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Estimating Pedigree-based Breeding Values and Stability Parameters of Elite Rice Breeding Lines for Yield Under Salt Stress During the Boro Season in Bangladesh

M. Ruhul Quddus
Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh,

M. Akhlasur Rahman
Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh,

Nusrat Jahan
Scientific officer
Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh,

Sanjoy K. Debsharma
Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh,

Ribed F. Disha
Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh,

M. Moniruzzaman Hasan
Advanced Seed Research & Biotech Centre (ASRBC), Dhaka 1212, Bangladesh

Tamal L. Aditya
Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh,

KM Iftekharuddaula
Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh,

Bertrand C.Y. Collard
International Rice Research Institute, Los Baños, Laguna 4031, Philippines 4 NSW Department of Primary Industries, Yanco NSW 2703, Australia

Abstract/ Executive Summary:

In salinity affected areas, variation in salinity level is the major cause of yield fluctuations in rice during the dry season (boro season). To sustain food security in Bangladesh, plant breeders need to develop new rice varieties which are higher yielding, salinity tolerant and stable across different environments. We evaluated the yield performance and stability of 51 rice elite genotypes including two salinity tolerant rice varieties (BRRI dhan67 and BINA dhan-10) and the dominant boro rice variety (BRRI dhan28) in three locations, including a salinity “hotspot”. Best linear unbiased predictions (BLUPs) were used to identify superior genotypes from multi-environment trials. Selection from a large set of candidates is required for evaluation and recommending a new variety for release. Estimated breeding values (EBVs) were used to select parents. Six parents with high EBVs (BR8982-5, IR 87870-6-1-1-1-1-B; BR8943-B-1-2-7, BR8940-B-17-4-7, A69-1 and BR8943-B-5-5-14) would be useful as parents to develop new breeding populations. To evaluate yield performance and stability across environments, we used additive main effects and multiplicative interaction (AMMI) model using a randomized complete block design with two replications. Genotype × environmental analysis was performed using GGE biplot analysis. Based on yield performance and stability, BR8982-5, IR 87870-6-1-1-1-1-B, BR8943-B-1-2-7, BR8940-B-17-4-7, A69-1 and BR8943-B-5-5-14 were promising across the tested environments, as they showed yield advantages over check varieties.The results in this study will be useful for selecting the elite lines suitable for salinity affected areas and parents for rapid breeding of salinity tolerance.

Keywords: Genetic gain, AMMI Model, Genotype × environment (GE) analysis, Stability, Best Linear Unbiased Prediction (BLUP), Estimated Breeding Value (EBV)

Location: Khulna, Satkhira, Gazipur

Start Date: 15-11-2015

End Date: 15-08-2019

Program Area: Variety and Species

Commodity: Rice

Objectives:

Our study aimed to identify high yielding, stable genotypes under salinity stress and estimate genetic gain of the current breeding program and estimated breeding values to make selection decisions.

Methodology:

Analysis of variance of the RCBD was performed for each of the three environments on the basis of individual plot observation using the STAR 2.0.1 software. Combined analysis of variance across the three environments was also performed using the STAR 2.0.1 software. Genotypes were considered as random effects in order to calculate BLUPs for yield using PBTools 1.4. software. Both software programs are open-source and were developed by the IRRI biometrics team (http://bbi.irri.org/products). Due to the parentage of the germplasm used, we were able to determine relatedness based on pedigrees. We used R (version 3.5.1) to analyze the relationship matrix, inbreeding coefficient for each individual and estimated breeding values and plots. Additive relationship matrix/ kinship coefficient (or coefficient of parentage: CoP) was calculated using pedigree information. The ‘editPed’ function used to construct pedigrees using R package ‘pedigreemm’ (Vazquez et al. 2010; Bates and Vazquez 2014). Data was coded following the animal model of dam (female/mother) and sire (male/father). We know coefficient of relationship (r) is double the coefficient of kinship (i.e. r =2* the coefficient of kinship). For example, the value of CoP is 0 (sparse matrix or sparse array) when crossed between unrelated parents/individuals and similarly this will be 0.5 when no inbreeding occurred for a parent/individual with themselves, 0.25 between mother and child, 0.125 between an uncle and niece, etc. Inbreeding coefficients were measured by subtracting additive relationship matrix from diagonal value. Estimated breeding values assessed on the basis of own performance of individual plant/parent, pedigree records and progeny data. We calculated the reliability of EBV derived from prediction error variance (PEV) as square root of 1 minus the ratio between PEV and additive genetic variance (VA) i.e. rel sqrt(1-PEV/VA). Grain yield data for dry season in 2016-17 at three locations were used for combined analyses via additive main effect and multiplicative interaction (AMMI) analysis of variance and genotype effect and genotype × environment interaction effect (GGE). AMMI uses ANOVA to analyze the main effects (additive part) and principal component analysis (PCA) to analyze the non-additive residuals by the ANOVA (Gauch 1993).

Source of Information: Plant Breed. Biotech. 2019 (September) 7(3):257~271 Online ISSN: 2287-9366 Print ISSN: 2287-9358

Article Link (Online Journal): https://doi.org/10.9787/PBB.2019.7.3.257

Funding Source:

1. Government

Budget:

Total Budget (Tk.) :

Achievement/Findings:

The use of EBVs and estimation of predicted genetic gain represent new a new awareness regarding the importance of genetic gain. Decisions about parent selection based on EBVs and conducting advanced trials in salinity-affected hotspots represent significant improvements to BRRI’s rice breeding program that will ultimately enable new, higher-yielding rice varieties to be developed for Bangladesh.

Knowledge Management: Journal