Prediction in high dimensional linear models and application to genomic selection under imperfect linkage disequilibrium
Résumé
Genomic selection (GS) consists in predicting breeding values of selection candidates, using a large number of genetic markers. An important question in GS is to determine the number of markers required for a good prediction.
For this purpose, we introduce new proxies for the accuracy of the prediction. These proxies are suitable under sparse genetic map where it is likely to observe some imperfect linkage disequilibrium, i.e. the situation where the alleles at a gene location and at a marker located nearby vary.
Moreover, our suggested proxies are helpful for designing cost-effective SNP chips based on a moderate density of markers.We analyze rice data from Los Banos, Philippines and focus on the flowering time collected during the dry season 2012. Using different densities of markers, we show that at least 1553 markers are required to implement GS.
Finding the optimal number of markers is crucial in order to optimize the breeding program.
| Origine | Fichiers produits par l'(les) auteur(s) |
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