Ali Zidi

Diversity and Ecophysiology of Cereals, Clermont-Ferrand

Genomic selection in wheat



Annual meeting: 2015

Fields-Topics: P2 Tissue and Individual

Type of talk: Fellows Speed Presentation

Genomic selection in wheat

Biography

I graduated in Agriculture Engineering in the “École Supérieure d’Agriculture de Mateur” (Tunisia). Subsequently, I obtained a MSc. in Animal Science in the context of a fellowship from Mediterranean Agronomic Institute of Zaragoza (CIHEAM-IAMZ, Spain). I presented my PhD. in Animal genetics at the Universitat Autònoma de Barcelona under the supervision of Dr. Marcel Amills Eras. The PhD research project was focused on the characterization and association analysis of candidate genes for milk production and composition. During my post-doctoral research, I was interested on the application of omics techniques on the improvement of traits economically important. The reduction of the cost of high-density panels SNPs makes opens the way to enhance a new selection scheme assisted by molecular markers, known as Genomic Selection. In this way, the success of the genomic selection especially in dairy cattle opens the door to apply this selection strategy in other species. In the context of AgreenSkills fellowship, my research interests were related to the optimisation of the accuracy of the selection genomic mainly that related with the reference populations used to train models.

Abstract

Genomic selection in wheat

The availability of whole genome typing tools in wheat has allowed the incorporation of new allelic variants into germplasm. In this way, genomic selection (GS) is an approach that is being applied in crop breeding to make decisions for advancing germplasm from one generation to the next. GS aims to predict breeding values from genome-wide marker data with high accuracy using model training and validation, prediction breeding values, and selecting based on these predictions. In this way, the present research work consists on finding the “Best” model training that could guarantee a high accuracy of the genomic predicted breeding value.

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