Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments

• Published in: The plant genome Vol. 17; no. 2; pp. e20454 - n/a
• Main Authors: DeSalvio, Aaron J., Adak, Alper, Murray, Seth C., Jarquín, Diego, Winans, Noah D., Crozier, Daniel, Rooney, William L.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.06.2024; Wiley
• Subjects: Accuracy; Corn; Crop yield; Datasets; Genomics; Genotypes; Genotyping; Grain; Infrared spectroscopy; Prediction models; Reflectance
• ISSN: 1940-3372; 1940-3372
• DOI: 10.1002/tpg2.20454

For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across‐environment sparse genomic prediction models. One phenomic data modality is whole grain near‐infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500‐kernel weight (KW) across 2 years (2011–2012) and two management conditions (water‐stressed and well‐watered) were conducted using combinations of reflectance data obtained from high‐throughput, F2 whole‐kernel scans and genomic data obtained from genotyping‐by‐sequencing within four different cross‐validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024—genomic vs. 0.612 ± 0.045—phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034—genomic vs. 0.617 ± 0.145—phenomic). Multi‐kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single‐kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single‐kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00. Core Ideas Near‐infrared spectroscopy (NIRS) phenomic and genomic prediction had similar accuracies for grain yield and 500‐kernel weight. Models with only genomic/phenomic data often outperformed multi‐kernel models. Lasso regression removed correlated NIRS bands with limited reduction in prediction ability. Plain Language Summary As a common method of identifying high‐performing crop varieties, genomic prediction has been successful in research and commercial crop improvement programs for nearly two decades. However, to determine if a nondestructive method of acquiring spectral data could perform comparably with genomic prediction, near‐infrared spectroscopy (NIRS) of intact maize kernels was conducted in this study using plants grown in multiple environments. Genomic and phenomic prediction were compared for predicting agronomic traits using combinations of genomic‐only, phenomic‐only, and combined prediction models. Though in many cases genomic prediction outperformed NIRS‐based phenomic prediction, these findings indicate that NIRS can still enable comparable prediction ability, highlighting its potential role as a high‐throughput method for predicting economically relevant crop traits. These findings are relevant for breeding programs seeking to screen varieties rapidly and nondestructively.