Browsing by Subject "Genom"

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Entwicklung, Charakterisierung und Kartierung von Mikrosatellitenmarkern bei der Zuckerrübe (Beta vulgaris L.)
(2001) Dörnte, Jost; Geiger, Hartwig H.
Simple sequence repeats (SSRs) or microsatellites were isolated from a sugarbeet (Beta vulgaris L.) genomic phage library. The size-fractionated library was screened for the occurrence of the motifes (GA)n, (GT)n, (TGA)n, (AGA)n and (CCG)n. The motifes (GA)n and (GT)n were found to occur most frequently in the sugarbeet genome (every 225 kb). In contrast, the trimer motifes were half as frequent (every 527 kb). A total of 217 microsatellite sequences were found in the sequenced clones. Most of the repeats were imperfect and/or compound. Sequence comparison revealed that 23% of the clones wich containing the (GT)n motif are variants of a previously described satellite DNA (SCHMIDT et al. 1991). Of 102 primer pairs tested on sugarbeet DNA, 71 gave a single product in the expected size. On 23 sugarbeet samples 64 of the 71 SSR-markers reveald length polymorphisms. The number of detected alleles per marker ranged from 2 to 13 (average 4,9) and the PIC-values ranged from 0,17 to 0,86 (average 0,58). A cluster analysis of the 23 samples confirms the pedigree data. The developed SSR markers were compared with RFLP and AFLP markers. Therefore nine sugarbeet lines, each with five single plants per line, were analysed. The SSR analyse shows the lowest similarity between the nine lines. The similarity inside the lines revealed no differences between the marker assays. Thirtythree SSR markers were genetically mapped into the RFLP framework maps of 2 F2-populations. The markers are randomly distributed over eight linkage groups of sugar beet.
Extensions of genomic prediction methods and approaches for plant breeding
(2013) Technow, Frank; Melchinger, Albrecht E.
Marker assisted selection (MAS) was a first attempt to exploit molecular marker information for selection purposes in plant breeding. The MAS approach rested on the identification of quantitative trait loci (QTL). Because of inherent shortcomings of this approach, MAS failed as a tool for improving polygenic traits, in most instances. By shifting focus from QTL identification to prediction of genetic values, a novel approach called 'genomic selection', originally suggested for breeding of dairy cattle, presents a solution to the shortcomings of MAS. In genomic selection, a training population of phenotyped and genotyped individuals is used for building the prediction model. This model uses the whole marker information simultaneously, without a preceding QTL identification step. Genetic values of selection candidates, which are only genotyped, are then predicted based on that model. Finally, the candidates are selected according their predicted genetic values. Because of its success, genomic selection completely revolutionized dairy cattle breeding. It is now on the verge of revolutionizing plant breeding, too. However, several features set apart plant breeding programs from dairy cattle breeding. Thus, the methodology has to be extended to cover typical scenarios in plant breeding. Providing such extensions to important aspects of plant breeding are the main objectives of this thesis. Single-cross hybrids are the predominant type of cultivar in maize and many other crops. Prediction of hybrid performance is of tremendous importance for identification of superior hybrids. Using genomic prediction approaches for this purpose is therefore of great interest to breeders. The conventional genomic prediction models estimate a single additive effect per marker. This was not appropriate for prediction of hybrid performance because of two reasons. (1) The parental inbred lines of single-cross hybrids are usually taken from genetically very distant germplasm groups. For example, in hybrid maize breeding in Central Europe, these are the Dent and Flint heterotic groups, separated for more than 500 years. Because of the strong divergence between the heterotic groups, it seemed necessary to estimate heterotic group specific marker effects. (2) Dominance effects are an important component of hybrid performance. They had to be included into the prediction models to capture the genetic variance between hybrids maximally. The use of different heterotic groups in hybrid breeding requires parallel breeding programs for inbred line development in each heterotic group. Increasing the training population size with lines from the opposite heterotic group was not attempted previously. Thus, a further objective of this thesis was to investigate whether an increase in the accuracy of genomic prediction can be achieved by using combined training sets. Important traits in plant breeding are characterized by binomially distributed phenotypes. Examples are germination rate, fertility rates, haploid induction rate and spontaneous chromosome doubling rate. No genomic prediction methods for such traits were available. Therefore, another objective was to provide methodological extensions for such traits. We found that incorporation of dominance effects for genomic prediction of maize hybrid performance led to considerable gains in prediction accuracy when the variance attributable to dominance effects was substantial compared to additive genetic variance. Estimation of marker effects specific to the Dent and Flint heterotic group was of less importance, at least not under the high marker densities available today. The main reason for this was the surprisingly high linkage phase consistency between Dent and Flint heterotic groups. Furthermore, combining individuals from different heterotic groups (Flint and Dent) into a single training population can result in considerable increases in prediction accuracy. Our extensions of the prediction methods to binomially distributed data yielded considerably higher prediction accuracies than approximate Gaussian methods. In conclusion, the developed extensions of prediction methods (to hybrid prediction and binomially distributed data) and approaches (training populations combining heterotic groups) can lead to considerable, cost free gains in prediction accuracy. They are therefore valuable tools for exploiting the full potential of genomic selection in plant breeding.
Genomic selection in synthetic populations
(2017) Müller, Dominik; Melchinger, Albrecht E.
The foundation of genomic selection has been laid at the beginning of this century. Since then, it has developed into a very active field of research. Although it has originally been developed in dairy cattle breeding, it rapidly attracted the attention of the plant breeding community and has, by now (2017), developed into an integral component of the breeding armamentarium of international companies. Despite its practical success, there are numerous open questions that are highly important to plant breeders. The recent development of large-scale and cost-efficient genotyping platforms was the prerequisite for the rise of genomic selection. Its functional principle is based on information shared between individuals. Genetic similarities between individuals are assessed by the use of genomic fingerprints. These similarities provide information beyond mere family relationships and allow for pooling information from phenotypic data. In practice, first a training set of phenotyped individuals has to be established and is then used to calibrate a statistical model. The model is then used to derive predictions of the genomic values for individuals lacking phenotypic information. Using these predictions can save time by accelerating the breeding program and cost by reducing resources spent for phenotyping. A large body of literature has been devoted to investigate the accuracy of genomic selection for unphenotyped individuals. However, training individuals are themselves often times selection candidates in plant breeding, and there is no conceptual obstacle to apply genomic selection to them, making use of information obtained via marker-based similarities. It is therefore also highly important to assess prediction accuracy and possibilities for its improvement in the training set. Our results demonstrated that it is possible to increase accuracy in the training set by shrinkage estimation of marker-based relationships to reduce the associated noise. The success of this approach depends on the marker density and the population structure. The potential is largest for broad-based populations and under a low marker density. Synthetic populations are produced by intermating a small number of parental components, and they have played an important role in the history of plant breeding for improving germplasm pools through recurrent selection as well as for actual varieties and research on quantitative genetics. The properties of genomic selection have so far not been assessed in synthetics. Moreover, synthetics are an ideal population type to assess the relative importance of three factors by which markers provide information about the state of alleles at QTL, namely (i) pedigree relationships, (ii) co-segregation and (ii) LD in the source germplasm. Our results show that the number of parents is a crucial factor for prediction accuracy. For a very small number of parents, prediction accuracy in a single cycle is highest and mainly determined by co-segregation between markers and QTL, whereas prediction accuracy is reduced for a larger number of parents, where the main source of information is LD within the source germplasm of the parents. Across multiple selection cycles, information from pedigree relationships rapidly vanishes, while co-segregation and ancestral LD are a stable source of information. Long-term genetic gain of genomic selection in synthetics is relatively unaffected by the number of parents, because information from co-segregation and from ancestral LD compensate for each other. Altogether, our results provide an important contribution to a better understanding of the factors underlying genomic selection, and in which cases it works and what information contributes to prediction accuracy.
Novel bacterial species from the chicken gastrointestinal tract and their functional diversity
(2023) Rios Galicia, Bibiana; Seifert, Jana
The digestive system of chicken presents different physicochemical conditions along the gastrointestinal tract (GIT), shaping an individual microbial profile along sections with different metabolic capacities and divergence on the adaptations to the environment. Efforts to obtain cultivable bacteria originating from the upper region of chicken GIT enrich the reference genome database and provide information about the site- specific adaptations of bacteria colonizing such GIT sections allowing to understand the metabolic profile and adaptive strategies to the environment. However, the lack of sufficient reference genomes limits the interpretation of sequencing data and restrain the study of complex functions. In this study, 43 strains obtained from crop, jejunum and ileum of chicken were isolated, characterised and genome analysed to observe their metabolic profiles, adaptive strategies and to serve as future references. Eight isolates represent new species that colonise the upper gut intestinal tract and present consistent adaptations that enable us to predict their ecological role, expanding our knowledge on the adaptative functions. Strains of Limosilactobacillus were found to be more abundant in the crop, while Ligilactobacillus dominated the ileal digesta. Isolates from crop encode a high number of glycosidases specialised in complex polysaccharides compared to strains isolated from jejunum and ileum. While isolates from jejunum and ileum encode a higher number of genes that interact with the host such as collagenases and hyaluronidases, indicating preferential persistence and adaptations along the GIT. These results represent the first repository of bacteria obtained from the crop and small intestine of chicken using culturomics, improving the potential handling of chicken microbiome with biotechnological applications