3 resultados para Genetic markers

em Universitätsbibliothek Kassel, Universität Kassel, Germany


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Mobile genetische Elementen wie Transposons wurden in unbelasteten Böden nachgewiesen. Hierzu wurden unterschiedliche Ansätze gewählt: Verschiedene, unbelastete Böden wurden mittels PCR auf das Vorhandensein von Markergenen, in diesem Fall Transposasen vom Typ Tn3, Tn21 und Tn501, hin untersucht. Hierzu wurde ein System entwickelt, welches es ermöglichte die Gesamt-DNA aus verschiedensten Böden mit einem System einfach und reproduzierbar zu extrahieren und anschließend mittels PCR zu untersuchen. Die mittlere Nachweisgrenze dieses Systems lag bei 9 x 10 *3 Templates / g Boden. Ein paralleler Ansatz erfolgte, indem aus den gleichen, unbelasteten Böden Bakterien mittels Selektivmedien isoliert wurden. Diese Isolate wurden anschließend auf genetische Marker hin untersucht. Transposons, bzw. Transposasen konnten in den unbelasteten Böden in weitaus geringerer Zahl als aus belasteten Böden bekannt nachgewiesen werden. Jedoch verhielten sich die unterschiedlichen Elemente in der Verteilung wie aus belasteten Böden bekannt. Am häufigsten wurde Tn21 dann Tn501 nachgewiesen. Tn3, nach dem auch gescreent wurde, konnte nicht nachgewiesen werden. Anschließend wurden diese Böden mittels Bodensäulen unter Laborbedingungen auf die Übertragung von potentiell transponierbaren Elementen aus der autochthonen Flora hin untersucht. Mittels dieses Experimentes konnte kein transponierbares Element nachgewiesen werden. Weiterhin wurden vorhandene Boden-Bakterienkollektive auf das Vorhandensein von Transposons mittels Gensondentechnik und PCR auf Transposasen hin gescreent. Auch hier konnten wiederum Signale zu Tn21, Tn501 und in diesem Falle auch Tn3 erhalten werden. Einige dieser Isolate wurden mittels Southern-Blot und Sequenzierung näher charakterisiert. Bei den Sequenzvergleichen einer so erhaltenen 2257 bp langen Sequenz wurde diese als Transposase der Tn21-Familie mit großer Homologie zur Transposase von Tn5060 bestimmt.

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Background: The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped. ----- Methods: Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets. ----- Results: Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams. ----- Conclusions: Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.

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• Premise of the study: Polymorphic microsatellite markers were developed in Vinca minor (Apocynaceae) to evaluate the level of clonality, population structure, and genetic diversity of the species within its native and introduced range. • Methods and Results: A total of 1371 microsatellites were found in 43,565 reads from 454 pyrosequencing of genomic V. minor DNA. Additional microsatellite loci were mined from publicly available cDNA sequences. After several rounds of screening, 18 primer pairs flanking di-, tri-, or tetranucleotide repeats were identified that revealed high levels of genetic diversity in two native Italian populations, with two to 11 alleles per locus. Clonal growth predominated in two populations from the introduced range in Germany. Five loci successfully cross-amplified in three additional Vinca species. • Conclusions: The novel polymorphic microsatellite markers are promising tools for studying clonality and population genetics of V. minor and for assessing the historical origin of Central European populations.