Genetic interactions matter more in less-optimal environments: a Focused Review of “Phenotype uniformity in combined-stress environments has a different genetic architecture than in single-stress treatments” (Makumburage and Stapleton, 2011)

Abstract

An increase in the distribution of data points indicates the presence of genetic or environmental modifiers. Mapping of the genetic control of the spread of points, the uniformity, allows us to allocate genetic difference in point distribution to adjacent, cis effects or to independently segregating, trans genetic effects. Our genetic architecture-mapping experiment elucidated the “environmental context specificity” of modifiers, the number and effect size of positive and negative alleles important for uniformity in single and combined stress, and the extent of additivity in estimated allele effects in combined stress environments. We found no alleles for low uniformity in combined stress treatments in the maize mapping population we examined. The major advances in this research area since early 2011 have been in improved methods for modeling of distributions and means and detection of important loci. Double hierarchical general linear models and, more recently, a likelihood ratio formulation have been developed to better model and estimate the genetic and environmental effects in populations. These new methods have been applied to real data sets by the method authors and we now encourage additional development of the software and wider application of the methods. We also propose that simulations of genetic regulatory network models to examine differences in uniformity and systematic exploration of models using shared simulations across communities of researchers would be constructive avenues for developing further insight into the genetic mechanisms of variation control.

Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Carthamus tinctorius L.)

“…GWAS identified MTAs for safflower traits GWAS has been widely used to study the genetic basis of the important agronomy traits with diverse germplasm in crops (Liu and Yan 2019). Multienvironment trials normally were combined to present the overall phenotypic variation for GWAS to detecting the associations between markers and traits (Landers and Stapleton 2014;Leamy et al 2017c). However, with diverse germplasm, the phenotypic variation displayed under differed environments can be used to measure the plasticity of the traits or trait G × E level with proper statistical models (Des Marais et al 2013;Malosetti et al 2013).…”

Caenorhabditis elegans Genes Affecting Interindividual Variation in Life-span Biomarker Gene Expression

“…Interestingly, some of these variation-increasing loci operate only in some environments. For example, some welldefined Quantitative Trait Loci in particular maize strains increase variation in all tested environments including normal cultivation, while others increase variation only in plants grown with reduced water or reduced nitrogen (58,59). Because in maize uniformity of plant height is considered a desirable trait and is under human selection, in this case, we might say that loci that decrease phenotypic uniformity decrease fitness.…”

Genome-Wide Association Studies Dissect the G×E Interaction for Agronomic Traits in a Worldwide Collection of Safflower (Carthamus Tinctorius L.)

“…GWAS identi ed MTAs for sa ower traits GWAS has been widely used to study the genetic basis of the important agronomy traits with diverse germplasm in crops [48]. Multi-environment trials normally were combined to present the overall phenotypic variation for GWAS to detecting the associations between markers and traits [49,50]. However, with diverse germplasm, the phenotypic variation displayed under differed environments can be used to measure the plasticity of the traits or trait G × E level with proper statistical models [51,52].…”