The environment (e.g. climate, soils, abiotic and biotic stresses) at a larger scale, such as the environment associated with a location (i.e. space) or year (i.e. time), also influences the response of complex quantitative trait. Genotypes sense and react to the environment in different ways causing large genotype by environment interactions where one genotype might be among the best in an environment and among the worst in another environment. We have exploited the genetic determinants of the genotype by environment interaction to understand abiotic (Quero et al., 2014) and biotic (Gutierrez et al., 2015) stresses. Strategies for dealing with genotype by environment interaction in large genomic studies were proposed for traditional quantitative trait loci mapping (Quero et al., 2014), genome-wide association studies (Locatelli et al., 2013; Gutierrez et al., 2015) and for predicting complex traits (Lado et al., 2016; Monteverde et al., 2018; Monteverde et al., 2019).
Finally, this is an area that we are leading as part of a $2 million federally funded collaborative project in which Dr. Gutierrez is a co-PI. Future work includes characterizing the biotic and abiotic plant-environment interactions to unveil the mechanisms employed by plants for local adaptation. We are extending genomic prediction models for new environments using environmental covariates in a large wheat dataset (Nalin et al., in prep.) and the modeling and prediction of new environments as a decision tool for variety selection from farmers in the Midwest (Bhatta et al., in review, USDA-SARE project). Additionally, we are trying to understand plant- microbiome interactions by studying the microbial community around a large oat mapping population evaluated in multiple environments and identifying the host (i.e. plant) genes responsible for recruiting beneficial soil microbial communities and their interaction (USDA-Hatch Multidisciplinary project).