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Abstract Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set’s genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
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Integrating genomic prediction and genotype specific parameter estimation in ecophysiological models: overview and perspectives
Abstract The Genome-to-Phenome (G2P) problem is one of the highest-priority challenges in applied biology. Ecophysiological crop models (ECM) and genomic prediction (GP) models are quantitative algorithms, which, when given information on a genotype and environment, can produce an accurate estimate of a phenotype of interest. In this article, we discuss how the GP algorithms can be used to estimate genotype-specific parameters (GSPs) in ECMs to develop robust prediction methods. In this approach, the numerical constants (GSPs) that ECMs use to distinguish and characterize crop cultivars/varieties are treated as quantitative traits to be predicted by genomic prediction models from underlying genetic information. In this article we provide information on which GP methods appear favorable for predicting different types of GSPs, such as vernalization sensitivity or potential radiation use efficiency. For each example GSP, we assess a number of GP methods in terms of their suitability using a set of three criteria grounded in genetic architecture, computational requirements, and the use of prior information. In general, we conclude that the most useful algorithms were dependent on both the nature of the particular GSP and the GP methods considered.
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- Award ID(s):
- 1826820
- PAR ID:
- 10501417
- Editor(s):
- Hammer, G.
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- in silico Plants
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2517-5025
- Page Range / eLocation ID:
- 1–14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/insilicoplants/diad007
