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Morpho-molecular genetic diversity and population structure analysis in garden pea (Pisum sativum L.) genotypes using simple sequence repeat markers.

Akhilesh SharmaShimalika SharmaNimit KumarRanbir Singh RanaParveen SharmaPrabhat KumarMenisha Rani
Published in: PloS one (2022)
Garden pea (Pisum sativum L.) is a self-pollinated plant species which played an important role for the foundation of modern genetics. Genetic diversity among 56 garden pea genotypes was assessed using 12 morphological descriptors, 19 quantitative traits and 8 simple sequence repeat (SSR) markers. Eight morphological descriptors were found polymorphic, and highest Shannon diversity index was recorded for pod curvature (1.18). Mahalanobis D2 illustrating genetic divergence arranged 56 genotypes into six clusters, with the highest inter-cluster distance between clusters IV and VI (18.09). The average values of Na (number of alleles), Ne (effective number of alleles), I (Shannon's Information index), PIC (polymorphism information content), Ho (observed heterozygosity) and He (expected heterozygosity) were 3.13, 1.85, 0.71, 0.36, 0.002 and 0.41, respectively. Pair wise genetic distance among all pairs of the genotypes varied from 0.33 to 1.00 with an average of 0.76. Based on genetic distance, the genotypes were classified into two main clusters (A and B) by cluster analysis, whereas structure analysis divided the genotypes into four sub-populations. The SSR makers indicated that present of genetic variability among the studied genotypes. When, we compared the groups formed by agro-morphological and molecular data, no genotypes were observed, indicating that both stages of characterization are crucial for a better understanding of the genetic variability. Hybridization between genetically diverse genotypes can be exploited to expend the genetic variability and introduce new traits in the pea breeding program.
Keyphrases
  • genetic diversity
  • genome wide
  • copy number
  • dna methylation
  • high resolution
  • single molecule
  • mass spectrometry
  • quality improvement
  • artificial intelligence