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

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

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 inter- preting 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 workfow 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 workfow 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 unifed 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 workfow and document presented are not limited to IRRI’s RRB program but are applicable to any organization or institute with full-fedged 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 research- ers, 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

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 qDTY2.1, qDTY3.1, qDTY12.1, qNR5.1, AG9.1, qEVV9.1, qRHD1.1, qRHD5.1, qRHD8.1qEMM1.1, qGY6.1, BPH3, BPH17, GM4, xa4, Xa21, Pita, and Pita2. The frequency of xa4 gene was highest followed by qAG9.1, GM4, qDTY3.1, qDTY2.1, qGY6.1, and qDTY12.1

Efficient power allocation for downlink MIMO-NOMA-based visible light communication systems

Visible light communication (VLC) networks are emerging as a viable option to meet the ever-growing wireless data demand, primarily for indoor environments in recent years. However, developing a high data rate VLC system using off-the-shelf light emitting diodes (LEDs) is difficult due to the narrow modulation bandwidth of the white light emitting diodes. The existing studies have integrated Multiple-Input Multiple-Output (MIMO) and Non-Orthogonal Multiple Access (NOMA) schemes into the downlink VLC system so as improve its performance. Despite its apparent advantages, the NOMA scheme suffers from multi-user interference problem. For an efficient NOMA scheme, which uses successive interference cancellation (SIC) technique to decode the superimposed received signal of multipleusers, adaptive and fair power allocation methods are required. The existing power allocation schemes are inefficient when the number of users increases. In this work, a low complexity and efficient power allocation strategy is proposed for MIMO-NOMA based VLC systems. The proposed parametric power allocation strategy is based on the order of channel gain among end-users and overcomes the limitations of the existing schemes. The performance of the proposed power allocation scheme in an indoor 4×4 MIMO-NOMA-based multi-user VLC was analyzed through simulation. A zero-forcing detection mechanism is used in the analysis. Simulation results using MATLAB depict the effectiveness of the proposed power allocation strategy. It achieves higher performance than the existing power allocation schemes, gain ratio power allocation (GRPA) and normalized gain difference power allocation (NGDPA) when the number of users are greater than two. Specifically, the proposed power allocation scheme improves the achievable sum rate of the NGDPA and GRPA schemes by 18.36% and 65.98%, respectively, in 4× 4 MIMO-based visible light communication networks with eight uniformly distributed users.</p

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

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

International audienceEstimating 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

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