Browsing by Subject "Genomische Selektion"

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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 methods for rotational crossbreeding in local dairy cattle breeds
(2022) Stock, Joana; Bennewitz, Jörn
Local dairy breeds, such as German Angler, usually have small population sizes and thus a reduced genetic gain, compared to high-yielding breeds. Especially since genomic selection is widely used in the latter, the performance gap between local breeds and high-yielding breeds increased further, as it requires large reference populations in order to achieve accurate estimated breeding values. As a result, many farmers switched to high-yielding breeds. On the other hand, to increase the performance of local breeds the introgression of high-yielding breeds was a common strategy in the past, which resulted in high amounts of foreign genetic material in many of them. Much of the original genetic background got lost, however, they do not achieve the same performance level as high-yielding breeds. Local breeds are therefore faced with the risk of two types of extinction, i.e. a numerical extinction due to the small and decreasing numbers of breeding animals, and a genetic extinction due to massive introgression from high-yielding breeds. To promote local dairy breeds, the implementation of a genomic rotational crossbreeding scheme can be a promising strategy. Local breeds can benefit from a genomic rotational crossbreeding scheme with a high-yielding breed due to 1) an enlarged reference population including both the local breed and crossbred animals, and 2) the increased performance level of crossbred animals. On the other hand, crossbreeding is particularly known to improve functional traits by the exploitation of heterosis. Thus, it appears to be an appealling option for high-yielding breeds, as well, as they tend to struggle with fitness related problems. This thesis aimed to develop genomic methods for numerically small local dairy breeds in crossbreeding schemes in order to improve their genetic gain, genetic uniqueness, and their ability to compete with high-yielding breeds. In Chapter 2 a review study conducted a comparison of different genomic models which are suitable for crossbred data. Different additive models (such as the parental model, a model with breed-specific allele effects, and a single step model) and dominance models, which were either line-dependent, line-independent or included imprinting were discussed. It was concluded that the model choice needs to be made based on desired accuracies, computational possibilities, and data availability. In general, dominance models showed to result in higher accuracies compared to additive models. A breed of origin of alleles model approach was introduced in Chapter 3, which assumes different SNP effects for different origins of haplotypes. This model is suitable for the multi-breed genomic prediction of breeding values of numerically small breeds (i.e. German Angler) that have experienced introgression from high-yielding breeds in the past. The breed of origin of alleles model approach tended to be advantageous for Angler over multi-breed and within-breed genomic predictions with GBLUP. Chapter 4 contains a simulation study about the implementation of a rotational crossbreeding scheme including German Angler x German Holstein, while introducing genomic selection in Angler. Different sizes and structures of growing reference populations and selection goals of Angler were examined. The results showed that crossbred animals had a small overall superiority to both Holstein and Angler populations. In addition, a reference population containing both Angler and crossbred animals, in combination with a selection based on the purebred performance of Angler, gave the highest response to selection in the purebred Angler population and in the crossbred population. The difference between selection methods for Angler individuals could only be observed in the long term, as the purebred-crossbred correlations decreased. In Chapter 5 a simulation study on rotational crossbreeding was performed including different Optimum Contribution Selection methods, in order to realize genetic gain while regaining the original genetic background of Angler. Different constraints regarding mean kinships, native kinships, and migrant contributions from Holstein were applied to investigate their effects on Angler, crossbred, and Holstein populations. Constraining the amount of migrant contribution in Angler increased their genetic uniqueness. However, it led to a notable reduction of genetic gain and thus a reduced superiority of the crossbred animals. The slowed rate of genetic gain and thus the large difference of the performance between the parental breeds could not be compensated by heterosis effects. In Chapter 6 the thesis ends with a general discussion about further genomic models for crossbreeding, and the practical relevance of crossbreeding in dairy cattle.
Gibberella ear rot resistance in European maize : genetic analysis by complementary mapping approaches and improvement with genomic selection
(2022) Han, Sen; Melchinger, Albrecht E.
During the last decades, implementation of molecular markers such as single nucleotide polymorphisms (SNPs) has transformed plant breeding practices from conventional phenotypic selection to marker-assisted selection (MAS) and genomic selection (GS) that are more precise, faster and less resource-consuming. In this dissertation, we investigated these three selection approaches for improving the polygenic trait Gibberella ear rot (GER) resistance in maize (Zea mays L.), which is an important fungal disease in Europe and North America leading to reduced grain yield and grain contaminated with mycotoxins such as deoxynivalenol (DON) and zearalenone (ZON). Three different sets of materials were evaluated in multiple environments and analyzed for different objectives. In the first study, five flint doubled-haploid (DH) families (with size 43 to 204) inter-connected at various levels through common parents, were generated in an incomplete half-diallel design with four parental lines developed by the University of Hohenheim. Significant genotypic variances and generally high heritabilities were observed for all three traits (i.e., GER, DON and days to silking (DS)) in all families, implying good prospects for resistance breeding and phenotypic selection against GER across different environments in European maize germplasm. Genetic correlations were extremely tight between DON and GER and moderately negative for DS with DON or GER, suggesting that indirect selection against GER would be efficient to reduce DON, but maturity should be considered in GER resistance breeding. Using a high-density consensus map with 2,472 marker loci, we compared classical bi-parental mapping of QTL (quantitative trait locus/loci) with multi-parental QTL mapping conducted with joint families and using four different biometric models. Multi-parental QTL mapping models identified all and even further QTL than the bi-parental QTL mapping model conducted within each family. Interestingly, QTL for DON and GER were mostly family-specific, yet multiple families had several common QTL for DS. Many QTL displayed large additive effects and most favorable alleles originated from the highly resistant parent. Interactions between detected QTL and genetic background (family) were rare and had comparatively small effects. Multi-parental QTL mapping models generally did not yield higher prediction accuracy than the bi-parental QTL mapping model for all traits. In the second study, two diversity panels consisting of 130 elite European dent and 114 flint lines, respectively, from the University of Hohenheim were evaluated and subject to a genome-wide association study within each pool. Similar to the first study, highly significant genotypic and genotype × environment interaction variances were observed for GER, DON and DS. Heritabilities were moderately high for GER and DON and high for DS in both pools. Estimated genomic correlations between pools were close to zero for DON and DS, and slightly higher for GER. The detected QTL for DON were all specific to each heterotic pool and none of them was in common with previously detected QTL. Furthermore, no QTL was detected for GER and DS in both pools. Genomic prediction (GP) across pools yielded low or even negative prediction accuracy for all traits. When the training set (TS) size was increased by combining lines from both heterotic pools, the combined-pool GP approaches had no higher prediction accuracy than the within-pool GP approach. Different from expectation, method BayesB did not outperform genomic best linear unbiased prediction (GBLUP). In the third study, we analyzed two backcross (BC) families derived from a resistant and a susceptible recurrent parent. Both BC populations differed substantially in their means for all traits, suggesting that the two recurrent parents have different QTL alleles for GER resistance. Relatively high correlations were observed between DON and ZON concentrations measured by immunoassays and GER visual severity scoring and NIRS (near-infrared spectroscopy) within each BC population. Thus, the mycotoxin content in grain can reliably be reduced by directional selection for GER severity and NIRS measurements that are less expensive and less laborious. In conclusion, GER resistance in European maize germplasm can be effectively improved through breeding with resistant donor lines. GER visual severity scoring and NIRS measurements were found to be reliable predictors for DON and ZON concentrations in grain. We observed that QTL for GER and DON are mostly specific to a few families or a limited number of materials, whereas QTL for DS are more commonly shared between families. The multi-parental QTL mapping approach is complementary to the classical bi-parental QTL mapping in that the latter has generally higher power to identify rare but large-effect QTL for traits such as GER and DON, whereas the former is superior in detecting common but small-effect QTL for traits such as DS. Composing the TS with materials more closely related to the prediction set and increasing the TS size generally resulted in higher prediction accuracy for MAS and GS, irrespective of the trait and statistical model.
Investigations on methodological and strategic aspects of genomic selection in dairy cattle using real and simulated data
(2018) Plieschke, Laura Isabel; Bennewitz, Jörn
In Chapter one a method was developed to separate the genomic relationship matrix into two independent covariance matrices. Here, the base group component describes the covariance that results from systematic differences in allele frequencies between groups at the pedigree base. The remaining segregation component describes the genomic relationship that is corrected for the differences between base populations. To investigate the proposed decomposition three different models were tested on six traits, where the covariance between animals was described either only by the segregation component or by a combination of the two components. An additional variant examining the effect of a fixed modeling of the group effects was included. In total, 7965 genotyped Fleckvieh and 4257 genotyped Brown Swiss and 143 genotyped Original Braunvieh bulls were available for this study. The proposed decomposition of the genomic relationship matrix helped to examine the relative importance of the effects of base groups and segregation component in a given population. It was possible to estimate significant differences between the means of base groups in most cases for both breeds and for the traits analyzed. Analysis of the matrix of base group contributions to the populations investigated revealed several general breed-specific aspects. Comparing the three models, it was concluded that the segregation component is not sufficient to describe the covariance completely. However, it also was found that the model applied has no strong impact on predictive power if the animals used for validation show no differences in their genetic composition with respect to the base groups and if the majority of them have complete pedigrees of sufficient depth. The subject of the chapter two was investigation to systematically increase the reliability of genomic breeding values by integrating cows into the reference population of genomic breeding value estimation. For this purpose a dataset was generated by simulation resembling the German-Austrian dual-purpose Fleckvieh population.. The concept investigated is based on genotyping a fixed number of daughters of each AI bull of the last or last two generation of the reference population and, together with their phenotypic performance, to integrate them into the reference population of the genomic evaluation. Different scenarios with different numbers of daughters per bull were compared. In the base scenario the reference population was made up of 4200 bulls. In the extended scenarios, more and more daughters were gradually integrated in the reference population. The reference population of the most extended scenario contained 4200 bulls and 420,000 cows. It was found that the inclusion of genotypes and phenotypes of female animals can increase the reliabilities genomic breeding values considerably. Changes in validation reliability of 6-54% for a trait with a heritability of 0.4 depending on scenario were found. As the number of daughters increased, the validation reliability increased as well. It should be noted that the composition of the daughter samples had a very great influence on whether the additional genotyped and phenotyped animals in the reference population can have a positive effect on the reliability of genomic breeding values. If pre-selected daughter samples were genotyped, the mean validation reliability decreased significantly compared to a correspondingly large unselected daughter sample. In addition, a higher bias was observable in these cases. Chapter three expands the investigations of chapter two by a low-heritability trait, as well as the aspect of so called new traits. The results found in chapter two were confirmed in chapter three for a low-heritability trait. Changes in validation reliability of 5-21% for a heritability of 0.05 depending on scenario were found. The negative effects of pre-selected daughter samples were even more pronounced in chapter three. In the case of an ‘old’ trait, the number of phenotypes is expected to be (nearly) unlimited, since a recording system is well established. In the case of a new trait recording of phenotypes just started, therefore the number of phenotypes is limited. Two different genotyping strategies were compared for new traits. On the one hand, the sires of the phenotyped cows were genotyped and on the other hand the phenotyped cows were genotyped themselves. It was found in all compared scenarios that it is more sensible to genotype cows themselves instead of the genotyping their sires. However, if usual strategy of phenotyping female animals and genotyping of males is applied, it is at least important to ensure that many daughters are phenotyped in a balanced system. If different numbers of daughters per bull are phenotyped and unbalancedness becomes severe, the average validation reliability decreased significantly.
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.