Development of new cowpea (Vigna unguiculata) mutant genotypes, analysis of their agromorphological variation, genetic diversity and population structure

• Published in: Biocell 2021-01, Vol.45 (2), p.345-362
• Main Authors: DIOUF, MADE, DIALLO, SARA, ABAYE BADIANE, FRAN荗IS, DIACK, OUMAR, DIOUF, DIAGA
• Format: Article
• Language: English
• Subjects: Coloration; Discriminant analysis; Genetic analysis; Genetic diversity; Genotypes; Heritability; Legumes; Mutants; Population genetics; Population structure; Seeds; Subpopulations; Vigna unguiculata
• ISSN: 1667-5746; 0327-9545
• DOI: 10.32604/biocell.2021.013706

Cowpea is one of the most important legume grains in the sub-Saharan region of Africa used for human consumption and animal feed, but its production is hampered by biotic and abiotic constraints raising the need to broaden its genetic basis. For this purpose, seeds of two cowpea varieties Melakh and Yacine were irradiated with 300 and 340 Gy of gamma-ray, respectively. The developed mutant populations were agromorphologically characterized from M5 to M7, while the genetic diversity of the latter was evaluated using 13 ISSR markers. Based on the agromorphological characterization, variation of flower color, pod length, seed coat color, and seed weight with 78.01, 68.29, 94.48, and 57.58% heritability, respectively, were recorded in the mutant lines. PCA analyses allowed to identify the elite mutants based on their agromorphological traits, while Pearson’s correlation results revealed a positive correlation between yield and yield component traits. Three subpopulations were identified through STRUCTURE analyses, but the assignment of the individuals in each group was improved using DAPC (Discriminant Analysis of Principal Components) analysis. Analysis of Molecular Variance revealed that the majority (85%) of the variance rather existed within groups than among (15%) groups. Finally, our study allowed us to select new promising mutant genotypes that could be tested for multi-locational trials to evaluate their agronomic performance.