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Epistasis and quantitative traits: using model organisms to study gene–gene interactions

Key Points

  • A major challenge of contemporary biology is to understand how naturally occurring genetic variation causes phenotypic variation in quantitative traits. Despite the biological plausibility that genetic variation affects nonlinear networks at multiple levels of biological organization, most efforts to explain the relationship between genetic and phenotypic variation concentrate on additive effects of individual loci.

  • Mapping gene–gene interactions (that is, epistasis) is challenging experimentally, statistically and computationally owing to the large number of interactions to be evaluated. This number is of the order of the square of the number of single-locus tests for pairwise interactions.

  • Epistatic interactions for quantitative traits result in a change of either the magnitude or the direction of allelic effects at one locus, depending on the genotype at the interacting locus. With epistasis, the additive effect (that is, the main effect) of a locus changes with the allele frequency of the interacting locus, such that estimates of effects at a single interacting locus will differ between populations with different allele frequencies.

  • Epistasis generates mostly additive variance for quantitative traits; therefore, the observation that most genetic variance for quantitative traits is additive is not inconsistent with an underlying epistatic genetic architecture. Experimental designs that are only possible in model organisms allow the exploration of the gene–gene interaction space, and the results of these analyses indicate that epistasis is pervasive.

  • Genetic interaction networks are derived by assessing quantitative trait phenotypes of wild-type, single-mutant and double-mutant genotypes. These networks show scale-free and small-world properties, such that the major features of network topology may be inferred by focusing on major hub genes and on interactions among the genes with which they interact. Combining genomics with mutant-interaction screens may aid the identification of network hubs.

  • Taking advantage of multifactorial perturbations in quantitative trait locus (QTL)-mapping populations is less laborious than constructing all pairwise combinations of mutant alleles, and the ability to construct chromosome substitution lines, introgression lines and near-isogenic lines in model organisms maximizes power to detect interactions. Epistasis is commonly observed, even between loci without significant main effects, but there are only a few cases in which the actual interacting variants have been identified.

  • Natural populations harbour hidden reservoirs of cryptic genetic variation that can be revealed by introducing mutations onto wild-derived backgrounds. When this approach is implemented in a QTL-mapping population, it is a powerful experimental design for identifying naturally occurring variants that either enhance or suppress the mutant phenotype.

  • Observations of cryptic genetic variation and less-than-additive epistatic interactions between QTLs suggest that natural populations have evolved suppressing epistatic interactions as homeostatic (that is, canalizing) mechanisms for quantitative traits. Pervasive epistasis has consequences for plant and animal breeding, evolutionary biology and human genetics.

  • In the future, assessment of the pleiotropic effects of genetic interactions on transcriptional, metabolic and protein–protein interaction networks will provide insights into the mechanistic basis of epistasis for organismal phenotypes.

Abstract

The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype–phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.

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Figure 1: Two-locus genotypic effects.
Figure 2: Quantitative genetics of additive-by- additive interactions.
Figure 3: Genotypes for mapping QTLs between two genetically divergent lines.
Figure 4: Two-dimensional search for epistatic interactions.
Figure 5: Epistasis between naturally occurring variation and mutations in D. melanogaster.

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Acknowledgements

The author thanks S. Zhou for helping with figures 2 and 5b, and R. Anholt for comments on the manuscript. Work in the Mackay laboratory is supported by the US National Institutes of Health grants R01 GM45146, R01 GM076083, R01 GM59469 and R01 AA016560.

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Correspondence to Trudy F. C. Mackay.

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Glossary

Main effect

The effect of a variable averaged over all other variables; also known as marginal effect.

Heterosis

The phenomenon whereby the mean value of a quantitative trait in the F1 progeny of two inbred lines exceeds, in the direction of increased fitness, either the mean value of the parental lines (that is, mid-parent heterosis) or the mean value of the best parent (that is, high parent heterosis); also known as hybrid vigour.

Missing heritability

The phenomenon whereby the fraction of total phenotypic variance that is explained by all individually significant loci in human genome-wide association analyses for common diseases and quantitative traits is typically much less than the heritability that is estimated from relationships among relatives.

Di-hybrid cross

A cross between parental lines that are fixed for alternative alleles at two unlinked loci (for example, A1A1B2B2 × A2A2B1B1, where A and B denote the loci and the subscripts represent the alleles) in which nine genotypes segregate in the F2 generation.

Dominance effects

Differences between the genotypic values of the heterozygous genotypes and the average genotypic values of the homozygous genotypes at loci that affect quantitative traits.

Standing variation

Allelic variation that is currently segregating within a population, as opposed to alleles that appear as the result of new mutation events.

Introgression

The substitution of a genomic region from one strain with that of another, typically by repeated backcrosses.

Diallel cross

A class of experimental designs that are used to estimate both additive and non-additive variance components for a quantitative trait from all possible crosses among a population of inbred lines. Full diallel designs include reciprocal crosses, whereas half-diallel designs do not; parental lines can be included or excluded in either case.

Synthetic enhancement

A type of epistatic interaction whereby the phenotype of a double mutant is more severe than that predicted from the additive effects of the single mutants.

Multiple testing penalty

The downward adjustment of the significance threshold for individual statistical tests that is required when multiple hypothesis tests are carried out on a single data set; for n independent tests, the Bonferroni-adjusted 5% significance threshold is 0.05/n.

Minor allele frequency

The frequency of the less common allele at a bi-allelic locus.

Founder-effect speciation models

A class of models for the evolution of reproductive isolation that is based on changes in selection pressures and on allele frequencies of epistatically interacting loci, which result from the establishment of a new population in a new environment from a small number of individuals.

Dobzhansky–Muller incompatibilities

Substitutions that occur during divergence of two lineages; these substitutions are neutral in the respective genetic backgrounds of the two lineages but cause a reduction in fertility and/or viability in hybrids between the two lineages.

Genomic prediction methods

Models that are derived from a discovery sample which consists of individuals with measured phenotypes and genome-wide marker data; these models are used to predict individual phenotypes in an independent sample from the same population using only genome-wide marker data.

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Mackay, T. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat Rev Genet 15, 22–33 (2014). https://doi.org/10.1038/nrg3627

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