Browsing by Subject "Population genetics"

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European population genomic differentiation and dispersal pattern of the invasive beetle Anoplophora glabripennis
(2023) Häussermann, Iris Hanna; Hasselmann, Martin
Anthropogenic activities (e.g. homogenized habitats, trade) are the main factors to facilitate the increasing rates of invasive alien species. In this study, the invasion of the Asian long-horned beetle (Anoplophora glabripennis) was examined. Its native distribution is eastern Asia (China, Korean peninsula), but by extensive trade, this beetle was introduced via wood packing materials to North-America (1996) and Europe (2001). ALB attacks healthy broadleaved trees (e.g. Acer spp., Salix spp., Populus spp.), which can become lethal due to larval feeding. This study aims to detect genetic differences and kinship between the European infestation sites in Germany, Switzerland and Italy, from which the introduction and dispersal patterns can be deviated. Therefore, mitochondrial (mt) DNA-markers of the Cytochrome oxidase subunits I and II genes (COI and II) were used (ch. A and B, Sanger sequencing), as well as genome wide single nucleotide polymorphisms (SNPs), which were obtained by a Genotype-by-sequencing (GBS) approach (ch. C, Illumina sequencing). The results of this population genomic study of invasive European ALB populations showed very complex introduction patterns into Europe including multiple independent introductions characterized by the high population structure between the European infestation sites and some cases of human mediated secondary dispersal.
Optimizing the prediction of genotypic values accounting for spatial trend andpopulation structure
(2010) Müller, Bettina Ulrike; Piepho, Hans-Peter
Different effects, like the design of the field trial, agricultural practice, competition between neighboured plots, climate as well as the spatial trend, have an influence on the non-genotypic variation of the genotype. This effects influence the prediction of the genotypic value by the non-genotypic variation. The error, which results from the influence of the non-genotypic variation, can be separated from the phenotypic value by field design and statistical models. The integration of different information, like spatial trend or marker, can lead to an improved prediction of genotypic values. The present work consists of four studies from the area of plant breeding and crop science, in which the prediction of the genotypic values was optimized with inclusion of the above mentioned aspects. Goals of the work were: (1) to compare the different spatial models and to find one model, which is applicable as routine in plant breeding analysis, (2) to optimize the analysis of unreplicated trials of plant breeding experiments by improving the allocation of replicated check genotypes, (3) to improve the analysis of intercropping experiments by using spatial models and to detect the neighbour effect between the different cultivars, and (4) to optimize the calculation of the genome-wide error rate in association mapping experiments by using an approach which regards the population structure. Different spatial models and a baseline model, which reflects the randomization of the field trial, were compared in three of the four studies. In one study the models were compared on basis of different efficiency criteria with the goal to find a model, which is applicable as routine in plant breeding experiments. In the second study the different spatial models and the baseline model were compared on unreplicated trials, which are used in the early generation of the plant breeding process. Adjacent to the comparison of the models in this study different designs were compared with the goal to see if a non-systematic allocation of check genotypes is more preferable than a systematic allocation of check genotypes. In the third study these different models were tested for intercropping experiments. In this study it should be tested, if an improvement is expectable for these non randomized or restricted randomized trials by using a spatial analysis. The results of the three studies are that no spatial model could be found, which is preferable over all other spatial models. In a lot of cases the baseline model, which regards only the randomization, but no spatial trend, was better than the spatial models, also for the restricted or non-randomized intercropping trials. In all three studies the basic principle was followed to start first with the baseline model, which is based on the randomization theory, and then to extend it by spatial trend, if the model fit can be improved. In the second study the systematic and non-systematic allocation of check plots in unreplicated trials were compared to solve the question if a non-systematic allocation leads to more efficient estimates of genotypes as the systematic allocation. The non-systematic allocation of check plots led to an unbiased estimation in three of four uniformity trials. As well as in the third study an analysis was done, if the border plots of the different cultivars are influenced by the neighboured cultivar and if there are significant differences to the inner plot. The position of the cultivars, border plot or inner plot, had a significant influence of the yield. If maize was cultivated adjacent to pea, the yield of the border plot of maize was much higher than the inner plot of maize. When wheat was cultivated behind maize, there were no significant differences in the yield, if the plot was a border plot or inner plot. In addition to optimizing the field design for unreplicated trials and the extension of the models by spatial trend the marker information was integrated in a fourth study. An approach was proposed in this study, which calculates the genome wide error for association mapping experiments and accounts for the population structure. Advantages of this approach in contrast to previously published approaches are that the approach on the one hand is not too conservative and on the other hand accounts the population structure. The adherence of the genome wide error rate was tested on three datasets, which were provided by different plant breeding companies. The results of these studies, which were obtained in this thesis, show that by the different extensions, like integration of spatial trend and marker information, and modifications of the field design, an improved prediction of the genotypic values can be achieved.
Population genomics of herbicide resistance in Alopecurus myosuroides
(2022) Kersten, Sonja; Schmid, Karl J.
Over the past 50 years, herbicides have often replaced mechanical and manual human weed control, thus representing a major factor in yield productivity in modern agriculture. Herbicide applications, however, exert strong selection pressures on weeds. As a consequence, these species have developed herbicide resistance through adaptive, beneficial alleles that increase in number to ensure the persistence of the populations, a phenomenon known as evolutionary rescue. A major research question is whether herbicide resistance adaptation is more likely to arise from standing genetic variation that was present before the onset of herbicide selection or from de novo mutations that arose after herbicide selection began. To address this question, I focused on target-site resistance (TSR) point mutations, which cause a lower binding affinity to the target protein of the respective herbicides. I first investigated the diversity of TSR haplotypes in populations of the grass species Alopecurus myosuroides (common name: blackgrass), and compared it with the TSR diversity outcome of simulated populations under both evolutionary scenarios. I first conducted a population genetics study of A. myosuroides, which is the most problematic weed in winter cereals across the European continent due to rapid resistance evolution. To obtain genome-wide polymorphic markers, I adapted a restriction site-associated DNA sequencing protocol to this species. I began by analyzing the diversity and population structure in a smaller local South German collection. The fact that I could differentiate populations on a local scale motivated me to extend the study to a European-wide collection, in which I found clear population structure, albeit with low differentiation and some evidence for admixture across Europe. In addition, I generated highly accurate long-read amplicons from single individuals of two loci, ACETYL-COA CARBOXYLASE (ACCase) and ACETOLACTATE SYNTHASE (ALS), which are the targets of the two main herbicide modes of action used in European cereal crops. I obtained completely phased haplotype information, supporting the analysis of haplotype diversity on a population level. I found a remarkable diversity of beneficial TSR mutations at the field level arising from multiple haplotypes of independent origin, so called soft sweeps. I used this information to perform forward simulations to investigate the evolutionary origin of these mutations. I found evidence that a majority of resistance mutations originated from standing genetic variation. While this at first may appear surprising, it is consistent with very large census and effective population sizes in blackgrass. Since long-read amplicon sequencing of single individuals could be costly and time consuming, I extended the analysis to pools of 150 to 200 individuals from Germany, Belgium, France, the Netherlands and the United Kingdom. By combining the power of a more stringent accuracy criterion in our long-reads and a novel clustering software (PacBio amplicon analysis), I was able to preserve individual haplotype information in pooled samples. Furthermore, in a proof of concept experiment, I was able to recover in our pools most haplotypes previously sequenced in individuals. The amplicon study provides a versatile workflow that can be easily adapted to any gene of interest in different species. In conclusion, I found that many A. myosuroides populations likely already have the genetic prerequisites not only for rapid evolution of resistance to currently used herbicides, but also to herbicides that have not yet been brought to market.