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.

Understanding Host-Pathogen Interactions with Expression Profiling of NILs Carrying Rice-Blast Resistance Pi9 Gene.

Magnaporthe oryzae infection causes rice blast, a destructive disease that is responsible for considerable decrease in rice yield. Development of resistant varieties via introgressing resistance genes with marker-assisted breeding can eliminate pesticide use and minimize crop losses. Here, resistant near-isogenic line (NIL) of Pusa Basmati-1(PB1) carrying broad spectrum rice blast resistance gene Pi9 was used to investigate Pi9-mediated resistance response. Infected and uninfected resistant NIL and susceptible control line were subjected to RNA-Seq. With the exception of one gene (Pi9), transcriptional signatures between the two lines were alike, reflecting basal similarities in their profiles. Resistant and susceptible lines possessed 1043 (727 up-regulated and 316 down-regulated) and 568 (341 up-regulated and 227 down-regulated) unique and significant differentially expressed loci (SDEL), respectively. Pathway analysis revealed higher transcriptional activation of kinases, WRKY, MYB, and ERF transcription factors, JA-ET hormones, chitinases, glycosyl hydrolases, lipid biosynthesis, pathogenesis and secondary metabolism related genes in resistant NIL than susceptible line. Singular enrichment analysis demonstrated that blast resistant NIL is significantly enriched with genes for primary and secondary metabolism, response to biotic stimulus and transcriptional regulation. The co-expression network showed proteins of genes in response to biotic stimulus interacted in a manner unique to resistant NIL upon M. oryzae infection. These data suggest that Pi9 modulates genome-wide transcriptional regulation in resistant NIL but not in susceptible PB1. We successfully used transcriptome profiling to understand the molecular basis of Pi9-mediated resistance mechanisms, identified potential candidate genes involved in early pathogen response and revealed the sophisticated transcriptional reprogramming during rice-M. oryzae interactions.

Marker-aided Incorporation of Xa38, a Novel Bacterial Blight Resistance Gene, in PB1121 and Comparison of its Resistance Spectrum with xa13 + Xa21.

Basmati rice is preferred internationally because of its appealing taste, mouth feel and aroma. Pusa Basmati 1121 (PB1121) is a widely grown variety known for its excellent grain and cooking quality in the international and domestic market. It contributes approximately USD 3 billion to India's forex earning annually by being the most traded variety. However, PB1121 is highly susceptible to bacterial blight (BB) disease. A novel BB resistance gene Xa38 was incorporated in PB1121 from donor parent PR114-Xa38 using a modified marker-assisted backcross breeding (MABB) scheme. Phenotypic selection prior to background selection was instrumental in identifying the novel recombinants with maximum recovery of recurrent parent phenome. The strategy was effective in delimiting the linkage drag to <0.5 mb upstream and <1.9 mb downstream of Xa38 with recurrent parent genome recovery upto 96.9% in the developed NILs. The NILs of PB1121 carrying Xa38 were compared with PB1121 NILs carrying xa13 + Xa21 (developed earlier in our lab) for their resistance to BB. Both NILs showed resistance against the Xoo races 1, 2, 3 and 6. Additionally, Xa38 also resisted Xoo race 5 to which xa13 + Xa21 was susceptible. The PB1121 NILs carrying Xa38 gene will provide effective control of BB in the Basmati growing region.

Improvement of Basmati rice varieties for resistance to blast and bacterial blight diseases using marker assisted backcross breeding.

Marker assisted backcross breeding was employed to incorporate the blast resistance genes, Pi2 and Pi54 and bacterial blight (BB) resistance genes xa13 and Xa21 into the genetic background of Pusa Basmati 1121 (PB1121) and Pusa Basmati 6. Foreground selection for target gene(s) was followed by arduous phenotypic and background selection which fast-tracked the recovery of recurrent parent genome (RPG) to an extent of 95.8% in one of the near-isogenic lines (NILs) namely, Pusa 1728-23-33-31-56, which also showed high degree of resemblance to recurrent parent, PB6 in phenotype. The phenotypic selection prior to background selection provided an additional opportunity for identifying the novel recombinants viz., Pusa 1884-9-12-14 and Pusa 1884-3-9-175, superior to parental lines in terms of early maturity, higher yield and improved quality parameters. There was no significant difference between the RPG recovery estimated based on SSR or SNP markers, however, the panel of SNPs markers was considered as the better choice for background selection as it provided better genome coverage and included SNPs in the genic regions. Multi-location evaluation of NILs depicted their stable and high mean performance in comparison to the respective recurrent parents. The Pi2+Pi54 carrying NILs were effective in combating a pan-India panel of Magnaporthe oryzae isolates with high level of field resistance in northern, eastern and southern parts of India. Alongside, the PB1121-NILs and PB6-NILs carrying BB resistance genes xa13+Xa21 were resistant against Xanthomonas oryzae pv. oryzae races of north-western, southern and eastern parts of the country. Three of NILs developed in this study, have been promoted to final stage of testing during the ​Kharif 2015 in the Indian National Basmati Trial.

Development and evaluation of near-isogenic lines for major blast resistance gene(s) in Basmati rice.

KEY MESSAGE: A set of NILs carrying major blast resistance genes in a Basmati rice variety has been developed. Also, the efficacy of pyramids over monogenic NILs against rice blast pathogen Magnaporthe oryzae has been demonstrated. Productivity and quality of Basmati rice is severely affected by rice blast disease. Major genes and QTLs conferring resistance to blast have been reported only in non-Basmati rice germplasm. Here, we report incorporation of seven blast resistance genes from the donor lines DHMASQ164-2a (Pi54, Pi1, Pita), IRBLz5-CA (Pi2), IRBLb-B (Pib), IRBL5-M (Pi5) and IRBL9-W (Pi9) into the genetic background of an elite Basmati rice variety Pusa Basmati 1 (PB1). A total of 36 near-isogenic lines (NILs) comprising of 14 monogenic, 16 two-gene pyramids and six three-gene pyramids were developed through marker-assisted backcross breeding (MABB). Foreground, recombinant and background selection was used to identify the plants with target gene(s), minimize the linkage drag and increase the recurrent parent genome (RPG) recovery (93.5-98.6 %), respectively, in the NILs. Comparative analysis performed using 50,051 SNPs and 500 SSR markers revealed that the SNPs provided better insight into the RPG recovery. Most of the monogenic NILs showed comparable performance in yield and quality, concomitantly, Pusa1637-18-7-6-20 (Pi9), was significantly superior in yield and stable across four different environments as compared to recurrent parent (RP) PB1. Further, among the pyramids, Pusa1930-12-6 (Pi2+Pi5) showed significantly higher yield and Pusa1633-7-8-53-6-8 (Pi54+Pi1+Pita) was superior in cooking quality as compared to RP PB1. The NILs carrying gene Pi9 were found to be the most effective against the concoction of virulent races predominant in the hotspot locations for blast disease. Conversely, when analyzed under artificial inoculation, three-gene pyramids expressed enhanced resistance as compared to the two-gene and monogenic NILs.