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

Abstract

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.

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

“…The pedigree data of 2,490 unique genotypes were retrieved from IRRIs “Breeding 4 Results” database (Breeding 4 Results (B4R), 2021, https://b4r.irri.org ). The pedigree data of immediate parents as well as grandparents upto seven generations was retrieved ( Hussain et al, 2022 ; Khanna et al, 2022 ). The number of parental lines was reduced from 2,490 lines to 361 lines by the seventh generation.…”

“…Before the statistical modeling, the data was pre-processed, and the quality of phenotype data was thoroughly checked to ensure only high-quality data trials and phenotypes were forwarded for downstream analysis. The pre-processing and quality procedure followed is thoroughly described in Khanna et al (2022) . Briefly, the trials with lack of proper experimental design, replications, and having more than 20% of missing data for grain yield were discarded.…”

“…Conventional breeding coupled with marker-based selection has been successful and led to positive increase in rice productivity. However, the rate of genetic improvement in rice has not been at a pace to meet the expected rice food demands of 1.5% or above for the growing population ( Khanna et al, 2022 ). For example, the rate of genetic gain in the International Rice Research Institute’s (IRRI) global rice breeding program is <1% ( Juma et al, 2021 ; Kumar et al, 2021 ; Khanna et al, 2022 ), which is not sufficient to meet future rice demands.…”

“…However, the rate of genetic improvement in rice has not been at a pace to meet the expected rice food demands of 1.5% or above for the growing population ( Khanna et al, 2022 ). For example, the rate of genetic gain in the International Rice Research Institute’s (IRRI) global rice breeding program is <1% ( Juma et al, 2021 ; Kumar et al, 2021 ; Khanna et al, 2022 ), which is not sufficient to meet future rice demands. Thus, it is necessary to increase the rate of genetic gain in rice to ensure future food security ( Peng et al, 2004 ; Li et al, 2018 ).…”

Optimizing QTL introgression via stochastic simulations: an example of the IRRI rice breeding program

“…In all, rice production is predicted to require an increase of 117% by 2050 to offset these various pressures (Ray et al 2013). Current trends in productivity due to breeding are 1% or less (Ladha et al 2021;Khanna et al 2022), far short of the 2.4% predicted to be required to meet these demands.…”