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.

DECUSSATE network with flowering genes explains the variable effects of qDTY12.1 to rice yield under drought across genetic backgrounds.

The impact of qDTY12.1 in maintaining yield under drought has not been consistent across genetic backgrounds. We hypothesized that synergism or antagonism with additive-effect peripheral genes across the background genome either enhances or undermines its full potential. By modeling the transcriptional networks across sibling qDTY12.1-introgression lines with contrasting yield under drought (LPB = low-yield penalty; HPB = high-yield penalty), the qDTY12.1-encoded DECUSSATE gene (OsDEC) was revealed as the core of a synergy with other genes in the genetic background. OsDEC is expressed in flag leaves and induced by progressive drought at booting stage in LPB but not in HPB. The unique OsDEC signature in LPB is coordinated with 35 upstream and downstream peripheral genes involved in floral development through the cytokinin signaling pathway. Results support the differential network rewiring effects through genetic coupling-uncoupling between qDTY12.1 and other upstream and downstream peripheral genes across the distinct genetic backgrounds of LPB and HPB. The functional DEC-network in LPB defines a mechanism for early flowering as a means for avoiding the drought-induced depletion of photosynthate needed for reproductive growth. Its impact is likely through the timely establishment of stronger source-sink dynamics that sustains a robust reproductive transition under drought.

Laboratory phenomics predicts field performance and identifies superior indica haplotypes for early seedling vigour in dry direct-seeded rice.

Seedling vigour is an important agronomic trait and is gaining attention in Asian rice (Oryza sativa) as cultivation practices shift from transplanting to forms of direct seeding. To understand the genetic control of rice seedling vigour in dry direct seeded (aerobic) conditions we measured multiple seedling traits in 684 accessions from the 3000 Rice Genomes (3K-RG) population in both the laboratory and field at three planting depths. Our data show that phenotyping of mesocotyl length in laboratory conditions is a good predictor of field performance. By performing a genome wide association study, we found that the main QTL for mesocotyl length, percentage seedling emergence and shoot biomass are co-located on the short arm of chromosome 7. We show that haplotypes in the indica subgroup from this region can be used to predict the seedling vigour of 3K-RG accessions. The selected accessions may serve as potential donors in genomics-assisted breeding programs.

Developing Climate-Resilient, Direct-Seeded, Adapted Multiple-Stress-Tolerant Rice Applying Genomics-Assisted Breeding.

There is an urgent need to breed dry direct-seeded adapted rice varieties in order to address the emerging scenario of water-labor shortage. The aim of this study was to develop high-yielding, direct-seeded adapted varieties utilizing biparental to multiparental crosses involving as many as six different parents in conventional breeding programs and 12 parents in genomics-assisted breeding programs. The rigorous single plant selections were followed from the F2 generation onwards utilizing phenotypic selection and quantitative trait locus (QTL)/gene-based/linked markers for tracking the presence of desirable alleles of targeted QTL/genes. In conventional breeding, multiparent lines had significantly higher yields (2,072-6,569 kg ha-1) than the biparental lines (1,493-6,326 kg ha-1). GAB lines derived from multiparent crosses had significantly higher (3,293-6,719 kg ha-1) yields than the multiparent lines from conventional breeding (2,072-6,569 kg ha-1). Eleven promising lines from genomics-assisted breeding carrying 7-11 QTL/genes and eight lines from conventional breeding with grain-yield improvement from 727 to 1,705 kg ha-1 and 68 to 902 kg ha-1, respectively, over the best check were selected. The developed lines may be released as varieties/parental lines to develop better rice varieties for direct-seeded situations or as novel breeding material to study genetic interactions.

Genomics-assisted breeding for successful development of multiple-stress-tolerant, climate-smart rice for southern and southeastern Asia.

Rice (Oryza sativa L.) in rainfed marginal environments is prone to multiple abiotic and biotic stresses, which can occur in combination in a single cropping season and adversely affect rice growth and yield. The present study was undertaken to develop high-yielding, climate-resilient rice that can provide tolerance to multiple biotic and abiotic stresses. An assembled first-crossing scheme was employed to transfer 15 quantitative trait loci (QTL) and genes-qDTY1.1 , qDTY2.1 , qDTY3.1 , qDTY12.1 (drought), Sub1 (submergence), Gm4 (gall midge), Pi9, Pita2 (blast), Bph3, Bph17 (brown plant hoppers), Xa4, xa5, xa13, Xa21, and Xa23 (bacterial leaf blight)-from eight different parents using genomics-assisted breeding. A funnel mating design was employed to assemble all the targeted QTL and genes into a high-yielding breeding line IR 91648-B-1-B-3-1. Gene-based linked markers were used in each generation from intercrossing to the F6 generation for tracking the presence of desirable alleles of targeted QTL and genes. Single-plant selections were performed from F2 onwards to select desirable recombinants possessing alleles of interest with suitable phenotypes. Phenotyping of 95 homozygous F6 lines carrying six to 10 QTL and genes was performed for nonstress, reproductive-stage (RS) drought, blast, bacterial leaf blight (BLB), gall midge (GM), and for grain quality parameters such as chalkiness, amylose content (AC), gelatinization temperature (GT), and head rice recovery (HRR). Finally, 56 F7 homozygous lines were found promising for multiple-location evaluation for grain yield (GY) and other traits. These multiple-stress-tolerant lines with the desired grain quality profiling can be targeted for varietal release in southern and southeastern Asia through national release systems.

Genetic Dissection of Epistatic Interactions Contributing Grain Yield Variability in Rice under Drought.

Aims: The aim of the present study was to evaluate the performance of 'high'-'low' yielding pyramided lines (PLs), having the same combinations of qDTYs in Samba Mahsuri, MR219 and IR64-Sub1 genetic backgrounds, and to understand the genetic interactions among QTL and/with genetic background affecting grain yield. Background: Epistasis regulates the expression of traits governed by several major/minor genes/QTL. Multiple pyramided lines (PLs) with the same grain yield QTL (qDTYs) combinations but possessing grain yield variability under different levels of reproductive stage drought stress were identified in different rice genetic backgrounds at International Rice Research Institute (IRRI). Objectives: The objectives of the present study were to evaluate the performance pyramided lines (PLs) with drought QTL in the backgrounds of Samba Mahsuri, MR219 and IR64-Sub1 under reproductive stage drought stress (RS) and NS (non-stress) conditions, to understand the effect of epistatic interactions among qDTYs and with genetic background on GY under the differential level of stress and to identify the promising drought-tolerant lines with high yield under drought and higher background recovery in different genetic backgrounds. Methods: The experiments were conducted in 2015 DS (dry season), 2015 WS (wet season) and 2017 DS at IRRI, Los Baños, Philippines, in a transplanted lowland ecosystem under lowland severe stress (LSS), lowland moderate stress (LMS) and lowland non-stress (LNS). The experiments were laid out in alpha lattice design with two replications. Results: Several digenic interactions were found in different genetic backgrounds, 13 interactions in Samba Mahsuri, 11 in MR219 and 20 in IR64-Sub1 backgrounds. Among all digenic interactions, one QTL × QTL interaction, 17 QTL × background and 26 background × background interactions resulted in GY reduction in low yielding PLs in different genetic backgrounds under LSS or LMS. Negative interaction of qDTY3.1 , qDTY4.1 and qDTY9.1 with background markers and background × background interactions caused up to 15% GY reduction compared to the high yielding PLs under LMS in the Samba Mahsuri PLs. In MR219 PLs, the negative interaction of qDTY2.2 , qDTY3.2 , qDTY4.1 and qDTY12.1 with the background marker interval RM314-RM539, RM273-RM349 and RM445-RM346, RM473D-RM16, respectively resulted 52% GY reduction compared to the high yielding PLs under LSS. In IR64-Sub1 PLs, qDTY6.1 interacted with background loci at RM16-RM135, RM228-RM333, RM202-RM287 and RM415-RM558A marker interval under LSS and at RM475-RM525 marker interval under LMS, causing GY reduction to 58% compared to the high yielding PLs. Conclusion: High yielding PLs in Samba Mahsuri (IR 99734:1-33-69-1-22-6), MR219 (IR 99784-156-87-2-4-1) and IR64-Sub1 (IR 102784:2-89-632-2-1-2) backgrounds without any negative interactions were identified. The identified selected promising PLs may be used as potential drought-tolerant donors or may be released as varieties for drought-prone ecosystems in different countries.

Genotyping-by-sequencing based QTL mapping for rice grain yield under reproductive stage drought stress tolerance.

QTLs for rice grain yield under reproductive stage drought stress (qDTY) identified earlier with low density markers have shown linkage drag and need to be fine mapped before their utilization in breeding programs. In this study, genotyping-by-sequencing (GBS) based high-density linkage map of rice was developed using two BC1F3 mapping populations namely Swarna*2/Dular (3929 SNPs covering 1454.68 cM) and IR11N121*2/Aus196 (1191 SNPs covering 1399.68 cM) with average marker density of 0.37 cM to 1.18 cM respectively. In total, six qDTY QTLs including three consistent effect QTLs were identified in Swarna*2/Dular while eight qDTY QTLs including two consistent effect QTLs were identified in IR11N121*2/Aus 196 mapping population. Comparative analysis revealed four stable and novel QTLs (qDTY2.4, qDTY3.3, qDTY6.3, and qDTY11.2) which explained 8.62 to 14.92% PVE. However, one of the identified stable grain yield QTL qDTY1.1 in both the populations was located nearly at the same physical position of an earlier mapped major qDTY QTL. Further, the effect of the identified qDTY1.1 was validated in a subset of lines derived from five mapping populations confirming robustness of qDTY1.1 across various genetic backgrounds/seasons. The study successfully identified stable grain yield QTLs free from undesirable linkages of tall plant height/early maturity utilizing high density linkage maps.

Evaluating the Performance of Rice Genotypes for Improving Yield and Adaptability Under Direct Seeded Aerobic Cultivation Conditions.

With the changing climatic conditions and reducing labor-water availability, the potential contribution of aerobic rice varieties and cultivation system to develop a sustainable rice based agri-food system has never been more important than today. Keeping in mind the goal of identifying high-yielding aerobic rice varieties for wider adaptation, a set of aerobic rice breeding lines were developed and evaluated for grain yield, plant height, and days to 50% flowering in 23 experiments conducted across different location in Philippines, India, Bangladesh, Nepal, and Lao-PDR between 2014 and 2017 in both wet and dry seasons. The heritability for grain yield ranged from 0.52 to 0.90. The season-wise two-stage analysis indicated significant genotype x location interaction for yield under aerobic conditions in both wet and dry seasons. The genotype × season × location interaction for yield was non-significant in both seasons indicating that across seasons the genotypes at each location did not show variability in the grain yield performance. Mean grain yield of the studied genotypes across different locations/seasons ranged from 2,085 to 6,433 Kg ha-1. The best-fit model for yield stability with low AIC value (542.6) was AMMI(1) model. The identified stable genotypes; IR 92521-143-2-2-1, IR 97048-10-1-1-3, IR 91326-7-13-1-1, IR 91326-20-2-1-4, and IR 91328-43-6-2-1 may serve as novel breeding material for varietal development under aerobic system of rice cultivation. High yield and stable performance of promising breeding lines may be due to presence of the earlier identified QTLs including grain yield under drought, grain yield under aerobic conditions, nutrient uptake, anaerobic germination, adaptability under direct seeded conditions, and tolerance to biotic stress resistance such as qDTY 2.1 , qDTY 3.1 , qDTY 12.1 , qNR 5.1 , AG 9.1 , qEVV 9.1 , qRHD 1.1 , qRHD 5.1, qRHD 8.1 qEMM 1.1 , qGY 6.1 , BPH3, BPH17, GM4, xa4, Xa21, Pita, and Pita2. The frequency of xa4 gene was highest followed by qAG 9.1, GM4, qDTY 3.1 , qDTY 2.1 , qGY 6.1, and qDTY 12.1.

Positive interactions of major-effect QTLs with genetic background that enhances rice yield under drought.

To improve the grain yield of the lowland-adapted popular rice variety Samba Mahsuri under reproductive-stage drought (RS) and to understand the interactions between drought QTLs, two mapping populations were developed using marker-assisted selection (MAS) and marker-assisted recurrent selection (MARS). The mean grain yield of pyramided lines (PLs) with qDTY 2.2 + qDTY 4.1 in MAS is significantly higher under RS and irrigated control than lines with single QTLs. Among MARS PLs, lines with four qDTYs (qDTY 1.1 + qDTY 2.1 + qDTY 3.1 + qDTY 11.1 ) and two QTLs (qDTY 1.1 + qDTY 11.1 ) yielded higher than PLs with other qDTY combinations. The selected PLs showed a yield advantage of 0.3-2.0 t ha-1 under RS. An allelic profile of MAS PLs having same qDTY combination but with different yields under drought was studied. Hierarchical clustering grouped together the selected lines with high yield under drought. Epistasis test showed the interaction of qDTY 4.1 and qDTY 9.1 loci with qDTY 7.1 significantly increased yield under drought and all the lines with higher yield under drought possessed the conserved region of qDTY 7.1 on chromosome 7. The positive interactions among QTLs, effectiveness of QTLs in different backgrounds, introgression of DTY QTLs together with resistance to biotic stresses shall help enhance grain yield under RS.

Genetic Loci Governing Grain Yield and Root Development under Variable Rice Cultivation Conditions.

Drought is the major abiotic stress to rice grain yield under unpredictable changing climatic scenarios. The widely grown, high yielding but drought susceptible rice varieties need to be improved by unraveling the genomic regions controlling traits enhancing drought tolerance. The present study was conducted with the aim to identify quantitative trait loci (QTLs) for grain yield and root development traits under irrigated non-stress and reproductive-stage drought stress in both lowland and upland situations. A mapping population consisting of 480 lines derived from a cross between Dular (drought-tolerant) and IR64-21 (drought susceptible) was used. QTL analysis revealed three major consistent-effect QTLs for grain yield (qDTY1.1, qDTY1.3 , and qDTY8.1 ) under non-stress and reproductive-stage drought stress conditions, and 2 QTLs for root traits (qRT9.1 for root-growth angle and qRT5.1 for multiple root traits, i.e., seedling-stage root length, root dry weight and crown root number). The genetic locus qDTY1.1 was identified as hotspot for grain yield and yield-related agronomic and root traits. The study identified significant positive correlations among numbers of crown roots and mesocotyl length at the seedling stage and root length and root dry weight at depth at later stages with grain yield and yield-related traits. Under reproductive stage drought stress, the grain yield advantage of the lines with QTLs ranged from 24.1 to 108.9% under upland and 3.0-22.7% under lowland conditions over the lines without QTLs. The lines with QTL combinations qDTY1.3 +qDTY8.1 showed the highest mean grain yield advantage followed by lines having qDTY1.1 +qDTY8.1 and qDTY1.1 +qDTY8.1 +qDTY1.3 , across upland/lowland reproductive-stage drought stress. The identified QTLs for root traits, mesocotyl length, grain yield and yield-related traits can be immediately deployed in marker-assisted breeding to develop drought tolerant high yielding rice varieties.