Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.

Publisher: Oxford University Press Country of Publication: England NLM ID: 101566598 Publication Model: Print Cited Medium: Internet ISSN: 2160-1836 (Electronic) Linking ISSN: 21601836 NLM ISO Abbreviation: G3 (Bethesda) Subsets: MEDLINE

Publication: 2021- : [Oxford] : Oxford University Press
Original Publication: Bethesda, MD : Genetics Society of America, 2011-

Phenomics* ; Genome-Wide Association Study*; Humans ; Phenotype ; Genotype ; Genomics

A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.
(© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.)

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Keywords: genomic prediction; high-throughput phenotyping; phenomic prediction

Date Created: 20221129 Date Completed: 20230116 Latest Revision: 20230203

20250114

PMC9836347

10.1093/g3journal/jkac294

36445027