account for thephenotypic variance ranging from9.06%to13.47%. Two additive QTL and1pair ofepistatic QTL were detected for stem weight in the two environments, which couldaccount for6.15%to20.38%of the phenotypic variance. Three additive QTL and Onepair of epistatic QTL were detected for biological mass in the two environments, whichcould account for4.64%to19.75%of the phenotypic variance. Three additive QTL and1pair of epistatic QTL were detected for thousand grain weight in the two environments,which could account for phenotypic variance ranging from7.21%to14.2%. One additiveQTL was detected for main grain numbers per spike in the two environments,which couldaccount for15.2%of the phenotypic variance. One additive QTL and1pair of epistaticQTL were detected for grain weight in the two environments, which could account for10.92%and10.58%of the phenotypic variance, respectively.