Institut für Pflanzenzüchtung, Saatgutforschung und Populationsgenetik

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Physical geography, isolation by distance and environmental variables shape genomic variation of wild barley (Hordeum vulgare L. ssp. spontaneum) in the Southern Levant
(2022) Chang, Che-Wei; Fridman, Eyal; Mascher, Martin; Himmelbach, Axel; Schmid, Karl
Determining the extent of genetic variation that reflects local adaptation in crop-wild relatives is of interest for the purpose of identifying useful genetic diversity for plant breeding. We investigated the association of genomic variation with geographical and environmental factors in wild barley ( Hordeum vulgare L. ssp. spontaneum ) populations of the Southern Levant using genotyping by sequencing (GBS) of 244 accessions in the Barley 1K+ collection. The inference of population structure resulted in four genetic clusters that corresponded to eco-geographical habitats and a significant association between lower gene flow rates and geographical barriers, e.g. the Judaean Mountains and the Sea of Galilee. Redundancy analysis (RDA) revealed that spatial autocorrelation explained 45% and environmental variables explained 15% of total genomic variation. Only 4.5% of genomic variation was solely attributed to environmental variation if the component confounded with spatial autocorrelation was excluded. A synthetic environmental variable combining latitude, solar radiation, and accumulated precipitation explained the highest proportion of genomic variation (3.9%). When conditioned on population structure, soil water capacity was the most important environmental variable explaining 1.18% of genomic variation. Genome scans with outlier analysis and genome-environment association studies were conducted to identify adaptation signatures. RDA and outlier methods jointly detected selection signatures in the pericentromeric regions, which have reduced recombination, of the chromosomes 3H, 4H, and 5H. However, selection signatures mostly disappeared after correction for population structure. In conclusion, adaptation to the highly diverse environments of the Southern Levant over short geographical ranges had a limited effect on the genomic diversity of wild barley. This highlighted the importance of nonselective forces in genetic differentiation.
Hybrid wheat: quantitative genetic parameters and heterosis for quality and rheological traits as well as baking volume
(2022) Schwarzwälder, Lea; Thorwarth, Patrick; Zhao, Yusheng; Reif, Jochen Christoph; Longin, C. Friedrich H.
Bread wheat cultivars have been selected according to numerous quality traits to fulfill the requirements of the bread making industry. These include beside protein content and quality also rheological traits and baking volume. We evaluated 35 male and 73 female lines and 119 of their single-cross hybrids at three different locations for grain yield, protein content, sedimentation value, extensograph traits and baking volume. No significant differences ( p < 0.05) were found in the mean comparisons of males, females and hybrids, except for higher grain yield and lower protein content in the hybrids. Mid-parent and better-parent heterosis values were close to zero and slightly negative, respectively, for baking volume and extensograph traits. However, the majority of heterosis values resulted in the finding that hybrids had higher grain yield than lines for a given level of baking volume, sedimentation value or energy value of extensograph. Due to the high correlation with the mid-parent values ( r > 0.70), an initial prediction of hybrid performance based on line per se performance for protein content, sedimentation value, most traits of the extensograph and baking volume is possible. The low variance due to specific combining ability effects for most quality traits points toward an additive gene action requires quality selection within both heterotic groups. Consequently, hybrid wheat can combine high grain yield with high bread making quality. However, the future use of wheat hybrids strongly depends on the establishment of a cost-efficient and reliable seed production system.
Phenomic prediction can be improved by optimization of NIRS preprocessing
(2025) Braun, Vincent; Zhu, Xintian; Meyenberg, Carina; Hahn, Volker; Maurer, Hans Peter; Würschum, Tobias; Thorwarth, Patrick
In recent years, phenomic prediction has emerged as a new method in plant breeding that has been shown to have great potential. However, there are still many open questions regarding its practical application. For example, in the field of spectroscopy, it is standard practice to optimize the preprocessing of spectra, which so far has only been done to a limited extent for phenomic prediction. In this study, we therefore used three different data sets of soybean, triticale and maize to identify the best combinations of Savitzky–Golay filter parameters for preprocessing near‐infrared spectra for phenomic prediction. We tested 677 combinations of polynomial order, derivative and window size and evaluated them with Monte Carlo cross‐validation. Our results showed that the predictive ability can be improved with the right settings. However, there was no global optimum that gave the best results for all data sets. Even for different traits within the same data set, different combinations of parameters were necessary to achieve the highest predictive ability. Nevertheless, we show that some combinations generally result in a very low predictive ability and should not be used for preprocessing. In addition, we used the normalized discounted cumulative gain to assess whether preprocessing affected the ranking of individuals, which revealed no major changes in the top 1%, 10% or 20% of predicted individuals. Taken together, our results show the potential of preprocessing near‐infrared spectroscopy data to improve the phenomic predictive ability, but there appears to be no global optimum of parameter settings across data sets and traits.
Maternal differences for the reaction to ergot in unfertilized hybrid rye (Secale cereale)
(2022) Kodisch, Anna; Schmiedchen, Brigitta; Eifler, Jakob; Gordillo, Andres; Siekmann, Dörthe; Fromme, Franz Joachim; Oberforster, Michael; Miedaner, Thomas
Claviceps purpurea causing ergot maintains to be a problem in commercial cytoplasmic male sterile (CMS)-based hybrid rye growing. The fungal spores compete with pollen during flowering and ergot incidence is reduced in highly pollen-shedding stands. This study was carried out to identify maternal differences in ergot infection in the absence of pollen. Ten male-sterile single crosses were tested by needle and spray inoculation and kept unfertilized in up to four field sites (Germany, Austria) and three greenhouse experiments, respectively, in two years. A medium to high correlation was observed between field (needle inoculation) and greenhouse (spray inoculation) experiments. The environments (=location × year combinations) differed in their ergot severity and ergot incidence. Significant ( P ≤ 0.05) genotypic and genotype × environment interaction variances were detected for the unfertilized male-sterile single crosses in both test systems for both traits. The single cross K_4 showed a significantly lower ergot severity averaged across all environments, thus being more resilient to ergot than the other genotypes. In conclusion, spray and needle inoculation are suitable for testing unfertilized male-sterile rye materials, testing across several environments (locations, years) is definitely necessary. Selection of specific females might give the potential for further reducing ergot contamination in hybrid rye in future. The frequency of such genotypes within larger breeding populations needs to be analyzed.
Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials
(2024) Meyenberg, Carina; Braun, Vincent; Longin, Carl Friedrich Horst; Thorwarth, Patrick
The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky–Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02–0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment (G×E)interaction variance.
Genome-wide association study for in vitro digestibility and related traits in triticale forage
(2024) De Zutter, Anneleen; Piro, Maria Chiara; Maenhout, Steven; Maurer, Hans Peter; De Boever, Johan; Muylle, Hilde; Roldán-Ruiz, Isabel; Haesaert, Geert
Background: Triticale is making its way on dairy farms as an alternative forage crop. This requires the availability of high-yielding triticale varieties with good digestibility. Triticale forage breeding mainly focussed on biomass yield, but efforts to improve digestibility are increasing. We previously investigated the interrelationships among different quality traits in soft dough triticale: starch, acid detergent fibre and in vitro digestibility of organic matter (IVOMD) and of neutral detergent fibre (IVNDFD) of the total plant, IVNDFD and Klason lignin of the stems, and ear proportion and stem length. Here we determine the genetic control of these traits, using a genome-wide association (GWAS) approach. A total of 33,231 DArTseq SNP markers assessed in a collection of 118 winter triticale genotypes, including 101 varieties and 17 breeding lines, were used. Results: The GWAS identified a total of 53 significant marker-trait associations (MTAs). The highest number of significantly associated SNP markers (n = 10) was identified for total plant IVNDFD. A SNP marker on chromosome 1A (4211801_19_C/T; 474,437,796 bp) was found to be significantly associated with ear proportion, and plant and stem IVNDFD, with the largest phenotypic variation for ear proportion (R²p = 0.23). Based on MTAs, candidate genes were identified which were of particular relevance for variation in in vitro digestibility (IVD) because they are putatively involved in plasma membrane transport, cytoskeleton organisation, carbohydrate metabolic processes, protein phosphorylation, and sterol and cell wall biogenesis. Interestingly, a xyloglucan-related candidate gene on chromosome 2R, SECCE2Rv1G0126340, was located in close proximity of a SNP significantly associated with stem IVNDFD. Furthermore, quantitative trait loci previously reported in wheat co-localized with significantly associated SNP markers in triticale. Conclusions: A collection of 118 winter triticale genotypes combined with DArTseq SNP markers served as a source for identifying 53 MTAs and several candidate genes for forage IVD and related traits through a GWAS approach. Taken together, the results of this study demonstrate that the genetic diversity available in this collection can be further exploited for research and breeding purposes to improve the IVD of triticale forage.
Buckwheat in Germany: The effect of variety and sowing date on agronomic traits
(2025) Grimes, Samantha J.; Afzal, Muhammad; Tako, Rea; Hahn, Volker; Graeff‐Hönninger, Simone; Longin, C. Friedrich H.; Grimes, Samantha J.; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Afzal, Muhammad; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Tako, Rea; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Hahn, Volker; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Graeff‐Hönninger, Simone; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Longin, C. Friedrich H.; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany
Buckwheat (Fagopyrum esculentum Moench) requires minimal agrochemical inputs and delivers grains with a high nutritional profile—the perfect prerequisites for future sustainable farming. However, it is currently consumed and produced in only a few countries. The aim of this study was to investigate the potential to successfully grow buckwheat in Germany and to elaborate first insights for local breeding. Therefore, a total of 33 buckwheat varieties were tested across three locations, 3 years, and two different sowing dates. The average yield was 2.3 t ha −1 , ranging from 1.4 to 3.1 t ha −1 across varieties. Similar yields were observed for both early and late sowing dates, and across all tested varieties. All but two of the very late‐maturing common buckwheat varieties could be safely harvested in all locations also on the late sowing date. Key prerequisites to establish local breeding were met, including large genetic variation and high heritability for important agronomic traits. In summary, this study highlights the importance of variety selection and targeted breeding focusing on early‐maturing buckwheat varieties, paving the way for potential double‐cropping systems in Germany that use buckwheat as a second crop and significantly enhance its profitability for farmers.
Genome-wide association study for resistances to yellow rust, powdery mildew, and Septoria tritici blotch in cultivated emmer
(2024) Miedaner, T.; Afzal, M.; Longin, C. F.
Emmer is a progenitor of bread wheat and evolved in the Levant together with the yellow rust (YR), powdery mildew (PM) fungi, and a precursor of Zymoseptoria tritici causing Septoria tritici blotch (STB). We performed a genome-wide association mapping for the three disease resistances with 143 cultivated emmer accessions in multi-environmental trials. Significant (P < 0.001) genotypic variation was found with high heritabilities for the resistances to the two biotrophs and a moderate heritability for STB resistance. For YR, PM, and STB severity nine, three, and seven marker-trait associations, respectively, were detected that were significant across all environments. Most of them were of low to moderate effect, but for PM resistance a potentially new major gene was found on chromosome 7AS. Genomic prediction abilities were high throughout for all three resistances (≥ 0.8) and decreased only slightly for YR and PM resistances when the prediction was done for the second year with the first year as training set (≥ 0.7). For STB resistance prediction ability was much lower in this scenario (0.4). Despite this, genomic selection should be advantageous given the large number of small QTLs responsible for quantitative disease resistances. A challenge for the future is to combine these multiple disease resistances with better lodging tolerance and higher grain yield.
Unravelling the genetic architecture of soybean tofu quality traits
(2025) Döttinger, Cleo A.; Steige, Kim A.; Hahn, Volker; Bachteler, Kristina; Leiser, Willmar L.; Zhu, Xintian; Würschum, Tobias
Tofu is a popular soybean ( Glycine max (L.) Merr.) food with a long tradition in Asia and rising popularity worldwide, including Central Europe. Due to the labour-intensive phenotyping procedures, breeding for improved tofu quality is challenging. Therefore, our objective was to unravel the genetic architecture of traits relevant for tofu production in order to assess the potential of marker-assisted selection and genomic selection in breeding for these traits. To this end, we performed QTL mapping with 188 genotypes from a biparental mapping population. The population was evaluated in a two-location field trial, and tofu was produced in the laboratory to evaluate tofu quality. We identified QTL for all investigated agronomic and quality traits, each explaining between 6.40% and 27.55% of the genotypic variation, including the most important tofu quality traits, tofu yield and tofu hardness. Both traits showed a strong negative correlation ( r = -0.65), and consequently a pleiotropic QTL on chromosome 10 was found with opposite effects on tofu hardness and tofu weight, highlighting the need to balance selection for both traits. Four QTL identified for tofu hardness jointly explained 68.7% of the genotypic variation and are possible targets for QTL stacking by marker-assisted selection. To exploit also small-effect QTL, genomic selection revealed moderate to high mean prediction accuracies for all traits, ranging from 0.47 to 0.78. In conclusion, inheritance of tofu quality traits is highly quantitative, and both marker-assisted selection and genomic selection present valuable tools to advance tofu quality by soybean breeding.
Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters
(2024) Melchinger, Albrecht E.; Fernando, Rohan; Melchinger, Andreas J.; Schön, Chris-Carolin
Plant breeding programs typically involve multiple families from either the same or different populations, varying in means, genetic variances and prediction accuracy of BLUPs or BLUEs for true genetic values (TGVs) of candidates. We extend the classical breeder's equation for truncation selection from single to multiple sets of genotypes, indicating that the expected overall selection response for TGVs depends on the selection response within individual sets and their post-selection proportions. For BLUEs, we show that maximizing requires thresholds optimally tailored for each set, contingent on their population parameters. For BLUPs, we prove that is maximized by applying a uniform threshold across all candidates from all sets. We provide explicit formulas for the origin of the selected candidates from different sets and show that their proportions before and after selection can differ substantially, especially for sets with inferior properties and low proportion. We discuss implications of these results for (a) optimum allocation of resources to training and prediction sets and (b) the need to counteract narrowing the genetic variation under genomic selection. For genomic selection of hybrids based on BLUPs of GCA of their parent lines, selecting distinct proportions in the two parent populations can be advantageous, if these differ substantially in the variance and/or prediction accuracy of GCA. Our study sheds light on the complex interplay of selection thresholds and population parameters for the selection response in plant breeding programs, offering insights into the effective resource management and prudent application of genomic selection for improved crop development.
Maximization through optimization? On the relationship between hybrid performance and parental genetic distance
(2023) Würschum, Tobias; Zhu, Xintian; Zhao, Yusheng; Jiang, Yong; Reif, Jochen C.; Maurer, Hans Peter
Heterosis is the improved performance of hybrids compared with their parental components and is widely exploited in agriculture. According to quantitative genetic theory, genetic distance between parents at heterotic quantitative trait loci is required for heterosis, but how heterosis varies with genetic distance has remained elusive, despite intensive research on the topic. Experimental studies have often found a positive association between heterosis and genetic distance that, however, varied in strength. Most importantly, it has remained unclear whether heterosis increases continuously with genetic distance or whether there is an optimum genetic distance after which heterosis declines again. Here, we revisit the relationship between heterosis and genetic distance and provide perspectives on how to maximize heterosis and hybrid performance in breeding, as well as the consequences for the design of heterotic groups and the utilization of more exotic material and genetic resources.
Participatory research at scale: a comparative analysis of four approaches to large-scale agricultural technology testing with farmers
(2024) Oberson, Nathalie; Moussa, Hannatou O; Aminou, Ali M; Kidane, Yosef Gebrehawaryat; Luo, Juliet Nangamba; Giuliani, Alessandra; Weltzien, Eva; Haussmann, Bettina IG
Tailoring agricultural technology options to the diverse conditions of smallholder farmers requires innovative approaches for testing these technologies with farmers across varied contexts, while incorporating their feedback into learning and decision-making processes. This study compares four such approaches: the Farmer Field School on Participatory Plant Breeding (FFS-PPB), Farmer Research Network (FRN), Crowdsourcing–Triadic comparisons of technologies (Tricot), and adapted Mother–Baby Trial (MBT) as implemented by four concrete projects. The objectives are to provide detailed descriptions of these approaches and their project-specific implementations, identify and analyze common aspects and differences, and derive insights to guide future farmer-inclusive projects aiming at contextual scaling of agricultural technologies. A literature review, key informant interviews, and a systematic content analysis were conducted for the analysis. Common features include cascade training models, simple farmer-managed experiments, and the use of digital tools for data collection. Major differences lie in the extent of farmer–researcher collaboration and decision-making, as well as how technology option-by-context interactions are addressed. The FRN, FFS-PPB, and adapted MBT approaches involve farmers in decision-making throughout most stages of research, including co-learning cycles that adapt the research design and technology options to farmers’ needs. Although these approaches require more training and expertise, they increase the likelihood of achieving relevant results that farmers can implement in practice. In contrast, more standardized approaches like the Crowdsourcing–Tricot streamline the implementation, data management and analysis of large-scale trials, but have limitations in capturing the underlying reasons for farmers’ preferences. Among the studied approaches, the FRN as implemented by the Women's Fields project in Niger is particularly effective in identifying which options best suit specific farming contexts.
Using landscape genomics to infer genomic regions involved in environmental adaptation of soybean genebank accessions
(2025) Haupt, Max; Schmid, Karl
Background: Understanding how crops adapt to specific environmental conditions is becoming increasingly important in the face of accelerating climate change, but the genetics of local adaptation remains little understood for many crops. Landscape genomics can reveal patterns of genetic variation that indicate adaptive diversification during crop evolution and dispersal. Here, we examine genetic differentiation and association signatures with environmental gradients in soybean ( Glycine max ) germplasm groups from China that were inferred from the USDA Soybean Germplasm Collection ( N = 17, 019 accessions) based on population structure and passport information. Results: We recover genes previously known to be involved in soybean environmental adaptation and report numerous new candidate genes in adaptation signatures implicated by genomic resources such as the genome annotation and gene expression datasets to function in flowering regulation, photoperiodism and stress reaction cascades. Linkage disequilibrium network analysis suggested functional relationships between genomic regions with signatures of genetic differentiation, consistent with a polygenic nature of environmental adaptation. We tested whether haplotypes associated with environmental adaptation in China were present in 843 North American and 160 European soybean cultivars and found that haplotypes in major genes for early maturity have been selected during breeding, but also that a large number of haplotypes exhibiting putative adaptive variation for cold regions at high latitudes are underrepresented in modern cultivars. Conclusions: Our results demonstrate the value of landscape genomics analysis of genebank accessions studying crop environmental adaptation and to inform future research and breeding efforts for improved adaptation of soybean and other crops to future climates.
Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies
(2025) Chang, Che-Wei; Schmid, Karl
Genome–environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limited by missing geographic origin data. To address this limitation, we explored the use of neural networks to predict the geographic origins of barley accessions and integrate imputed environmental data into GEA. Neural networks demonstrated high accuracy in cross-validation but occasionally produced ecologically implausible predictions as models solely considered geographical proximity. For example, some predicted origins were located within non-arable regions, such as the Mediterranean Sea. Using barley flowering time genes as benchmarks, GEA integrating imputed environmental data ( N=11,032) displayed partially concordant yet complementary detection of genomic regions near flowering time genes compared to regular GEA ( N=1,626), highlighting the potential of GEA with imputed data to complement regular GEA in uncovering novel adaptive loci. Also, contrary to our initial hypothesis anticipating a significant improvement in GEA performance by increasing sample size, our simulations yield unexpected insights. Our study suggests potential limitations in the sensitivity of GEA approaches to the considerable expansion in sample size achieved through predicting missing geographical data. Overall, our study provides insights into leveraging incomplete geographical origin data by integrating deep learning with GEA. Our findings indicate the need for further development of GEA approaches to optimize the use of imputed environmental data, such as incorporating regional GEA patterns instead of solely focusing on global associations between allele frequencies and environmental gradients across large-scale landscapes.
Order from entropy: big data from FAIR data cohorts in the digital age of plant breeding
(2025) Gogna, Abhishek; Arend, Daniel; Beier, Sebastian; Rezaei, Ehsan Eyshi; Würschum, Tobias; Zhao, Yusheng; Chu, Jianting; Reif, Jochen C.
Lack of interoperable datasets in plant breeding research creates an innovation bottleneck, requiring additional effort to integrate diverse datasets—if access is possible at all. Handling of plant breeding data and metadata must, therefore, change toward adopting practices that promote openness, collaboration, standardization, ethical data sharing, sustainability, and transparency of provenance and methodology. FAIR Digital Objects, which build on research data infrastructures and FAIR principles, offer a path to address this interoperability crisis, yet their adoption remains in its infancy. In the present work, we identify data sharing practices in the plant breeding domain as Data Cohorts and establish their connection to FAIR Digital Objects. We further link these cohorts to broader research infrastructures and propose a Data Trustee model for federated data sharing. With this we aim to push the boundaries of data management, often viewed as the last step in plant breeding research, to an ongoing process to enable future innovations in the field.
Genomic prediction in hybrid breeding: I. Optimizing the training set design
(2023) Melchinger, Albrecht E.; Fernando, Rohan; Stricker, Christian; Schön, Chris-Carolin; Auinger, Hans-Jürgen
Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents (nTS) and crosses per parent (c) has received little attention. Our objective was to examine prediction accuracy (ra) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of nTS and c. In the theory, we developed estimates for ra of GBLUPs for hybrids: (i)r^a based on the expected prediction accuracy, and (ii) r~a based on ra of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion (τSCA = 1%, 6%, 22%) of SCA variance in σG2 and heritability (h2 = 0.4, 0.8). Values of r~a and r^a closely agreed with ra for hybrids. For given size NTS = nTS × c of TS, ra of H0 hybrids and GCA of I0 lines was highest for c = 1. Conversely, for GCA of I1 lines and H1 and H2 hybrids, c = 1 yielded lowest ra with concordant results across all scenarios for both data sets. In view of these opposite trends, the optimum choice of c for maximizing selection response across all types of hybrids depends on the size and resources of the breeding program.
Exploring adaptive genetic variation in exotic barley germplasm with landscape genomics
(2025) Chang, Che-Wei; Schmid, Karl
Understanding genetic variation underlying local adaptation is essential for improving crop resilience to address challenges posed by climate change. Barley (Hordeum vulgare L. ssp. vulgare), one of the most important crops, is suitable for studying local adaptation due to its remarkable adaptability. This PhD dissertation investigated adaptive genetic variation in exotic barley germplasm, including wild barley (Hordeum vulgare ssp. spontaneum) and barley landraces, from diverse environments and explored strategies to improve the use of genebank accessions for harnessing valuable genetic variants. In the first study, local adaptation in wild barley populations from the Southern Lev- ant was explored using landscape genomics approaches, combining genomic data with the climatic and soil properties of geographical origins. Through redundancy analysis (RDA), we found spatial autocorrelation explained 45% of genomic variation, and environmental factors accounted for 15%. Adaptive signatures were identified in the pericentromeric regions by the population-genetics-based scans and genome- environment association (GEA) scans, but they mostly disappeared when the population structure was considered. Our findings overall highlighted the role of nonselective forces in shaping the genetic variation of wild barley even in divergent environments. The second study addressed challenges in passport data quality control for large- scale samples, such as germplasm collections in genebanks. The R package GGoutlieR was developed in this work to tackle the shortcomings of traditional manual data cleaning. It efficiently detects and visualizes samples with unusual geo-genetic patterns by characterizing geography-genotype associations with distance-based statis- tics via K-nearest neighbors and calculating empirical p-values accordingly. By stream- lining data cleaning and quality control, GGoutlieR supports more reliable landscape genomics studies, which is crucial for studying loci involved in local adaptation. The third study explored the use of neural networks to predict geographical origins for genebank accessions lacking passport data, enabling their integration into genome- environment association (GEA) analyses. Neural network models demonstrated high prediction accuracy in cross-validation. Incorporating imputed environmental data (N = 11,032) into GEA, using barley flowering time genes as benchmarks, revealed complementary detection of genomic regions near flowering time genes compared to regular GEA (N = 1,626). Furthermore, simulations of polygenic local adaptation in selfing species showed that GEA power is insensitive to large sample sizes. These findings suggest that GEA with imputed environmental data can be a complementary approach for uncovering novel adaptive loci that might remain undetected in conventional GEA, rather than improving the statistical power of GEA. Overall, this dissertation contributes to understanding the adaptive genetic variation in barley and expanding methodologies in landscape genomics, providing a direction for the future development of GEA approaches to better support allele mining for prebreeding to enhance crop resilience.
Rapid cycling genomic selection in maize landraces
(2025) Polzer, Clara; Auinger, Hans-Jürgen; Terán-Pineda, Michelle; Hölker, Armin C.; Mayer, Manfred; Presterl, Thomas; Rivera-Poulsen, Carolina; da Silva, Sofia; Ouzunova, Milena; Melchinger, Albrecht E.; Schön, Chris-Carolin
Key message: A replicated experiment on genomic selection in a maize landrace provides valuable insights on the design of rapid cycling recurrent pre-breeding schemes and the factors contributing to their success. Abstract: The genetic diversity of landraces is currently underutilized for elite germplasm improvement. In this study, we investigated the potential of rapid cycling genomic selection for pre-breeding of a maize ( Zea mays L.) landrace population in replicated experiments. We trained the prediction model on a dataset (N = 899) composed of three landrace-derived doubled-haploid (DH) populations characterized for agronomic traits in 11 environments across Europe. All DH lines were genotyped with a 600 k SNP array. In two replications, three cycles of genomic selection and recombination were performed for line per se performance of early plant development, a major sustainability factor in maize production. From each cycle and replication, 100 DH lines were extracted. To evaluate selection response, the DH lines of all cycles and both replications (N = 688) were evaluated for per se performance of selected and unselected traits in seven environments. Selection was highly successful with an increase of about two standard deviations for traits under directional selection. Realized selection response was highest in the first cycle and diminished in following cycles. Selection gains predicted from genomic breeding values were only partially corroborated by realized gains estimated from adjusted means. Prediction accuracies declined sharply across cycles, but only for traits under directional selection. Retraining the prediction model with data from previous cycles improved prediction accuracies in cycles 2 and 3. Replications differed in selection response and particularly in accuracies. The experiment gives valuable insights with respect to the design of rapid cycling genomic selection schemes and the factors contributing to their success.
Reference proteomes of five wheat species as starting point for future design of cultivars with lower allergenic potential
(2023) Afzal, Muhammad; Sielaff, Malte; Distler, Ute; Schuppan, Detlef; Tenzer, Stefan; Longin, Friedrich
Wheat is an important staple food and its processing quality is largely driven by proteins. However, there is a sizable number of people with inflammatory reactions to wheat proteins, namely celiac disease, wheat allergy and the syndrome of non-celiac wheat sensitivity. Thus, proteome profiles should be of high importance for stakeholders along the wheat supply chain. We applied liquid chromatography-tandem mass spectrometry-based proteomics to establish the flour reference proteome for five wheat species, ancient to modern, each based on 10 cultivars grown in three diverse environments. We identified at least 2540 proteins in each species and a cluster analyses clearly separated the species based on their proteome profiles. Even more, >50% of proteins significantly differed between species - many of them implicated in products’ quality, grain-starch synthesis, plant stress regulation and proven or potential allergic reactions in humans. Notably, the expression of several important wheat proteins was found to be mainly driven by genetics vs. environmental factors, which enables selection and refinement of improved cultivars for the wheat supply chain as long as rapid test methods will be developed. Especially einkorn expressed 5.4 and 7.2-fold lower quantities of potential allergens and immunogenic amylase trypsin inhibitors, respectively, than common wheat, whereas potential allergen content was intermediate in tetraploid wheat species. This urgently warrants well-targeted clinical studies, where the developed reference proteomes will help to design representative test diets.
Influence of the mating design on the additive genetic variance in plant breeding populations
(2023) Lanzl, Tobias; Melchinger, Albrecht E.; Schön, Chris-Carolin
The additive genetic variance VAinherent to a breeding population is a major determinant of short- and long-term genetic gain. When estimated from experimental data, it is not only the additive variances at individual loci (QTL) but also covariances between QTL pairs that contribute to estimates of VA. Thus, estimates of VAdepend on the genetic structure of the data source and vary between population samples. Here, we provide a theoretical framework for calculating the expectation and variance of VAfrom genotypic data of a given population sample. In addition, we simulated breeding populations derived from different numbers of parents ( P = 2, 4, 8, 16) and crossed according to three different mating designs (disjoint, factorial and half-diallel crosses). We calculated the variance of VAand of the parameter b reflecting the covariance component in VA,standardized by the genic variance. Our results show that mating designs resulting in large biparental families derived from few disjoint crosses carry a high risk of generating progenies exhibiting strong covariances between QTL pairs on different chromosomes. We discuss the consequences of the resulting deflated or inflated VAestimates for phenotypic and genome-based selection as well as for applying the usefulness criterion in selection. We show that already one round of recombination can effectively break negative and positive covariances between QTL pairs induced by the mating design. We suggest to obtain reliable estimates of VAand its components in a population sample by applying statistical methods differing in their treatment of QTL covariances.

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