The Development of Thematic Core Collections in Cassava Based on Yield, Disease Resistance, and Root Quality Traits.

Autor: Dos Santos CC; Centro de Ciências Agrárias, Ambientais e Biológicas, Universidade Federal do Recôncavo da Bahia, Cruz das Almas 44380-000, BA, Brazil., de Andrade LRB; Embrapa Mandioca e Fruticultura, Nugene, Cruz das Almas 44380-000, BA, Brazil., do Carmo CD; Embrapa Mandioca e Fruticultura, Nugene, Cruz das Almas 44380-000, BA, Brazil., de Oliveira EJ; Embrapa Mandioca e Fruticultura, Nugene, Cruz das Almas 44380-000, BA, Brazil.
Jazyk: angličtina
Zdroj: Plants (Basel, Switzerland) [Plants (Basel)] 2023 Oct 04; Vol. 12 (19). Date of Electronic Publication: 2023 Oct 04.
DOI: 10.3390/plants12193474
Abstrakt: Thematic collections (TCs), which are composed of genotypes with superior agronomic traits and reduced size, offer valuable opportunities for parental selection in plant breeding programs. Three TCs were created to focus on crucial attributes: root yield (CC_Yield), pest and disease resistance (CC_Disease), and root quality traits (CC_Root_quality). The genotypes were ranked using the best linear unbiased predictors (BLUP) method, and a truncated selection was implemented for each collection based on specific traits. The TCs exhibited minimal overlap, with each collection comprising 72 genotypes (CC_Disease), 63 genotypes (CC_Root_quality), and 64 genotypes (CC_Yield), representing 4%, 3.5%, and 3.5% of the total individuals in the entire collection, respectively. The Shannon-Weaver Diversity Index values generally varied but remained below 10% when compared to the entire collection. Most TCs exhibited observed heterozygosity, genetic diversity, and the inbreeding coefficient that closely resembled those of the entire collection, effectively retaining 90.76%, 88.10%, and 88.99% of the alleles present in the entire collection (CC_Disease, CC_Root_quality, and CC_Disease, respectively). A PCA of molecular and agro-morphological data revealed well-distributed and dispersed genotypes, while a discriminant analysis of principal components (DAPC) displayed a high discrimination capacity among the accessions within each collection. The strategies employed in this study hold significant potential for advancing crop improvement efforts.
Databáze: MEDLINE