A novel high-density grapevine (Vitis vinifera L.) integrated linkage map using GBS in a half-diallel population.

KEY MESSAGE: A half-diallel population involving five elite grapevine cultivars was generated and genotyped by GBS, and highly-informative segregation data was used to construct a high-density genetic map for Vitis vinifera L. Grapevine is one of the most relevant fruit crops in the world. Deeper genetic knowledge could assist modern grapevine breeding programs to develop new wine grape varieties able to face climate change effects. To assist in the rapid identification of markers for crop yield components, grape quality traits and adaptation potential, we generated a large Vitis vinifera L. population (N = 624) by crossing five red wine cultivars in a half-diallel scheme, which was subsequently sequenced by an efficient GBS procedure. A high number of fully informative genetic variants was detected using a novel mapping approach capable of reconstructing local haplotypes from adjacent biallelic SNPs, which were subsequently used to construct the densest consensus genetic map available for the cultivated grapevine to date. This 1378.3-cM map integrates 10 bi-parental consensus maps and orders 4437 markers in 3353 unique positions on 19 chromosomes. Markers are well distributed all along the grapevine reference genome, covering up to 98.8% of its genomic sequence. Additionally, a good agreement was observed between genetic and physical orders, adding confidence in the quality of this map. Collectively, our results pave the way for future genetic studies (such as fine QTL mapping) aimed to understand the complex relationship between genotypic and phenotypic variation in the cultivated grapevine. In addition, the method used (which efficiently delivers a high number of fully informative markers) could be of interest to other outbred organisms, notably perennial fruit crops.

New stable QTLs for berry weight do not colocalize with QTLs for seed traits in cultivated grapevine (Vitis vinifera L.).

BACKGROUND: In grapevine, as in other fruit crops, fruit size and seed content are key components of yield and quality; however, very few Quantitative Trait Loci (QTLs) for berry weight and seed content (number, weight, and dry matter percentage) have been discovered so far. To identify new stable QTLs for marker-assisted selection and candidate gene identification, we performed simultaneous QTL detection in four mapping populations (seeded or seedless) with various genetic backgrounds. RESULTS: For berry weight, we identified five new QTLs, on linkage groups (LGs) 1, 8, 11, 17 and 18, in addition to the known major QTL on LG 18. The QTL with the largest effect explained up to 31% of total variance and was found in two genetically distant populations on LG 17, where it colocalized with a published putative domestication locus. For seed traits, besides the major QTLs on LG 18 previously reported, we found four new QTLs explaining up to 51% of total variance, on LGs 4, 5, 12 and 14. The previously published QTL for seed number on LG 2 was found related in fact to sex. We found colocalizations between seed and berry weight QTLs only for the major QTL on LG 18 in a seedless background, and on LGs 1 and 13 in a seeded background. Candidate genes belonging to the cell number regulator CNR or cytochrome P450 families were found under the berry weight QTLs on LGs 1, 8, and 17. The involvement of these gene families in fruit weight was first described in tomato using a QTL-cloning approach. Several other interesting candidate genes related to cell wall modifications, water import, auxin and ethylene signalling, transcription control, or organ identity were also found under berry weight QTLs. CONCLUSION: We discovered a total of nine new QTLs for berry weight or seed traits in grapevine, thereby increasing more than twofold the number of reliable QTLs for these traits available for marker assisted selection or candidate gene studies. The lack of colocalization between berry and seed QTLs suggests that these traits may be partly dissociated.

Across-population genomic prediction in grapevine opens up promising prospects for breeding.

Crop breeding involves two selection steps: choosing progenitors and selecting individuals within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

Interest of phenomic prediction as an alternative to genomic prediction in grapevine.

BACKGROUND: Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. RESULTS: For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. CONCLUSION: NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.