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Beating the Red Queen:
How can evolution help us
define (and refine) research
for the farm of the future?
Bruce Walsh, [email protected]
University of Arizona
Depts. of Ecology & Evolutionary Biology,
Molecular & Cellular Biology,
Plant Sciences,
Animal Sciences,
Epidemiology & Biostatistics
Walsh’s Philosophy of
Science/Research
Lemma:
ideastake
in science
are or
wrong
(at least at
Axiom 1:ALL
NEVER
yourself,
an idea,
some
level), so get over it.
too seriously
Axiom 2: If you are not living on the edge,
you are taking up too much space.
Axiom 3: Avoid the lollypop of mediocrity.
Lick it once, and you will suck forever.
The importance of being lazy
(Homage to Dave Swain)
Smart
Industrious
Lazy
Stupid
XXXX
Create the most
problems
Most creative
solutions to
problems
XXXX
Inputs to consider for research on
the farm science
of the future
Basic Biological
Systems Biology
Genetics
Genomics
Modeling
Economics
Ecology
Development
Statistics
Physiology
Environment
Regulatory Issues Consumer Issues
How can we effectively (and ideally, optimally),
manage all of this information?
Evolution deals with a similar problem in complexity, with
a large number of inputs that are considered when
trying to improve a population
Does evolution offer any suggestions for how investigators
can plan for research on the farm of the future?
It may offer some insight
Equally important, modern evolutionary biology offers
several useful tools for researchers to help them on
the farm of the future
The
Red
Queen
Lewis Carroll's Through the Looking Glass. The Red Queen said,
"It takes all the running you can do, to keep in the same place."
The “Red Queen Hypothesis”, due to Leigh Van Valen (1973),
states that species most continually evolve just to keep
caught up with their fellow species who are also evolving.
This “evolutionary arms-race” means that a lot of effort is
required simply not to lose ground. Moving ahead is even
more difficult.
Many researchers feel this way in trying to stay current in
their particular field, especially with the exponential
explosions in molecular biology, genomics, and new
statistical methods of analysis (to name a few)
Can the evolutionary process provide us with some insight
about coping with all this knowledge, specifically how to
“evolve” so as to at least keep up with the red queen
Evolution: The objective is to improve fitness
Thus, all of the complexity is distilled into a single
objective function: fitness
Researchers often try to balance multiple objectives,
especially when considering new information/methods
Suggestion One: Evolution suggests to focus on a single
overall major objective to achieve.
Just as evolution has components of fitness, a researcher
can have components that contribute to the major
objective and work on advancing each of these to achieve
the overall objective.
Variation: The Raw Materials of Evolution
Mutation (new variation) and recombination (shuffling of
existing variation) provide the raw material for continued
evolution.
For the researcher, mutation corresponds to new
ideas/methods (some are useful, many are deleterious!)
The key is how to sort out those that contribute to the longterm objective of the research from those that simply add
noise, clutter, or confusion
Suggestion Two: Evolution suggests to weigh new ideas
and methods by how they contribute to the long-term
objective of the research. Those that initially prove useful
show have their “weight” increased.
Evolution can be enhanced by migration
Between Subdivided populations
The parallel here is obvious: fields that one does not often
consider may offer very useful ideas and/or methods when
viewed from the context of your specific long-term
objectives
Example:
SuggestionMany
Three:
“new”
Evolution
methods
suggests
developed
to look
for the
at statistical
analysis
developments
of microarrays.
in other fields,
Almost
especially
all are equivalent
those which
to may
methods
used
appear
for
rather
years(apparently)
by plant breeders
disjunct
looking
fromfor
theGfield
x E inoffield
the
trails.
overall objective
Communication is the key, and “common” words are a problem.
The “same” word may mean very different things in different
fields (e.g. epistatis in QG vs. Mol biol)
Evolutionary Change often more
Regulatory that Structural
In the evolution of new gene function, it is
often seen that regulatory changes (changes
in the timing and amount of a protein/gene
product) are more important than actual
structural changes (changes in the gene
product itself).
The “erector set” model of evolution
New use of old tools vs. development of new tools
Suggestion Four: Evolution suggests regulatory
changes (new uses of old ideas and methods) are
at least as productive as structural changes
(new ideas and new methods).
Shortly, we will examine some potentially useful
tools from evolutionary biology that may be of
some use to livestock researchers. However,
two important caveats are in order first
Caveat 1: Adaptive vs. Neutral Evolution
By
Andefinition,
observed evolution
change is is
often
simply
viewed
change
as over
adaptive
time
(increasing fitness), but this not need be the case,
as it could simply be change due to neutral drift,
with no positive effect on fitness.
Similarly, scientific “progress” can be rather
elusive to measure. A large scientific literature
on a particular subject can be akin to neutral
drift, a change without any adaptive consequences
Hence, be wary of equating the volume of work
on a particular problem as a measure of progress.
Caveat 2: Run-away Sexual selection
Counterpart
toof
obtaining
in research
the
One
component
fitness mates
is sexual
selection,isthe
quest to
to acquire
appear sufficiently
attractive
for funding,
ability
mates. Can lead
to reduction
in
regardless
of thecomponents
actual “worth”
of the proposed
viability,
fertility
of fitness
QuickTime™ and a
work!
TIFF (Uncompressed) decompressor
are needed to see this picture.
This can lead to a run-away process wherein one focuses
on strategies to simply obtain new grants, rather than
actually attempting something daring or over-the-top
Avoid the lollipop!
Useful tools from Evolutionary Biology
for Animal Scientists
• Assessing (genetic) risks of GMOs
• Genetic constraints and selection response
• Selection for group traits (such as for
lowered aggression)
• Detection of traits under natural selection
• Detection of loci under selection
• Evolution and constraints in complex
pathways
Evaluating Risks Of GMOs
One concern about Genetically Modified Organisms
(GMOs) is that production benefits may be offset in
some cases by ecological risks
One potential example is transgenic fish with an
added growth hormone gene (GH). The resulting fish
grow much faster to a larger size
What potential risks are there if the gene somehow
enters natural populations?
Population genetics, and in particular Prout’s fitness
components, allows us to address potential genetic
risks from GMOs
One view is that, being essentially macro-mutations,
trans-genes are unlikely to spread throughout a
population because they typically have reduced viability
Muir & Howard (1999) showed that a reduction in
viability can easily be offset by increased mating
advantage
In the worse-case scenario, the Trojan gene hypothesis,
the transgene spreads as a result of increased mating
advantage, but its lower viability can cause local extinction
of a population
They measures the appropriate fitness parameters in a
transgenic (GH) fish, Japanese medaka, and showed that
the larger GH males do have a mating advantage, but
viability disadvantage
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Predicted time to extinction of a wild-type medaka population as a function of the mating
advantage (numbers above curves) of transgenic males relative to that of wild-type males
and the relative viability of transgenic offspring.
Muir & Howard 1999. PNAS 96: 13853
Genetic Constraints and Selection
Response
Selection does not act on a single trait, rather it acts on
some multidimensional phenotype.
A classic result from evolutionary genetics, due to
Lande (1977), is that the vector of response R = Gb, where
b is the vector (direction) favored by selection, and G the
matrix of the genetic variances (and covariances) on
the traits.
Thus, the actual response R is not in the most favored
directions, due to genetic constraints imposed by G.
Actual change in the
traits. z1 increases
(as desired), but z2 also
increases, a response in
the opposite direction
favored by selection
Direction favored by selection -- increase trait
z1, decrease trait z2.
Key point: Genetic constraints prevent optimal
selection response.
In a changing environment, it is important to know
if selection can indeed keep pace with the required
changes
Recent work (Mark Blows, UQ) in evolutionary biology
suggests that genetic constraints may be much more
common than initially thought
Blows examined 8 chemical traits involved in mate
choice between two overlapping Drosophila species
While all traits had high heritability, most of the total
genetic variation (close to 80%) resided in only two
dimensions of the potential 8-D space.
Direction favored by natural selection was over 70 degrees
(i.e., very close to right angles) away from this variation.
Net result: very little response, although all traits have lots
of genetic variation. However, little variation in the
particular direction favored by selection
Trait 1
Trait 2
While there is considerable variation in both traits, most
Selection in this direction yields very little response
resides in one dimension (one particular linear combination
of trait values)
These underlying constrains can greatly reduce response
and may create apparent selection limits (lack of
significant progress in a breeding program)
Group Selection
Evolutionary biology has been concerned with how the
interactions among individuals influence trait evolution
While typically framed in terms of traits such as altruism
(e.g., warning calls in birds), this machinery equally applies
to agronomic traits
Examples: Cannibalism in fish, pecking in chickens
Selection on individuals in such cases (for example,
body weight when individuals live in groups) may result
in little (or even negative) response in the trait
The basic model to handle this has both direct and
associate effects
Bruce Griffing’s Model (1967, Aust. J. Biol. Sci)
The phenotype Pi of a particular individual i embedded in a
group of n other interacting individuals can be
decomposed into a direct effect PD,i from individual i
plus the sum of all of the associate effects PA,j
of others in its group
Pi = PD,i + S PA,j
Final
phenotype
Example: Growth rate:
direct feeding efficiency
of individual i plus the
effects of feeding
efficiencies of
others in its groups
Bill Muir (Purdue) and colleagues (Bijma et al, Genetics
in press) have built a formal theory of selection response
and parameter estimation around the Griffins model
Consider a selection index C that weights individual and
group values, Ci = Pi + f S Pj
R = S * [f(n-1)r + 1]Var(TBV) + (1-f)Cov(P, TBV)
Example: survival in chickens. Large mortality from pecking
from others in group (4 chickens/cage).
Classic response (R = h2S): increase of 7.8 days in survival
Mild group selection on full sibs (f = 0.3, r = 1/2),
expected response is 22 day increase
Finding Traits under Natural
Selection
While the traits on which artificial selection acts are
generally set by the breeder, this occurs in a background
of natural selection on other traits.
It is potentially of great benefit to an animal producer to
know which traits are under natural selection.
Likewise, breeders trying to exploit the genetic variation
in natural population in extreme environments would also
benefit from knowing if particular traits are under selection.
Evolutionary biologists have developed a general approach
for testing whether a trait is under selection
Why not measure changes in the mean value of a
trait (before/after selection) to detect those traits under
selection?
Problem: A change in trait i could be due to direct selection
in trait i, or due to selection on other traits that are
phenotypically correlated with this trait,
How can we untangle direct selection from correlated
effects?
Trait
Within-generation change
Body size
Dm = -4
Which traits are under
Weight
Dm = -6
selection vs. those whose
Metabolic Rate
Dm = 3
change is due to phenotypic
Food intake
Dm = 4
correlations?
Milk yield
Dm = 2
Lande-Arnold Fitness Estimation
For the individuals in the study, measure their fitness
w (survival, fecundity) as well as k potential traits
z1 , … , zk under selection.
Fit a multiple regression of w as a function of the
measured traits,
n
w = a+
X
bj zj
j =1
A non-zero bj indicates direct selection on trait j
Detecting Loci Under Selection
The method of QTL mapping (when we have a pedigree)
or association mapping (with a dense set of markers) allows
us to find genes that influence a specified trait.
More generally, we would like to be able to detect those
loci that have recently been under selection. This would
allow us to detect genes involved in domestication,
improvement, and (in wild populations) adaptation
Such an approach does not require us to specify a particular
trait (as is required for QTL/association mapping) and hence
offers a potentially more unbiased view of which traits/genes
are critical to improvement
A scan of levels of polymorphism can
suggest sites under selection
Directional selection
(selective sweep)
Local region with
reduced mutation rate
Balancing selection
Local region with
elevated mutation rate
This approach has been used to detect potential
domestication genes in plant breeding (maize vs. teosinte)
Wang et al (1999) Nature 398: 236.
Evolutionary Features of
Complex networks
The future of biology will be rich in graphs and networks
Yeast proteinprotein map
Small worlds, Scale-free Graphs and Power Laws
Regulatory and metabolic graphs examined to date
share two critical features:
First, they are small-world graphs, which means that
the mean path distance between any two nodes is short.
The members live in a small world
The critical feature of small-world graphs is that
they propagate information very efficiently.
The second feature that studied regulatory/
metabolic networks show is that the degree
distribution (probability distribution that a node
is connected to k other others) follows a power law
Under a power law, no modal
value, but the probability of
many links falls off as a power,
not exponentially, resulting in a few
nodes with a large number of links
Graphs with a power distribution of links are called
scale-free graphs.
In a scale-free graph, a few of the nodes will
have very many connections. Such nodes are often
called hubs.
Scale-free graphs show the very important feature
that they are fairly robust to perturbations. Most
randomly-chosen nodes can be removed with little
effect on the system.
While removing a hub has a critical effect, the chance
that a randomly-chosen node is a hub is small.
Gene knock-out experiments in yeast, where every
single gene was deleted one at a time, showed that
only a very small fraction had any effect on phenotype.
This is entirely consistent with the developmental
pathways leading to phenotypes resulting from scalefree graphs.
Since a scale-free structure gives a regulatory network
inherent stability, biological homeostasis may just be
a simply consequence of this structure, rather than a
highly evolved feature.
How might such scale-free graphs evolve? The answer
turns out to be rather simple: when we add new nodes,
they have a slight preference to attach to already
established nodes.
Conclusions
• Focus on a single overall major objective to achieve,
and those components that feed this objective
• Dynamically weigh new ideas and methods by how they
contribute to the long-term objective of the research.
• Look at developments in other fields, but beware
of language, as the same common term refer to quite
different concepts in different fields
• Never underestimate the power of new uses of “old”
ideas and methods!