Genomic selection (GS) has been the most prominent topic in breeding science in the last two decades. The continued interest is promoted by its huge potential impact on the efficiency of breeding. Predicting a breeding value based on molecular markers and phenotypic values of relatives may be used to manipulate three parameters of the breeder's equation. First, the accuracy of the selection may be improved by predicting the genetic value more reliably when considering the records of relatives and the realized genomic relationship. Secondly, genotyping and predicting may be more cost effective than comprehensive phenotyping. Resources can instead be allocated to increasing population sizes and selection intensity. The third, probably most important factor, is time. As shown in dairy cattle breeding, reducing cycle time by crossing selection candidates earlier may have the strongest impact on selection gain.
Many different prediction models have been used, and different ways of using predicted values in a breeding program have been explored. We would like to address the questions:
i. How did GS change breeding schemes of different crops in the last 20 years?
ii. What was the impact on realized selection gain?
iii. What would be the best structure of a crop-specific breeding scheme to exploit the full potential of GS?
iv. What is the potential of hybrid prediction, epistasis effect models, deep learning methods and other extensions of the standard prediction of additive effects?
v. What are the long-term effects of GS?
vi. Can predictive breeding approaches also be used to harness genetic resources from germplasm banks in a more efficient way to adapt current germplasm to new environmental challenges?
This Research Topic welcomes submissions of Original Research papers, Opinions, Perspectives, Reviews, and Mini-Reviews related to these themes:
1. Genomic selection: statistical methodology
2. The (optimal) use of GS in breeding schemes
3. Practical experiences with GS (selection gain, long-term effects, negative side effects)
4. Predictive approaches to harness genetic resources
Concerning point 1): If an original research paper compares different methods empirically without theoretical considerations on when one or the other method should be better, the methods should be compared with at least five different data sets. The data sets should differ either in crop, genotyping method or its source, for instance from a breeding program or gene bank accessions.
Concerning point 2): Manuscripts addressing the use of GS in breeding schemes should illustrate breeding schemes that are run in practice. General ideas about schemes that may be run in the future may be considered as 'Perspective' articles.
Conflict of Interest statements:
- Topic Editor Valentin Wimmer is affiliated to KWS SAAT SE & Co. KGaA, Germany.
- Topic Editor Brian Gardunia is affiliated to Bayer Crop Sciences and has a collaboration with AbacusBio, and is an author on patents with Bayer Crop Sciences.
The other Topic Editors did not disclose any conflicts of interest.
Image credit: CIMMYT, reproduced under the CC BY-NC-SA 2.0 license
Genomic selection (GS) has been the most prominent topic in breeding science in the last two decades. The continued interest is promoted by its huge potential impact on the efficiency of breeding. Predicting a breeding value based on molecular markers and phenotypic values of relatives may be used to manipulate three parameters of the breeder's equation. First, the accuracy of the selection may be improved by predicting the genetic value more reliably when considering the records of relatives and the realized genomic relationship. Secondly, genotyping and predicting may be more cost effective than comprehensive phenotyping. Resources can instead be allocated to increasing population sizes and selection intensity. The third, probably most important factor, is time. As shown in dairy cattle breeding, reducing cycle time by crossing selection candidates earlier may have the strongest impact on selection gain.
Many different prediction models have been used, and different ways of using predicted values in a breeding program have been explored. We would like to address the questions:
i. How did GS change breeding schemes of different crops in the last 20 years?
ii. What was the impact on realized selection gain?
iii. What would be the best structure of a crop-specific breeding scheme to exploit the full potential of GS?
iv. What is the potential of hybrid prediction, epistasis effect models, deep learning methods and other extensions of the standard prediction of additive effects?
v. What are the long-term effects of GS?
vi. Can predictive breeding approaches also be used to harness genetic resources from germplasm banks in a more efficient way to adapt current germplasm to new environmental challenges?
This Research Topic welcomes submissions of Original Research papers, Opinions, Perspectives, Reviews, and Mini-Reviews related to these themes:
1. Genomic selection: statistical methodology
2. The (optimal) use of GS in breeding schemes
3. Practical experiences with GS (selection gain, long-term effects, negative side effects)
4. Predictive approaches to harness genetic resources
Concerning point 1): If an original research paper compares different methods empirically without theoretical considerations on when one or the other method should be better, the methods should be compared with at least five different data sets. The data sets should differ either in crop, genotyping method or its source, for instance from a breeding program or gene bank accessions.
Concerning point 2): Manuscripts addressing the use of GS in breeding schemes should illustrate breeding schemes that are run in practice. General ideas about schemes that may be run in the future may be considered as 'Perspective' articles.
Conflict of Interest statements:
- Topic Editor Valentin Wimmer is affiliated to KWS SAAT SE & Co. KGaA, Germany.
- Topic Editor Brian Gardunia is affiliated to Bayer Crop Sciences and has a collaboration with AbacusBio, and is an author on patents with Bayer Crop Sciences.
The other Topic Editors did not disclose any conflicts of interest.
Image credit: CIMMYT, reproduced under the CC BY-NC-SA 2.0 license