| Autor: |
Mario Henrique Murad Leite Andrade, Janam P. Acharya, Juliana Benevenuto, Ivone deBem Oliveira, Yolanda Lopez, Patricio Munoz, Marcio F. R. Resende Jr., Esteban F. Rios |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
The Plant Genome, Vol 15, Iss 3, Pp n/a-n/a (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1940-3372 |
| DOI: |
10.1002/tpg2.20235 |
| Popis: |
Abstract Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high‐quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full‐sib and 35 half‐sib families was bulk‐genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single‐nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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