title: Genome-wide prediction methods in highly diverse and heterozygous species: proof-of-concept through simulation in grapevine.
abstract: Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.
authors.affiliation: UMT Geno-Vigne, IFV-INRA-Montpellier Supagro, Montpellier, France; UMR AGAP, INRA, Montpellier, France.
authors.forename: Agota
authors.initials: A
authors.lastname: Fodor
authors.affiliation: UR 588 AGPF, INRA, Orléans, France.
authors.forename: Vincent
authors.initials: V
authors.lastname: Segura
authors.affiliation: UMR AGAP, CIRAD, Montpellier, France.
authors.forename: Marie
authors.initials: M
authors.lastname: Denis
authors.affiliation: University of Lausanne, Department of Ecology and Evolution, Lausanne, Switzerland; University of Lausanne, Swiss Institute of Bioinformatics, Vital-IT, Lausanne, Switzerland.
authors.forename: Samuel
authors.initials: S
authors.lastname: Neuenschwander
authors.affiliation: Department of Genetics, The University of Melbourne, Parkville, Australia.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Fournier-Level
authors.affiliation: UMR AGAP, INRA, Montpellier, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Chatelet
authors.affiliation: UMR AGAP, CIRAD, Montpellier, France.
authors.forename: Félix Abdel Aziz
authors.initials: FA
authors.lastname: Homa
authors.affiliation: UMR AGAP, INRA, Montpellier, France.
authors.forename: Thierry
authors.initials: T
authors.lastname: Lacombe
authors.affiliation: UMT Geno-Vigne, IFV-INRA-Montpellier Supagro, Montpellier, France; UMR AGAP, INRA, Montpellier, France.
authors.forename: Patrice
authors.initials: P
authors.lastname: This
authors.affiliation: UMT Geno-Vigne, IFV-INRA-Montpellier Supagro, Montpellier, France; UMR AGAP, INRA, Montpellier, France.
authors.forename: Loic
authors.initials: L
authors.lastname: Le Cunff
mesh_terms: D044822:Biodiversity; D005075:Biological Evolution; D001947:Breeding; D003198:Computer Simulation; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D006579:Heterozygote; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results; D012641:Selection, Genetic; D027843:Vitis / Q000235:genetics