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BackgroundEstimation of 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 success of the breeding program. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes.ResultsA 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 0.20-0.94 under the drought trials, and 0.43-0.83 under non-stress trials. Under non-stress conditions the genetic gain of 0.44% (21.20 kg/ha/year) for genotypes and 0.17 % (7.90 kg/ha/year) for checks was observed. The genetic trend under the drought conditions exhibited a positive trend with the genetic gain of 0.11% (1.98kg/ha/year) for genotypes and 0.55% (9.52kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.39% (12.13 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).ConclusionsA 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.

The frequent fluctuations in global climate variability (GCV), decreases in farmland and irrigation water, soil degradation and erosion, and increasing fertilizer costs are the significant factors in declining rice productivity, mainly in Asia and Africa. Under GCV scenarios, it is a challenging task to meet the rice food demand of the growing population. Identifying green traits (tolerance of biotic and abiotic stresses, nutrient-use efficiency, and nutritional grain quality) and stacking them in high-yielding elite genetic backgrounds is one promising approach to increase rice productivity. To this end, the Green Super Rice (GSR) breeding strategy helps to pool multi-stress-tolerance traits by stringent selection processes and to develop superior GSR cultivars within a short span of 4–5 years. In the crossing and selection process of GSR breeding, selective introgression lines (SILs) derived from sets of early backcross BC1F2 bulk populations through both target traits and non-target traits were selected. Genotyping of SILs with high-density SNP markers leads to the identification of a large number of SNP markers linked with the target green traits. The identified SILs with superior trait combinations were used for designed QTL pyramiding to combine different target green traits. The GSR breeding strategy also focused on nutrient- and water-use efficiency besides environment-friendly green features primarily to increase grain yield and income returns for resource-poor farmers. In this chapter, we have highlighted the GSR breeding strategy and QTL introgression of green traits in rice. This breeding strategy has successfully dissected many complex traits and also released several multi-stress-tolerant varieties with high grain yield and productivity in the target regions of Asia and Africa.

Rice (Oryza sativa L.) is one of the world’s most vital staple grains, and 90% of it is produced and consumed in Asia alone. It plays a significant role in the entry of mineral nutrients into the food chain. Arsenic (As) is a toxic heavy metal that threatens the major rice-growing regions in the world, particularly in Asia. Arsenic is ubiquitously present in moderate concentrations in the environment because of natural geological processes and anthropogenic impacts. However, rapid industrialization and excessive use of arsenic-rich groundwater are further fueling the increased arsenic concentration in agricultural topsoil. Arsenic accumulation in rice plants has a significant adverse effect on plant, human, and livestock health. Although arsenic contamination in rice is well documented, its interaction and accumulation in rice are poorly understood. So far, no candidate genes or QTLs associated with arsenic interaction are used in breeding programs for the development of low-arsenic-accumulating rice varieties. The development and adaptation of new low-arsenic-accumulating rice cultivars resilient to arsenic toxicity constitute safe ways to mitigate arsenic contamination in rice. Recent scientific advances in rice genetics, genomics, and physiology have opened up new opportunities to speed up the process of developing low-arsenic-accumulating rice cultivars for the rapidly growing human population.

Phenotypic data analysis is a key component in crop breeding to extract meaningful insights from data in making better breeding decisions. Each year the rainfed rice breeding (RRB) program at IRRI conducts trials in the national agricultural research and extension systems (NARES) network-partner sites across South Asia, Southeast Asia and Africa. Analyzing the data from the network trials and sharing the results with the partners in the best possible format is a daunting task. It is crucial to demystify data analysis to the NARES partners for making better breeding decisions. Here, we provide an overview of how RRB program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly , LaTeX and HTML to develop a unique data analysis workflow and redesigned it to a reproducible document for better interpretation, visualization and seamlessly sharing with partners. The generated report is the state-of-the-art implementation of analysis workflow and outputs either in text, tables or graphics in a unified way as one document. The analysis is highly reproducible and can be regenerated based at any time. The plots are built with enhanced dynamic and interactive visualizations to aid in better understanding and extract information with ease. Tables are highly interactive and manageable rendering liberty to be exported within the document in numerous formats. The source code and demo data set for download and use is available at https://github.com/whussain2/Analysis-pipeline . Conclusively, the analysis workflow and document we presented is not limited to IRRI’s RRB program but is applicable to any organization or institute with full-fledged breeding programs.

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.

The development of green super rice varieties with improved nutrient use efficiency (NuUE) is a vital target area to increase yield and make it more stable under rainfed conditions. In the present study, we followed an early backcross (BC) breeding approach by using a high-yielding and widely adapted Xian variety, Weed Tolerant Rice 1 (WTR-1), as a recipient and a Geng variety, Hao-An-Nong (HAN), as a donor. Starting from the BC1F2 generation, the BC population went through one generation of selection under irrigated, low-input, and rainfed conditions, followed by four consecutive generations of screening and selection for high grain yield (GY) under six different nutrient conditions (NPK, 75N, -N, -P, -NP, and -NPK), leading to the development of 230 BC1F6 introgression lines (ILs). These 230 ILs were evaluated under the same six nutrient conditions for 13 agro-morphological and grain yield component traits in comparison to four checks and parents. Significant trait variations were observed between the treatments and ILs. Positive correlations were identified for GY with biomass, panicle length, flag-leaf area, flag-leaf width, filled grain number per panicle, 1000-grain weight, and tiller number under -N, -P, -NP, and -NPK conditions. Out of 230 ILs, 12 were identified as promising under two or more nutrient deficiency conditions. The results demonstrated an efficient inter-subspecific BC breeding procedure with a first round of selection under rainfed-drought conditions, followed by four generations of progeny testing for yield performance under six nutrient conditions. The promising ILs can be useful resources for molecular genetic dissection and understanding the physiological mechanisms of NuUE. Keywords: Nutrient use efficiency, Grain yield, Nitrogen, Phosphorus, Green super rice

Identification of a small core germplasm set representing the available genetic diversity is essential for its proper evaluation and subsequent utilization in rice improvement programmes. For constituting a small diverse mini-core panel of Indian rice germplasm, a representative set of 6912 accessions drawn based on their geographic origin from the whole rice germplasm collection available in the National Gene Bank was genotyped using 36 microsatellite markers. Automated fragment analysis of amplicons yielded a total of 435 alleles, with an average 12.4 and range of 3–29 alleles per locus. Polymorphism information content (PIC) ranged from 0.08 (RGNMS190) to 0.86 (RM552) with an average of 0.528. Based on genotyping data, a mini-core consisting of 98 genotypes was identified. Ninety-four per cent of the alleles present in the core set were present in the mini-core. The identified small but diverse panel will be useful for further intensive trait-specific evaluation and utilization in allele mining.