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dc.contributor.authorMasha, Justus Charo
dc.date.accessioned2020-10-02T10:22:44Z
dc.date.available2020-10-02T10:22:44Z
dc.date.issued2017-09
dc.identifier.otherEVALUATION OF COCONUT (Cocos nucifera L.) GENETIC DIVERSITY FOR GERMPLASM CONSERVATION AND UTILIZATION IN COASTAL LOWLANDS OF KENYA JUSTUS CHARO
dc.identifier.urihttp://elibrary.pu.ac.ke/handle/123456789/717
dc.descriptionThe genetic diversity of coconut (Cocos nucifera L), the most important cash crop along the Kenyan Coast, has not been fully characterised, which limits formulation of sound conservation strategies and sustainable utilization of these genetic resources. Molecular marker technology, such as simple/short sequence repeats SSRs, has been used in other International Coconut Genetic Resources Network (COGENT) countries to characterize coconut genetic resources (CGRs). In a previous study by Oyoo et al (2015), 48 coconut genotypes were sampled and characterized morphologically for stem, crown, leaf and fruit morphology. The genotypes were obtained from four coastal lowlands counties of Kenya i.e. Kwale, Kilifi, Tana River and Lamu which are the coconut diversity hotspots. They comprised 37 East African tall, 3 East African dwarf and 8 hybrid varieties. In this study a genetic diversity assessment was undertaken of the same 48 coconut varieties using a set of 30 coconut specific SSR markers. DNA was extracted from leaf samples using a modified CTAB method described by Doyle and Doyle (1987) followed by polymerase chain reaction (PCR) amplification using SSR markers. SSR analysis was performed using GeneMapper while data analysis was done with PowerMarker and DARwin softwares. Seventeen markers tested, were monomorphic and not useful and were excluded whilst the 13 remaining markers were polymorphic and were selected for final analysis. These markers detected a total of 68 alleles ranging from 2 to 11 per locus with a mean of 5.23. Polymorphic information content (PIC) ranged from 0.36 to 0.79 with a mean of 0.589. Major allele frequency ranged from 0.21 to 0.71 with an average of 0.47. Heterozygosity ranged from 0.00 to 0.02 with an average of 0.01. Analysis of molecular variance (AMOVA) indicated that variation among individuals within populations was 101 %, among populations was -1.59 % while within all 48 genotypes was 0.49 %. The negative percentage indicated absence of genetic structure i.e genotypes among the populations were more related than within populations. Cluster vi analysis indicated that the 48 coconut individuals were grouped into three clusters. The results of the study showed that the markers used could discern the 48 coconut varieties and that there was substantial diversity among coconuts along the Kenyan coast. However the results did not clearly group coconut genotypes according to place where they were sampled or their heights (tall, semi-talls and dwarf). This indicated that future studies should aim to use more markers that can be associated with important traits of coconut.en_US
dc.description.abstractThe genetic diversity of coconut (Cocos nucifera L), the most important cash crop along the Kenyan Coast, has not been fully characterised, which limits formulation of sound conservation strategies and sustainable utilization of these genetic resources. Molecular marker technology, such as simple/short sequence repeats SSRs, has been used in other International Coconut Genetic Resources Network (COGENT) countries to characterize coconut genetic resources (CGRs). In a previous study by Oyoo et al (2015), 48 coconut genotypes were sampled and characterized morphologically for stem, crown, leaf and fruit morphology. The genotypes were obtained from four coastal lowlands counties of Kenya i.e. Kwale, Kilifi, Tana River and Lamu which are the coconut diversity hotspots. They comprised 37 East African tall, 3 East African dwarf and 8 hybrid varieties. In this study a genetic diversity assessment was undertaken of the same 48 coconut varieties using a set of 30 coconut specific SSR markers. DNA was extracted from leaf samples using a modified CTAB method described by Doyle and Doyle (1987) followed by polymerase chain reaction (PCR) amplification using SSR markers. SSR analysis was performed using GeneMapper while data analysis was done with PowerMarker and DARwin softwares. Seventeen markers tested, were monomorphic and not useful and were excluded whilst the 13 remaining markers were polymorphic and were selected for final analysis. These markers detected a total of 68 alleles ranging from 2 to 11 per locus with a mean of 5.23. Polymorphic information content (PIC) ranged from 0.36 to 0.79 with a mean of 0.589. Major allele frequency ranged from 0.21 to 0.71 with an average of 0.47. Heterozygosity ranged from 0.00 to 0.02 with an average of 0.01. Analysis of molecular variance (AMOVA) indicated that variation among individuals within populations was 101 %, among populations was -1.59 % while within all 48 genotypes was 0.49 %. The negative percentage indicated absence of genetic structure i.e genotypes among the populations were more related than within populations. Cluster vi analysis indicated that the 48 coconut individuals were grouped into three clusters. The results of the study showed that the markers used could discern the 48 coconut varieties and that there was substantial diversity among coconuts along the Kenyan coast. However the results did not clearly group coconut genotypes according to place where they were sampled or their heights (tall, semi-talls and dwarf). This indicated that future studies should aim to use more markers that can be associated with important traits of coconut.en_US
dc.description.sponsorshipPwani Universityen_US
dc.language.isoenen_US
dc.publisherPwani Unversityen_US
dc.subjectCoconuts, Germplasmen_US
dc.titleEVALUATION OF COCONUT (Cocos nucifera L.) GENETIC DIVERSITY FOR GERMPLASM CONSERVATION AND UTILIZATION IN COASTAL LOWLANDS OF KENYA JUSTUS CHAROen_US
dc.typeThesisen_US


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