Appropriate statistical methods for analysis of safflower genetic diversity using agglomerative hierarchical cluster analysis through combination of phenotypic traits and molecular markers

Abstract

Combining phenotypic and genotypic germplasm characterization is a key to efficient and successful safflower (Carthamus tinctorius L.) breeding program by identifying valuable and confirmed parents. This study aimed to investigate and use appropriate statistical methods for such a characterization, and to identify potential genetic pools in safflower germplasm that may be useful for breeding program implementation. The genetic diversity of 45 accessions from different countries, provided by the USDA-ARS, was assessed during two cropping seasons, using agromorphological traits and inter simple sequence repeat (ISSR) molecular markers. Agglomerative hierarchical cluster analysis (AHCA) was used with appropriate similarity distances, and Ward and unweighted pair group method with arithmetic mean (UPGMA) linkages. Agreement between distance-linkage combinations was evaluated using cophenetic correlation, Mantel test, Fisher exact test, Cramer's V, overall accuracy, and Cohen's κ. Both agromorphological phenotyping and molecular genotyping revealed significant genetic diversity. Ward linkage was better than UPGMA, using simple matching distance for molecular markers and Gower distance for phenotypic traits as well as for combined phenotypic traits and molecular markers. It delineated the studied accessions into four main clusters. Some accessions showed desirable profiles that can be used in future breeding programs. This is the first report of a series of appropriate statistical methods that can be used for assessing genetic diversity in safflower, combining phenotypic traits and molecular markers, and thus identifying relevant genetic pools for breeding program.

Characterization of Cyclamen genotypes using morphological descriptors and DNA molecular markers in a multivariate analysis

“…UPGMA method was demonstrated to generate the highest coefficient values for the majority of matrices between morphological, colorimetric, and molecular data, implying that these matrices and distances are well represented as dendrograms. In line with the current study’s findings compared to other methods, the UPGMA clustering approach was shown to provide significant correlation coefficients for genetic diversity investigations in safflower ( Houmanat et al., 2021 ), yam ( Granato et al., 2018 ), sweet potato ( Paliwal et al., 2022 ), and gladiolus ( Singh et al., 2018 ).…”

“…Although molecular markers have been extensively used to evaluate species variety, extremely low or insignificant correlations have been observed among dissimilarity matrices generated using both phenotypic and molecular data ( Gupta et al., 2018 ). As a result, if the non-overlapping data is derived from phenotypic and genotypic divergence matrix, combining them may offer a full picture of a population’s variety ( Houmanat et al., 2021 ). Multivariate analysis approaches (HCA or PCA), are commonly used to precisely classify various plants based on their agro-morphological, molecular, chemical composition, or bioactivities that are regularly compared with correlation coefficients ( Granato et al., 2018 ).…”