Genomic and phenomic prediction for soybean seed yield, protein, and oil.

Abstract

Developments in genomics and phenomics have provided valuable tools for use in cultivar development. Genomic prediction (GP) has been used in commercial soybean [Glycine max L. (Merr.)] breeding programs to predict grain yield and seed composition traits. Phenomic prediction (PP) is a rapidly developing field that holds the potential to be used for the selection of genotypes early in the growing season. The objectives of this study were to compare the performance of GP and PP for predicting soybean seed yield, protein, and oil. We additionally conducted genome-wide association studies (GWAS) to identify significant single-nucleotide polymorphisms (SNPs) associated with the traits of interest. The GWAS panel of 292 diverse accessions was grown in six environments in replicated trials. Spectral data were collected at two time points during the growing season. A genomic best linear unbiased prediction (GBLUP) model was trained on 269 accessions, while three separate machine learning (ML) models were trained on vegetation indices (VIs) and canopy traits. We observed that PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new random forest model was trained with the inclusion of selected SNPs. These models did not outperform the original GP and PP models. These results show the capability of using ML for in-season predictions for specific traits in soybean breeding and provide insights on PP and GP inclusions in breeding programs.

Plant phenotyping relevance

大豆の収量・種子成分を対象に、圃場スペクトルデータ、植生指数、樹冠形質、機械学習を用いたフェノミック予測を構築・比較しており、表現型取得・推定手法が研究の中心である。

Code and data availability

The supplied blocks describe soybean phenomic/genomic prediction methods (spectral reflectance, VIs, GBLUP, PLSR/RF/XGBoost) but contain no authors' public data or code deposit. The only URLs present are the license and generic CRAN pages for the inti and heritability R packages, which are third-party libraries, not a,

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