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.

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To cope with the challenges facing agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennial species such as grapevine, but requires understanding the genetic architecture of target traits. To go beyond the mapping of quantitative trait loci (QTLs) in bi-parental crosses, we exploited a diversity panel of 279 Vitis vinifera L. cultivars planted in five blocks in the vineyard. This panel was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. The panel was genotyped for 63k single nucleotide polymorphisms (SNPs) by combining an 18K microarray and genotyping-by-sequencing (GBS). The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via GBS markedly increased the proportion of genetic variance explained by SNPs, and two multi-SNP models identified QTLs not found by a SNP-by-SNP model. Overall, 489 reliable QTLs were detected for 41% more response variables than by a SNP-by-SNP model with microarray-only SNPs, many new ones compared to the results from bi-parental crosses. A prediction accuracy higher than 0.42 was obtained for 50% of the response variables. Our overall approach as well as QTL and prediction results provide insights into the genetic architecture of target traits. New candidate genes and the application into breeding are discussed.

In the original publication, the numbers assigned to each haplotype in Fig. 2 and its expanded version (Supple-mentary Fig. S2) are not consistent with the numbers used in the article. The haplotype numbers are corrected and the haplotypes are reordered according to these new numbers in the attached revised Fig. 2 and Supplementary Fig. S2. Fig. 2 Marker allele composition of each haplotype across the five haploblocks for the sweet cherry QTL hotspot on chromosome 2 illustrated using the smallest number of markers needed to differentiate the haplotypes. SSR alleles are recorded as fragment sizes in base pairs. Haplotypes were assigned by the PediHaplotyper software (Voorrips et al. 2016). Haplotypes containing missing marker scores were omitted from the table. The complete marker composition is in Supplementary Fig. S2 Supplementary Fig. S2 Marker allele composition of each haplotype across five haploblocks for the sweet cherry QTL hotspot on chromosome 2. SSR alleles are recorded as fragment sizes in base pairs. The smallest subset of markers needed to differentiate the haplotypes within each haploblock are highlighted in red font. Haplotypes were assigned by the PediHaplotyper software (Voorrips et al., 2016). Haplotypes containing missing marker scores were omitted from the table Mol Breeding (2017) 37: 100