Perspectives for Genomic Selection Applications and Research in Plants
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.306 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
ABSTRACT Genomic selection (GS) has created a lot of excitement and expectations in the animal‐ and plant‐breeding research communities. In this review, we briefly describe how genomic prediction can be integrated into breeding efforts and point out achievements and areas where more research is needed. Genomic selection provides many opportunities to increase genetic gain in plant breeding per unit time and cost. Early empirical and simulation results are promising, but for GS to deliver genetic gains, careful consideration of the problem of optimal resource allocation is needed. Consideration of the cost‐benefit balance of using markers for each trait and stage of the breeding cycle is needed, moving beyond only focusing on recurrent selection with GS on a few complex traits, using prediction on unphenotyped individuals. With decreasing marker cost, phenotype data is quickly becoming the most valuable asset and marker‐assisted selection strategies should focus on making the most of scarce and expensive phenotypes. It is important to realize that markers can also improve accuracy of selection for phenotyped individuals. Use of markers as an aid to phenotype analysis suggests a number of new strategies in terms of experimental design and multi‐trait models. GS also provides new ways to analyze and deal with genotype by environment interactions. Lastly, we point to some recent results showing that new models are needed to improve predictions particularly with respect to the use of distantly related individuals in the training population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Crop Science
- Topic
- Genetic and phenotypic traits in livestock
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- —
- Funders
- HatchU.S. Department of Agriculture
- Keywords
- Selection (genetic algorithm)Genomic selectionBiologyTraitResource (disambiguation)PopulationGenetic gainQuantitative trait locusBiotechnologyPlant breedingMarker-assisted selectionAsset (computer security)Computational biologyComputer scienceGeneticsMachine learningGenotypeGenetic variationGene
- Has abstract in OpenAlex
- yes