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Identifying Mutations by High Resolution Melting in a TILLING Population of Rice
Published on: September 2, 2019
Assessing the Genetic Diversity of Parents for Developing Hybrids Through Morphological and Molecular Markers in Rice
Rakkimuthu Nivedha1, Swaminathan Manonmani2, Thiyagarajan Kalaimagal3
1Department of Rice, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.
Abstract:
The advancement of hybrid technology plays a crucial role in addressing yield plateau and diminishing resources in rice cultivating regions. The knowledge of genetic diversity among parental lines is a prerequisite for effective hybrid breeding program. In the current study, a set of 66 parental lines was studied for diversity based on both morphological characters and microsatellite SSR markers. The genetic variability parameters unveiled that number of productive tillers per plant, single plant yield and hundred grain weight exhibited additive gene action. Mahalanobis D2 statistics grouped the genotypes into ten clusters based on yield and grain traits. The principal component analysis identified four PCs with eigen value more than one accounting for 71.28% of cumulative variance. The polymorphic SSR markers produced 122 alleles among which the marker RM474 recorded the highest values for Polymorphic Information Content (0.83) and heterozygosity index (0.85). The genotypes were assembled in seven clusters based on jaccard distances using the Unweighted Pair Group method with Arithmetic Mean (UPGMA). The population structure divided the entire population into 3 subpopulations. In both clustering, there was difference in the assembling of genotypes, but, good performing genotypes identified through PCA were positioned in different clusters in both approaches. The genotypes CBSN 495 and CBSN 494 located in different clusters were identified as the potential restorers for high yielding and short duration hybrids. The hybridization among CRR Dhan 310, CRR Dhan 315, IR64 DRT, CB 17135 and WGL 347 can be performed to develop climate smart varieties with improved nutrition.
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