No abstract

Development of core collections is an effective tool to extensively characterize large germplasm collections, and the use of a mini-core subsampling strategy further increases the effectiveness of genetic diversity analysis at detailed phenotype and molecular levels. We report the formation of a mini-core subset containing 217 entries derived from 1794 core entries representing the genetic diversity found in more than 18,000 accessions of the USDA-ARS rice (Oryza sativa L.) germplasm collection. The mini-core was developed with PowerCore software on the basis of 26 phenotypic traits and 70 molecular markers. The 217 entries in the mini-core had a similar distribution over 15 geographical regions, with 1794 entries in the original core collection. The resultant mini-core had 6.3% of mean difference (MD%), 16.5% of variance difference (VD%), 102.7% of variable rate (VR%), and 97.5% of coincidence rate (CR%) with the core collection, which brought about full coverage of 26 traits. All 962 alleles identifi ed by the 70 markers in the core collection were captured in the mini-core, which maximized allelic richness up to 100% and resulted in an average genetic diversity (Nei index) of 0.76, ranging from 0.37 to 0.97 among the markers. In conclusion, the mini-core presented in this study is a highly suitable and representative subset of the USDA rice core collection as well as the entire USDA-ARS rice germplasm holdings.

Harvest index is a measure of success in partitioning assimilated photosynthate. An improvement of harvest index means an increase in the economic portion of the plant. Our objective was to identify genetic markers associated with harvest index traits using 203 O. sativa accessions. The phenotyping for 14 traits was conducted in both temperate (Arkansas) and subtropical (Texas) climates and the genotyping used 154 SSRs and an indel marker. Heading, plant height and weight, and panicle length had negative correlations, while seed set and grain weight/panicle had positive correlations with harvest index across both locations. Subsequent genetic diversity and population structure analyses identified five groups in this collection, which corresponded to their geographic origins. Model comparisons revealed that different dimensions of principal components analysis (PCA) affected harvest index traits for mapping accuracy, and kinship did not help. In total, 36 markers in Arkansas and 28 markers in Texas were identified to be significantly associated with harvest index 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 constitutively identified at both locations, while 32 and 24 markers were identified specifically in Arkansas and Texas, respectively. Allelic analysis of four constitutive markers demonstrated that allele 253 bp of RM431 had significantly greater effect on decreasing plant height, and 390 bp of RM24011 had the greatest effect on decreasing panicle length across both locations. Many of these identified markers are located either nearby or flanking the regions where the QTLs for harvest index have been reported. Thus, the results from this association mapping study complement and enrich the information from linkage-based QTL studies and will be the basis for improving harvest index directly and indirectly in rice.

A rice mini-core collection consisting of 217 accessions has been developed to represent the USDA core and whole collections that include 1,794 and 18,709 accessions, respectively. To improve the efficiency of mining valuable genes and broadening the genetic diversity in breeding, genetic structure and diversity were analyzed using both genotypic (128 molecular markers) and phenotypic (14 numerical traits) data. This mini-core had 13.5 alleles per locus, which is the most among the reported germplasm collections of rice. Similarly, polymorphic information content (PIC) value was 0.71 in the mini-core which is the highest with one exception. The high genetic diversity in the mini-core suggests there is a good possibility of mining genes of interest and selecting parents which will improve food production and quality. A model-based clustering analysis resulted in lowland rice including three groups, aus (39 accessions), 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 from 128 molecular markers and phenotypic (Mahalanobis) distance D(2) from 14 traits. Both dendrograms built by Fst and D(2) reached similar-differentiative relationship among these genetic groups, and the correlation coefficient showed high value 0.85 between Fst matrix and D(2) matrix. The information of genetic and phenotypic differentiation could be helpful for the association mapping of genes of interest. Analysis of genotypic and phenotypic diversity based on genetic structure would facilitate parent selection for broadening genetic base of modern rice cultivars via breeding effort.

Stigma and spikelet characteristics play an essential role in hybrid seed production. A mini-core of 90 accessions developed from USDA rice core collection was phenotyped in field grown for nine traits of stigma and spikelet and genotyped with 109 DNA markers, 108 SSRs plus an indel. Three major clusters were built upon Rogers’ genetic distance, indicative of indicas, and temperate and tropical japonicas. A mixed linear model combining PC-matrix and K-matrix was adapted for mapping marker-trait associations. Resulting associations were adjusted using false discovery rate technique. We identified 34 marker-trait associations involving 22 SSR markers for eight traits. Four markers were associated with single stigma exsertion (SStgE), six with dual exsertion (DStgE) and five with total exsertion. RM5_Chr1 played major role indicative of high regression with not only DStgE but also SStgE. Four markers were associated with spikelet length, three with width and seven with L/W ratio. Numerous markers were co-associated with multiple traits that were phenotypically correlated, i.e. RM12521_Chr2 associated with all three correlated spikelet traits. The co-association should improve breeding efficiency because single marker could be used to assist breeding for multiple traits. Indica entry 1032 (cultivar 50638) and japonica entry 671 (cultivar Linia 84 Icar) with 80.65 and 75.17% of TStgE, respectively are recommended to breeder for improving stigma exsertion.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-009-9290-y) contains supplementary material, which is available to authorized users.