ArticleHeredity2019
Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.
Article in Heredity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Lipid Metabolism and Actin Cytoskeleton Regulation Underlie Yield and Disease Resistance in TwoPlants (Basel, Switzerland) · 2025Article
- Genomic insights into population structure and predictive breeding for climate-resilient coffee.Heredity · 2025Article
- Genomic Prediction of Adaptation in Common Bean (International journal of molecular sciences · 2025Article
- Article
- Multienvironment genomic prediction in tetraploid potato.G3 (Bethesda, Md.) · 2024Article
- Genomic-assisted breeding for climate-smart coffee.The plant genome · 2024Article
- Using visual scores for genomic prediction of complex traits in breeding programs.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genomic selection for genotype performance and environmental stability in Coffea canephora.G3 (Bethesda, Md.) · 2023Article
- CRISPR technology towards genome editing of the perennial and semi-perennial crops citrus, coffee and sugarcane.Frontiers in plant science · 2023Review
- Multi-Environment and Multi-Year Bayesian Analysis Approach inPlants (Basel, Switzerland) · 2022Article
- Genomic selection in tropical perennial crops and plantation trees: a review.Molecular breeding : new strategies in plant improvement · 2022Review
- Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks.The plant genome · 2022Article
- Factor analysis applied in genomic selection studies in the breeding ofEuphytica: Netherlands journal of plant breeding · 2022Article
- Genetic diversity of wild and cultivated Coffea canephora in northeastern DR Congo and the implications for conservation.American journal of botany · 2021Article
- Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine.G3 (Bethesda, Md.) · 2021Article
- Review
- Article
- Genomic Selection in an Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding.Frontiers in plant science · 2021Article
- Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.G3 (Bethesda, Md.) · 2020Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee-production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.
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