Dokument: Effective strategies incorporating genomic selection to improve short- and long-term genetic gain in plant breeding programs
| Titel: | Effective strategies incorporating genomic selection to improve short- and long-term genetic gain in plant breeding programs | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=65970 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20240611-125441-8 | |||||||
| Kollektion: | Dissertationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Abschlussarbeiten » Dissertation | |||||||
| Medientyp: | Text | |||||||
| Autor: | Wu, Po-Ya [Autor] | |||||||
| Dateien: |
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| Beitragende: | Prof. Dr. Stich, Benjamin [Gutachter] Prof. Dr. Longin, Friedrich [Gutachter] | |||||||
| Dewey Dezimal-Klassifikation: | 500 Naturwissenschaften und Mathematik » 580 Pflanzen (Botanik) | |||||||
| Beschreibung: | Genomic selection (GS) based on single nucleotide polymorphisms (SNP) has emerged as a powerful tool to increase the genetic gain of complex traits in breeding programs of various animal and plant species. However, its optimal integration especially in clone breeding programs, and its combination with the cross-selection (CS) method in heterozygous and tetraploid crops to balance genetic gain and diversity in long-term breeding programs are still lacking. Another important aspect affecting the success of genetic gain is the degree of prediction accuracy/ability of a GS model. The use of additional or alternative layers of omics datasets closer to phenotypes as predictors may improve the prediction ability. The main objectives of this thesis were to (1) optimize potato breeding programs incorporating GS using computer simulation; and (2) improve the efficiency of GS using different omic datasets and structural variants as predictors compared to SNP array, taking barley as an experimental example. Both approaches have the final goal to further enhance the genetic gain in breeding programs. In the simulation results, implementing GS with optimal selection intensities had a higher short- and long-term genetic gain compared to the phenotypic selection solely. In addition, implementing GS in consecutive selection stages largely increased genetic gain compared to using GS in one stage. Furthermore, the results of my computer simulations suggest that the optimal selection intensities require to be adjusted under different scenarios considering cost, selection strategies, prediction accuracy of the GS model, etc. When studying the long-term selection response, the CS method considering additive and dominance effects to predict progeny mean based on simulated progenies (MEGV-O) reached the highest accuracy in predicting progeny mean and the highest long-term gain among the CS methods that only consider the progeny mean. However, it accompanied the loss of genetic variance quickly. The linear combination of usefulness criteria (UC) and genome-wide diversity, which was called EUCD, kept the same level of genetic gain compared to UC and MEGV-O. However, EUCD simultaneously kept a higher diversity as well as a certain degree of genetic variance compared to UC and MEGV-O. Therefore, these results of my thesis can provide breeders with a concrete method to improve their potato breeding programs and are presumably also helpful for other clone breeding programs. In the frame of the other aspect studied in this thesis, the prediction ability of the GS model using deleterious sequence variants, structural variants, transcriptome, and metabolome as a single predictor, was higher than using SNP array on average across the assessed traits. Optimally combining the information of several layers of omic datasets in the GS model outperformed single predictors alone. Therefore, the results of my thesis will open the path to perform such analysis on a large scale segregating populations and even apply for potato breeding programs to boost genetic gain. | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät » WE Biologie | |||||||
| Dokument erstellt am: | 11.06.2024 | |||||||
| Dateien geändert am: | 11.06.2024 | |||||||
| Promotionsantrag am: | 25.01.2024 | |||||||
| Datum der Promotion: | 13.05.2024 |
