Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models

doi:10.1016/j.rsci.2021.04.006

摘要/Abstract

. [J]. Rice Science, 2021, 28(3): 268-278.

Fig. 1. Predictive ability of genomic prediction in cross validation experiments for arsenic content in grains (AsG), grain yield (GY) and days to flowering (DTF) observed under alternate watering and drying (AWD) and continued flooding (CF) irrigation systems over two years. BL, Bayesian Lasso; GBLUP, Genomic best linear unbiased prediction; RKHS, Reproducing kernel Hilbert spaces.Data are presented as Mean ± SD (n = 100).

Fig. 2. Effects of presence and weight of 64 trait-specific markers on predictive ability of genomic prediction for arsenic content in grains (AsG), grain yield (GY) and days to flowering (DTF) observed in four environments. AWD1 and AWD2, Alternate watering and drying in Year 1 and Year 2, respectively; CF1 and CF2, Continued flooding in Year 1 and Year 2, respectively. 0, Establishment of the trait-specific Genomic relationship matrix G' with 17K SNP; nw, Establishment of genomic relationship matrix with 17K SNP + 64 trait-specific markers; 0.25, 0.50, 0.75 and 1.00, Establishment of genomic relationship matrix with 17K SNP + 64 trait-specific markers with a weight of 0.25, 0.50, 0.75 and 1.00, respectively.Data are presented as Mean ± SD (n = 100).

Fig. 3. Predictive ability of genomic prediction experiment with single environment (SE), and multi-environment (ME) models obtained with the genomic best linear unbiased prediction (GBLUP) and reproducing kernel Hilbert spaces (RKHS) statistical methods, for arsenic content in grains (AsG), grain yield (GY) and days to flowering (DTF).ME models are implemented with two cross-validation strategies CV1 and CV2. Alternate watering and drying is shown in orange and continued flooding in blue. Data are presented as Mean ± SD (n = 100).

Table 1 Genetic correlation between days to flowering (DTF), arsenic content in grains (AsG) and grain yield (GY).

Environment	Trait	Genetic correlation		Residual genetic correlation
Environment	Trait	AsG	GY	AsG	GY
Year 1 & Year 2	DTF	0.000	-0.157	-0.061	-0.051
Year 1 & Year 2	AsG		0.144		-0.083
Year 1	DTF	0.027	0.005	-0.023	-0.023
Year 1	AsG		0.057		-0.036
Year 2	DTF	-0.018	-0.119	0.012	-0.087
Year 2	AsG		0.126		0.094

Table 2 Genetic correlation between alternate wetting and drying (AWD) and continued flooding (CF) irrigation systems.

Irrigation system	AWD2	CF1	CF2
AWD1	0.642	0.673 (0.504)	0.607 (0.461)
AWD2		0.706	0.814
CF1			0.639

Fig. 4. Predictive ability of genomic prediction experiment with three multi-trait and multi-environment prediction models. SE, Single environment; BMTME, Bayesian multi-trait and multi-environment; BMORS, Bayesian multi-output regressor stacking; BMORS_Env, BMORS that allow predicting whole environments using the remaining environments as training. Four environments are considered: alternate watering and drying (AWD1 and AWD2) and continued flooding (CF1 and CF2). AsG, Arsenic in grains; GY, Grain yield; DTF, Days to flowering.Data are presented as Mean ± SD (n = 100).

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