| As one of the main staple food crops, rice has been widely grown, and has the highest yield per acre among the other crops. Up till now, rice yield is still the first priority in the rice breeding in our country. With the advance in biological technology, a huge amount of researches have been focused on mapping of major gene(s)/QTLs controlling rice important traits: yield, quality, and resistance, among the others. However, further fine-mapping of QTLs and their interactions are wanting, and therefore can't meet the requirements for map-based gene cloning and marker-assisted selection. The rice genomic sequence available online enables us to develop high-density markers on the chromosomal regions harboring targeted QTLs. The selected tightly-linked molecular markers can be used to select one or more traits, and hence to improve the efficiency of marker-assisted selection in plant breeding.A DH population, derived from the cross of Balilla(japonica)/Nanjing11(indica), was used in the present research. Mapping software Joinmap 3.0 was applied to construct a linkage map of 137 molecular markers, including 10 STS, 23 CAPS, and 104 SSR markers. The map covers a genetic distance of 1429.2cM, with an average of 10.43cM. Software QTLmapper 1.0 was used to search QTLs of nine yield-associated traits, and then to analyze their locations and genetic effects. The main results are as followings:1. Nine yield-associated traits have been investigated in the present research. These nine traits include grain number per panicle (GN), spikelet number per panicle (SN), grain fertility (GF), panicle length (PL), panicle density (PD), kilo-grain weight (GW), panicle number per plant (PN), pollen fertility (PF), phenol response (PH). Among the nine traits, the values vary distinctively between the two parents except grain weight and panicle density. A continuous segregation was observed for each of the nine traits in the DH population, and some individuals showed 'super' values compared to theirparents (except phenol response). This phenomenon was characterized with typical characters of quantitative traits.2. A QTLmapperl.O that based on a mixed linear model was used to locate QTLs and their effectiveness on traits. The results showed:(l) For the additive and epistasis effects, variations were present in the contribution, the effectiveness and the interaction mode among different QTLs. the contribution values varied widely, ranging from 3.19% to 47.48%. (2) Of all QTLs, some showed either additive or epistasis effect, others exhibited the mixture effects. (3) Some QTLs interact with only one other QTL, others can interact with more than one QTLs, that is to say, having two epistasis effects. These results reveal the complex effects of the genes involved in yield traits.3. The results suggest that the QTLs with addictive effects for a given trait are usually distributed in both parental lines, as a result, superior individual were frequently appeared in the DH population. If the favorable QTLs could be pyramided into one individual by marker-assisted approach, this may lead to superior lines with over-dominant performances.4. The QTLs controlling the same trait are often clustered at the adjacent chromosomal regions. Furthermore, the QTLs controlling the relevant traits are often located at the same or adjacent regions. Up till now, we don't know whether the close localizations of the QTLs controlling relevant traits were originated from pleiotropy or from genetic linkage. Compared with the results published elsewhere, most of major QTLs frequently discovered from different populations or under different environments are found to locate in the same or nearby chromosomal regions.5. Fine-mapping of qPH4-3 controlling phenol response in grain was conducted based on the preliminary QTL mapping. The rice genome sequence available online was used to develop high-density markers on the qPH4-3 region. With a big BC1 population, qPH4-3 was finally mapped within two markers RM5611 and F17 with the physical dis...
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