Ecological genomics

Ecological genomics aims to understand how organisms’ genes have been shaped by their natural environments. Technological advances help to link genetic information with ecological studies and unify disparate biological disciplines. Over the next 5–10 years, the remit of genetics will expand to include more ecological and evolutionary context.

Contemporary biology research ranges from basic cellular processes to worldwide climate patterns. This spectrum has not always been continuous: although organisms physically adapt to their natural environments, the underlying genetic processes have remained a mystery. The emerging multidisciplinary field of ecological genomics is helping to illuminate the relationship between individual genetic variability and the evolutionary history of species in their natural environments.


Because of the technological investments required, genetic analyses have traditionally been applied to only a small set of model species, such as nematode worms or fruit flies, which have short life spans and are amenable to experimental manipulation. These organisms typically represent abstractions of biological systems, so it has been difficult to infer from these studies how species in the wild interact with their natural surroundings. Moreover, although traditional genetics has focused on the action of single genes with discrete phenotypic effects, the traits that determine the interactions of organisms with their environment are usually controlled by multiple genes producing a continuum of phenotypes (Fig. 1). New combinations of genetic and genomic approaches applied in the context of natural populations now provide a means to trace the genetic basis of adaptation and the interactions of organisms with their environment1-3.

Quantitative trait locus (QTL) analysis is used to examine a continuous trait in a natural or experimental population. It identifies multiple stretches of DNA in the population’s genome that are associated with the trait in question as a first step towards identifying the contributing genes. A more recent alternative is the genome-wide association study (GWAS), which compares phenotypic information in a natural population against a panel of sequence markers throughout the genomes of thousands of individuals4.Through statistical association, genomic regions that correlate with the trait of interest can be identified.

>> Ecological genomics is unifying disparate biological disciplines to provide a clearer understanding of patterns in biodiversity.

It is also possible to screen systematically for genes involved in recent adaptations without first selecting a trait. This requires evolutionary signature techniques, such as selective sweep mapping, which analyse patterns of DNA variation in natural populations5. Once an evolutionary signature has been identified, a phenotypic trait can be associated with it. Genetics-level studies of parallel adaptations provide a means to confirm the validity of results from single systems.

Through a combination of these approaches, specific genes responsible for adaptive traits, such as coat colour in deer mice, plate armour in sticklebacks and flowering time in plants, have been identified6,7. However, technological advances in sequence analysis, such as microarray-based single-polynucleotide polymorphism (SNP) detection and next-generation sequencing techniques8, permit larger systematic screens to identify complete sets of adaptive trait genes in non-model organisms. The next generation of sequencing technologies will allow population and ecosystem analysis on an unprecedented scale.

Alongside the advances in genetic technologies, there have been improvements in imaging and remote sensing. Until recently, techniques such as satellite-based, aerial and land-based capturing of information have been used to record only broad trends in ecosystems. However, as technology such as digital cameras becomes cheaper and more integrated through wireless networks, detailed images of ecosystem dynamics can be obtained and shared to reveal spatial and temporal components of the interactions between individuals and their natural environments. Data acquisition is becoming easier; the challenge will be to ensure that the speed of data generation does not outstrip the capacity of data analysis.


These advances will have a dramatic effect on the new field of ecological genomics. Within the next two years, it will be feasible to perform comparative sequencing of thousands of individual genomes from a species, providing insights into natural variation and the signatures of adaptation. Researchers will not be limited to laboratory-bred organisms and will be able to study populations in the wild, including ecologically and evolutionarily interesting species previously unavailable for genetic analysis. Similarly, it will be possible to sequence the genomes of complex communities of micro-organisms in environmental samples to understand species composition, gene content and biochemical pathways. The challenge will be to understand how genomes interact.

Over the next 5–10 years, gene-function analysis will include more ecological and evolutionary context. Genetics will move from a perspective largely focused on biomedicine and agriculture to include ecology, with special relevance to questions of global change. By understanding the genetics of adaptation and community interactions, it will be possible to comprehend the ecosystem, its services and its stability. This will help move the field from being largely descriptive to being more predictive.


As ecological genomics is established, experimental approaches need to be developed to take into account the differences between selection under natural and experimental conditions. For instance, laboratory-based experiments are limited in population size; they can identify only those new variants that provide an organism with a relative advantage over its siblings — in the order of 10%. Nature, by contrast, can select for improvements that are merely one-hundredth of a percent better (Fig. 2).

There is also a limitation in studying single-gene effects, as interactions between genes can be overlooked. These epistatic interactions are difficult to detect because there are so many possible combinations. Improved analysis of natural genotypes — with their positively selected epistatic pairs of genes — will rectify this, although the statistical challenge is enormous9.

As data acquisition becomes more efficient, new conceptual advances will be required for analysis. Current algorithms that analyse sequence data were devised when data were comparatively scarce and computational efficiency was not a priority. Current circumstances and technologies demand change, and computer scientists will need to develop new solutions10.

Ecological genomics is unifying disparate biological disciplines to provide a clearer understanding of patterns in biodiversity. The technological revolution will reveal how organisms and their genes have been shaped by their natural environments. Now, the challenge is to understand the relationship.

Even in model organisms, the isolation of genes defined merely by a mutant phenotype previously took months, if not years. Scientists at the Max Planck Institute for Developmental Biology have demonstrated that mutant genes can now be identified in days, using new sequencing technologies. Extending the mapping technique to wild species will allow the rapid identification of any gene with large phenotypic effects that is segregating in natural populations (Schneeberger, K. et al. Nature Methods 6, 550–551, 2009).

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