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<p>This study utilized a comprehensive approach, integrating multi-trait mixed models with multivariate statistical analyses, to assess genetic diversity and trait associations among 110 diverse mungbean (<i>Vigna radiata</i> L.) accessions sourced from India and Taiwan. Field experiments were conducted over two consecutive <i>kharif</i> seasons (2023–2025) under irrigated conditions at Sumerpur, Rajasthan, using a randomized alpha lattice design. Phenotypic data on nine agro-morphological traits were analyzed using a linear mixed-effects model to estimate variance components, broad-sense heritability, and best linear unbiased predictions (BLUPs), effectively accounting for genotype × environment interactions. Substantial genetic variability and moderate-to-high heritability for key traits—particularly seed yield (SY), number of pods per plant (NPPP), and 1000-seed weight (TSW)—indicated the predominance of additive gene effects, supporting their potential for direct selection. Trait association analyses, including Pearson’s correlation, stepwise regression, and path coefficient analysis, identified TSW, number of seeds per pod (NSPP), and NPPP as the primary determinants of seed yield. Traits associated with early maturity and compact plant type were also found beneficial for improving yield stability. Genetic divergence assessed through Mahalanobis D<sup>2</sup>-based Ward’s clustering, K-means, and Euclidean hierarchical clustering consistently grouped genotypes into five distinct clusters, with Ward’s method providing the most robust resolution. Principal Component Analysis (PCA) further validated these groupings, with the first three components explaining over 62% of total variation. Based on yield data from both 2023 and 2024, five genotypes (G8, G97, G58, G87, and G60) demonstrated higher and more stable yields than the check genotype G18 (4.15 &amp; 4.32 q/ha), with yields ranging from 4.38 to 7.00&#xa0;q/ha across both years. Overall, this integrated framework effectively identified genetically diverse, high-performing mungbean genotypes offering valuable insights for strategic hybridization, selection, and the development of climate-resilient cultivars.</p>

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Integrating multi-traits mixed model and multivariate statistics for genetic diversity assessment in mungbean (Vigna radiata L.)

  • 期刊论文

  • 发表日期：

    2025年8月7日

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  • Vijay Kamal Meena,
  • Vijay Singh Meena,
  • Shahil Kumar,
  • Harshvardhan Singh Shekhawat,
  • Kapil Choudhary,
  • Jitendar Kumar Sharma,
  • Lekha Meal,
  • Rahul Bhardwaj,
  • Sitaram Kumawat

摘要

This study utilized a comprehensive approach, integrating multi-trait mixed models with multivariate statistical analyses, to assess genetic diversity and trait associations among 110 diverse mungbean (Vigna radiata L.) accessions sourced from India and Taiwan. Field experiments were conducted over two consecutive kharif seasons (2023–2025) under irrigated conditions at Sumerpur, Rajasthan, using a randomized alpha lattice design. Phenotypic data on nine agro-morphological traits were analyzed using a linear mixed-effects model to estimate variance components, broad-sense heritability, and best linear unbiased predictions (BLUPs), effectively accounting for genotype × environment interactions. Substantial genetic variability and moderate-to-high heritability for key traits—particularly seed yield (SY), number of pods per plant (NPPP), and 1000-seed weight (TSW)—indicated the predominance of additive gene effects, supporting their potential for direct selection. Trait association analyses, including Pearson’s correlation, stepwise regression, and path coefficient analysis, identified TSW, number of seeds per pod (NSPP), and NPPP as the primary determinants of seed yield. Traits associated with early maturity and compact plant type were also found beneficial for improving yield stability. Genetic divergence assessed through Mahalanobis D2-based Ward’s clustering, K-means, and Euclidean hierarchical clustering consistently grouped genotypes into five distinct clusters, with Ward’s method providing the most robust resolution. Principal Component Analysis (PCA) further validated these groupings, with the first three components explaining over 62% of total variation. Based on yield data from both 2023 and 2024, five genotypes (G8, G97, G58, G87, and G60) demonstrated higher and more stable yields than the check genotype G18 (4.15 & 4.32 q/ha), with yields ranging from 4.38 to 7.00 q/ha across both years. Overall, this integrated framework effectively identified genetically diverse, high-performing mungbean genotypes offering valuable insights for strategic hybridization, selection, and the development of climate-resilient cultivars.