Genetic diversity and population structure analysis of bold type rice collection from Southern India

CEREAL RESEARCH COMMUNICATIONS(2020)

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摘要
It is essential to elucidate genetic diversity and relationships among related varieties of origin and landraces for improving the breeding process. Since rice breeding has improved agronomic traits such as yield and eating quality during green revolution, modern rice varieties are originated from narrow genetic resource and closely related. To resolve the population structure and genetic diversity in bold type rice varieties of southern India, we used a total of 81 rice genotypes by 100 simple sequence repeat markers composed of 36 improved varieties and 45 landraces, which are representative and important for bold type grain rice breeding. The landraces exhibit greater gene diversity than improved lines, suggesting that landraces can provide additional genetic diversity for future breeding. Clustering by Ward method was done to establish a relationship among the 81 rice genotypes. All the genotypes were clustered into mainly 5 clusters. Principle component analysis revealed that the first principal component revealed 42.87% variation, while the second component showed 14.01% variation. Among the eight morpho-physiological and plant production traits studied, the relative water content and spikelet fertility percentage contributed towards maximum diversity. Principle co-ordinate analysis evidently differentiated the genotypes to high yielding varieties with common ancestry. Population structure analysis also obviously classified the genotypes into high yielding susceptible and indigenous tolerant groups. These old varieties and landraces present in crop germplasm collections represent a strategic reserve of genetic variation that can be tapped for varieties and understanding of stress response and developing new varieties that are physiologically adapted to highly variable, climate-resilient environments.
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关键词
Rice,Population structure,Principal component analysis,Principal co-ordinate analysis,Cluster analysis
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