Meta-QTL Based Molecular Marker Development And Its Efficiency For Germplasm Evaluation

Barley (Hordeum vulgare L.) is the fourth largest crop in China. Its quality will directly affect the product's final commercial use. How to effective screen out useful genes from wild and breeding materials to breed higher yields, better quality, greater disease resistance and adaptability new varieties is our current objective.Based on large amount of information provided by QTL mapping, the selection of reasonable molecular markers to evaluate genetic resources more effective has important theoretical and practical value. However, the influence of genetic background in crosses and other factors, such as population type, measurement standards, statistical methods, environment, made results of QTL mapping differently. Therefore, the pre-conditions of exploit core markers could ensure the facticity of QTLs.The core markers were screened out via real QTLs through integration of QTLs from different barley mapping populations followed by statistical method. The main results and conclusions are summarized as following:1. Based on 150 DH lines derived from Steptoe×Morex,7 agronomic traits, plant height, ear length, grain number per spike,1000-grains weight, tiller number, effective spike per plant, and spike number per plant, were investigated in two year at different location. Four pairs of positive correlation (P<0.01) were found in two years.2.150 DH lines were used for QTL mapping and epistasis analysis with inclusive composive interval mapping method. Eventually,21 QTLs were found for four traits, namely 1000-grains weight, plant height, ear length, and grain number per ears. On the other hand, the spike number per plant, tiller number, effective spike per plant were controlled mainly by spistasis effect. The marker interval ABG399-MWG571B which control of 4 traits on chromosome 3 was stable expressioned in two years. At least 53 positonal candidate genes were found in this regions after map intergration3. Through published papers, we collected 3875 QTLs which were almost all the QTLs mapped in barley and involving agronomic, quality, diseases resistance and physiology. We sort out these data firstly according to there trait names, population type and size, map, chromosome location, and the peak interval. 4. Using simulation method, we deduced empirical formula to compute 95% confidence interval for DH and RIL populations, namely CI= 97.462×(N×R2) -0.835, CI= 99.128×(N×R2) -0.88, N is the population size, R2 is proportion of the variance explained by QTL. It is important conditions for future meta-analysis.5. After integration of 178 plant height QTLs,53 real QTLs were found with average confidence interval 5.53cM. Compare with origin, it reduced 9.27cM for each. The distribution of these QTLs across chromosomes was 8,12,15,8,3,5 and 6. Eventually, we selected core markers for future resource evaluation after thought of detection threshold and R2.6. Based on meta-QTL of plant height in barley, we evaluated 3 conventional methods for selection markers with QTL information, eg. direct method, razor and overlapping times, and the addition of direct method above razor would get similar results as meta-analysis.7. Among the 3875 QTLs, we projected 842 QTLs in 2H to reference map and then developed molecular markers after analysis the distribution of these QTLs. We found that 5 important marker intervals (i.e 25-33.3,50-58.3,75-83.3,116.7-125 and 150-158.3 cM) were suitable for molecular development for globalized evaluation of germplasm.