Browsing by Subject "Auslese"

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Genome-wide prediction of testcross performance and phenotypic stability for important agronomic and quality traits in elite hybrid rye (Secale cereale L.)
(2016) Wang, Yu; Miedaner, Thomas
Genomic selection offers a greater potential for improving complex, quantitative traits in winter rye than marker-assisted selection. Prediction accuracies for grain yield for unrelated test populations have, however, to be improved. Nevertheless, they are already favorable for selecting phenotypic stability of quality traits.
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.
Mulitdimensionale Informationen im Kontext wertorientierte Unternehmensführung von Versicherern
(2017) Trautinger, Max-Josef; Schiller, Jörg
The cumulative dissertation analyzes how multi-dimensional information influences customer behavior and how insurers can use that information efficiently as a key factor in customer interaction. Information per se is multilayered and can be multidimensional. Multidimensional information in this context is understood as known or generally available information about customers, which should help in the interaction between the insurer and the customer to fulfill the customer expectations. As an additional research question, this dissertation analyzes how insurers can use the information economically profitably and generate added value. Provided that information can be used effectively value orientation can be generated. For example, having data in a pure form does not add value to insurers. If this information can at least be used to satisfy customer expectations, it can be assumed that customers want to use offered services and are disposed to a higher willingness to pay. In three analyzes this question is taken up separately and discussed. Analysis 1: In a competitive insurance market, claims settlement is a central task of insurers. Customers indicate after an event of loss specific expectations and further the adept service is of customers point of view a ‘moment of truth’. Insurers may align their claims settlement and optimise it. This paper analyse which determinants influence the customer satisfaction. The hypotheses were shown in a model and discussed by the author. Also, the hypotheses evaluated on the basis of empirical data which is derived from a set of interviews by a german insurer. The results of the analysis show variables which can be influenced in order to improve the customer satisfaction. Analysis 2: Customer behavior is managed by customer satisfaction in two dimensions: Insurer can profit by a higher customer loyalty und in addition, by a sensitive price behavior of customers. The findings of moderating effects are mean considered and thus, customer satisfaction is a too strong indicator of economic success in established concepts. To manage an insurance company effective, it is a good advice to implement a model that is specific for each company. This model should respect the heterogeneous factors of influence due to customer satisfaction by multidimensional instruments. Hence, insurer may identify drivers of service and work with analysis of correlations to describe the coherence between customer satisfaction and economic success exactly. The alignment for customer satisfaction is worth for traditional insurance companies, but only, if customer satisfaction is understood as an economic valued management that is culturally based in the firm. Manager should account for this suggestion to follow a sustainable story in a saturated competitive environment. Analysis 3: In this analysis we analyze in a project selection effects in the German market for private complementary long-term care insurance contracts (CompLTCI) within a static and dynamic framework. Using data on more than 98,000 individuals from a German insurance company, we provide evidence that advantageous selection is dominating in this market, with respect to both the decision to buy a CompLTCI policy and the decision about the extent of CompLTCI coverage. We identify occupational status, residential location and the holding of further supplementary health insurance policies as unused observables contributing to selection effects in this market. Our results suggest that non-linearities in the relationship of potential sources of selection to insurance coverage and risk should be considered. A panel data analysis shows that an increase in health insurance payouts is positively correlated with the uptake of CompLTCI, while a decrease in those costs is positively associated with the lapse of CompLTCI. In addition, we find that people in financial distress and of lower socioeconomic status are more likely to let their CompLTCI policies lapse.
Optimum strategies to implement genomic selection in hybrid breeding
(2022) Marulanda Martinez, Jose Joaquin; Melchinger, Albrecht E.
To satisfy the rising demand for more agricultural production, a boost in the annual expected selection gain (ΔGa) of traits such as grain yield and especially yield stability has to be rapidly achieved. Hybrid breeding has contributed to a notable increment in performance for numerous allogamous species and has been proposed as a way to match the increased demand for autogamous cereals such as rice, wheat, and barley. An additional tool to increase the rate of annual selection gain is genomic selection (GS), a method to assess the merit of an individual by simultaneously accounting for the effects associated with hundreds to thousands of DNA markers. Successful integration of GS and hybrid breeding should go beyond the study of GS prediction accuracy and focus on the design of breeding strategies, for which GS maximizes ΔGa and optimizes the allocation of resources. The main goal of this thesis was to examine strategies for optimum implementation of GS in hybrid breeding with emphasis on estimation set design to perform GS within biparental populations and on the optimization of hybrid breeding strategies through model calculations. One strategy, GSrapid, with moderate nursery selection, one stage of GS, and one stage of phenotypic selection, reached the greatest ΔGa for single trait selection regardless of the budget, costs, variance components, and accuracy of genomic prediction. GSrapid was also the most efficient strategy for the simultaneous improvement of two traits regardless of the correlation between traits, selection index chosen, and economic weights assigned to each trait. The success of this strategy relies principally on the reduction of breeding cycle length and marginally on the increase in selection intensity. Moving from traditional breeding strategies based on phenotypic selection to strategies using GS for single trait improvement in hybrid breeding could lead not only to increments in ΔGa but also to large savings in the budget. The implementation of nursery selection in breeding strategies boosted the importance of efficient systems for inbred generation accompanied by improvements in the methods of hybrid seed production for experimental tests. When it comes to multiple trait improvement, the choice between optimum and base selection indices had minor impact on the net merit. However, considerable differences for ΔGa of single traits were observed when applying optimum or base indices if the variance components of the traits to be improved differed. The role of the economic weights assigned to each trait was determinant and small variations in the weights led to a remarkable genetic loss in one of the traits. The optimum design of estimation sets to perform GS within biparental populations should be based on phenotypic data, rather than molecular marker data. This finding poses major challenges for GS-based strategies aiming to select the best new inbreds within second cycle breeding populations, as breeding cycle length might not be reduced. Then, the ES design to optimize GS within biparental populations would have a defined application on the exploitation of within-family variation by increasing selection intensity in biparental populations with the largest potential of producing high-performing inbreds. Based on the results of this thesis, future challenges for the optimum implementation of GS in hybrid breeding strategies include (i) reductions in breeding cycle length and increments in selection intensity by refinements of DH technology or implementation of speed breeding, (ii) improvements in the methods for hybrid seed production, facilitating the reallocation of resources to the production of more candidates tested during the breeding cycle, and (iii) precise estimation of economic weights, reflecting the importance of the traits for breeding programs and farmers, and maximizing long term ΔGa for the most relevant traits.
Strategies for selecting high-yielding and broadly adapted maize hybrids for the target environment in Eastern and Southern Africa
(2012) Windhausen, Sandra Vanessa; Melchinger, Albrecht E.
Maize is a major food crop in Africa and primarily grown by small-holder farmers under rain-fed conditions with low fertilizer input. Projections of decreasing precipitation and increasing fertilizer prices accentuate the need to provide farmers with maize varieties tolerant to random abiotic stress, especially drought and N deficiency. Genetic improvement for the target environment in Eastern and Southern Africa can be achieved by: (i) direct selection of grain yield in random abiotic stress environments, (ii) indirect selection for a secondary trait or grain yield in optimal, low-N and/or managed stress environments, or (iii) index selection using information from all test environments. At present, the maize hybrid testing programs of the International Maize and Wheat Improvement Center (CIMMYT) select primarily for grain yield under managed stress and optimal environments and subdivide the target environment according to geographic and climatic differences. It is not known to what extend the current strategy contributes to selection gains. The same holds true for genomic prediction, a strategy that is not yet implemented into the CIMMYT maize breeding program but that may accelerate breeding progress and reduce cycle length by predicting genotype performance based on molecular markers. Regarding the different strategies mentioned for selecting high-yielding and broadly adapted maize hybrids, the breeder needs to decide which of them are most promising to increase genetic gains. Consequently, the objectives of my thesis were to (1) evaluate the potential of leaf and canopy spectral reflectance as novel secondary traits to predict grain yield across different environments, (2) estimate to what extent indirect selection in managed drought and low-N stress environments is predictive of grain yield in random abiotic stress environments, (3) investigate whether subdividing the target environment into climate, altitude, geographic, yield level or country subregions is likely to increase rates of genetic gain, and (4) evaluate the prospects of genomic prediction in the presence of population structure. The measurement of spectral reflectance (495 ? 1853 nm) of both leaves and canopy at anthesis and milk grain stage explained less than 40% of the genetic variation in grain yield after validation. Consequently, selection based on predicted grain yield is only suitable for pre-screening, while final yield evaluation will still be necessary. Nevertheless, the prospect of developing inexpensive and easy to handle devices that can provide, at anthesis, precise estimates of final grain yield warrants further research. Based on a retrospective analysis across 9 years, more than 600 trials and 448 maize hybrids, it was shown that maize hybrids were broadly adapted to climate, altitude, geographic and country subregions in Eastern and Southern Africa. Consequently, I recommend that the maize breeding programs of CIMMYT in the region should be consolidated. Within the consolidated breeding programs, genotypes should be selected for performance in low- and high yielding environments as the genotype-by-yield level interaction variance was high relative to the genetic variance and genetic correlations between low- and high-yielding environments were moderate. Genetic gains were maximized by index selection, considering the yield-level effect as fixed and appropriately weighting information from all trials. To allow better allocation of resources, locations with high occurrence of random abiotic stress need to be identified. Heritability in trials conducted at these locations may be increased by the use of row- and column designs and/or spatial adjustment. Furthermore, resources invested into managed drought trials should be maintained during early breeding stages but shifted to the conduct of low-N trials at later breeding stages. Investments in a larger number of low-N trials may increase selection gain, because performance under low-N and random abiotic stress was highly correlated and genotypes can be easily selected under different levels of soil N. Prospects are promising to accelerate breeding cycles by the use of genomic prediction. Based on two large data sets on the performance of eight breeding populations, it was shown that prediction accuracy resulted primarily from differences in mean performance of these populations. Genomic prediction may be implemented into the CIMMYT maize breeding program to predict the performance of lines from a diversity panel, segregating lines from the same or related crosses, and progenies from closed populations within a recurrent selection program. The breeding scenarios in which genomic prediction is most promising still need to be defined. Generally, the construction of larger training sets with strong relationship to the validation set and a detailed analysis of the population structure within the training and validation sets are required. In conclusion, combining index and genomic selection is the most promising strategy for providing high-yielding and broadly adapted maize genotypes for the target environments in Eastern and Southern Africa.
The development of phenotypic protocols and adjustment of experimental designs in Pelargonium zonale breeding
(2018) Molenaar, Heike; Piepho, Hans-Peter
Ornamental plant variety improvement is limited by current phenotyping approaches and the lack of use of experimental designs. Robust phenotypic data obtained from experiments laid out to best control local variation by blocking allow adequate statistical analysis and are crucial for any breeding purpose, including MAS. Often experiments consist of multiple phases like in P. zonale breeding, where in the first phase stock plants are cultivated to obtain the stem cutting count and in the second phase the stem cuttings are further assess for root formation. The first analyses of rooting experiments raised questions regarding options for improving the two-phase experimental layout, for example whether there is a disadvantage to using exactly the same design in both phases. The other question was, whether a design can be optimized across both phases, such that the MVD can be decreased. Instead of generating a separate layout for each phase. Moreover, optimal selection methods that maximize selection gain in P. zonale breeding based on available data collected from unreplicated trials and containing pedigree information were sought. This thesis was conducted to evaluate the benefits of using two-phase experimental designs and corresponding analysis in P. zonale for production related traits, for which it was necessary to establish phenotyping protocols. To optimize the rooting experiments with their two-phase nature, alternative approaches were explored involving two-phase design generation either in phase wise order or across phases. Furthermore, selection methods considering pedigreeinformation (family-index selection) or not (individual selection), were evaluated to enhance selection efficiency in P. zonale breeding. The benefits of using experimental designs in P. zonale breeding was shown by the simulated response to selection. Alternative designs were evaluated by the MVD obtained by the intrablock analysis and the joint inter-block-intra-block analysis. The efficiency of individual and family-index selection was evaluated in terms of heritability obtained from linear mixed models implementing the selection methods. Simulated response to selection varied greatly, depending on the genotypic variances of the breeding population and traits. However, by using efficient designs allowing adequate analysis, a varietal improvement of over 20% of stock plant reduction is possible for stem cutting count, root formation, branch count and flower count. The smallest MVD for alternative designs was most frequently obtained for designs generated across phases rather than for each phase separately, in particular when both phases of the design were separated with a single pseudolevel. Family-index selection was superior to individual selection in P. zonale indicating that the pedigree-based BLUP procedure can further enhance selection efficiency in productionrelated traits in P. zonale. The quantification of genotypic variation by phenotypic protocols and the optimized two-phase designs for estimating genotypic values were necessary and successful steps in laying the foundation for effective MAS. Phenotypic protocols effectively characterized the genetic material on an observational unit level, while the two-phase experimental designs enabled effective characterization on a genotype level by adjusting entry means using linear mixed models. The resulting adjusted entry means are the basis for future genotype phenotype association for MAS.