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The multi-environment genomic selection enables plant breeders to select varieties resilient to diverse environments or particularly adapted to specific environments, which holds a great potential to be used in rice breeding. To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering the huge potential of genomic prediction enhanced sparse phenotyping on the cost saving of multi-environment trials (MET), the establishment of a multi-environment training set could also benefit from it. Optimizing the genomic prediction methods is also crucial to enhance the multi-environment genomic selection. Using haplotype-based genomic prediction models is able to capture local epistatic effects which could be conserved and accumulated across generations much like additive effects thereby benefitting breeding. However, previous studies often used fixed length haplotypes composed by a few adjacent molecular markers disregarding the linkage disequilibrium (LD) which is of essential role in determining the haplotype length. In our study, based on three rice populations with different sizes and compositions, we investigated the usefulness and effectiveness of multi-environment training sets with varying phenotyping intensities and different haplotype-based genomic prediction models based on LD-derived haplotype blocks for two agronomic traits, i.e., days to heading (DTH) and plant height (PH). Results showed that phenotyping merely 30% records in multi-environment training set is able to provide a comparable prediction accuracy to high phenotyping intensities; the local epistatic effects are much likely existent in DTH; dividing the LD-derived haplotype blocks into small segments with two or three single nucleotide polymorphisms (SNPs) helps to maintain the predictive ability of haplotype-based models in large populations; modelling the covariances between environments improves genomic prediction accuracy. Our study provides means to improve the efficiency of multi-environment genomic selection in rice. [ABSTRACT FROM AUTHOR]

Acireductone dioxygenase (ARD) is a metal-binding metalloenzyme and involved in the methionine salvage pathway. In rice, OsARD1 binds Fe2+ and catalyzes the formation of 2-keto-4-methylthiobutyrate (KMTB) to produce methionine, which is an initial substrate in ethylene synthesis pathway. Here, we report that overexpression of OsARD1 elevates the endogenous ethylene release rate, enhances the tolerance to submergence stress, and reduces the sensitivity to drought, salt, and osmotic stresses in rice. OsARD1 is strongly induced by submergence, drought, salinity, PEG6000, and mechanical damage stresses and exhibits high expression level in senescent leaves. Transgenic plants overexpressing OsARD1 (OsARD1-OE) display fast elongation growth to escape submergence stress. The ethylene content is significantly maximized in OsARD1-OE plants compared with the wide type. OsARD1-OE plants display increased shoot elongation and inhibition of root elongation under the submergence stress and grow in dark due to increase of ethylene. The elongation of coleoptile under anaerobic germination is also significantly promoted in OsARD1-OE lines due to the increase of ethylene content. The sensitivity to drought and salt stresses is reduced in OsARD1-OE transgenic lines. Water holding capacity is enhanced, and the stomata and trichomes on leaves increase in OsARD1-OE lines. Drought and salt tolerance and ethylene synthesis–related genes are upregulated in OsARD1-OE plants. Subcellular localization shows that OsARD1 displays strong localization signal in cell nucleus, suggesting OsARD1 may interact with the transcription factors. Taken together, the results provide the understanding of the function of OsARD1 in ethylene synthesis and abiotic stress response in rice. [ABSTRACT FROM AUTHOR]

Genome-wide association study (GWAS) is an effective approach for the identification of marker-trait associations (MTA) using more diverse germplasm not constrained by specific crossing. This study used a collection of 327 advanced indica breeding lines and varieties genotyped using a genotyping-by-sequencing method and phenotyped in eight environments to identify MTA for grain yield (GY) and 10 related traits. The current panel of indica breeding lines were divided into two subpopulations by three analytical methods including STRUCTURE, principal component analysis (PCA) and neighbour joining (NJ) tree. STRUCTURE and PCA gave the same classification of genotypes while the NJ tree results are slight difference in assigning genotypes to subpopulation from STRCUTURE and PCA. Wide variation was observed for all the 11 traits in the whole panel and the two subpopulations inferred by STRUCTURE analysis. Linkage disequilibrium (LD) analysis showed that LD decay varied across the chromosomes and average pairwise squared correlation coefficient (r 2 ) dropped to half of its maximum value (r 2 = 0.25) within a physical distance of 200 kb in the whole population. A MLM model controlling both population structure and cryptic relatedness was chosen to identify MTA for all the 11 tested traits. A total of 452 MTA that were delineated into 43 QTL were identified for all traits but PB, SB, and SR with 39 QTL being not reported before. Three QTL on chromosome 6, 9 and 12 were identified for GY but only in DS2. The numbers of QTL identified for the remaining traits varied from two to 26. Most of the detected QTL were found in only one environment. Four QTL were located in the regions containing genes/QTL previously identified for other related traits. The effects of identified QTL were relatively small with the highest percentage of phenotypic variance explained by a single QTL being 9.6%. The identified QTL are directly relevant to and can be more effectively used in breeding programs. [ABSTRACT FROM AUTHOR]

Effects of nitrogen application, genotype, seasons, grain shapes and amylose content (AC) on head rice yield (HRY) and chalkiness were determined in 2012 dry (DS) and wet seasons (WS) in IRRI using 351 indica advanced breeding lines/cultivars from several breeding programs and three N rates (0, 90, and 180 kg ha −1 and 0, 45, and 90 kg ha −1 for DS and WS, respectively). HRY was improved with increasing N rates in WS while no significant differences were observed in DS. The average HRY was 10% higher in DS than in WS. Degree of endosperm chalkiness (DEC) and percentage of grains with chalkiness (PGWC) were decreased with increasing N rates in both seasons. AC was only slightly affected by season and N treatment. DEC, PGWC, grain length (GL) and the ratio of grain length to width (LWR) were negatively correlated with HRY in both seasons. 78 and 23 lines had high HRY and low DEC under 180 (DS) and 90 N (WS) rates, respectively. 74 and 21 of these high quality lines had PGWC less than 30%, indicating that visual selection on PGWC could be a quick and simple method for screening for HRY. The low AC subset had lowest DEC but highest HRY in both seasons. Furthermore, the proportions of lines with high HRY and low DEC in different shape classes were significantly different, suggesting that it is possible to select for low chalkiness and high HRY by selecting for PGWC and grain shape. [ABSTRACT FROM AUTHOR]