Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

Abstract

Pearl millet (Pennisetum glaucum (L.) R. Br., 2n = 2x = 14, Poaceae), is a cross-pollinated, warm-season crop grown worldwide. To select genotypes for breeding pearl millet cultivars that adapt to drought condition in southern Tunisia, we evaluated the grain yield (GY) and yield-related traits using a set of 27 landraces at two locations in southern Tunisia for two grown seasons (2019 and 2020). The genetic variability, phenotypic and genotypic association, and path coefficient (PC), based on grain yield (GY) and different yield-related agronomic traits, were evaluated. Analysis of variance and BLUPs value revealed a wide range of variability and the possibility of genetic selection for traits that are advantageous. Broad sense heritability (H) for all the traits ranged from 24.10% for grain yield (GY) to 57.11% for spike length (SL), indicating low to moderate inheritability. Genetic advance as a percentage of the mean (GAM) ranged from high (29.56%) for principal panicle weight (PPW) to moderate for all the traits except from plant high (PH) (7.31%). For all the traits, the phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV), indicating the magnitude of environmental conditions. GY was significantly correlated with all the traits at the genotypic and phenotypic level. According to the path coefficient, the traits PPW and SL displayed the highest direct effects on GY. Heatmap analysis demonstrated a clear segregation between the early and late genotypes based on their geographic origin. Based on the cluster analysis and FAI-BLUPS analysis, genotypes G11, G13, G12, G17 and G18 were selected as the best-performing genotypes with the shortest cycle.

Estimation of Genetic Parameters and Identification of Leaf Blast-Resistant Rice RILs Using Cluster Analysis and MGIDI

“…In this study, the accuracy values of the majority of variables, especially AUDPC and IT, were very high, as were the accuracy values of the rest of the variables; in general, estimates above 0.70 are sufficient to explicitly conclude the genetic value of genotypes [31,32]. In addition, AUDPC showed the highest selection genetic gain percent (−61.13%), and in general, the MGIDI provided a total SD of −59.12% for traits with decreased sense.…”

“…According to the results, we found that the selection based on MGIDI provided satisfactory gain. The measured MGIDI, based on the BLUP data from multiple traits in two years, selected 20 resistant genotypes (17,80,111,127,81,38,37,61,132,59,Dorfak,58,46,141,18A,54,27,140,51,and 32) that highly overlapped with selected genotypes using cluster analysis.…”

“…The MGIDI is calculated based on BLUPs that have the ability to eliminate environmental variance and provide accurate estimations of individual breeding values. As a result, they are becoming more prevalent among plant breeders for precise genotypic value estimation [32]. Also, this method is free from the multi-collinearity problem [38].…”

“…The selection of these parents was based on their distinct genetic and phenotypic responses to blast disease, enabling the creation of a population with maximal genetic diversity for targeted selection purposes. Through the implementation of cluster analysis and the MGIDI method, 17 genotypes, including 17,111,Dorfak,38,127,54,81,80,18A,27,61,46,37,59,32,132, and 51, were identified as the most resistant genotypes over two years. The mean value of the AUDPC for these resistant genotypes was determined to be 2.435 ± 0.114.…”