Using phenomic selection to predict hybrid values with NIR spectra measured on the parental lines: proof of concept on maize

Abstract

Phenomic selection based on parental spectra can be used to predict GCA and SCA in a sparse factorial design. Prediction approaches such as genomic selection can be game changers in hybrid breeding. They allow predicting the genetic values of hybrids without the need for their physical production. This leads to significant reductions in breeding cycle length, and so to the increase in genetic progress. However, these methods are often underutilized in breeding programs due to the substantial cost involved in genotyping thousands of candidate parental lines annually. To address this limitation, we propose a cost-effective alternative based on phenomic selection, where genotyping of parental lines is replaced by NIR spectroscopy. Standard prediction models are then applied for genomic and phenomic selection, using similarity matrices derived from either genotyping data (genomic selection) or NIR spectral data (phenomic selection). Our hypothesis is that the chemical composition of parental tissues captured by NIRS reflects the genetic similarity between parental lines. We evaluated both strategies using a sparse factorial design, whose hybrids have been phenotyped in a multi-environment trial network, and with NIR spectra acquired on the parental lines on two independent environments. Both genomic and phenomic prediction approaches demonstrated moderate-to-high predictive abilities across various cross-validation scenarios. Our results also showcase the capability of phenomic selection to predict Mendelian sampling. This study serves as a proof of concept that low-cost high-throughput phenomics of parental lines can effectively be used to predict maize hybrids in independent trials. This paves the way for widespread adoption of prediction approaches at the very first stages of hybrid breeding, benefiting both major and orphan species.

Keywords: Genomic selection; Hybrid breeding; Maize; Phenomic selection; Sparse factorial design.

Fig. 1

Correlation plot between each pair of traits (Pearson correlations)

Fig. 2

The proportion of genetic (red), genetic-environment interaction (green), and residual (blue) of the spectra variance measured in A dent silage, B flint silage in Ploudaniel, C dent leaves, and D flint leaves. NIRS analysis from leaves and dent silage was performed for two locations: Ploudaniel and Estrées-Mons. The variance decompositions were obtained from a bivariate model if spectra were acquired in the two trials or with a univariate model if the spectra were obtained only in Ploudaniel (flint silage) (color figure online)

Fig. 3

Predictive abilities of the pedigree, genomic, and phenomic prediction models for the agronomic traits in the CV_SparseTesting scenario. These were fivefold random cross validations with 5 repeats. The boxes represent the medians, as well as the first and third quartiles and the triangle dots marked the mean. Each plot was labeled corresponding to a given trait of which DMC: Dry Matter Content, DMY: Dry Matter Yield, FLOF: Flowering Time, and PH: Plant Height

Fig. 4

Predictive abilities for the CV_newDentFlint scenario for all traits. Only the four calibration set/prediction set partitions with more than 10 hybrids in the prediction set were considered. The boxes represent the median, as well as the first and third quartiles and the triangle dots marked the mean. There is no boxplot for P-BLUP on this figure, as this model cannot be used for this scenario, in which the pedigree coefficient is the same for all the predicted hybrids