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SUMMARY: Mesocotyl length (ML) is a crucial factor in determining the establishment and yield of rice planted through dry direct seeding, a practice that is increasingly popular in rice production worldwide. ML is determined by the endogenous and external environments, and inherits as a complex trait. To date, only a few genes have been cloned, and the mechanisms underlying mesocotyl elongation remain largely unknown. Here, through a genome‐wide association study using sequenced germplasm, we reveal that natural allelic variations in a mitochondrial transcription termination factor, OsML1, predominantly determined the natural variation of ML in rice. Natural variants in the coding regions of OsML1 resulted in five major haplotypes with a clear differentiation between subspecies and subpopulations in cultivated rice. The much‐reduced genetic diversity of cultivated rice compared to the common wild rice suggested that OsML1 underwent selection during domestication. Transgenic experiments and molecular analysis demonstrated that OsML1 contributes to ML by influencing cell elongation primarily determined by H2O2 homeostasis. Overexpression of OsML1 promoted mesocotyl elongation and thus improved the emergence rate under deep direct seeding. Taken together, our results suggested that OsML1 is a key positive regulator of ML, and is useful in developing varieties for deep direct seeding by conventional and transgenic approaches. Significance Statement: Mesocotyl elongation (ML) is important for seedling emergence under dry direct seeding in rice. Here, we report the identification and functional validation of a gene, OsML1 (LOC_Os07g24090.1), which contributes greatly to the natural variation of ML, has been under selection during domestication and breeding, and functions mainly by affecting cell elongation via H2O2 homeostasis and the expression of expansin genes. [ABSTRACT FROM AUTHOR]

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]

Direct seeding has been widely adopted as an economical and labor-saving technique in rice production, though problems such as low seedling emergence rate, emergence irregularity and poor lodging resistance are existing. These problems are currently partially overcome by increasing seeding rate, however it is not acceptable for hybrid rice due to the high seed cost. Improving direct seeding by breeding is seen as the ultimate solution to these problems. For hybrid breeding, identifying superior hybrids among a massive number of hybrids from crossings between male and female parental populations by phenotypic evaluation is tedious and costly. Contrastingly, genomic selection/prediction (GS/GP) could efficiently detect the superior hybrids capitalizing on genomic data, which holds a great potential in plant hybrids breeding. In this study, we utilized 402 rice inbred varieties and 401 hybrids to investigate the effectiveness of GS on rice mesocotyl length, a representative indicative trait of direct seeding suitability. Several GP methods and training set designs were studied to seek the optimal scenario of hybrid prediction. It was shown that using half-sib hybrids as training set with the phenotypes of all parental lines being fitted as a covariate could optimally predict mesocotyl length. Partitioning the molecular markers into trait-associated and -unassociated groups based on genome-wide association study using all parental lines and hybrids could further improve the prediction accuracy. This study indicates that GS could be an effective and efficient method for hybrid breeding for rice direct seeding. [ABSTRACT FROM AUTHOR]

Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain. [ABSTRACT FROM AUTHOR]

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders' main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs. [ABSTRACT FROM AUTHOR]

Zinc (Zn) malnutrition is a major public health issue. Genetic biofortification of Zn in rice grain can alleviate global Zn malnutrition. Therefore, elucidating the genetic mechanisms regulating Zn deprivation response in rice is essential to identify elite genes useful for breeding high grain Zn rice varieties. Here, a meta-analysis of previous RNA-Seq studies involving Zn deficient conditions was conducted using the weighted gene co-expression network analysis (WGCNA) and other in silico prediction tools to identify modules (denoting cluster of genes with related expression pattern) of co-expressed genes, modular genes which are conserved differentially expressed genes (DEGs) across independent RNA-Seq studies, and the molecular pathways of the conserved modular DEGs. WGCNA identified 16 modules of co-expressed genes. Twenty-eight and five modular DEGs were conserved in leaf and crown, and root tissues across two independent RNA-Seq studies. Functional enrichment analysis showed that 24 of the 28 conserved modular DEGs from leaf and crown tissues significantly up-regulated 2 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 15 Gene Ontology (GO) terms, including the substrate- specific transmembrane transporter and the small molecule metabolic process. Further, the well-studied transcription factors (OsWOX11 and OsbHLH120), protein kinase (OsCDPK20 and OsMPK17), and miRNAs (OSA-MIR397A and OSA-MIR397B) were predicted to target some of the identified conserved modular DEGs. Out of the 24 conserved and up-regulated modular DEGs, 19 were yet to be experimentally validated as Zn deficiency responsive genes. Findings from this study provide a comprehensive insight on the molecular mechanisms of Zn deficiency response and may facilitate gene and pathway prioritization for improving Zn use efficiency and Zn biofortification in rice.