Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource.

KEY MESSAGE: Estimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains. Genetic gain is a crucial parameter to check the breeding program's success and help optimize future breeding strategies for enhanced genetic gains. To estimate the genetic gains in IRRI's salinity breeding program and identify the best genotypes based on high breeding values for grain yield (kg/ha), we analyzed the historical data from the trials conducted in the IRRI, Philippines and Bangladesh. A two-stage mixed-model approach accounting for experimental design factors and a relationship matrix was fitted to obtain the breeding values for grain yield and estimate genetic trends. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha was observed in IRRI, Philippines. In Bangladesh, we observed a genetic gain of 0.31% per annum with a yield advantage of 14.02 kg/ha. In the released varieties, we observed a genetic gain of 0.12% per annum with a 2.2 kg/ha/year yield advantage in the IRRI, Philippines. For the Bangladesh dataset, a genetic gain of 0.14% per annum with a yield advantage of 5.9 kg/ha/year was observed in the released varieties. Based on breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of > 2400 kg/ha in the IRRI, Philippines, and > 3500 kg/ha in Bangladesh with a reliability of > 0.4 were selected to develop the elite breeding panel. Conclusively, a recurrent selection breeding strategy integrated with novel technologies like genomic selection and speed breeding is highly required to achieve higher genetic gains in IRRI's salinity breeding programs.

Optimizing predictions in IRRI's rice drought breeding program by leveraging 17 years of historical data and pedigree information.

Prediction models based on pedigree and/or molecular marker information are now an inextricable part of the crop breeding programs and have led to increased genetic gains in many crops. Optimization of IRRI's rice drought breeding program is crucial for better implementation of selections based on predictions. Historical datasets with precise and robust pedigree information have been a great resource to help optimize the prediction models in the breeding programs. Here, we leveraged 17 years of historical drought data along with the pedigree information to predict the new lines or environments and dissect the G × E interactions. Seven models ranging from basic to proposed higher advanced models incorporating interactions, and genotypic specific effects were used. These models were tested with three cross-validation schemes (CV1, CV2, and CV0) to assess the predictive ability of tested and untested lines in already observed environments and tested lines in novel or new environments. In general, the highest prediction abilities were obtained when the model accounting interactions between pedigrees (additive) and environment were included. The CV0 scheme (predicting unobserved or novel environments) reveals very low predictive abilities among the three schemes. CV1 and CV2 schemes that borrow information from the target and correlated environments have much higher predictive abilities. Further, predictive ability was lower when predicting lines in non-stress conditions using drought data as training set and/or vice-versa. When predicting the lines using the data sets under the same conditions (stress or non-stress data sets), much better prediction accuracy was obtained. These results provide conclusive evidence that modeling G × E interactions are important in predictions. Thus, considering G × E interactions would help to build enhanced genomic or pedigree-based prediction models in the rice breeding program. Further, it is crucial to borrow the correlated information from other environments to improve prediction accuracy.

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.

Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding.

BACKGROUND: Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators. RESULTS: We reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document. The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis is highly reproducible and can be regenerated at any time. The analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipeline . CONCLUSION: The analysis workflow and document presented are not limited to IRRI's RRB program but are applicable to any organization or institute with full-fledged breeding programs. We believe this is a great initiative to modernize the data analysis of IRRI's RRB program. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way.

Development and validation of cross-transferable and polymorphic DNA markers for detecting alien genome introgression in Oryza sativa from Oryza brachyantha.

African wild rice Oryza brachyantha (FF), a distant relative of cultivated rice Oryza sativa (AA), carries genes for pests and disease resistance. Molecular marker assisted alien gene introgression from this wild species to its domesticated counterpart is largely impeded due to the scarce availability of cross-transferable and polymorphic molecular markers that can clearly distinguish these two species. Availability of the whole genome sequence (WGS) of both the species provides a unique opportunity to develop markers, which are cross-transferable. We observed poor cross-transferability (~0.75 %) of O. sativa specific sequence tagged microsatellite (STMS) markers to O. brachyantha. By utilizing the genome sequence information, we developed a set of 45 low cost PCR based co-dominant polymorphic markers (STS and CAPS). These markers were found cross-transferrable (84.78 %) between the two species and could distinguish them from each other and thus allowed tracing alien genome introgression. Finally, we validated a Monosomic Alien Addition Line (MAAL) carrying chromosome 1 of O. brachyantha in O. sativa background using these markers, as a proof of concept. Hence, in this study, we have identified a set molecular marker (comprising of STMS, STS and CAPS) that are capable of detecting alien genome introgression from O. brachyantha to O. sativa.

Population Structure, Diversity and Trait Association Analysis in Rice (Oryza sativa L.) Germplasm for Early Seedling Vigor (ESV) Using Trait Linked SSR Markers.

Early seedling vigor (ESV) is the essential trait for direct seeded rice to dominate and smother the weed growth. In this regard, 629 rice genotypes were studied for their morphological and physiological responses in the field under direct seeded aerobic situation on 14th, 28th and 56th days after sowing (DAS). It was determined that the early observations taken on 14th and 28th DAS were reliable estimators to study ESV as compared to 56th DAS. Further, 96 were selected from 629 genotypes by principal component (PCA) and discriminate function analyses. The selected genotypes were subjected to decipher the pattern of genetic diversity in terms of both phenotypic and genotypic by using ESV QTL linked simple sequence repeat (SSR) markers. To assess the genetic structure, model and distance based approaches were used. Genotyping of 96 rice lines using 39 polymorphic SSRs produced a total of 128 alleles with the phenotypic information content (PIC) value of 0.24. The model based population structure approach grouped the accession into two distinct populations, whereas unrooted tree grouped the genotypes into three clusters. Both model based and structure based approach had clearly distinguished the early vigor genotypes from non-early vigor genotypes. Association analysis revealed that 16 and 10 SSRs showed significant association with ESV traits by general linear model (GLM) and mixed linear model (MLM) approaches respectively. Marker alleles on chromosome 2 were associated with shoot dry weight on 28 DAS, vigor index on 14 and 28 DAS. Improvement in the rate of seedling growth will be useful for identifying rice genotypes acquiescent to direct seeded conditions through marker-assisted selection.