Genetic Analysis For Rice Quality Traits And Qtl Mapping

With the stability of rice production and the improvement of people’s life quality, rice quality improvement has become one of the most important goals in rice breeding programs. The primary components of rice grain quality include appearance, eating, cooking, and milling quality, and nutrient qualities, all values that are determined by their physical-chemical properties and other socio-cultural factors. Among these quality properties, consumers pay more attention to the fine appearance, high eating and nutrient quality. Therefore, these are major goals in rice quality improvement. To better elucidate genetic basis and improve rice nutrition quality by marker-assisted selection and gene engineering procedure, the following studies were carried out:1. The analysis of QTL and the stability across three environments underlying grain quality traits by BILsUsing a population of Koshihikari/Kasalath (Backcross Inbred Lines, BIL) from a backcross of Koshihikari (japonica)/Kasalath(indica)//Koshihikari, QTL and the stability of QTL of12quality traits in three environments were analyzed. Altogether64QTL were identified in three environments;5QTL of nutrition traits;3QTL of appearance traits, others of starch RVA. Six QTL were identified using Koshihikari/Kasalath BIL population in three environments, including qPI-3-1、qPGWC-6、qPKV-6、qHPV-6-1、qBDV-6、 qSBV-6-1、qPaT-6. qPGWC-6was identified only in two environments. Then all the7QTL were further confirmed across two or three different environments by some chromosome segment substitution lines (CSSLs). In addition, some QTL affecting several quality traits were mapped on the same genome regions. Co-localization of these QTL can provide an explanation for the genetic basis of correlation between nutrient traits and RVA.2. Identification of stably expressed QTL for grain quality traitsA population of Sasanishiki×Habataki (Backcross Inbred Lines, BIL) from a backcross of Sasanishiki (japonica)/Habataki (indica)//Sasanishiki in2007Nanjing and2007Jinhu were used to detect QTL for10rice quality traits, a total of43QTL were detected,including5of protein content,38QTL of RVA. Ten QTL were identified in two environments, including qPC-8、qAAC-4、qAAC-10、qPKV-2、qPKV-7、qCPV-1、 qBDV-4、qBDV-7and qSBV-7. Then all the10QTL were further confirmed across two different environments by some chromosome segment substitution lines (CSSLs). In addition, some QTL affecting several quality traits were mapped on the same genome regions. Co-localization of these QTL can provide an explanation for the genetic basis of correlation between nutrient traits and RVA.3. Dynamic QTL analysis of protein content and protein index during grain filling stage of riceProtein content (PC) and protein index (PI) play an important role in determining nutrition quality in rice. Seventy-one lines derived from Asominori/IR24were used to analyze the developmental behavior of PC and PI by unconditional and conditional QTL mapping methods. Ten unconditional QTL and6conditional QTL for PC and11unconditional QTL and9conditional QTL for PI were identified at four measuring stages. More QTL were identified at the earlier three stages than at the final stage. The temporal patterns of gene expression for PC and PI were different at different stages. Several QTL expressed across two or three measuring stages while many QTL expressed at only one stage. In addition, a few QTL were closely linked with maturity QTL reported previously. Many QTL for PC and PI were co-localized, in agreement with the significant correlation between PC and PI. The present study suggested that dynamic QTL mapping might be a valid way to reveal more genetic information about protein accumulation.4. Primary mapping of WB geneWe isolated a larger white-belly mutant (WB).Comparing with wildtype Nip., WB has distinguished phenotype on grain size,1000-grain weight, protein content and fat content. Significant difference was observed in starch granule beween WB and Nip. To map theWB locus, we generated F2mapping population derived from a cross of the WB and the cultivar Pusher. Then we mapped the WB to an interval between RM13061and RM13195on Chromosome2, a3.0cM DNA region. These results are useful in map-based cloning of WB gene.