Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits.

Abstract

The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.

Plant phenotyping relevance

NIR分光によるサトウキビの繊維・ショ糖含量という植物品質形質の推定性能を、ゲノム予測および統合モデルと比較検証しており、形質取得・推定手法が研究の中心である。

Code and data availability

The paper's underlying phenotype (fiber and sucrose content BLUPs), NIR spectra, and SNP marker data for the 385 sugarcane clones are publicly deposited on figshare, as stated in the Data Availability statement. No author analysis code repository is mentioned. The figshare DOI appears in the text but its URL is not in;