| Maize is a key raw material for food, forage and chemical products. In China, the corn production can basically meet the needs of modern corn industry and forage processing field. However, our maize production is facing two major problems now, for one thing is the bottleneck of maize grain field; for another thing is the low quality of marketing and processing, which seriously affects the competitive strength in the international market. The test weight is a key yield trait for maize, as well as the essential index for maize processing and marketing quality, and a key objective of crop breeding for hyper-grain field. While in the recent years, the researches about maize quality are mostly focusing on the nutrition and processing quality, instead of studying processing quality especially the test weight; due to the low-level studies for the genetic basis of test weight, a complex trait, the work of improving maize test weight moves slowly, which causes seriously erosion of our competitiveness in the world trade market. Thus, choosing maize test weight as the study objective, employing the high-density linkage map Bin map based on the next-generation sequencing to detect the key QTLs for maize test weight, the analyze its genetic mechanism and biology function so as to dissect the genetic basis of test weight, is of great importance for speeding up the genetic improvement of maize test weight, improving our maize quality and competitiveness in the international market, and can provide theory basis for MAS in improving the crop high-grain yield and quality.In this study, the IBM Syn10 DH population(B73×Mo17) provided by ISU was employed as the research material, by combing with the high-density linkage map IBM SynlO DH bin map, the QTL identification for maize test weight was conducted. By combining genotype data from high density SNP linkage map IBM syn10 bin map constructed by our research team with phenotype data from multi-places and multi-replications, CIM method was employed in the QTL mapping for maize test weight related traits, the research was aimed to detect the key QTLs controlling maize test weight via closely linked SNP markers and favorable variations, as well as analyze the traits related to test weight so as to detect QTL loci related to maize test weight; then to explain the components of maize test weight and genetic mechanism, so as to underlie the MAS for maize test weight and map cloning for related genes, the results are as follows:1. Phenotype data from multi-places and replications were analyzed, including six test-weight related traits, TW(test weight), HKW(hundred kernels weight), WC(the water content of kernel), KL(kernel length), KW(kernel width), KT(kernel thickness), and each of the trait showed a normal distribution; in the correlation analysis, the TW and HKW were significantly in both of the places and association analysis; TW and WC of materials in Mianyang were significant; WC and HKW were significantly in both of the places and association analysis, proving that WC is a che results are in accordance with the quantitative theory, indicating that those traits were controlled by multi-genes.2. The genetic linkage map for bin marker of 125 IBM DH population materials was reconstructed based on the previous constructed map for 280 IBM Syn10 DH lines by our team, and the constructed map contains 6227 bin markers, covers 4558.4 cM in the maize genome, the length of chromosomes ranges from 277.37 cM to 722.64 cM, the intervals between markers ranges from 14.90 cM to 0.01 cM with an average value of 0.73 cM. The linkage map was drew by R package’wgaim’.3. QTL analysis was conducted using QTL Cartographer Unix version 1.17f by employing CIM method for test weight and related traits.159 related QTLs were detected in the Chr 1-10. Among them,40 QTLs are related to TW, with an phenotype explanation rate range from 4.37% to 16.8%; 42 QTLs are related to TW, with a phenotype explanation rate range from 3.79% to 16.44%; 39 QTLs are related to TW, with anphenotype explanation rate range from 4.06% tol5.1%; 10 QTLs are related to KL, with an phenotype explanation rate range from 6.86% to 10.28%; 16 QTLs are related to KW, with an phenotype explanation rate range from 5.36% to 12.97%; 10 QTLs are related to KT, with an phenotype explanation range from 6.21% to 12.95%; Qtw5-1, Qtw3-7 and Qtw7-1 for test weight, Qhkw8-2, Qhkw 10-2 for hundred kernel’s weight, Qk12-1 for kernel length, Qkw8-1 for kernel width, Qktl-1 for kernel thickness were detected and in accordance with previous researches with a more accurate position.4. Qtw5-1, Qtw3-7 and Qtw7-1 for test weight, Qhkw8-2, Qhkw10-2 for hundred kernel’s weight, Qk12-1 for kernel length, Qkw8-1 for kernel width, Qktl-1 for kernel thickness were detected and in accordance with previous researches with a more accurate position.7 stably expressed QTLs were detected in the two plots of Mianyang and Harbin and the associated study for two plots. The QTL for HKW, Qhkw3-5, was located in the interval of 304.17-315.77 cM in Chr 3 with a phenotype explanation rate of 7.64%, and the addictive effect is -0.7192; Qwc9-4 for kernel water content was located in the interval of 364.38-381.54 cM, with a phenotype explanation rate of 6.43%, and the addictive effect is-0.3156; two QTLs for kernel length were detected, the Qkl5-1 was in the interval of 28.66-45.52 cM with an phenotype explanation rate of 7.83%, and the addictive effect is-0.2736, while the Qk19-3 was in the interval of 85.56-94.79 cM with an phenotype explanation rate of 8.74%, and the addictive effect is 0.3217;two QTLs for kernel width were detected, Qkwl-3 was in the interval of 106.27-112.57 cM in the chromosome 1 with an phenotype explanation rate of 10.37%, and the addictive effect is 0.3358, while the Qkw5-3 was located in the 11.57-17.35 cM in interval in the chromosome 5 with an explanation rate 7.21%, and the addictive effect is -0.2753; the Qkt1-3 for kernel thickness was located in the interval of 422.48-441.53cM with a explanation rate of 8.64%, and the addictive effect is -4.4956. |
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