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Knowledge of the pattern of trait variation among accessions, and the trait profiles of the accessions is crucial for improvement of a crop. Twenty-one Amaranthus accessions were evaluated in 2018 and 2019 to investigate the extent of genotypic diversity among the amaranth accessions and their trait profiles. Data were subjected to analysis of variance, and correlation and principal component analyses. Taking stem weight (SWT) as the yield, the accession × yield-trait combination (GYT) biplot was employed to investigate the trait profiles of the accessions. Accession, year, and accession × year mean squares were significant (P≤0.05/0.01) for most of the measured traits. The first three principal components explained 88.18% of observable variation among the accessions and identified plant height (PHT), number of leaves per plant (NOL), and root weight (RWT) as the major contributors. Significant (P≤0.01) correlation was observed in the association of SWT with NOL, TBM, and RWT. Accessions NGB00019 and NGB00061 were associated with the sector containing all the YT combinations considered. However, NGB00019 was identified as the best combiner of yield with other traits. Further studies involving more traits should determine the trait profiles of the remaining accessions.

Genetic analysis of phenotypic variations of traits under drought stress to identify associated loci will facilitate crop improvement and performance in marginalized drought-prone areas. To this end, we identified genomic regions/loci underlying variations among recombinant inbred lines (RILs) of WAB638-1/BRS-PRIMAVERA rice under drought stress and control environments using diversity array technology (DArT)-based markers in linkage analysis. The linkage map of 94 RILs and 581 DArT-Based markers (295 SNP and 286 SilicoDArT) covers all the 12 rice chromosomes. The total length of the linkage map was 3057.85 cM, with an average of 254.82 cM per chromosome. The average interval between two adjacent markers was 5.82 cM and ranged from 0.56 cM to 39.8 cM, with 429 (73.8%) markers less than 5.0 cM from their nearest neighbours. A total of 28 additive effect QTLs associated with seven agronomic traits across the two environments were localized on chromosomes 1, 2, 3, 8, 10, and 11. The QTLs individually explained 0.96% to 41.6% of the phenotypic variance (PVE), with 11 (40%) major-effect QTLs, each explaining more than 10% PVE. Four major-effect QTLs were mapped to regions consistent with other studies, while seven are newly reported. The consistent QTLs can be deployed for marker-assisted breeding, and the new QTLs should be further characterized and validated. While the QTLs detected for various agronomic traits advances our understanding of phenotypic variability, those associated with drought treatments are significant for improving rice performance in drought-prone areas of the upland ecology.