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[email protected] or [email protected] Makerere Deviations from Hardy-Weinberg equilibrium, HWE, for each locus were examined using Fisher. [email protected] Understand that Hardy-Weinberg Equilibrium is a null hypothesis (there will be no change in allele or Date Posted: 3/17/ hardy-weinberg. Art of Trolling · Favorite By Unknown. Repost. Create a Site - ; Vote; -. Recaption · Comments · hardy-weinberg · equation · math.

Epidemiological data showed a reduction in vitamin D deficiency in a London Bangladeshi population at risk for T2DM compared with subjects not at risk [ 5 ]. Vitamin D primarily known to be involved in phospho-calcium homeostasis also regulates growth and differentiation of diverse types of cells through specific receptor [ 6 ]. In the nonobese diabetic mouse model for insulin-dependent diabetes mellitus, vitamin D is necessary for normal insulin release and maintenance of glucose tolerance [ 9 ].

Genetic alterations of the VDR gene may lead to defects in gene activation or changes in the protein structure of the VDR, both of which could affect the cellular functions of vitamin D. Genotyping and TaqI polymorphism The genomic DNA was isolated from peripheral leukocytes obtained from anticoagulant whole blood. Genotyping for TaqI rs was performed with the following primers: Digestion with Taq1 yields 3 genotypes: Homozygote tt showed three bands, The value for the T allele frequency in T2DM patients was These finding suggest a protective role for the t allele in contrast to the role of T allele which seems to be a predisposing factor to T2DM in the Iraqi population.

Results of this work is inconsistent with Errouagul [ 14 ] who observed that the tt allele was associated with T2DM in Morocco population. On the other hand the Hardy-Weinberg Equilibrium HWE equation analysis is performed by calculating the allele frequencies and the resulting expected frequencies of the genotype based on these.

If the observed frequencies of genotype are close to the expected genotype frequencies calculated from the observed allele frequencies, then the population is in Hardy—Weinberg Equilibrium and allele combinations are likely to be independent of one another.

Information about genetic diversity and relationships among sesame landraces is vital for crop improvement. Information on the use of molecular markers for the characterisation of genetic diversity in sesame is limited. SSRs are clusters of short tandemly repeated nucleotide bases and have proved the most valuable polymerase chain reaction PCR based DNA markers in genetic diversity analysis of many crop species.

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They are characterised by high variability, co-dominant nature, great abundance and even distribution throughout genomes. For SSRs, each location in a chromosome that contains core repeats may have a different number of copies of the repeat.

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Polymorphism is based on the number of tandem repeats and, therefore, the length of PCR products Dreisigacker et al. The development of SSRs markers could be based on the screening of genomic DNA libraries specifically constructed for the discovery of repeated sequences in the genome. Ten SSR markers have been developed Dixit et al. However, they have not yet been used to assess the genetic diversity of different sesame landraces. The objective of this work was, therefore, to study the genetic diversity of cultivated sesame in Ethiopia based on SSR markers.

Seeds of 47 landraces of sesame collected from different sites Fig. Young leaves leaves free of disease infestation were collected and dried in filter paper bags on the silica gel for 48 hours. DNA extraction, primer selection and polymerase chain reaction.

A total of 13 SSR markers were selected for this study. Ten sesame SSR markers were selected from literature Dixit et al. The forward primers of each of these 13 primers were labeled with fluorescent dyes, namely tetrachlorocarboxyfluorescien TET; green colourHexachlorocarboxyfluorescein HEX; black colour or 6-carboxyflourescein 6-FAM; blue at the 5' end of the primer.

The 10 SSRs primers that produce amplified products are listed in Table 2. The PCR reaction was carried out under the same conditions for all the primers except for the annealing temperatures. Individual PCR products were pooled markers run-1 according to their fluorescent label to permit discrimination of the individual markers after electrophoretic separation.

A 96 well PCR plate format and transfers using eight channel pipettes were used throughout. The final cocktail of the samples contained 0. The genetic diversity of each SSR marker was measured in terms of number of alleles per locus, allele frequencies, observed heterozygosity Ho and Expected heterozygosity He also known as Nei's average gene diversity using the "Population Genetics'' PopGene software version of 1.

The polymorphic information content PIC for each marker was determined as described by Anderson et al. Where p2i is the frequency of ith allele. Cluster analysis was performed by using MRD estimate values and dendrogram constructed to visualise the relationship among the 50 population based on the Neighbour-Joining method using the software package "Molecular Evolutionary Genetics Analysis'' MEGA, Kumar et al.

The fixation index Fst which characterises the partitioning of diversity between and within population was calculated as described by Wright as follows: Where HT is the expected heterozygosity in the total population and Hs the mean expected heterozygosity within subpopulations. Allele frequencies of SSR loci were used to calculate mean expected heterozygosity within sub populations Hs and expected heterozygosity in the total populations HT.

The outcrossing rate was estimated based on the equilibrium inbreeding coefficient Fe under partial selfing: For estimating the outcrossing rate, He was replaced by the observed heterozygosity, and Hr replaced by Nei's average gene diversity, which equals the expected heterozygosity assuming Hardy-Weinberg Equilibrium.

The resulting estimate is designated outcrossing at inbreeding equilibrium te. Ten additional SSRs were selected from literature. Two markers GBssr-sa and GBssr-sa from literature and one makers developed from publicly available ESTs for sesame Si-ssr failed to produce amplification products and were discarded from the study.

All SSR markers showed polymorphism. Overall, alleles were detected using the 10 SSR markers Table 3. A wide range of fragment sizes was observed from bp to bp Table 3. The SSR markers in our study showed a higher number of alleles than previous analysis of the diversity of sesame accessions conducted with SSR markers Dixit et al.

This was probably because Ethiopia is considered the origin and centre of diversity of cultivated sesame Seegeler, This is further shown by the observed heterozygosity Ho that ranged from 0.

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The higher heterozygosity indicates a higher outcrossing rate of Ethiopian sesame populations, which seemed to be line mixtures rather than pure lines. Genetic diversity measurements of the 50 sesame populations are presented in Table 4. The average numbers of alleles Na per locus were 2. The number of alleles ranged from 1. The heterozygosity for landraces ranged from 0.

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Landrace EW showed the highest heterozygosity of 0. High average heterozygosity was observed both in the landraces 0.

The average of polymorphic loci was Among the 50 lines evaluated, landrace EW was the most diverse with Nei's average gene diversity of 0. The landraces were more diverse than the cultivars. Nei's average gene diversity for landraces was 0.

However, the differences in all genetic diversity indices between landraces and cultivars was not statistically significant P Genetic differentiation and outcrossing. The higher value of Fst in landraces was unexpected as usually greater differentiation is expected between more advanced materials or cultivars Hamrick and Godt, The small sample size of 5 individuals used in our study could be one explanations for this result.

However, the result may also illustrate that the cultivars were not at all pure lines but still diverse line mixtures. Outcrossing is a very important aspect to be considered in order to establish strategies for plant breeding or landrace management and conservation Rheenen, In this study, we used a rough estimate of outcrossing from observed heterozygosity and Nei's average gene diversity.

Different authors reported contradicting results concerning the outcrossing the percentage of sesame Khidir, ; Rheenen, We also observed a wide range of outcrossing in the landraces 9. This indicated a mixed mating system. The observed heterozygosity exceeded that expected under Hardy-Weinberg Equilibrium in some cases Table 4. According to Lowe et al. Overall, 3 clusters were formed. Cluster I comprised of 12 landraces, 1 each from Gambella and Benishangul-Gumuz regions and 10 of them originated from Amara and Tigray two major sesame growing regions.

The two regions are found in the northern part of the country. Amara and Tigray regions are close to each other Anonymous,have similar ecological conditions and exchange of seed material expected from farmer to farmer.

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Six out of eight landraces from Tigray region were clustered together in Cluster I, indicating the lower genetic diversity of the landraces from this region. The long tradition of sesame farming in this region might have lead to the selection of the landraces by local farmers Anonymous,that might have caused lower genetic diversity within sesame landraces from Tigray.

Within the Cluster I, two landraces Hirhir and GAappeared to be distinct from all others and can be considered as a separate group. The first subcluster I comprised of 7 landraces and the rest 6 landraces in the second II subcluster.

Cluster III comprised of 22 landraces, 9 of them collected from Amara region, 7 from Oromia region, 3 from Afar region, 2 from Benishangul-Gumuz region, 1 from Gambella region. In this Cluster there was also 1 cultivar. The first I subcluster comprised of 16 landraces and 1 cultivar and the remaining 6 landraces were in the II subgroups.

All landraces collected from Oromia region excluding landrace Acc were grouped in subcluster I of Cluster III, which indicated the close genetic relatedness of landraces from the Oromia region.

Landraces from Afar region were grouped in Clusters II and III, which indicated the existence of genetic variation among the landraces. The 18 landraces collected from Amara region were distributed over all three different clusters.