Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize

Abstract

Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. In light of the investment required to characterize root anatomy, we present a machine learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment and were used subsequently in genotype-environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial conductance in cooler, drier highland areas. We discuss our results in the context of previously described water-banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.

Plant phenotyping relevance

環境記述子と機械学習により、少数の根解剖学的形質データから大規模なトウモロコシ集団へ形質変異を推定・投影する手法が研究の中心であり、再利用可能な植物形質推定ワークフローとして扱える。

Code and data availability

The supplied blocks describe root-anatomy phenotyping, random forest models, and GRANAR-MECHA simulations, but contain no public deposit, availability statement, or authors' URL for phenotype data, images, code, or trained models. The only URL present (paperpile.com/c/jHvuxa/s6Ua) is a citation link to prior work, nota

No evidence-backed public reproduction asset is currently recorded.