Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information

Abstract

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.

Otimizando a seleção de milho tropical resistente a fumonisina: implementação da abordagem single-step

“…Breeding programs also are interested in the development of superior cultivars that get a favorable response for a diverse of environmental conditions (Jarquin et al 2021). Our results was consistent with those obtained by several researchers that observed better predictive ability in models that consider that genetic and environmental interaction effect (Jarquin et al 2014(Jarquin et al , 2021Lado et al 2016;Basnet et al 2019;Khanna et al 2022). Showing the importance of this effect for accurate prediction of complex traits.…”

Genetic gains in IRRI’s rice salinity breeding and elite panel development as a future breeding resource

“…1 d) based on pedigrees was incorporated in the second stage of mixed-model analysis. The establishment of the connectivity among the unbalanced historical datasets using the pedigree relationship matrix has been shown in the drought data (Khanna et al 2022b , a ). Additionally, the suitable connectivity in the current datasets can be attributed to the common saline tolerant (FL478, Pokkali-8558) and susceptible checks and varieties (IR29, IRRI 104, IRRI 165, etc.)…”

“…Days to flowering (DTF) was used as a covariate in the model to reduce error due to the difference in the flowering synchronization and ensure the selections for best genotypes would be across different maturity groups. The strategy of covariance adjustment of DTF would significantly reduce variance due to differentiation in flowering time among the genotypes in the analysis (Moreno-Amores et al 2020 ; Juma et al 2021 ; Khanna et al 2022b , a ). The baseline model used in the first stage of analysis is given below: where represents the response variable grain yield (kg/ha) for ith observation, μ is the overall mean, g i is the fixed effect of ith genotype, s j is the fixed effect of jth season, and is the residual error.…”

Fingerprint Finder: Identifying Genomic Fingerprint Sites in Cotton Cohorts for Genetic Analysis and Breeding Advancement

“…Pedigree formation is a common phenomenon in crop breeding [ 7 , 9 , 10 , 44 , 45 , 46 , 47 , 48 , 49 ]. Our previous study detected the featured structural variations within an elite cotton pedigree, the pedigree of CRI12, based on long-reads sequencing [ 7 ], while the fingerprint genomic sites within this cotton pedigree could not be characterized by long reads due to their low single base sequencing accuracy.…”