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Ecological genomics
FROM GENES TO ECOSYSTEMS
C
ontemporary 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.
NEW GENETIC TECHNOLOGY
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
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Ecological genomics is unifying disparate biological disciplines
to provide a clearer understanding of patterns of biodiversity.
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.
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,
E
ven 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,
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Research Perspectives of the Max Planck Society | 2010+
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.
FUTURE IMPLICATIONS
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 laboratorybred 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 microorganisms 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
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).
BIOLOGY AND MEDICINE
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.
Fig. 1 | Variation in wild strains of the flowering plant Arabidopsis
thaliana
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.
Individuals of this species show considerable variation in vegetative morphology
and size, even when grown under identical conditions in the greenhouse. As is
typical for such traits, the variation is continuous and indicates the presence of
much natural variation in multiple genes controlling the trait.
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.
➟ For references see pages 38 and 39
Fig. 2 | Graph of the relationship between the power of natural
selection (the selection coefficient) and genetic function
The minimum selection coefficient that can maintain a genetic function is 1/2Ne,
where Ne is the effective population size. Tracing such functions will require
complex experiments ranging up to population-scale approaches.
standard genetic
experiments
1
complex
experiments
SELECTION COEFFICIENT
Fig. 1: Janne Lempe and Detlef Weigel, Max Planck Institute for Developmental Biology.
NEW AREAS OF RESEARCH
basic function
population scale
experiments
ecological function
1
2Ne
2010+ | Research Perspectives of the Max Planck Society
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