Abstract:
Morphological and genetic diversity are important components for cultivar development and
are a pre-requisite to cultivar improvement. The probability of producing unique genotypes
increases in proportion to the number of genes for which parents differ (genetic distance). The
objectives of this study were to determine the morphological and genetic diversity among
native blackberry (Rubus L. subgenus Rubus Watson) accessions in selected counties in Kenya
and their relationship with Plant Introductions (PIs) using morphological and SSR markers.
Eleven out of thirteen available blackberry SSR primer sets were used to screen 90 blackberry
accessions in this study. Molecular data were scored in binary fashion for SSR marker loci
amplified and were analysed using DARwin 6.0, PowerMarker 3.25 and GenAlEx 6.5
software. Each individual blackberry accession was nested within its county of collection and
morphological data were taken in-situ on all the accessions including erect, semi-erect and
trailing types. Morphological data were analysed using GENSTAT 15th Edition programme, SAS
ver. 9.1 (SAS Institute Inc., Cary, 2001) and R for statistical computing version 3.4.1 software. Both molecular and morphological data analysed detected considerable diversity within and among the blackberry accessions studied. Analysis of Molecular Variance (AMOVA) showed that much of the genetic diversity existed within the accessions (95%) with estimated genetic variation of 4.12. The expected heterozygosity (HE) of the blackberry accessions ranged from 0.48 to 0.89. Principal component analysis (PCA) conducted on morphological data generated 10 axes, out of which, 7 had a cumulative variation of 96.30%, with the first two axes having a discriminatory variance of 52.71 % sufficient to identify variables able to differentiate blackberry accessions in Kenya. Further, out of the 10 important morphological traits subjected to PCA, 8 were able to
differentiate the collected accessions and were considered as variables capable of
discriminating them on the basis of morphology. Molecular data cluster analysis using the
Jaccard’s similarity coefficient grouped the accessions into three classes; I, II and III consisting
of 31, 52 and 7 accessions, respectively, while a phylogenetic tree constructed for morphological
data, using the Gower’s coefficient, grouped the accessions into two classes; I and II consisting of 1 and 89 accessions, respectively. Both clusters were random and did not group the accessions
according to their geographical origin, indicating that the accessions found in Kenya are closely
related. This study revealed high levels of within genetic diversity in the blackberry genetic
resources studied which can be used in blackberry breeding programs.
