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

Abstract

Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal 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. 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. We found that the co-inertia between spectra and genomic data 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. 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, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of −0.2, thus suggesting that phenomic prediction could be applied for any trait.

Knowledge-driven approaches to improve genomic prediction in plants

“…Each dataset can be used as a standalone information resource for predicting phenotypes. For instance, using only the multi-/ hyperspectral data, often called phenomics prediction (Edlich-Muth, Muraya et al 2016, Adak, Murray et al 2021, Brault, Lazerges et al 2021, Robert, Auzanneau et al 2022, has been reported to potentially be as effective as genomic prediction. In principle, anyomics dataset can serve for GP, where in many cases metabolomics measurements could be considered to be closest to the observable phenotypes, as metabolism is influenced by other levels in the cell (the genome, transcriptome and proteome).…”