Browsing by Person "Longin, Carl Friedrich Horst"

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Insights into a genomics‐based pre‐breeding program in wheat
(2025) Meyenberg, Carina; Thorwarth, Patrick; Spiller, Monika; Kollers, Sonja; Reif, Jochen Christoph; Longin, Carl Friedrich Horst; Meyenberg, Carina; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Thorwarth, Patrick; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Spiller, Monika; KWS LOCHOW GmbH, Bergen, Germany; Kollers, Sonja; KWS LOCHOW GmbH, Bergen, Germany; Reif, Jochen Christoph; Leibnitz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany; Longin, Carl Friedrich Horst; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany
Continuous intercrossing of the best‐performing wheat ( Triticum aestivum L.) elite lines has resulted in genetic gains for a wide range of traits. However, this approach can also reduce genetic diversity, which potentially limits the long‐term genetic improvement. The use of plant genetic resources (PGRs) is therefore considered as crucial to maintain, or even increase, genetic variability in breeding to address future challenges in agriculture in a sustainable manner. Pre‐breeding programs aim to incorporate untapped genetic diversity into an elite germplasm background. Since there is limited knowledge exchange and few publications on how to run pre‐breeding programs efficiently, we report here our latest pre‐breeding scheme and key lessons learned from a decade of wheat pre‐breeding. Our study is based on genotypic and phenotypic data from 390 pre‐breeding lines coming from multiple locations and 4 years of yield trials. We used the genotypic data to estimate the genetically estimated parental contribution (GEPC) of PGRs in pre‐breeding lines. Considerable variation in GEPC between pre‐breeding lines were found even within the same cross. Combining both genotypic and phenotypic data, we compared different scenarios for genome‐wide predictions. Predicting new lines based on calibrations developed across previous years, we determined prediction abilities ranging between 0.34 and 0.69 for grain yield and 0.53 and 0.71 for sedimentation volume, depending on the predicted dataset. Finally, we showed that targeted pre‐breeding yields a small number of promising pre‐breeding lines that perform at the level of the most important commercial varieties.
Multiomics based association mapping in wheat reveals genetic architecture of quality and allergenic related proteins
(2023) El Hassouni, Khaoula; Afzal, Muhammad; Steige, Kim A.; Sielaff, Malte; Curella, Valentina; Neerukonda, Manjusha; Tenzer, Stefan; Schuppan, Detlef; Longin, Carl Friedrich Horst; Thorwarth, Patrick
Wheat is an important staple crop since its proteins contribute to human and animal nutrition and are important for its end-use quality. However, wheat proteins can also cause adverse human reactions for a large number of people. We performed a genome wide association study (GWAS) on 114 proteins quantified by LC-MS-based proteomics and expressed in an environmentally stable manner in 148 wheat cultivars with a heritability > 0.6. For 54 proteins, we detected quantitative trait loci (QTL) that exceeded the Bonferroni-corrected significance threshold and explained 17.3–84.5% of the genotypic variance. Proteins in the same family often clustered at a very close chromosomal position or the potential homeolog. Major QTLs were found for four well-known glutenin and gliadin subunits, and the QTL segregation pattern in the protein encoding the high molecular weight glutenin subunit Dx5 could be confirmed by SDS gel-electrophoresis. For nine potential allergenic proteins, large QTLs could be identified, and their measured allele frequencies open the possibility to select for low protein abundance by markers as long as their relevance for human health has been conclusively demonstrated. A potential allergen was introduced in the beginning of 1980s that may be linked to the cluster of resistance genes introgressed on chromosome 2AS from Triticum ventricosum. The reported sequence information for the 54 major QTLs can be used to design efficient markers for future wheat breeding.
Optimum allocation of test resources and comparison of alternative breeding schemes for hybrid maize breeding with doubled haploids
(2007) Longin, Carl Friedrich Horst; Melchinger, Albrecht E.
A major objective in hybrid maize breeding is the development of inbred lines with superior testcross performance. Inbred lines have commonly been derived in maize by recurrent selfing for five to six generations. The use of doubled haploids (DHs) enables the generation of completely homozygous lines in one step, representing a promising alternative to recurrent selfing. The implementation of the new DH technique in maize breeding requires an optimization of the entire breeding scheme in order to maximize progress from selection. The objectives of this study were to (i) compare selection gain (¢G) per breeding cycle with the probability of identifying superior genotypes with respect to the optimum allocation of test resources, (ii) evaluate several breeding schemes for an optimum use of the DH technique, (iii) determine the optimum number of test candidates and test locations as well the optimum type and number of testers for the different breeding schemes, and (iv) investigate the potential and limitations in the current DH technique in hybrid maize breeding. Monte Carlo simulations and numerical integration techniques were used to calculate the optimization criteria. The choice of G and the probability of identifying superior genotypes seems not to be crucial for the optimization of breeding schemes. The use of the new probability criterion supported the large optimum number of test locations determined by G. However, a larger impact of varying economic and quantitative-genetic parameters on the probability criterion than on ¢G was found, emphasizing their importance to maximize the chances of identifying a superior genotype. The use of Monte Carlo simulations for optimizing the allocation of test resources seems promising because of the possibility to calculate various optimization criteria for multi-stage selection in finite populations. However, the large computing power required for them can rapidly become prohibitive. Numerical integration techniques allow the calculation of G in multi-stage selection under the simplified assumption of infinite population size. The differences between finite and infinite population size were negligible for both, G and the optimum allocation of test resources. Thus, the simplifying assumption of infinite population size is justified as long as a tremendous reduction in computing time is warranted. Two-stage selection of DH lines was important to increase G and the probability of identifying superior genotypes, because it combines the evaluation of a large number of initial DH lines with the use of a large number of test locations. Consideration of an economic seed production indicated the necessity of separate breeding schemes for seed and pollen parent heterotic groups. For the pollen parent heterotic group, two-stage selection on testcross performance in both stages was most suitable, whereas for the seed parent heterotic group, line per se performance in the first stage followed by evaluation of testcross performance in the second stage was most appealing. The concentration of test resources on the most promising S1 families in early testing prior to DH production was superior to the evaluation of DH lines from the beginning of the selection process. The allocation of test resources was crucial to maximize G for a given scenario. Testers with broad genetic base allow a reduction of the number of testers in favor of an increased number of test locations and a largely increased G. An evaluation of progenies of each tester only in a single location instead of evaluating the progenies of each testers in all locations further increased G. With early testing prior to DH production, similar optimum numbers of testers and test locations were determined for evaluation of testcross performance of S1 families and DH lines within S1 families. This resulted in (i) a large optimum number of S1 families for the first stage and (ii) a small optimum number of S1 families but a large optimum number of DH lines within S1 families for the second stage. Current limitations in the DH technique with a low number of DH lines, which can be produced from a single maize plant, and high costs, affected the selection gain and the optimum allocation of test resources only marginally for breeding schemes with evaluation of DH lines from the beginning of the selection process. However, substantial improvements of the DH technique are required to realize the high potential of early testing prior to DH production in combination with a short cycle length. In conclusion, the optimum allocation of test resources is of utmost importance to increase selection gain under given economic resources. The implementation of DHs into maize breeding enables to shorten the length of the breeding cycle, but a careful evaluation of the breeding alternatives is required to maximize progress from selection.