Association Mapping And Linkage Mapping Of Several Quantitative Traits In Rice(Oryza S Ativa L.)

Most of agronomic traits of rice such as yield, harvest index and resistant trait such as resistance of physiological disorder are controlled by quantitative traits loci (QTL). Dissection of the genetic basis for these traits is important for improvement of the traits through marker assisted selection (MAS). Genetic mapping is one of the most efficient strategies to dissect the genetic basis of complex quantitative traits. Mapping generally uses two main types of populations:one is a bi-parental mapping population developed from a cross of two parents, used for linkage mapping; the other is a natural population or germplasm collection including tens or hundreds of diverse lines used for association mapping or linkage disequilibrium (LD) mapping. To improve the efficiency of mining valuable genes, a rice core collection (USDA rice core collection, URCC) consisting of1,794entries originating from114countries were developed. Futher, a mini-core subset (USDA rice mini-core collection, URMC) from the core collection by further reduction of accessions were selected. The wide range in genetic diversity along with the manageable number of accessions in the URMC make it one of the best collections of rice for mining valuable genes based on association mapping. After the markers associated with the gene of interest are identified, an essential step must be taken for their confirmations via different strategies, because the marker alleles are correlated with, but not entirely predictive of, the gene alleles. Linkage mapping is such a strategy for the confirmation by using the segregating populations,i. e. F2population. In the present study, the genetic structure and diversity of URCC and URMC were analyzed. Association mapping were applied in yield and harvest index. Linkage mapping and association mapping were used to study genetic basis for straighthead. The main results are summarized as follows:Genetic structure and diversity of URMC were analyzed using both genotypic (128molecular markers) and phenotypic (14numerical traits) data. This mini-core had13.5alleles per locus and polymorphic information content (PIC) value was0.71. A model-based clustering analysis resulted in lowland rice including three groups, aus (39accessions), indica (71) and their admixtures (5), upland rice including temperate japonica (32), tropical japonica (40), aromatic (6) and their admixtures (12) and wild rice (12) including glaberrima and four other species of Oryza. Group differentiation was analyzed using both genotypic distance Fst from128molecular markers and phenotypic (Mahalanobis) distance D2from14traits. Both dendrograms built by Fst and D2reached similar-differentiative relationship among these genetic groups, and the correlation coefficient showed high value0.85between Fst matrix and D2matrix. The information of genetic and phenotypic differentiation could be helpful for the association mapping of genes of interest.Yield is the most important and complex trait for genetic improvement in crops, and marker-assisted selection enhances the improvement efficiency.203O.sativa accessions from URMC were phenotyped for14agronomic traits and identified five mghly and significantly correlated with grain yield i.e. plant height, plant weight, tillers, panicle length, and kernels/branch per panicle. Genotyping with genome-wide154SSRs and an indel demonstrated five groups. Linkage disequilibrium (LD) decayed at least20cM and marker pairs with significant LD ranged from4.64to6.06%in four main groups. Model comparisons revealed that different dimensions of principal components analysis (PCA) effect on yield and yield correlated traits and kinship did not improved models in this collection. Thirty marker-trait associations were highly significant, four for yield, three for plant height, six for plant weight, nine for tillers, five for panicle length and three for kernels/panicle branch. Twenty-one markers contributed to the30associations because seven were co-associated with two and more. Allelic analysis of OSR13, RM471and RM7003for their co-associations with yield traits demonstrated that allele126bp of RM471and108bp of RM7003had the greatest positive effect on yield traits.Harvest index is a measure of success in partitioning assimilated photosynthate. An improvement of harvest index means an increase of economic portion of the plants, i.e. grain yield.203O.sativa accessions from URMC were phenotyped for14traits in both temperate (Arkansas) and subtropical (Texas) climates and the genotyping used154SSRs and an indel marker. Across both locations, four traits of heading, plant height, plant weight and panicle length had negative correlations, while two traits of seed set and grain weight/panicle had positive correlations with harvest index. By association mapping,36markers in Arkansas and28markers in Texas were identified to be significantly associated with harvest index and its correlated traits. Seven and two markers were consistently associated with two or more harvest index correlated traits in Arkansas and Texas, respectively. Additionally, four markers were identified at both locations constitutively, while32and24markers were identified specifically adaptive to Arkansas and Texas, respectively. Allelic analysis of four constitutive markers demonstrated that allele253bp of RM431had significantly greater effect on decreasing plant height, and390bp of RM24011had the greatest effect on decreasing panicle length across both locations. Thus, the results from our association mapping could complement and enrich the information from linkage-based QTL studies, and help improve harvest index directly and indirectly in rice.Straighthead, a physiological disorder in rice(Oryza sativa L.) that can cause yield losses approaching100%, is a serious threat to rice production in the US and worldwide. We conducted both association and linkage mapping to identify candidate QTLs for straighthead. The association mapping was applied to405accessions extracted from the URCC and genotyped with71genome-wide SSR markers. By model comparison, we selected a best fit model that yielded five marker loci significantly associated with straighthead. The linkage mapping was based on a F2population developed from resistant'Zhe733'and susceptible'R312.A total of147SSR markers were found to be polymorphic between two parents among the361screened SSRs. Using phenotypic data of F2plants and their F2:3families, two major QTLs, qSH-2and qSH-8, were identified using bulk segregant analysis (BSA), which was supported by two independent genes model in a segregation ratio of9resistant:6moderately resistant:1susceptible plants in the F2population (x2=5.65, P=0.06>0.05). QTLs qSH-2was mapped between RM475and RM13489on Chr2, and qSH-8between RM8271/RM72and RM6838on Chr8, explaining11.10%and18.10%of phenotypic variation, respectively. qSH-2was also confirmed by the resistant allele194bp of RM475identified in association mapping. Theses findings should be useful for fine mapping of straighthead resistant genes and improvement of commercial cultivars for straighthead resistance using marker-assisted selection.