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

被引:14
|
作者
Debsharma, Sanjoy K. [1 ]
Syed, Md. Abu [1 ]
Ali, Md. Hannan [2 ]
Maniruzzaman, Sheikh [1 ]
Roy, Popy R. [3 ]
Brestic, Marian [4 ]
Gaber, Ahmed [5 ]
Hossain, Akbar [6 ]
机构
[1] Bangladesh Rice Res Inst, Breeding Div 1Plant, Gazipur 1701, Bangladesh
[2] Bangladesh Rice Res Inst, Irrigat & Water Management Div, Gazipur 1701, Bangladesh
[3] Seed Certificat Agcy, Minist Agr, Govt Peoples Republ Bangladesh, Gazipur 1701, Bangladesh
[4] Slovak Univ Agr, Inst Plant & Environm Sci, Tr A Hlinku 2, Nitra 94901, Slovakia
[5] Taif Univ, Coll Sci, Dept Biol, POB 11099, Taif 21944, Saudi Arabia
[6] Bangladesh Wheat & Maize Res Inst, Div Soil Sci, Dinajpur 5200, Bangladesh
关键词
genetic diversity; trait association; principal component analysis; clustered heatmap; MGIDI; PATH COEFFICIENT ANALYSIS; CHARACTER ASSOCIATION; YIELD; HERITABILITY; SELECTION; AREAS; LINES;
D O I
10.3390/genes14010010
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
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页数:21
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