ArticleScientific reports2021
A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- New insights into trait introgression with the look-ahead intercrossing strategy.G3 (Bethesda, Md.) · 2023Article
- Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean.The plant genome · 2023Article
- Identification of Disease Resistance Parents and Genome-Wide Association Mapping of Resistance in Spring Wheat.Plants (Basel, Switzerland) · 2022Article
- Benefit of Introgression Depends on Level of Genetic Trait Variation in Cereal Breeding Programmes.Frontiers in plant science · 2022Article
- Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.Scientific reports · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. Our objective is to select the most promising individuals for further backcrossing or selfing. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches. Our proposed selection method can assist breeders to efficiently design trait introgression projects.
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Registered trials
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