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The environmental contribution to gene expression profiles

Nature Reviews Genetics volume 9, pages 575581 (2008) | Download Citation


Microarray analysis provides a bridge between the molecular genetic analysis of model organisms in laboratory settings and studies of physiology, development, and adaptation in the wild. By sampling species across a range of environments, it is possible to gain a broad picture of the genomic response to environmental perturbation. Incorporating estimates of genetic relationships into study designs will facilitate genomic analysis of environmental plasticity by aiding the identification of major regulatory loci in natural populations.

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The author is grateful to Y. Idaghour and E. Kennerly for discussions.

Author information


  1. Greg Gibson is at the School of Integrative Biology, The University of Queensland, Goddard Building, St Lucia Campus, Brisbane, Queensland 4072, Australia.

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Association study

A genetic mapping approach in which historical recombination in outbred populations ensures that only markers closely linked to a causal polymorphism are associated with a trait, yielding high resolution mapping of common variants.

Baldwin effect

An evolutionary response to environmental change that preserves or increases the phenotypic plasticity observed within the species.

Bonferroni adjustment

A conservative statistical adjustment for significance across an entire experiment, performed by dividing the nominal p-value for a single test by the number of comparisons performed.


Evolved resistance to genetic or environmental perturbation in a population of organisms.

Cis eSNP

A regulatory SNP that is associated with and linked to expression of a gene (that is, abundance of the transcript) in an outbred sample of organisms.

Directional selection

Positive selection that tends to push a trait towards a new optimum, as opposed to stabilizing selection, which keeps a trait constant at an intermediate value.


In statistical genetics, environment represents all non-genetic contributions to variation for a trait. In common biological use, the term is restricted to all biotic and abiotic circumstances experienced by an organism, whether internal or external to it. Thus, genes experience tissue differences and technical effects during growth in culture, but these are not considered to be environmental factors in colloquial use of the term.

Genetic accommodation

An evolutionary response to environmental change by means of natural selection; it results in an increased proportion of individuals with the environmentally induced adaptive phenotype.

Genetic assimilation

An evolutionary response to environmental change that results in canalization, particularly leading to the appearance of individuals with an adaptive trait even in the absence of the original environmental stimulus that produced it.

Genetical genomics

The strategy of using joint gene expression profiling and genome-wide genotyping to map the genetic determinants of gene expression variation; usually used in the context of segregating crosses.

Genotype-by-environment interaction

The phenomenon that the effect a genotype has on a trait is a function of the environment; it is measured, when possible, by comparing clones in different environments (a reaction norm), or alternatively by averaging the phenotypes of organisms with similar genotypes that experience different environments.


The proportion of the variance for a trait in a population that is explained by genetic differences among individuals.

Linkage study

A genetic mapping approach in which chromosomal intervals influencing a trait are mapped by following marker segregation among relatives, so that recombination within the pedigree or cross ensures linkage between the markers and the QTLs.

Population structure

The existence of differences in allele frequency between two populations of individuals, often inferred from genome-wide genotype data.

Principal component

Principal component analysis is a statistical method for reducing the dimensionality of complex data sets. It captures the major axes (principal components) of variation as orthogonal variables that are made up of partial contributions of each of the individual data elements. Typically, three or four principal components capture most of the variance in the data.

Surrogate variable analysis

A method for detecting hidden sources of variation in a gene expression data set; these can then be added to the statistical model to improve the estimation of the contribution of known or suspected sources of variance.

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