Identification Of QTL Of Important Agronomic Traits In Soybean And Fine Mapping Of QoilF-1

Soybean is an important oil and grain crops, also is the main source of vegetable protein and vegetable oil. Due to the lack of internationally competitive soybean production in China is slow, high yield and good quality of soybean varieties, the imported soybeans occupy the vast majority of the domestic market share. Through modern breeding technology to shorten the breeding period, cultivating soybean varieties of high yield and good quality is of great significance to promote the competitiveness of the domestic soybean, the agronomic traits and quality traits of soybean genetic regulation mechanism research is the foundation of soybean molecular breeding. To resolve important agronomic traits and quality traits of soybean molecular genetic pattern, mining QTL loci contain the important traits, In this study‘Ji Yu82’ as the donor parent of high-yield soybean varieties, with high oil soybean farmers ‘Dong Nong47’as recurrent parent, a hybrid, three backcross build BC3F2 and BC3 F 3 backcross import department group as the research material, For these two groups of 193 family of protein content, fat content, plant height, the grain weight per plant, pod number and grain weight per plant phenotype analysis of important agronomic characters such as; Combined with phenotypic data mining control important agronomic traits of soybean QTL, molecular marker assisted breeding for soybean provide strong theoretical support. And stability for more than a generation found related to the oil content of QTL qoil F- 2 for precise positioning.Specific results are as follows:（1） the BC3F2 and BC3F3 group of main agronomic traits phenotypic statistical results show that: Two groups of protein content, fat content, plant height, number of pods per plant, grain weight per plant, hundred grain weight, and other important agronomic traits are normally distributed, with smaller skewness and kurtosis. Plant height of the value of skewness negative（left） distribution, number of pods per plant, grain weight per plant, protein content and fat content of skewness value is positive（present right distribution）. Number of pods per plant and grain weight per plant of variation coefficient is bigger, show that the two groups of data of discrete degree is bigger.The protein content, fat content variation coefficient is small, its discrete degree is smaller.Kao Zhong BC3F2 and BC3F3 two generations group data to present a more consistent trend.（2） the backcross population BC3F2 per plant and BC3F3 family agronomic traits related analysis, the results show that the grain weight and protein content and total fat were significantly positive correlation;Grain number per plant and protein content were not significant negative correlation, grain weight per plant showed significant negative correlation with fat content;Showed significant negative correlation between fat and protein content.（3） using 1600 soybean genome-wide screening, SSR molecular marker polymorphism of parent and genotype in 193 BC3F2 group analysis, we obtained 155 polymorphism markers present polymorphism in parent and offspring populations, using 155 SSR markers to construct a soybean 20 chain group of molecular genetic linkage map, map the total length of 267.621 c M and the average distance of 1.76 c M, the average distance between the longest chain group mark E average distance of 5.81 c M, the average distance of the shortest C1 chain group mark average distance of 0.48 c M.On average each linkage group tag number is 7.（4） through to the group of 193 BC3F2 strain background recovery rate calculation, found that all strains are reaching a higher recovery rate, the range of 91.20% 99.00%, the average is 96.79%, and significantly higher than the theoretical value of 87.5%.（5） of this study was to use QTL Ici Mapping Version 3.2 and Win QTLCartographer software V2.0, the ‘Dong Nong47’ x ‘Ji Yu82’BC3F2 and precocious BC3F3 generation groups of protein content, fat content, plant height, number of pods per plant, grain number per plant and hundred kernel weight 6 traits QTL analysis, to set the LOD values of 2.0, a total of 38 QTL is detected.The QTL associated with fat content has 5, can explain the genetic variation in the range of 16.33% 18.12%;QTL associated with protein content has three, can explain the genetic variation in the range of 4.61% 8.20%;The QTL associated with plant height have eight, can explain the genetic variation in the range of 4.36% 18.71%;QTL associated with pod number per nine, can explain the genetic variation in the range of 14.10% 46.40%;The QTL associated with grain weight per plant had ten, can explain the genetic variation in the range of 5.54% 47.36%;QTL associated with hundred grain weight has seven, can explain the genetic variation in the range of 3.42% 36.74%, mainly distributed in D1 a, N, F, A2, L, E and K chain group.Among them, the two generations can steady point of QTL QTL associated with fat between Satt151- Satt185 tags in two generations have been detected, between Sat₃70- SSR₁6₀840 marker was detected in two generations, grain weight per plant and related QTL position Sat₀90- Sat₁33 in two generations have been detected.QTL associated with hundred grain weight between Satt349- Sat₁19 markers in two generations have been detected.Character to make use of the QTL can be a good choice, then auxiliary soybean breeding work.（6） on the linkage group F on Satt663-Sat₀39 range related to the oil content of qoil F- 1 for precise positioning, using the selected hybrid system for the remainder of the group, and group by 30 to there is polymorphism between the parents of primers screened, finally has six have polymorphism markers between groups, 20% of the total.Restructuring the extreme value on both sides of the oil content rate calculation, high oil and low oil group 50 strains are selected, the results show that the SSR₁3₁818 separation is a common tag, SSR₁3₁802 is a closely connected with the tags, completed the precise positioning..