Beating the Red Queen: How can evolution help us define (and refine) research for the farm of the future?

1. Beating the Red Queen: How can evolution help us define (and refine) research for the farm of the future? Bruce Walsh, jbwalsh@u.arizona.edu University of Arizona Depts. of Ecology & Evolutionary Biology, Molecular & Cellular Biology, Plant Sciences, Animal Sciences, Epidemiology & Biostatistics

2. Walsh’s Philosophy of Science/Research Lemma: ALL ideas in science are wrong (at least at some 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.

3. The importance of being lazy (Homage to Dave Swain) Smart Stupid Create the most problems Industrious XXXX Most creative solutions to problems Lazy XXXX

4. Development Systems Biology Genetics Statistics Ecology Modeling Genomics Physiology Economics Environment Regulatory Issues ConsumerIssues Inputs to consider for research on the farm of the future Basic Biological science

5. 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

6. 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."

7. 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

8. 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.

9. 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 long- term 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.

10. 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: Many “new” methods developed for the statistical analysis of microarrays. Almost all are equivalent to methods used for years by plant breeders looking for G x E in field trails. Suggestion Three: Evolution suggests to look at developments in other fields, especially those which may appear rather (apparently) disjunct from the field of the 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)

11. 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

13. 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

14. Caveat 1: Adaptive vs. Neutral Evolution By definition, evolution is simply change over time An 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 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.

15. Caveat 2: Run-away Sexual selection Counterpart to obtaining mates in research is the quest to appear sufficiently attractive for funding, regardless of the actual “worth” of the proposed work! One component of fitness is sexual selection, the ability to acquire mates. Can lead to reduction in viability, fertility components of fitness 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!

16. 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

17. 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

18. 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

19. 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

20. 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.

21. 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

22. 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

23. Trait 1 Trait 2 Selection in this direction yields very little response While there is considerable variation in both traits, most 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)

24. 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

25. Bruce Griffing’s Model (1967, Aust. J. Biol. Sci) The phenotype Piof a particular individual i embedded in a group of n other interacting individuals can be decomposed into a direct effect PD,ifrom individual i plus the sum of all of the associate effects PA,j of others in its group Pi = PD,i + S PA,j Example: Growth rate: direct feeding efficiency of individual i plus the effects of feeding efficiencies of others in its groups Final phenotype

26. 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

27. 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

28. 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 sizeDm = -4 WeightDm = -6 Metabolic RateDm = 3 Food intake Dm = 4 Milk yieldDm = 2 Which traits are under selection vs. those whose change is due to phenotypic correlations?

29. n X w = a + b z j j j =1 A non-zero bj indicates direct selection on trait j 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,

30. 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

31. 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

32. This approach has been used to detect potential domestication genes in plant breeding (maize vs. teosinte) Wang et al (1999) Nature 398: 236.

33. Yeast protein- protein map Evolutionary Features of Complex networks The future of biology will be rich in graphs and networks

34. 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.

35. 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 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

36. 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.

37. 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 scale- free 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.

38. 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!