SSR marker-based genetic diversity and marker-trait association analysis in aromatic rice (Oryza sativa) landraces

被引:0
|
作者
Burman, Maumita [1 ,2 ]
Nair, Sunil Kumar [1 ]
Sao, Abhinav [1 ]
Gauraha, Deepak [1 ]
Sharma, Deepak [1 ]
机构
[1] Indira Gandhi Krishi Vishwavidyalaya, Raipur 492012, Chhattisgarh, India
[2] Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh, India
来源
关键词
Aromatic rice; Genetic diversity; Landraces; Molecular markers; Variability; POPULATION-STRUCTURE; SOFTWARE;
D O I
10.56093/ijas.v93i4.133177
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
The present study on SSR marker-based genetic diversity and marker-trait association analysis in aromatic rice (Oryza sativa L.) landraces was carried out at R. H. Richharia Research Laboratory, Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh during 2020. A total of 25 PCR-based simple sequence repeats (SSR) markers were evaluated in a set of 90 aromatic rice landraces along with 6 checks which includes 1 non-aromatic and 5 aromatic check varieties. Phenotypic data for marker-trait association analysis were taken for 24 yield attributing traits. Polymorphic Information Content (PIC) value ranged from 0.52 (RM316) to 0.79 (RM553) with a mean of 0.69 which reveals that all the markers used in this study were highly informative and useful for diversity analysis of a wide range of genotypes. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and Rho's similarity based cluster analysis revealed that all 5 aromatic check varieties falls in one cluster while the 1 non-aromatic check variety (Mahamaya) forms a separate cluster. Mixed linear model (MLM) was applied to perform genome-wide association mapping where 41 significant marker-trait associations were observed for 24 yield attributing traits. The potential markers identified in the study may provide new opportunities for rice breeder to improve yield and its attributing traits through marker assisted selection approach.
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页码:365 / 370
页数:6
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