Beating the Red Queen:How can evolution help us

define (and refine) researchfor the farm of the future?

Bruce Walsh, jbwalsh@u.arizona.eduUniversity of Arizona

Depts. of Ecology & Evolutionary Biology,Molecular & Cellular Biology,

Plant Sciences,Animal Sciences,

Epidemiology & Biostatistics

Walsh’s Philosophy of Science/Research

Lemma: ALL ideas in science are wrong (at least atsome level), so get over it.Axiom 1: NEVER take yourself, or an idea, 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 Stupid

Industrious

Lazy

XXXX

XXXX

Create the mostproblems

Most creativesolutions to

problems

Basic Biological science

Genomics Modeling

EcologyGenetics

Systems Biology Development

Statistics

Physiology

Environment

Consumer IssuesRegulatory Issues

Economics

Inputs to consider for research on the farm of the future

How can we effectively (and ideally, optimally), manage all of this information?

Evolution deals with a similar problem in complexity, witha large number of inputs that are considered whentrying 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

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."

TheRed

Queen

The “Red Queen Hypothesis”, due to Leigh Van Valen (1973),states that species most continually evolve just to keepcaught up with their fellow species who are also evolving.

This “evolutionary arms-race” means that a lot of effort isrequired simply not to lose ground. Moving ahead is evenmore difficult.

Many researchers feel this way in trying to stay current intheir particular field, especially with the exponentialexplosions in molecular biology, genomics, and newstatistical methods of analysis (to name a few)

Can the evolutionary process provide us with some insightabout 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

Suggestion One: Evolution suggests to focus on a single overall major objective to achieve.

Just as evolution has components of fitness, a researchercan have components that contribute to the major objective and work on advancing each of these to achievethe overall objective.

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

Variation: The Raw Materials of Evolution

Mutation (new variation) and recombination (shuffling ofexisting variation) provide the raw material for continuedevolution.

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 long-term objective of the research from those that simply addnoise, clutter, or confusion

Suggestion Two: Evolution suggests to weigh new ideasand methods by how they contribute to the long-termobjective of the research. Those that initially prove usefulshow have their “weight” increased.

Evolution can be enhanced by migration Between Subdivided populations

Communication is the key, and “common” words are a problem.The “same” word may mean very different things in differentfields (e.g. epistatis in QG vs. Mol biol)

The parallel here is obvious: fields that one does not oftenconsider may offer very useful ideas and/or methods whenviewed from the context of your specific long-termobjectives

Example: Many “new” methods developed for the statisticalanalysis of microarrays. Almost all are equivalent to methodsused for years by plant breeders looking for G x E in fieldtrails.

Suggestion Three: Evolution suggests to look at developments in other fields, especially those which may appear rather (apparently) disjunct from the field of theoverall objective

In the evolution of new gene function, it isoften seen that regulatory changes (changesin the timing and amount of a protein/geneproduct) are more important than actualstructural changes (changes in the geneproduct itself).

The “erector set” model of evolution

Evolutionary Change often moreRegulatory that Structural

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 usefultools from evolutionary biology that may be ofsome use to livestock researchers. However,two important caveats are in order first

Caveat 1: Adaptive vs. Neutral EvolutionBy definition, evolution is simply change over timeAn observed change is often viewed as adaptive

(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 ratherelusive to measure. A large scientific literatureon a particular subject can be akin to neutraldrift, 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

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One component of fitness is sexual selection, theability to acquire mates. Can lead to reduction inviability, fertility components of fitness

Counterpart to obtaining mates in research is thequest to appear sufficiently attractive for funding,regardless of the actual “worth” of the proposedwork!

This can lead to a run-away process wherein one focuseson strategies to simply obtain new grants, rather thanactually 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

Population genetics, and in particular Prout’s fitness components, allows us to address potential geneticrisks from 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 somehowenters natural populations?

One view is that, being essentially macro-mutations, trans-genes are unlikely to spread throughout apopulation because they typically have reduced viability

Muir & Howard (1999) showed that a reduction inviability can easily be offset by increased matingadvantage

In the worse-case scenario, the Trojan gene hypothesis, the transgene spreads as a result of increased matingadvantage, 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

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Muir & Howard 1999. PNAS 96: 13853

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.

Genetic Constraints and SelectionResponse

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 = G, where 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.

Direction favored by selection -- increase traitz1, decrease trait z2.

Actual change in the traits. z1 increases(as desired), but z2 alsoincreases, a response inthe opposite directionfavored by selection

Key point: Genetic constraints prevent optimal selection response.

In a changing environment, it is important to knowif selection can indeed keep pace with the requiredchanges

Recent work (Mark Blows, UQ) in evolutionary biologysuggests that genetic constraints may be much morecommon than initially thought

Blows examined 8 chemical traits involved in matechoice 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 lotsof genetic variation. However, little variation in the particular direction favored by selection

Trait 1

Trait 2

Selection in this direction yields very little responseWhile there is considerable variation in both traits, most resides in one dimension (one particular linear combinationof trait values)

These underlying constrains can greatly reduce response and may create apparent selection limits (lack ofsignificant progress in a breeding program)

Group Selection

Evolutionary biology has been concerned with how theinteractions among individuals influence trait evolution

While typically framed in terms of traits such as altruism(e.g., warning calls in birds), this machinery equally appliesto 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 resultin little (or even negative) response in the trait

The basic model to handle this has both direct and associate effects

Pi = PD,i + PA,j

Bruce Griffing’s Model (1967, Aust. J. Biol. Sci)

The phenotype Pi of a particular individual i embedded in agroup of n other interacting individuals can bedecomposed into a direct effect PD,i from individual iplus the sum of all of the associate effects PA,j of others in its group

Finalphenotype

Example: Growth rate:direct feeding efficiencyof individual i plus the

effects of feeding efficiencies of

others in its groups

Bill Muir (Purdue) and colleagues (Bijma et al, Geneticsin press) have built a formal theory of selection responseand parameter estimation around the Griffins model

Consider a selection index C that weights individual andgroup values, Ci = Pi + f Pj

R = S * [f(n-1)r + 1]Var(TBV) + (1-f)Cov(P, TBV)

Example: survival in chickens. Large mortality from peckingfrom 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

Evolutionary biologists have developed a general approachfor testing whether a trait is under selection

While the traits on which artificial selection acts aregenerally set by the breeder, this occurs in a backgroundof natural selection on other traits.

It is potentially of great benefit to an animal producer toknow which traits are under natural selection.

Likewise, breeders trying to exploit the genetic variationin natural population in extreme environments would alsobenefit from knowing if particular traits are under selection.

Why not measure changes in the mean value of atrait (before/after selection) to detect those traits underselection?

Problem: A change in trait i could be due to direct selectionin trait i, or due to selection on other traits that arephenotypically correlated with this trait,

How can we untangle direct selection from correlated effects?

Trait Within-generation changeBody size = -4Weight = -6Metabolic Rate = 3Food intake = 4Milk yield = 2

Which traits are underselection vs. those whosechange is due to phenotypiccorrelations?

Lande-Arnold Fitness Estimation

For the individuals in the study, measure their fitnessw (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,

w = a+nX

j=1

bj zj

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) allowsus to find genes that influence a specified trait.

More generally, we would like to be able to detect thoseloci that have recently been under selection. This wouldallow us to detect genes involved in domestication,improvement, and (in wild populations) adaptation

Such an approach does not require us to specify a particulartrait (as is required for QTL/association mapping) and henceoffers a potentially more unbiased view of which traits/genesare critical to improvement

A scan of levels of polymorphism can suggest sites under selection

Directional selection(selective sweep)

Balancing selection

Local region withreduced mutation rate

Local region withelevated mutation rate

Wang et al (1999) Nature 398: 236.

This approach has been used to detect potential domestication genes in plant breeding (maize vs. teosinte)

Evolutionary Features ofComplex networks

The future of biology will be rich in graphs and networks

Yeast protein-protein map

Small worlds, Scale-free Graphs and Power Laws

Regulatory and metabolic graphs examined to dateshare 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 thatthey propagate information very efficiently.

Under a power law, no modalvalue, but the probability ofmany links falls off as a power,not exponentially, resulting in a fewnodes with a large number of links

The second feature that studied regulatory/metabolic networks show is that the degreedistribution (probability distribution that a node is connected to k other others) follows a power law

Graphs with a power distribution of links are called scale-free graphs.

In a scale-free graph, a few of the nodes willhave very many connections. Such nodes are oftencalled hubs.

Scale-free graphs show the very important featurethat they are fairly robust to perturbations. Mostrandomly-chosen nodes can be removed with littleeffect on the system.

While removing a hub has a critical effect, the chancethat a randomly-chosen node is a hub is small.

Gene knock-out experiments in yeast, where everysingle gene was deleted one at a time, showed thatonly a very small fraction had any effect on phenotype.This is entirely consistent with the developmentalpathways leading to phenotypes resulting from scale-free graphs.

Since a scale-free structure gives a regulatory networkinherent stability, biological homeostasis may just bea simply consequence of this structure, rather than ahighly evolved feature.

How might such scale-free graphs evolve? The answerturns 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 bewareof language, as the same common term refer to quitedifferent concepts in different fields

• Never underestimate the power of new uses of “old”ideas and methods!