Frontiers in Nutrition (Sep 2022)

Assessing the genetic diversity of guava germplasm characterized by morpho-biochemical traits

  • Nayan Deepak Gangappa,
  • Chandu Singh,
  • Mahendra Kumar Verma,
  • Madhubala Thakre,
  • Amitha Mithra Sevanthi,
  • Rakesh Singh,
  • Manish Srivastav,
  • K. Raghunandan,
  • Chukkamettu Anusha,
  • Vivek Yadav,
  • Arumugam Nagaraja

DOI
https://doi.org/10.3389/fnut.2022.1017680
Journal volume & issue
Vol. 9

Abstract

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Amid environmental crises, a galloping population, and changing food habits, increasing fruit production with nutritional quality is a global challenge. To address this, there is a necessity to exploit the germplasm accessions in order to develop high-yielding varieties/hybrids with good adaptability and high quality fruit under changing environmental and biological conditions. In the study, a total of 33 morpho-biochemical traits enabled an assessment of the genetic variability, diversity, and structure in a collection of 28 diverse germplasm lines of guava. Results showed that highly significant genetic variability existed in the studied traits in the guava germplasm. The coefficient of variation values for the qualitative and quantitative traits varied from 23.5–72.36 to 1.39–58.62%, respectively. Germplasm Thai, Lucknow-49, Punjab Pink, Psidium friedrichsthalianum, and Shweta had the highest fruit weight (359.32 g), ascorbic acid content (197.27 mg/100 g fruit), total phenolic content (186.93 mg GAE/100 g), titratable acidity (0.69 percent), and antioxidant capacity (44.49 μmolTrolox/g), respectively. Fruit weight was positively correlated with ascorbic acid content; however, titratable acidity was negatively correlated with fruit weight. The principal component analysis (PCA) was 84.2% and 93.3% for qualitative and quantitative traits, respectively. Furthermore, K-mean clustering was executed; the population was grouped into three clusters for both traits. Additionally, the dendrogram using agglomerative hierarchical clustering (AHC), where all the germplasm were grouped into four clusters, revealed that among the clusters, clusters III and IV were highly divergent. The high variability, diversity, and structure could be utilized for the breeding programme of guava and also explored for molecular analysis using next-generation technology to enhance the guava yield and nutrition properties and also develop the climate resilient technology to fulfill the existing demand gap and nutrition availability, which could not only mitigate the nutrition requirement but also enhance the easy availability of fruits year-round.

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