| Most of the important agronomic traits in crops are quantitative, and variations in these traits are usually controlled by many minor genes called QTLs. Improvement of these quantitative traits is one of the major objectives in crop genetic breeding program. Many elite varieties and hybrids have been developed using traditional breeding methods, but the efficiency is still low because the genetic principles .of the quantitative traits are still unclear. With the advent of molecular makers and QTL mapping methods in the last decade, there is an opportunity to dissect the quantitative traits.In this study, a RILs population consisting of 241 recombination lines, derived from an elite hybrid, Shanyou 63, was used-to construct a genetic map and measure 12 traits. Field experiments with 3 replicates following a randomized complete block design were implemented in 1997 and 1998. QTLs for the different traits were mapped and a large amount of epistasis was detected. The main results are summarized as following:1. Transgressive segregation was observed for all traits in the RILs, There existed significant difference in all traits between the two years. The interactions between genotype and environment were observed for all traits, and the environment effects on plant yield and tillers per plant were relatively larger than the other traits.2. In the population, the allele frequencies of MH63 and ZS97 in all 175 informative loci were 51.4% and 48.6% respectively. The allele ratio approximated to the expected 1:1 (MH63:ZS97). The heterozygosity genotype frequency was 0.79%, twice as the expected (0.39%). There were 39 markers skewed from the expected 1:1 genotypic segregation. Of the 39 markers, the genotype of 12 markers skewed towards ZS97, 27 markers towards MH63.3. A genetic linkage map, covering 12 rice chromosomes, was constructed using 176 markers, spanning 1494.2 cM , with an average interval of 9.3 cM. which well integrated the markers from RGP and Cornell maps. The linear order of markers in the map in the chromosomes was in good agreement with the one constructed using F2 population.4. The entire genome was searched for QTLs conferring significant effects on all scored traits by interval mapping. The number of QTLs greatly varied according to the different traits. A total of 16 QTLs were detected for yield and its component traits in two years, and of these 9 QTLs were simultaneously detected in two years. For other traits, there were 25 QTLs located in 7 chromosomes. Only 8 QTLs were shared in both years. For grain shape, length and width, 10 QTLs were recovered.5. A total of 59 QTLs were detected for all 12 traits using composite interval mapping with mixed model. 22 of the 59 QTLs. with relative large effects, were located in the same chromosome intervals by interval mapping. 11 QTLs were detected in theadjacent intervals of those detected by interval mapping. All QTLs for different traits cumulatively explained 3.24% to 75.02% of total phenotypic variations.6. QTLs for the same trait with positive and negative additive effects were dispersed in both parents.7. Epistasis was detected in all 12 chromosomes. All possible two-way ANOVAs between the 175 markers were analyzed for plant height, heading date, plant yield and its components. Amounts of significant digenic interactions were detected at the level p<0.01. Some marker pairs showed significant epistatic gene actions in both years. Four digenic significant interactions were detected for plant yield, the least one, and 52 for plant height, the most one.8. Interactions were divided into 3 types according to the combination locus effect. Fpistasis between non-significant effect loci (non-QTL) was dominant, epistasis between QTL and non-QTL was very rare, and epistasis between QTL and non-QTL was moderate. Each interaction pair explained 3% - 9% of the phenotypic variation.9. Some QTLs appeared to be pleiotropic effects, and pleiotropic effects of digenic interactions were detected between or among relat...
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