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.

Plant phenotyping relevance

スペクトルに基づくフェノミック予測をブドウで適用し、複数組織・年・集団・形質で遺伝予測との性能比較を行っており、表現型取得・予測手法が研究の中心である。

Code and data availability

The paper's Data availability statement deposits spectra, R scripts, and result tables in the INRAE data portal (DOI 10.15454/BICRFX), and genotypic values/genotypic data at DOI 10.15454/PNQQUQ. Both are paper-specific, public, and actionable.