A new evolution-based genomic prediction model forecasts yield performance across environments and future climates and identifies adapted maize landraces

Abstract

Forecasting vulnerability of cultivated and wild species to climate changes is highly challenging. Evolutionary genomic models enable the prediction of mal-adaptation (genomic offset - GO) across environments and future climates under the assumption that populations are currently locally adapted but do not predict but the resulting phenotypic changes. To do so, we developed a new genomic prediction model (GP) integrating both genomic offset (GO) and within-population gene diversity (Hs) to capture genotype by environment interaction and inbreeding effects, respectively (GP-HO-Hs). As proof of concept, we applied this GP-GO-Hs model to a collection of 397 maize populations (landraces) evaluated across 25 environments in Europe using high-throughput DNA pool genotyping. GP-GO-Hs model accurately predicted yield, plant height and flowering time. It increased by 13% the predictive abilities of GP model for predicting yield of new landraces in new environments. GP-GO-Hs model also predicted that the more diverse the landrace, the more stable its agronomic performance across environments. GP-GO-Hs model generated phenotypic adaptive landscapes for each landrace in future climatic scenarios, enabling the identification of landraces with enhanced potential to adapt to future or emerging cultivation conditions. This GP-GO-Hs model could be easily applied to other wild and cultivated species. Teaser Identify promising landrace adapted to new and future environments by combining genomic selection and offset

Plant phenotyping relevance

ゲノム情報と環境適応指標を統合した新規予測モデルを開発し、収量・草丈・開花期という植物形質を予測することが研究の中心であるため、計算的フェノタイピング手法として収録する。

Code and data availability

The supplied blocks describe phenotyping of 397 maize landraces across 25 EVA environments and GP/GO analyses, but contain no data or code availability statement, no public repository deposit, and no authors' URL for datasets, phenotypes, images, or scripts. The only URL present is the bioRxiv DOI itself, which is the预

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