Distinct genetic architectures for phenotype means and plasticities in Zea mays

Abstract

Phenotypic plasticity describes the phenotypic variation of a trait when a genotype is exposed to different environments. Understanding the genetic control of phenotypic plasticity in crops such as maize is of paramount importance for maintaining and increasing yields in a world experiencing climate change. Here, we report the results of genome-wide association analyses of multiple phenotypes and two measures of phenotypic plasticity in the maize nested association mapping (US-NAM) population grown in multiple environments and genotyped with ~2.5 million single nucleotide polymorphisms (SNPs). We show that across all traits the candidate genes for mean phenotype values and plasticity measures form structurally and functionally distinct groups. Such independent genetic control suggests that breeders will be able to select semi-independently for mean phenotype values and plasticity, thereby generating varieties with both high mean phenotype values and levels of plasticity that are appropriate for the target performance environments.

Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds

“…In most cases, performance was not correlated with any measure of stability. A previous study of 23 phenotypes in maize showed that candidate genes for mean performance and for linear and nonlinear plasticity (i.e., slope and MSE from the Finley-Wilkinson regression) were structurally and functionally distinct (Kusmec, Srinivasan, Nettleton, & Schnable, 2017). If different genetic regions control trait performance as opposed to trait stability, it may be easier to exploit G × E while also breeding for improved performance.…”

Crop genome‐wide association study: a harvest of biological relevance

“…Even though the statistical framework (Wang et al ., ; Zhai et al ., ; Zhou et al ., ) has been established for years and representative studies (Kusmec et al ., ; Li et al ., ) have been performed, our understanding of the mechanisms of plasticity and its effect on shaping crop diversity along environmental gradients is still limited. The lack of deep insights into this common and important issue may be because of the massive data sets needed, including environmental measurements in addition to standard genotypic and phenotypic variations.…”

“…In a recent pioneering study, Kusmec et al . () analyzed 23 agronomic traits in 4−11 environments using a nested association mapping (NAM) population consisting of about 5000 RILs. By partitioning the trait into phenotypic mean, linear, and non‐linear plasticities with the Bayesian Finlay−Wilkinson Regression (FWR) (Su et al ., ), structurally and functionally distinct candidate genes were found in association with mean and plastic phenotypes.…”

Distinct characteristics of genes associated with phenome-wide variation in maize (Zea mays)

“…The current statistical procedure also encounters challenges once the number of input traits exceed the number of individuals in the population. In these cases, it would be best to avoid the common practice of employing BLUP scores 48 as this approach strips out information on trait plasticity across environments, and trait plasticity is often controls by distinct sets of genes from genes controlling variation in multi-environment trait means 49 . Automatic variable selection and/or dimensional reduction could be incorporated into future GPWAS implementations.…”