A GWAS approach to find SNPs associated with salt removal in rice leaf sheath.

BACKGROUND AND AIMS: The ability for salt removal at the leaf sheath level is considered to be one of the major mechanisms associated with salt tolerance in rice. Thus, understanding the genetic control of the salt removal capacity in leaf sheaths will help improve the molecular breeding of salt-tolerant rice varieties and speed up future varietal development to increase productivity in salt-affected areas. We report a genome-wide association study (GWAS) conducted to find single nucleotide polymorphisms (SNPs) associated with salt removal in leaf sheaths of rice. METHODS: In this study, 296 accessions of a rice (Oryza sativa) diversity panel were used to identify salt removal-related traits and conduct GWAS using 36 901 SNPs. The sheath:blade ratio of Na+ and Cl- concentrations was used to determine the salt removal ability in leaf sheaths. Candidate genes were further narrowed via Gene Ontology and RNA-seq analysis to those whose putative function was likely to be associated with salt transport and were up-regulated in response to salt stress. KEY RESULTS: For the association signals of the Na+ sheath:blade ratio, significant SNPs were found only in the indica sub-population on chromosome 5. Within candidate genes found in the GWAS study, five genes were upregulated and eight genes were downregulated in the internal leaf sheath tissues in the presence of salt stress. CONCLUSIONS: These GWAS data imply that rice accessions in the indica variety group are the main source of genes and alleles associated with Na+ removal in leaf sheaths of rice under salt stress.

Salinity tolerance, Na+ exclusion and allele mining of HKT1;5 in Oryza sativa and O. glaberrima: many sources, many genes, one mechanism?

BACKGROUND: Cultivated rice species (Oryza sativa L. and O. glaberrima Steud.) are generally considered among the crop species most sensitive to salt stress. A handful of lines are known to be tolerant, and a small number of these have been used extensively as donors in breeding programs. However, these donors use many of the same genes and physiological mechanisms to confer tolerance. Little information is available on the diversity of mechanisms used by these species to cope with salt stress, and there is a strong need to identify varieties displaying additional physiological and/or genetic mechanisms to confer higher tolerance. RESULTS: Here we present data on 103 accessions from O. sativa and 12 accessions from O. glaberrima, many of which are identified as salt tolerant for the first time, showing moderate to high tolerance of high salinity. The correlation of salinity-induced senescence (as judged by the Standard Evaluation System for Rice, or SES, score) with whole-plant and leaf blade Na+ concentrations was high across nearly all accessions, and was almost identical in both O. sativa and O. glaberrima. The association of leaf Na+ concentrations with cultivar-groups was very weak, but association with the OsHKT1;5 allele was generally strong. Seven major and three minor alleles of OsHKT1;5 were identified, and their comparisons with the leaf Na+ concentration showed that the Aromatic allele conferred the highest exclusion and the Japonica allele the least. A number of exceptions to this association with the Oryza HKT1;5 allele were identified; these probably indicate the existence of additional highly effective exclusion mechanisms. In addition, two landraces were identified, one from Thailand and the other from Senegal, that show high tissue tolerance. CONCLUSIONS: Significant variation in salinity tolerance exists within both cultivated Oryza species, and this is the first report of significant tolerance in O. glaberrima. The majority of accessions display a strong quantitative relationship between tolerance and leaf blade Na+ concentration, and thus the major tolerance mechanisms found in these species are those contributing to limiting sodium uptake and accumulation in active leaves. However, there appears to be genetic variation for several mechanisms that affect leaf Na+ concentration, and rare cases of accessions displaying different mechanisms also occur. These mechanisms show great promise for improving salt tolerance in rice over that available from current donors.

Development of early maturing salt-tolerant rice variety KKL(R) 3 using a combination of conventional and molecular breeding approaches.

Introduction: Soil salinity poses a severe threat to rice production, resulting in stunted growth, leaf damage, and substantial yield losses. This study focuses on developing an early maturing seedling stage salinity tolerant rice variety by integrating conventional breeding methods with marker assisted breeding (MAB) approaches. Methods: Seedling-stage salinity tolerance Quantitative Trait Locus (QTL) "Saltol" from the salt-tolerant parent FL478 was introduced into the high-yielding but salt-sensitive rice variety ADT 45. This was achieved through a combination of conventional breeding and MAB. The breeding process involved rigorous selection, screening, and physiological parameter assessments. Results: KKL(R) 3 (KR 15066) identified as the top performing Recombinant Inbred Line (RIL), consistently demonstrating maximum mean grain yields under both salinity (3435.6 kg/ha) and normal (6421.8 kg/ha) conditions. In comparison to the early maturing, salt-tolerant national check variety CSR 10, KKL(R) 3 exhibited a substantial yield increase over 50%. Discussion: The notable improvement observed in KKL(R) 3 positions it as a promising variety for release, offering a reliable solution to maximize yields, ensure food security, and promote agricultural sustainability in both saline and non-saline environments. The study highlights the effectiveness of MAB in developing salt-tolerant rice varieties and emphasizes the significance of the Saltol QTL in enhancing seedling stage salinity tolerance. The potential release of KKL(R) 3 has the capacity to revolutionize rice production in salt affected regions, providing farmers with a reliable solution to maximize yields and contribute to food security while ensuring agricultural sustainability.

Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets.

Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines.

Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI's rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43-0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI's drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

Criteria for evaluating molecular markers: Comprehensive quality metrics to improve marker-assisted selection.

Despite strong interest over many years, the usage of quantitative trait loci in plant breeding has often failed to live up to expectations. A key weak point in the utilisation of QTLs is the "quality" of markers used during marker-assisted selection (MAS): unreliable markers result in variable outcomes, leading to a perception that MAS products fail to achieve reliable improvement. Most reports of markers used for MAS focus on markers derived from the mapping population. There are very few studies that examine the reliability of these markers in other genetic backgrounds, and critically, no metrics exist to describe and quantify this reliability. To improve the MAS process, this work proposes five core metrics that fully describe the reliability of a marker. These metrics give a comprehensive and quantitative measure of the ability of a marker to correctly classify germplasm as QTL[+]/[-], particularly against a background of high allelic diversity. Markers that score well on these metrics will have far higher reliability in breeding, and deficiencies in specific metrics give information on circumstances under which a marker may not be reliable. The metrics are applicable across different marker types and platforms, allowing an objective comparison of the performance of different markers irrespective of the platform. Evaluating markers using these metrics demonstrates that trait-specific markers consistently out-perform markers designed for other purposes. These metrics also provide a superb set of criteria for designing superior marker systems for a target QTL, enabling the selection of an optimal marker set before committing to design.

1k-RiCA (1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice.

BACKGROUND: While a multitude of genotyping platforms have been developed for rice, the majority of them have not been optimized for breeding where cost, turnaround time, throughput and ease of use, relative to density and informativeness are critical parameters of their utility. With that in mind we report the development of the 1K-Rice Custom Amplicon, or 1k-RiCA, a robust custom sequencing-based amplicon panel of ~ 1000-SNPs that are uniformly distributed across the rice genome, designed to be highly informative within indica rice breeding pools, and tailored for genomic prediction in elite indica rice breeding programs. RESULTS: Empirical validation tests performed on the 1k-RiCA showed average marker call rates of 95% with marker repeatability and concordance rates of 99%. These technical properties were not affected when two common DNA extraction protocols were used. The average distance between SNPs in the 1k-RiCA was 1.5 cM, similar to the theoretical distance which would be expected between 1,000 uniformly distributed markers across the rice genome. The average minor allele frequencies on a panel of indica lines was 0.36 and polymorphic SNPs estimated on pairwise comparisons between indica by indica accessions and indica by japonica accessions were on average 430 and 450 respectively. The specific design parameters of the 1k-RiCA allow for a detailed view of genetic relationships and unambiguous molecular IDs within indica accessions and good cost vs. marker-density balance for genomic prediction applications in elite indica germplasm. Predictive abilities of Genomic Selection models for flowering time, grain yield, and plant height were on average 0.71, 0.36, and 0.65 respectively based on cross-validation analysis. Furthermore the inclusion of important trait markers associated with 11 different genes and QTL adds value to parental selection in crossing schemes and marker-assisted selection in forward breeding applications. CONCLUSIONS: This study validated the marker quality and robustness of the 1k-RiCA genotypic platform for genotyping populations derived from indica rice subpopulation for genetic and breeding purposes including MAS and genomic selection. The 1k-RiCA has proven to be an alternative cost-effective genotyping system for breeding applications.