Venkateshwarlu, C. | bioRxiv Science

bioRxiv2025

Future flooding tolerant rice germplasm: resilience afforded beyond Sub1A gene

Developing high-yielding, flood-tolerant rice varieties is essential for enhancing productivity and livelihoods in flood-prone ecologies. We explored genetic avenues beyond the well-known SUB1A gene to improve flood resilience in rice. We screened a collection of 6,274 elite genotypes from IRRIs germplasm repository for submergence and stagnant flooding tolerance over multiple seasons and years. This rigorous screening identified 89 outstanding elite genotypes, among which thirty-seven exhibited high submergence tolerance, surpassing the survival rate of SUB1A introgression genotypes by 40-50%. Thirty-five genotypes showed significant tolerance to stagnant flooding, and 17 demonstrated dual tolerance capabilities, highlighting their adaptability to varying flood conditions. The genotypes identified have a broader genetic diversity and harbor 86 key QTLs and genes related to traits such as grain quality, grain yield, herbicide resistance, and various biotic and abiotic traits, highlighting the richness of the identified elite collection. Besides germplasm, we introduce an innovative breeding approach called Transition from Trait to Environment (TTE). TTE leverages a parental pool of high-performing genotypes with complete submergence tolerance to drive population improvement and enable genomic selection in the flood breeding program. Our approach of TTE achieved a remarkable 65% increase in genetic gain for submergence tolerance, with the resulting fixed breeding genotypes demonstrating exceptional performance in flood-prone environments of India and Bangladesh. The elite genotypes identified herein represent invaluable genetic resources for the global rice research community. By adopting the TTE approach, which is trait agonistic, we establish a robust framework for developing more resilient genotypes using advanced breeding tools. Plain Language SummaryTo address climate challenges, an urgent focus is necessary to identify and develop flood-tolerant rice varieties, particularly for flood-prone ecosystems across Asia and Africa. We screened 6,274 elite genotypes from IRRIs germplasm and identified 89 promising lines with improved tolerance to submergence and stagnant flooding. Among these, 37 demonstrated 40-50% greater submergence tolerance than SUB1A introgression lines, 35 exhibited stagnant flooding tolerance, and 17 showed dual tolerance. These genotypes contain 86 key QTLs and genes associated with yield, grain quality, and biotic and abiotic tolerance traits. A new breeding strategy, the Transition from Trait to Environment (TTE) approach, was developed. We achieved a genetic gain of 65% for submergence tolerance in rice using this method. The newly identified germplasm provides invaluable genetic resources for the global rice research community to develop flood-tolerant rice genotypes. Core ideas The SUB1A gene, enabling rice to survive underwater for 14 days, marked a significant breakthrough. We have identified elite genotypes with submergence tolerance significantly surpassing the SUB1A gene-mediated tolerance. The diverse elite genotypes identified harbor 86 key genes and QTLs that affect various traits positively. Developed a unique breeding strategy for implementing population improvement in challenging environments. The new breeding strategy demonstrated a genetic gain of 65% for submergence tolerance.

plant biology↗

bioRxiv2023

DeepMap: A deep learning-based model with four-line code for prediction-based breeding in crops

Prediction of phenotype through genotyping data using the emerging machine or deep learning technology has been proven successful in genomic prediction. We present here a graphical processing unit (GPU) enabled DeepMap configurable deep learning-based python package for the genomic prediction of quantitative phenotype traits. We found that deep learning captures non-linear patterns more efficiently than conventional statistical methods. Furthermore, we suggest an additional module inclusion of epistasis interactions and training of the model on Graphical Processing Units (GPUs) in addition to Central Processing Unit (CPU) to enhance efficiency and increase the models performance. We developed and demonstrated the application of DeepMap using a 3K rice genome panel and 1K-Rice Custom Amplicon (1kRiCA) data for several phenotypic traits including days to 50% flowering (DTF), number of productive tillers (NPT), panicle length (PL), plant height (PH), and plot yield (PY). We have found that DeepMap outperformed the best existing state-of-the-art models by giving higher predictive correlation and low mean squared error for the datasets studied. This prediction performance was higher than other compared models in the range of 13-31%. Similarly for Dataset-2, significantly higher predictions were observed than the compared models (16-20% higher prediction ability). On Dataset-3, we have also shown the better and versatile performance of our model across crops (wheat, maize, and soybean) for yield and yield-related traits. This demonstrates the potentiality of the framework and ease of use for future research in crop improvement. The DeepMap is accessible at https://test.pypi.org/project/DeepMap-1.0/. Short SummaryDeepMap is a deep learning-based breeder-friendly python package to perform genomic prediction. It utilizes epistatic interactions for data augmentation and outperforms the existing state-of-the-art machine/deep learning models such as Bayesian LASSO, GBLUP, DeepGS, and dualCNN. DeepMap developed for rice and tested across crops such as maize, wheat, soybean etc.

genomics↗