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Estimating genetic variability among diverse lentil collections through novel multivariate techniques

Author

Listed:
  • Syed Atiq Hussain
  • Muhammad Sajjad Iqbal
  • Muhammad Akbar
  • Noshia Arshad
  • Saba Munir
  • Muhammad Azhar Ali
  • Hajra Masood
  • Tahira Ahmad
  • Nazra Shaheen
  • Ayesha Tahir
  • Muhammad Ahson Khan
  • Muhammad Kashif Ilyas
  • Abdul Ghafoor

Abstract

Lentil is an important food legume throughout the world and Pakistan stands at 18th position with 8,610 tons production from 17,457 hectares. It is rich in protein, carbohydrates, fat, fiber, and minerals that can potentially meet food security and malnutrition issues, particularly in South Asia. Two hundred and twenty lentil genotypes representing Pakistan (178), Syria (14), and the USA (22) including 6 from unknown origins were studied for yield, yield contributing traits, and cooking time (CT). Genotype 6122 (Pakistan) performed the best during both years with seed yield per plant (SY) 68±1.7 g, biological yield per plant (BY) 264±2.8 g, pod size (PS) 0.61±0.01 cm, number of seeds per pod (NSP) 2, cooking time (CT) 11 minutes, with no hard seed (HS). The genotypes 6122 (Pakistan) and 6042 (Syria) produced the highest BY, hence these have the potential to be an efficient source of fodder, particularly during extreme winter months. The genotypes 5698 (Pakistan) and 6015 (USA) were late in maturity during 2018–19 while 24783 and 5561 matured early in 2019. A minimum CT of 10 minutes was taken by the genotypes 6074 and 5745 of Pakistani origin. The lowest CT saves energy, time, and resources, keeps flavor, texture, and improves protein digestibility, hence the genotypes with minimum CT are recommended for developing better lentil cultivars. Pearson correlation matrix revealed significant association among several traits, especially SY with BY, PS, and NSP which suggests their use for the future crop improvement program. The PCA revealed a considerable reduction in components for the selection of suitable genotypes with desired traits that could be utilized for future lentil breeding. Structural Equational Model (SEM) for SY based on covariance studies indicated the perfect relationship among variables. Further, hierarchical cluster analysis establishes four clusters for 2017–18, whereas seven clusters for 2018–19. Cluster 4 of 2017–18 and cluster 5 of 2018–19 exhibited the genotypes with the best performance for most of the traits (SY, BY, PS, NSP, CT, and HS). Based on heritability; HSW, SY, BY, NSP were highly heritable, hence these traits are expected for selecting genotypes with genes of interest and for future lentil cultivars. In conclusion, 10 genotypes (5664, 5687, 6084, 6062, 6122, 6058, 6087, 5689, 6042 and 6074) have been suggested to evaluate under multi-location environments for selection of the best one/s or could be utilized in hybridization in future lentil breeding programs.

Suggested Citation

  • Syed Atiq Hussain & Muhammad Sajjad Iqbal & Muhammad Akbar & Noshia Arshad & Saba Munir & Muhammad Azhar Ali & Hajra Masood & Tahira Ahmad & Nazra Shaheen & Ayesha Tahir & Muhammad Ahson Khan & Muhamm, 2022. "Estimating genetic variability among diverse lentil collections through novel multivariate techniques," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-20, June.
  • Handle: RePEc:plo:pone00:0269177
    DOI: 10.1371/journal.pone.0269177
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