Climate change will involve negative impacts on staple (major or orphan) yields and on their nutritional quality. To this predicament must be added the uncertainty of climatic phenomena (frequency, intensity), which makes it essential to speed up the times for obtaining varieties adapted to the new conditions.
GWAS (Genome-wide association studies) and GS (genomic selection) are powerful approaches to investigate marker-trait associations and reduce the time/cost of breeding. The heritability and the genetic architecture influence the efficiency of these approaches, and it is not always completely successful, therefore, new methodologies are needed to complement and to achieve the objectives in shorter times.
The rapid advances in high-throughput technologies provide an opportunity to generate these new alternatives for plant breeding. For example, there is growing evidence that Omics Data improves the performance of Genomic Prediction. Moreover, integrating genome and functional omics data with genetic and phenotypic information can lead to discovering genes and pathways responsible for critical agronomic phenotypes.
The massive amount of data generated for the above-mentioned methodologies is bridged to the phenotype basically by machine learning and emerging branches, e.g. deep learning. This discipline is capable of dealing with the dimensionality and complexity of data, allowing the translation of biological knowledge and omics data into precision-designed plant breeding (although this task cannot always be solved in real time).
This Special Issue is focused on empirical studies addressing precision-designed breeding for yield, yield-related or nutritional traits.
We welcomes submissions of Research papers, Reviews, and Mini-Reviews related to:
• Empirical work combining GWAS with omics data.
• Empirical work reporting the enhancing the predictability of GS for breeding through machine learning empowered cost effective applications of “omics” data.
• Aplication of GWAS/GS and omics to harness genetic resources from germplasm banks in a more efficient way and adapt current germplasm to new environmental challenges
• Description of the machine learning methodology developed to apply in the previous cases.
Climate change will involve negative impacts on staple (major or orphan) yields and on their nutritional quality. To this predicament must be added the uncertainty of climatic phenomena (frequency, intensity), which makes it essential to speed up the times for obtaining varieties adapted to the new conditions.
GWAS (Genome-wide association studies) and GS (genomic selection) are powerful approaches to investigate marker-trait associations and reduce the time/cost of breeding. The heritability and the genetic architecture influence the efficiency of these approaches, and it is not always completely successful, therefore, new methodologies are needed to complement and to achieve the objectives in shorter times.
The rapid advances in high-throughput technologies provide an opportunity to generate these new alternatives for plant breeding. For example, there is growing evidence that Omics Data improves the performance of Genomic Prediction. Moreover, integrating genome and functional omics data with genetic and phenotypic information can lead to discovering genes and pathways responsible for critical agronomic phenotypes.
The massive amount of data generated for the above-mentioned methodologies is bridged to the phenotype basically by machine learning and emerging branches, e.g. deep learning. This discipline is capable of dealing with the dimensionality and complexity of data, allowing the translation of biological knowledge and omics data into precision-designed plant breeding (although this task cannot always be solved in real time).
This Special Issue is focused on empirical studies addressing precision-designed breeding for yield, yield-related or nutritional traits.
We welcomes submissions of Research papers, Reviews, and Mini-Reviews related to:
• Empirical work combining GWAS with omics data.
• Empirical work reporting the enhancing the predictability of GS for breeding through machine learning empowered cost effective applications of “omics” data.
• Aplication of GWAS/GS and omics to harness genetic resources from germplasm banks in a more efficient way and adapt current germplasm to new environmental challenges
• Description of the machine learning methodology developed to apply in the previous cases.