Progress | Published:

The environmental contribution to gene expression profiles

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

Abstract

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Breakthrough of the year: human genetic variation. Science 318, 1842–1843 (2007).

  2. 2.

    & Uncovering evolutionary patterns of gene expression using microarrays. Trends Ecol. Evol. 21, 29–37 (2006).

  3. 3.

    et al. Global transcript profiles of fat in monozygotic twins discordant for BMI: pathways behind acquired obesity. PLoS Medicine 5, e51 (2008).

  4. 4.

    et al. Gene-expression profiling of HIV-1 infection and perinatal transmission in Botswana. Genes Immun. 7, 298–309 (2006).

  5. 5.

    & Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, e161 (2007).

  6. 6.

    et al. Host gene expression profiling of Dengue virus infection in cell lines and patients. PLoS Negl. Trop. Dis. 1, e86 (2007).

  7. 7.

    et al. Activation of inflammation/NF-kappa B signaling in infants born to arsenic-exposed mothers. PLoS Genet. 3, e207 (2007).

  8. 8.

    et al. Diesel exhaust inhalation and assessment of peripheral blood mononuclear cell gene transcription effects: an exploratory study of healthy human volunteers. Inhal. Toxicol. 19, 1107–1119 (2007).

  9. 9.

    et al. Gene expression signature in peripheral blood cells from medical students exposed to chronic psychological stress. Biol. Psychol. 76, 147–155 (2007).

  10. 10.

    et al. Effect of menopause on gene expression profiles of circulating monocytes: a pilot in vivo microarray study. J. Genet. Genom. 34, 974–983 (2007).

  11. 11.

    & The biological importance of measuring individual variation. J. Exp. Biol. 210, 1613–1621 (2007).

  12. 12.

    et al. A gene expression signature of confinement in peripheral blood of red wolves (Canis rufus). Mol. Ecol. 17, 2782–2791 (2008).

  13. 13.

    et al. Selection for tameness has changed brain gene expression in silver foxes. Curr. Biol. 15, R915–R916 (2005).

  14. 14.

    et al. Transcriptome comparison of winter and spring wheat responding to low temperature. Genome 48, 913–923 (2005).

  15. 15.

    et al. Correlating gene expression to physiological parameters and environmental conditions during cold acclimation of Pinus sylvestris, identification of molecular markers using cDNA microarrays. Tree Physiol. 26, 1297–1313 (2006).

  16. 16.

    , & Evolution of gene expression in the Drosophila melanogaster subgroup. Nature Genet. 33, 138–144 (2003).

  17. 17.

    , , & Mixed-model reanalysis of primate data suggests tissue and species biases in oligonucleotide-based gene expression profiles. Genetics 165, 747–757 (2003).

  18. 18.

    et al. Human and chimpanzee gene expression differences replicated in mice fed different diets. PLoS ONE 1, e1504 (2008).

  19. 19.

    , & Heritability in the genomics era — concepts and misconceptions. Nature Rev. Genet. 9, 255–266 (2008).

  20. 20.

    , , & A genome-wide gene expression signature of environmental geography in leukocytes of Moroccan Amazighs. PLoS Genet. 4, e100052 (2008).

  21. 21.

    & Genetic variation in human gene expression. Mamm. Genome 17, 503–508 (2006).

  22. 22.

    et al. Functional deficit of T regulatory cells in Fulani, an ethnic group with low susceptibility to Plasmodium falciparum malaria. Proc. Natl Acad. Sci. USA 105, 646–651 (2008).

  23. 23.

    et al. Genetic inheritance of gene expression in human cell lines. Am. J. Hum. Genet. 75, 1094–1105 (2004).

  24. 24.

    et al. A genome-wide association study of global gene expression. Nature Genet. 39, 1202–1207 (2007).

  25. 25.

    et al. Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nature Genet. 39, 1208–1216 (2007).

  26. 26.

    et al. Population genomics of human gene expression. Nature Genet. 39, 1217–1224 (2007).

  27. 27.

    et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).

  28. 28.

    et al. JNK1 in hematopoietically derived cells contributes to diet-induced inflammation and insulin resistance without affecting obesity. Cell Metab. 6, 386–397 (2007).

  29. 29.

    & Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001).

  30. 30.

    & Genetics of global gene expression. Nature Rev. Genet. 7, 862–872 (2007).

  31. 31.

    et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008).

  32. 32.

    , et al. Mapping determinants of gene expression plasticity by genetical genomics in C. elegans. PLoS Genet. 2, e222 (2007).

  33. 33.

    & . Gene–environment interaction in yeast gene expression. PLoS Biol. 6, e83 (2008).

  34. 34.

    , et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genet. 38, 203–208 (2006).

  35. 35.

    et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2, e41 (2006).

  36. 36.

    et al. Pedigree-free animal models: the relatedness matrix reloaded. Proc. Biol. Sci. 275, 639–647 (2008).

  37. 37.

    Developmental plasticity and evolution (Oxford Univ. Press, Oxford, 2003).

  38. 38.

    The Baldwin effect and genetic assimilation: revisiting two mechanisms of evolutionary change mediated by phenotypic plasticity. Evolution 61, 2469–2479 (2007).

  39. 39.

    , , , & Comparison of gene expression between upland and lowland rice cultivars under water stress using cDNA microarray. Theor. Appl. Genet. 115, 1109–1126 (2007).

  40. 40.

    Canalization of development and genetic assimilation of acquired characters. Nature 183, 1654–1655 (1959).

  41. 41.

    , & Regulatory changes underlying expression differences within and between Drosophila species. Nature Genet. 40, 346–350 (2008).

Download references

Acknowledgements

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

Author information

Affiliations

  1. Greg Gibson is at the School of Integrative Biology, The University of Queensland, Goddard Building, St Lucia Campus, Brisbane, Queensland 4072, Australia.  g.gibson1@uq.edu.au

    • Greg Gibson

Authors

  1. Search for Greg Gibson in:

Glossary

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.

Canalization

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.

Environment

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.

Heritability

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.

About this article

Publication history

Published

DOI

https://doi.org/10.1038/nrg2383

Further reading