Prediction in High-Dimensional Linear Models and Application to Genomic Selection Under Imperfect Linkage Disequilibrium
Year: 2021
Type: article
Abstract: 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, that is, 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 analyse 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.
DOI: 10.1111/rssc.12496
Authors: Charles-Elie Rabier, Simona Grusea
Institutions: Centre National de la Recherche Scientifique, Université de Montpellier, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Institut des Sciences de l'Evolution de Montpellier, Institut Montpelliérain Alexander Grothendieck, Institut de Recherche pour le Développement, Institut National des Sciences Appliquées de Toulouse, Université de Toulouse, Institut de Mathématiques de Toulouse
Language: English
Subfield: Genetics
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W2990954751