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Evaluating maize phenotypic variance, heritability, and yield relationships at multiple biological scales across agronomically relevant environments
Author(s) -
Tucker Sarah L.,
Dohleman Frank G.,
Grapov Dmitry,
Flagel Lex,
Yang Sean,
Wegener Kimberly M.,
Kosola Kevin,
Swarup Shilpa,
Rapp Ryan A.,
Bedair Mohamed,
Halls Steven C.,
Glenn Kevin C.,
Hall Michael A.,
Allen Edwards,
Rice Elena A.
Publication year - 2020
Publication title -
plant, cell and environment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.646
H-Index - 200
eISSN - 1365-3040
pISSN - 0140-7791
DOI - 10.1111/pce.13681
Subject(s) - germplasm , heritability , biology , phenology , phenotypic trait , phenotype , yield (engineering) , agronomy , gene , evolutionary biology , genetics , materials science , metallurgy
A challenge to improve an integrative phenotype, like yield, is the interaction between the broad range of possible molecular and physiological traits that contribute to yield and the multitude of potential environmental conditions in which they are expressed. This study collected data on 31 phenotypic traits, 83 annotated metabolites, and nearly 22,000 transcripts from a set of 57 diverse, commercially relevant maize hybrids across three years in central U.S. Corn Belt environments. Although variability in characteristics created a complex picture of how traits interact produce yield, phenotypic traits and gene expression were more consistent across environments, while metabolite levels showed low repeatability. Phenology traits, such as green leaf number and grain moisture and whole plant nitrogen content showed the most consistent correlation with yield. A machine learning predictive analysis of phenotypic traits revealed that ear traits, phenology, and root traits were most important to predicting yield. Analysis suggested little correlation between biomass traits and yield, suggesting there is more of a sink limitation to yield under the conditions studied here. This work suggests that continued improvement of maize yields requires a strong understanding of baseline variation of plant characteristics across commercially‐relevant germplasm to drive strategies for consistently improving yield.