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Rice grain quality is a multifaceted quantitative trait that impacts crop value and is influenced by multiple genetic and environmental factors. Chemical, physical, and visual analyses are the standard methods for measuring grain quality. In this study, we evaluated high-throughput hyperspectral imaging for quantification of rice grain quality and classification of grain samples by genetic sub-population and production environment. Whole grain rice samples from the USDA mini-core collection grown in multiple locations were evaluated using hyperspectral imaging and compared with results from standard phenotyping. Loci associated with hyperspectral values were mapped in the mini-core with 3.2 million SNPs in a genome-wide association study (GWAS). Our results show that visible and near infra-red (Vis/NIR) spectroscopy can classify rice according to sub-population and production environment based on differences in physicochemical grain properties. The 702–900 nm range of the NIR spectrum was associated with the chalky grain trait. GWAS revealed that grain chalk and hyperspectral variation share genomic regions containing several plausible candidate genes for grain chalkiness. Hyperspectral quantification of grain chalk was validated using a segregating bi-parental mapping population. These results indicate that Vis/NIR can be used for non-destructive high throughput phenotyping of grain chalk and potentially other grain quality properties. [ABSTRACT FROM AUTHOR]

Tiller number (TN) is a key determinant of final grain yield in the small grains crops, including rice (Oryza sativa L.), and is desired to enhance competition with weeds. Quantitative trait loci (QTLs) associated with TN were mapped within a set of recombinant inbred lines developed from a cross between the low-tillering tropical japonica cultivar 'Kaybonnet-lpa1-1' and the high-tillering, high-yielding, early-maturing indica cultivar 'Zhe733' using data collected from greenhouse potted plants evaluated weekly. Relationships between the five identified TN QTLs and related traits including panicle number (PN), flag leaf number, seedling leaf number (SLN), and earliness of tiller production were also evaluated. The three TN QTLs that were linked with early tiller emergence were also associated with PN; two additional TN not associated with PN were also identified. All five TN QTLs were also SLN QTLs, though other loci affecting SLN were also identified. Several of the TN and SLN QTLs identified using data from young plants (e.g., ≤6 wk after planting) were not detectible in data collected just before or after heading. The three TN QTLs also associated with PN showed stronger effects (higher logarithm of the odds ratios and additive effects) on TN as observed in seedling stages than on final TN at heading time, and their effects diminished over time to the point that they were not always detectible in TN data collected at or after heading. However, the three TN QTLs that were also PN QTLs were robust enough that they were detected in the less intensive (one-fourth the labor and resources) Arkansas trials, where tillers were counted from two single-plant replications just twice, at 6 wk after planting and after heading. The three loci associated with both TN and PN are the TN loci most likely to increase rice yield, and the markers presently found linked to them can be used for marker assisted breeding. [ABSTRACT FROM AUTHOR]

An increase in early tiller production is desired in rice (Oryza sativa L.) to increase yield potential and enhance ability to shade and suppress weeds. Unfortunately, tiller production and survival are sensitive to many environmental cues, making tillering pattern a difficult trait to reliably evaluate in field plots. The present objective was to use pot-grown plants where seeding depth and the environment could be controlled to identify quantitative trait loci (QTLs) associated with rate of seedling development (seedling leaf number, SLN) and tiller production (tiller number, TN) as well as the node from which the first tiller originated (N1T).The QTLs were identified in two related mapping populations, the first being a set of 280 'Lemont' × 'TeQing' recombinant inbred lines (RILs) observed over four trials, in which nine QTLs associated with TN, three for SLN, and two for N1T were detected. Eight of the TN QTLs were verified in a second population consisting of 123 TeQing-into-Lemont backcross introgression lines (TILs). Each SLN and N1T QTL was colocated with a TN QTL, and all nine detected TN QTLs were located in genomic regions previously reported to contain TN major genes or QTLs, demonstrating their robustness under diverse genetic backgrounds and growth conditions. Early tillering was associated with rapid seedling development as detected by an increased number of leaves and nodes on 8-wk-old seedlings. The present data indicated that breeders could effectively select for enhanced early tiller production among segregating genotypes based on tiller counts conducted at a single time point approximately 8 to 9 wk after germination under greenhouse conditions. Marker-trait linkages reported here can support marker-assisted selection of these nine TN QTLs. [ABSTRACT FROM AUTHOR]

The mineral concentrations in cereals are important for human health, especially for individuals who consume a cereal subsistence diet. A number of elements, such as zinc, are required within the diet, while some elements are toxic to humans, for example arsenic. In this study we carry out genome-wide association (GWA) mapping of grain concentrations of arsenic, copper, molybdenum and zinc in brown rice using an established rice diversity panel of ∼300 accessions and 36.9 k single nucleotide polymorphisms (SNPs). The study was performed across five environments: one field site in Bangladesh, one in China and two in the US, with one of the US sites repeated over two years. GWA mapping on the whole dataset and on separate subpopulations of rice revealed a large number of loci significantly associated with variation in grain arsenic, copper, molybdenum and zinc. Seventeen of these loci were detected in data obtained from grain cultivated in more than one field location, and six co-localise with previously identified quantitative trait loci. Additionally, a number of candidate genes for the uptake or transport of these elements were located near significantly associated SNPs (within 200 kb, the estimated global linkage disequilibrium previously employed in this rice panel). This analysis highlights a number of genomic regions and candidate genes for further analysis as well as the challenges faced when mapping environmentally-variable traits in a highly genetically structured diversity panel. [ABSTRACT FROM AUTHOR]

Background: Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers. Results: To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value < 1.53 × 10− 8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~ 20%) of the 250 QTLs were coinciding with previously reported QTLs/genes, about 201 (~ 80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods. Conclusions: While 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other. [ABSTRACT FROM AUTHOR]