The Genomic Architecture of Interactions Between Natural Genetic Polymorphisms and Environments in Yeast Growth

Abstract

Gene-environment interaction (G3E) refers to the phenomenon that the same mutation has different phenotypic effects in different environments. Although quantitative trait loci (QTLs) exhibiting G3E have been reported, little is known about the general properties of G3E, and those of its underlying QTLs. Here, we use the genotypes of 1005 segregants from a cross between two Saccharomyces cerevisiae strains, and the growth rates of these segregants in 47 environments, to identify growth rate QTLs (gQTLs) in each environment, and QTLs that have different growth effects in each pair of environments (g3eQTLs) . The average number of g3eQTLs identified between two environments is 0.58 times the number of unique gQTLs identified in these environments, revealing a high abundance of G3E. Eighty-seven percent of g3eQTLs belong to gQTLs, supporting the practice of identifying g3eQTLs from gQTLs. Most g3eQTLs identified from gQTLs have concordant effects between environments, but, as the effect size of a mutation in one environment enlarges, the probability of antagonism in the other environment increases. Antagonistic g3eQTLs are enriched in dissimilar environments. Relative to gQTLs, g3eQTLs tend to occur at intronic and synonymous sites. The gene ontology (GO) distributions of gQTLs and g3eQTLs are significantly different, as are those of antagonistic and concordant g3eQTLs. Simulations based on the yeast data showed that ignoring G3E causes substantial missing heritability. Together, our findings reveal the genomic architecture of G3E in yeast growth, and demonstrate the importance of G3E in explaining phenotypic variation and missing heritability.

Patterns and Mechanisms of Diminishing Returns from Beneficial Mutations

“…The incorporation of the environmental contribution in a similar way as that of the genetic contribution appears to be a major distinction of our model from previous models. This feature may be important because, by allowing genetic and environmental factors to contribute to the same function, our modular life model simultaneously considers gene-by-gene interactions and gene-by-environment interactions, both of which are known to be prevalent (He et al 2010;Qian et al 2012;Li et al 2016;Wei and Zhang 2017;Li and Zhang 2018).…”

“…Following a recent analysis of the same data set (Wei and Zhang 2017), we mapped quantitative trait loci (QTLs) underlying the growth rate variation among the segregants (at a false discovery rate of 0.05) in each of the 47 environments. The number of QTLs identified ranged from 0 to 33 in the 47 environments, with a mean of 15.8 (Wei and Zhang 2017). In one environment, there is no mapped QTL.…”

Gene–gene and gene–environment interactions in complex traits in yeast

“…A study that mapped GEI in a biparental segregating population phenotyped for growth in 12 environments showed that a large proportion (~40%) of the loci show trade‐off across at least one pair of environments (Wei & Zhang, ; Yadav et al, ). Moreover, loci tend to show trade‐off across different types of stresses, i.e.…”

Patterns and mechanisms of diminishing returns from beneficial mutations

“…Following a recent analysis of the same dataset (Wei and Zhang 2017), we mapped quantitative trait loci (QTLs) underlying the growth rate variation among the segregants (at a false discover rate of 0.05) in each of the 47 environments. The number of QTLs identified ranged from 0 to 33 in the 47 environments, with a mean of 15.8 (Wei and Zhang 2017). Zero QTL was mapped in one environment.…”