QTL Mapping Of Kernel And Plant Type Using A RILs Population In Barley

Barley is the fourth largest cereal crop ranking after rice, maize and wheat, with a long history. Because of a variety of essential amino acids and mineral elements, barley is mainly used for brewing, feed and food. Due to its nutritional components comply with the requirements of modern health, health care products of barley are popular with more and more people. So, the global demand for barley is increasing. This requires researchers to foster higher yields, better quality and more resistant barley. The researches on quantitative trait loci (QTL) mapping of barley are not as deep as rice, maize and wheat. In addition, because of artificial selection, genetic basis of cultivated barley becomes narrow, and can not provide the abundent genetic diversity. These will hinder the development of barley breeding research. Therefore, utilizing QTL genetic mapping and introducting new germplasm resources have a very important significance on researches of important agronomic traits in barley.In this study, recombinant inbred lines (RILs) population comprising of 128 lines derived from the barley cultivar ’Baudin’(cultivated barley)and AWCS276 (Middle East wild barley) was used to construct a high-density genetic linkage map using the genome-wide diversity array technology (DArT) markers by QTL IciMapping. And after the data correlation analysis of kernel length, kernel width, kernel length-width ratio and other agronomic traits, we detected the QTL for these 14 important agronomic traits using Map QTL6.0. Besides, we used bioinformatics to predict candidate genes. The main results are listed as follows:1. Using QTL IciMapping,6904 DArT markers were used for constructing a high-density genetic linkage map spanning a total of 3940.43cM with an average interval of approximately 0.57 cM, and the number of linkage groups is equivalent to barley chromosomes.2. The correlation and frequency distribution analysis were calculated for 14 important agronomic traits, and the results showed that: the correlation coefficients of plant height and panicle length, number of spikelets and kernels per spike both exceeded 0.8. respectively. Except plant height and panicle length, the rest 12 traits showd a normal distribution and had a good distribution continuity.3. Using the high-density map and interval mapping of Map QTL6.0 (Permutation Text calfulates the threshold) for QTL analysis,67 QTL had been found for 14 important agronomic traits. A total of 35 QTL were detected for kernel-related traits throuth three years, in which 27 QTL were detected for the ruler measurements and 8 QTL for hundred kernel weight.32 QTL were detected for plant types, including 14 QTL for the flag leaf,12 QTL for spike,1 QTL for plant height,4 QTL for rachis internode length and 1 QTL for tiller angle. Analysis showed that,2H-6H had QTL clusters. For example, we detected QTLs for kernel width and kernel length-width ratio, plant height and rachis internode length in the same regions respectively. This may be a results of pleiotropism or multiple QTL closely linked.4. There were 6 stable QTL in grains, including 2 QTL for kernel length,1 QTL for kernel width,2 QTL for kernel length-width ratio and 1 QTL for hundred kernel weight. And we found a hundred kernel weight QTL repeated for three consecutive years and the contribution rate distributed between 11.9%～21.7%. The contribution rate of plant height and rachis internode length in the same site had reached more than 60%, which indicated the presence of a major gene. This explained the frequency distribution in plant height and rachis internode length were not normally distributed. In the 6 stable QTL, QTL for kernel length located on 3H and QTL for kernel length-width ratio located on 4H had not been reported.5. Using IPK and NCBI database to predict candidate genes, we found a total of 9 candidate genes. Moreover, plant height and rachis internode length, kernel length and number of spikelets had the same gene, respectively. In accordance with the function, these genes can be divided into two categories:Class 1 genes encoding enzymes of various biochemical pathways, including ptr1, XEB,pap-4 and UG40773; Class 2 genes encoding transcription factor, including dof12, Cly1 and NAC012.