![Empirical prediction accuracy of genomic selection between experimental designs and generations in oil palm. [W667]](/search/themes/bootstrap3/images/libros.png)
Empirical prediction accuracy of genomic selection between experimental designs and generations in oil palm. [W667]
There is still a large potential for the genetic improvement of yield in oil palm, as it has only been submitted to a few generations of modern breeding. Selection candidates are traditionally evaluated in progeny tests, as some yield components have a low heritability. The progeny-tests allow selecting with a high accuracy, but constrain the rate of genetic gain as they increase the generation interval and limit the number of evaluated individuals. Genomic selection (GS) is an appealing alternative to the current phenotypic selection, as it could allow selecting without progeny tests. Here, we studied the prediction accuracy of GS in the two heterotic groups used to produce oil palm commercial hybrids, ie the correlation between the genomic estimated breeding values and the breeding values obtained from the conventional method of phenotypic selection. We used two independent experimental designs located in Sumatra (Indonesia), comprising over 700 hybrid crosses. The GS model was calibrated with the first experimental design (training set) and applied to predict the breeding values of the individuals progeny tested in the second design (validation set). The genomic breeding values were obtained with the GBLUP statistical method, using the phenotypes of the training hybrids and the genotypes of all the progeny tested individuals. The genotypes consisted in several thousand SNPs produced with genotyping-by-sequencing (GBS). The validation set included sibs and progenies of the individuals progeny tested in the training set. We found that GBLUP could reach intermediate to high values and capture within families differences for some traits in both heterotic groups, when taking into account pedigree to impute molecular missing data and when subsetting SNPs on quality criteria (% missing data, MAF and / or distribution along genome). GBS therefore appeared as a relevant method to produce the molecular data necessary for GS in oil palm. Other studies are required to take advantage of the full potential of GS in this species. (Texte intégral)
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
PAG
|
| Subjects: | F30 - Génétique et amélioration des plantes, U10 - Informatique, mathématiques et statistiques, |
| Online Access: | http://agritrop.cirad.fr/579056/ http://agritrop.cirad.fr/579056/1/Cros%20et%20al%202016%20PAGXXIV_v3.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | There is still a large potential for the genetic improvement of yield in oil palm, as it has only been submitted to a few generations of modern breeding. Selection candidates are traditionally evaluated in progeny tests, as some yield components have a low heritability. The progeny-tests allow selecting with a high accuracy, but constrain the rate of genetic gain as they increase the generation interval and limit the number of evaluated individuals. Genomic selection (GS) is an appealing alternative to the current phenotypic selection, as it could allow selecting without progeny tests. Here, we studied the prediction accuracy of GS in the two heterotic groups used to produce oil palm commercial hybrids, ie the correlation between the genomic estimated breeding values and the breeding values obtained from the conventional method of phenotypic selection. We used two independent experimental designs located in Sumatra (Indonesia), comprising over 700 hybrid crosses. The GS model was calibrated with the first experimental design (training set) and applied to predict the breeding values of the individuals progeny tested in the second design (validation set). The genomic breeding values were obtained with the GBLUP statistical method, using the phenotypes of the training hybrids and the genotypes of all the progeny tested individuals. The genotypes consisted in several thousand SNPs produced with genotyping-by-sequencing (GBS). The validation set included sibs and progenies of the individuals progeny tested in the training set. We found that GBLUP could reach intermediate to high values and capture within families differences for some traits in both heterotic groups, when taking into account pedigree to impute molecular missing data and when subsetting SNPs on quality criteria (% missing data, MAF and / or distribution along genome). GBS therefore appeared as a relevant method to produce the molecular data necessary for GS in oil palm. Other studies are required to take advantage of the full potential of GS in this species. (Texte intégral) |
|---|