Harnessing on Genetic Variability and Diversity of Rice (Oryza sativa L.) Genotypes Based on Quantitative and Qualitative Traits for Desirable Crossing Materials

Abstract

Yield is a complex parameter of rice due to its polygonal nature, sometimes making it difficult to coat the selection process in the breeding program. In the current study, 34 elite rice genotypes were assessed to evaluate 3 locations for the selection of desirable rice cultivars suitable for multiple environments based on genetic diversity. In variance analysis, all genotypes have revealed significant variations (p ≤ 0.001) for all studied characters, signifying a broader sense of genetic variability for selection purposes. The higher phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were found for yield-associated characteristics such as the number of grains panicle^-1 (GP), panicles hill^-1 (PPH), and tillers hill^-1 (TILL). All of the characters had higher heritability (greater than 60%) and higher genetic advance (greater than 20%), which pointed out non-additive gene action and suggested that selection would be effective. The most significant traits causing the genotype variants were identified via principal component analysis. In the findings of the cluster analysis, 34 elite lines were separated into 3 categories of clusters, with cluster II being chosen as the best one. The relationship matrix between each elite cultivar and traits was also determined utilizing a heatmap. Based on multi-trait genotype-ideotype distance index (MGIDI), genotypes Gen2, Gen4, Gen14, Gen22, and Gen30 in Satkhira; Gen2, Gen6, Gen7, Gen15, and Gen30 in Kushtia; and Gen10, Gen12, Gen26, Gen30, and Gen34 in Barishal were found to be the most promising genotypes. Upon validation, these genotypes can be suggested for commercial release or used as potential breeding material in crossing programs for the development of cultivars suitable for multiple environments under the future changing climate.

Keywords: MGIDI; clustered heatmap; genetic diversity; principal component analysis; trait association.

Figure 1

Correlation coefficient matrix, scatter plot, and phenotypic frequency distribution among grain yield and yield-related traits over the locations. Each variable’s distribution is displayed diagonally. The bivariate scatter plots with a trend line are shown at the bottom of the diagonal. The correlation coefficient and the level of significance are displayed as stars at the top of the diagonal. * p ≤ 0.05, ** p ≤ 0.01, and *** p > 0.001 show significance level; Note: X50F = days to 50% flowering, PH = plant height (cm), TILL = number of tillers per hill, PPH = number of panicles per hill, PL = panicle length (cm), GP = grains per panicle, Fertility = fertility (%), YPP = grain yield per plant (gm), TGW = thousand grain weight (gm), and YTH = grain yield (t/ha).

Figure 2

Scree plot on variability explained by each component of 34 elite rice genotypes.

Figure 3

Biplot for the 34 rice genotypes and 10 agronomic traits along the first 2 principal components. Note: X50F = days to 50% flowering, PH = plant height (cm), TILL = number of tillers per hill, PPH = number of panicles per hill, PL = panicle length (cm), GP = grains per panicle, Fertility = fertility (%), YPP = grain yield per plant (gm), TGW = thousand grain weight (gm), and YTH = grain yield (t/ha).

Figure 4

Graphical representation on (A) percent of contribution and (B) quality of representation of top 5 variables to principal components 1-2-3-4-5.

Figure 5

Graphical representation of (A) percent of contribution and (B) quality of representation of top 10 genotypes to principal components 1-2-3-4-5.

Figure 6

Graphical method (A) Hubert index and (B) Dindex for detecting the optimum number of clusters.

Figure 7

The heatmap displays the clustering pattern of 34 rice genotypes with 10 quantitative characters. Each column indicates a character, whereas each row represents a genotype. The various colours and intensities (−2 to 3) were adjusted based on the genotype–characters relationship. The orange colour represents a lower value, the white colour for mid value, and the dark red indicates a higher value.

Figure 8

The circular preview indicates the ranking of genotypes based on the MGIDI selection index as well as the best rice genotypes with the associated locations (A) Satkhira, (B) Kushtia, and (C) Barishal. The selected ones are marked in red colour.

Figure 9

The strengths and weaknesses view of the selected genotypes represent the proportion of each factor on the computed MGIDI index. (A) Satkhira, (B) Kushtia, and (C) Barishal.