Genetic relationships of cotton (Gossypium barbadense L.) genotypes as studied by morphological and molecular markers

Fifteen (15) morphological traits and three different types of molecular markers [inter simple sequence repeats (ISSR), simple sequence repeat (SSR) and expressed sequence tag (EST) markers] were used to study the genetic relationships among 24 cotton (Gossypium barbadense L.) genotypes (commercial varieties and new germplasm). High significant differences were observed among the genotypes for all the studied traits and the interaction between genotypes and years ranged from highly significant to significant for the most studied traits. The value of phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV) for all studied traits which means that the apparent variation is not only due to genotypes but also due to the influence of environmental factors. The cluster analysis of the 24 cotton genotypes depending upon the morphological traits divided them into two main groups (A and B) while molecular data divided them into six groups. The cotton genotypes were distributed according to principal coordinate analysis (PCOORDA) analysis of both morphological traits and molecular markers regardless of their fiber characteristics. According to this analysis, the cotton genotypes were distributed into three distinct parts. Most molecular markers showed polymorphism in their patterns. The highest number of total and polymorphic bands was generated from ISSR markers while the least number of total and polymorphic bands was obtained from the ESTSSR markers. According to both morphological and molecular analyses, the following genotypes could be used to hybridize and produce high growth and yield potential: Giz87, Giza45, Giza88 and Giza70 as a first parent and Karshansky, Giza80, Giza83, Australian10229 and Russian6022 as a second parent in the cross.Keywords: Cotton, simple sequence repeat (SSR), expressed sequence tag (EST), inter simple sequence repeats (ISSR), morphological traits, cluster analysis, principal coordinate analysis (PCOORDA).African Journal of Biotechnology Vol. 12(30), pp. 4736-474