Browsing by Person "Thorwarth, Patrick"

Now showing 1 - 3 of 3

Evaluation of association mapping and genomic prediction in diverse barley and cauliflower breeding material
(2018) Thorwarth, Patrick; Schmid, Karl J.
Due to the advent of new sequencing technologies and high-throughput phenotyping an almost unlimited amount of data is available. In combination with statistical methods such as Genome-wide association mapping (GWAM) and Genomic prediction (GP), these information can provide valuable insight into the genetic potential of individuals and support selection and crossing decisions in a breeding program. In this thesis we focused on the evaluation of the aforementioned methods in diverse barley (Hordeum vulgare L.) and cauliflower (Brassica oleracea var. botrytis) populations consisting of elite material and genetic resources. We concentrated on the dissection of the influence of specific parameters such as marker type, statistical models, influence of population structure and kinship, on the performance of GWAM and GP. For parts of this thesis, we additionally used simulated data to support findings based on empirical data. First, we compared four different GWAM methods that either use single-marker or haplotypes for the detection of quantitative trait loci in a barley population. To find out the required population size and marker density to detect QTLs of varying effect size, we performed a simulation study based on parameter estimates of the empirical population. We could demonstrate that already in small populations of about 100 individuals, QTLs with a large effect can be detected and that at least 500 individuals are necessary to detect QTLs with an effect < 10%. Furthermore, we demonstrated an increased power of haplotpye based methods in the detection of very small QTLs. In a second study we used a barley population consisting of 750 individuals as training set to compare different GP models, that are currently used by scientists and plant breeders. From the training set 33 offspring families were derived with a total of 750 individuals. This enabled us to assess the prediction ability not only based on cross-validation but also in a large offspring population with varying degree of relatedness to the training population. We investigated the effects of linkage disequilibrium and linkage phase, population structure and relatedness of individuals, on the prediction ability. We could demonstrate a strong effect of the population structure on the prediction ability and show that about 11,203 evenly spaced SNP markers are necessary to predict even genetically distant populations. This implies that at the current marker density prediction ability is based on the relatedness of the individuals. In a third study we focused on the evaluation of GWAM and GP in cauliflower. We focused on the evaluation of genotyping-by-sequencing and compared the influence of imputation methods on the prediction ability and the number of significant associations. We obtained a total 120,693 SNPs in a random collection of 174 cauliflower genebank accessions. We demonstrated that imputation did not increase prediction ability and that the number of detected QTLs only slightly differed between the imputed and the unimputed data set. GP performed well even in such a diverse gene bank sample, but population structure again influenced the prediction ability. We could demonstrate the usefulness and limitations of Genome-wide association mapping and genomic prediction in two species. Even though a lot of research in the field of statistical genetics has provided valuable insight, the usage of Genomic prediction should still be applied with care and only as a supporting tool for classical breeding methods.
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.