The theory on and software simulating large-scale genomic data for genotype-by-environment interactions

Background With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. Results We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. Conclusions This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

Multiple trait breeding programs with genotype-by-environment interactions based on reaction norms, with application to genetic improvement of disease resilience

Background Genotype-by-environment interactions for a trait can be modeled using multiple-trait, i.e. character-state, models, that consider the phenotype as a different trait in each environment, or using reaction norm models based on a functional relationship, usually linear, between phenotype and a quantitative measure of the quality of the environment. The equivalence between character-state and reaction norm models has been demonstrated for a single trait. The objectives of this study were to extend the equivalence of the reaction norm and character-state models to a multiple-trait setting and to both genetic and environmental effects, and to illustrate the application of this equivalence to the design and optimization of breeding programs for disease resilience. Methods Equivalencies between reaction norm and character-state models for multiple-trait phenotypes were derived at the genetic and environmental levels, which demonstrates how multiple-trait reaction norm parameters can be derived from multiple-trait character state parameters. Methods were applied to optimize selection for a multiple-trait breeding goal in a target environment based on phenotypes collected in a healthy and disease-challenged environment, and to optimize the environment in which disease-challenge phenotypes should be collected. Results and conclusions The equivalence between multiple-trait reaction norm and multiple-trait character-state parameters allow genetic improvement for a multiple-trait breeding goal in a target environment to be optimized without recording phenotypes and estimating parameters for the target environment.

Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions

Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F2 (60 out of 629 plants) and the F5 stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha−1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.

The mutation effect reaction norm (MERN) highlights environmentally dependent mutation effects and genetic interactions

Measuring the fitness effects of mutations and epistasis remain central yet provocative concepts in evolutionary and population genetics. In addition to the baseline complexity that arises from the notion that genetic information can interact in a nonlinear way, recent studies have revealed that interactions can change as a function of environmental context. Here I propose the fusion of measurements of the effect of mutations and physiological epistasis with the reaction norm, a central abstraction used to depict genotype by environment interactions. In doing so, I formalize the notion of a "mutation effect reaction norm" (MERN) as an instrument through which one can analyze or depict the phenotypic consequences of interactions between mutations across environmental contexts. I demonstrate its utility through a discussion of the signature of mutations that undermine reverse evolution of antimicrobial resistance. In closing, I argue that the mutation effect reaction norm may help us resolve the dynamism of evolution across fitness seascapes through specific insight into how mutation effects and interactions are modulated by environmental context.

Mapping of genotype-by-environment interactions in phenology identifies two cues for flowering in switchgrass (Panicum virgatum)

Plant phenological timings are major fitness components affected by multiple environmental cues; thus, phenological traits can have important genotype-by-environment interactions (GxE). We use a flexible, data-driven method to characterize GxE in the timing of vegetative growth ('green-up') and flowering across eight environments and in two highly divergent switchgrass (Panicum virgatum) populations. We classified polygenic GxE patterns as suggestive of modulation of genetic effects via weather-based cues-or other, unknown modulators. >26% of Gulf population SNPs affecting flowering had effects that covaried with photoperiodicity and >34% of Midwest upland population SNPs affecting flowering had effects that covaried with cumulative growing degree days. 76% of SNP effects on green-up showed antagonistic pleiotropy, a change in effect sign, between environments native to Gulf plants (Texas) and environments native to Midwest plants (North). In contrast, <2% of flowering effects showed antagonistic pleiotropy; the majority (>64%) showed no GxE. Top GxE-associated SNPs were highly enriched in the top associations from an independent pseudo-F2 cross of individuals from the same two populations. Breeding for particular alleles at GxE-associated loci could change flowering responsiveness to photoperiod cues in switchgrass. More broadly, this approach could be used to flexibly characterize patterns of GxE across species, populations and environments.

Analysis of genotype by environment interactions in a maize mapping population

Genotype by environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype by environment interaction in a maize multi-parent advanced generation intercross population grown across five environments. We found that genotype by environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. In order to understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype by environment variance. We also performed a genome-wide association study to identify markers associated with genotype by environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype by environment interactions in this population.