SNP Based Trait Characterization Detects Genetically Important and Stable Multiple Stress Tolerance Rice Genotypes in Salt-Stress Environments

Abstract

Soil salinity is a major constraint to rice production in coastal areas around the globe, and modern high-yielding rice cultivars are more sensitive to high salt stress, which limits rice productivity. Traditional breeding programs find it challenging to develop stable salt-tolerant rice cultivars with other stress-tolerant for the saline environment in Bangladesh due to large yield variations caused by excessive salinity fluctuations during the dry (boro) season. We examined trait characterization of 18 advanced breeding lines using SNP genotyping and among them, we found line G6 (BR9621-B-1-2-11) (single breeding line with multiple-stress-tolerant QTL/genes) possessed 9 useful QTLs/genes, and two lines (G4:BR9620-2-7-1-1 and G14: IR 103854-8-3-AJY1) carried 7 QTLs/genes that control the desirable traits. To evaluate yield efficiency and stability of 18 rice breeding lines, two years of field experiment data were analyzed using AMMI (additive main effect and multiplicative interaction) and GGE (Genotype, Genotype Environment) biplot analysis. The AMMI analysis of variance demonstrated significant genotype, environment, and their interaction, accounting for 14.48%, 62.38%, and 19.70% of the total variation, respectively, and revealed that among the genotypes G1, G13, G14, G17, and G18 were shown to some extent promising. Genotype G13 (IR 104002-CMU 28-CMU 1-CMU 3) was the most stable yield based on the AMMI stability value. The GGE biplot analysis indicates 76% of the total variation (PC1 48.5% and PC2 27.5%) which is performed for revealing genotype × environment interactions. In the GGE biplot analysis, genotypes were checked thoroughly in two mega-environments (ME). Genotype G14 (IR103854-8-3-AJY1) was the winning genotype in ME I, whereas G1 (BR9627-1-3-1-10) in ME II. Because of the salinity and stability factors, as well as the highest averages of grain yield, the GGE and AMMI biplot model can explain that G1 and G13 are the best genotypes. These (G1, G6, G13, G14, G17, and G18) improved multiple-stress-tolerant breeding lines with stable grain yield could be included in the variety release system in Bangladesh and be used as elite donor parents for the future breeding program as well as for commercial purposes with sustainable production.

Keywords: Oryza sativa; advanced breeding lines; multiple stress; salinity stress; stable yield; trait genotyping.

Figure 1

Salinity level of tested entries varied at different locations in coastal areas in Bangladesh.

Figure 2

AMMI 1 biplot for grain yield (t/ha) of 18 elite rice genotypes (G) and six environments (E) indicating genotypic and environmental IPCA scores. (First principal component of the interaction = IPCA1, G1 = BR9627-1-3-1-10, G2 = IR92860-33-CMU1-1-CMU2-AJYB, G3 = BR9154-2-7-1-2, G4 = BR9620-2-7-1-1, G5 = BR9620-2-4-1-5, G6 = BR9621-B-1-2-11, G7 = BR9625-4-1-2-8, G8 = BR9625-B-2-4-9, G9 = BR9625-B-1-4-6, G10 = BR9625-3-1-12, G11 = BR9626-1-2-12, G12 = IR 103512-B-AJY 2-2, G13 = IR 104002-CMU 28-CMU 1-CMU 3, G14 = IR 103854-8-3-AJY 1, G15 = IR 103499-B-2-AJY 1, G16 = BRRI dhan28 (Sus. Ck.), G17 = BRRI dhan67 (Res. Ck.) G18 = Binadhan-10 (Res. Ck.)).

Figure 3

AMMI 2 biplot for grain yield (t/ha) exhibiting the interrelation of IPCA2 against IPCA1 scores of eighteen elite rice genotypes in six environments in Bangladesh.

Figure 4

Interrelation among the tested environments of elite rice genotypes for yield was assessed transversely across six environments in Bangladesh.

Figure 5

Relationship among the test environments constructed on the average environment axis (AEA), regarding stability and adaptability of elite rice genotypes for grain yield estimated over six environments.

Figure 6

The mean and stability of elite rice genotypes for yield and definite genotype-environment interrelations during two consecutive seasons.

Figure 7

Mega-environments with winning genotypes during two consecutive seasons.

Figure 8

Heatmap of linear Pearson’s correlation coefficients between grain yield tested in diverse environments (ASA = Assasuni 2017–18, DEB = Debhata 2018–19, KOYRA1 = Koyra 2017–18, KOYRA2 = Koyra 2018–19, FARM1 = Satkhira BRRI farm 2017–18, FARM2 = Satkhira BRRI farm 2018–19).

Figure 9

Cluster analysis showing the genotypic diversity and relevancy along with the 18 salinity tolerant rice genotypes based on yield traits.

Figure 10

Geographical location of the experimental sites.