Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar

Abstract

ABSTRACT Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits and coined this new approach “phenomic selection” (PS). We tested PS on two species of economic interest ( Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information. ARTICLE SUMMARY Despite its widely adopted interest in breeding, genomic selection - the prediction of breeding values using DNA polymorphisms - remains difficult to implement for many species because of genotyping costs. As an alternative or complement depending on the context, we propose “phenomic selection” (PS) as the use of low-cost and high-throughput phenotypic records to reconstruct similarities between genotypes and predict their performances. As a proof of concept of PS, we made use of near infrared spectroscopy applied to different tissues in poplar and wheat to predict various key traits and showed that PS could reach predictions as accurate as with molecular markers.

Plant phenotyping relevance

NIRSを用いた低コスト・高スループットな表現型記録から遺伝型間類似性を再構成し、複雑形質を予測する手法を提案・実証しており、表現型取得と解析ワークフローが研究の中心である。

Code and data availability

The supplied blocks describe NIRS spectra, phenotypes, and custom R analysis for wheat and poplar, but contain no data or code availability statement, no public repository, and no authors' URL for any dataset or script. Only the bioRxiv DOI is present, which is the article itself, not a qualifying asset.

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