contributions of genomics to life-history theory

Evolution proceeds because of variation in fitness, and directly contributing to this fitness are life-history traits, such as age of maturity, growth rate, fecundity and sur-vival. Other traits contribute indirectly (for example, in most invertebrates, plants and some small mammals, fecundity increases with body size) and are classified as morphological, physiological or behavioural traits. Variation in life-history traits among species can be extreme, as shown by variation in age at maturity and allocation to reproduction. Some species mature within a year of birth and allocate up to 50% of their body mass to reproduction, whereas others take several decades before reproducing and allocate just a few percent of their body mass to each reproductive episode. In the extreme, species such as Pacific salmon reproduce once and die, whereas related species such as Atlantic salmon undergo repeated bouts of reproduction. Even within a single species there is large variation; for example, the flatfish, Hippoglossus hippoglossus, matures at an age of 3 years and a length of 20 cm in Scotland, whereas in Newfoundland the same species does not mature until an age of 15 years and a length of 40 cm. Furthermore, the Scottish fish live to a maximum age of 6 years, but the Newfoundland fish live beyond 20 years1. The purview of life-history theory and analysis is this variation in life-his-tory traits, with a central aim of producing a conceptual, mathematical and biological framework in which the evo-lution of the diversity in life histories both in and among species can be understood. So, life-history theory seeks to produce both mathematical and biological models for the evolution of life-history variation1,2.

The study of the evolution of life histories encom-passes not only the integration of all other fields of biology — from molecular, to cellular, to organismal — but also mathematics and statistics. It is no surprise that one of the founders of life-history theory, Sir Ronald Fisher, was

also a founding father of modern statistical theory, quan-titative and population genetics. Further to contributing to our understanding of how life on Earth has evolved, life-history theory has profound practical implica-tions in conservation biology3, resource management4, evolutionary anthropology5 and human medicine6.

Two fundamental assumptions of life-history theory are that there is some operationally definable measure of fitness that is maximized by natural selection, and that there are trade-offs that limit the possible set of trait combinations (BOX 1). For example, in many species, fecundity increases with body size, which is achieved by extending the period of growth; on the other hand, the probability of surviving to reproduce will decrease as the time taken to reach maturity is increased. Therefore, there is a trade-off between fecundity and survival that is mediated through body size and development time. A third assumption that is typically made is that evo-lutionary trajectories are not generally inhibited by the lack of genetic variation. Although it is generally agreed that genetic variation per se is not likely to be lacking, a central issue in life-history theory is the extent to which evolutionary trajectories and end points are determined and modulated by the genetic and functional basis of trade-offs7.

Life-history theory can be approached from a genetic or non-genetic perspective. As shown by the example given in BOX 1, much of the theory has been developed and tested without reference to the genetic underpin-nings of the trait; it is simply assumed that selection maximizes some operationally definable measure of fitness and that trait combinations are constrained by trade-offs, but not by genetic architecture (‘constraint’ is used throughout this Review both in the sense of bias-ing evolutionary trajectories and in the absolute sense of restricting the set of possible trajectories). On the other

Department of Biology, University of California, Riverside, California 92521, USA.Correspondence to e-mail: [email protected]:10.1038/nrg2040

Contributions of genomics to life-history theoryDerek A. Roff

Abstract | Life-history theory seeks to understand the factors that produce variation in life histories that are found both among and within species. At the organismal level there is a well developed mathematical framework, and an important focus of the current research is determining the biological underpinnings of this framework, with particular attention to the causal mechanisms that underlie trade-offs. Genomic approaches are proving useful in addressing this issue.

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hand, a line of enquiry that has an equally long heritage derives from Mendelian genetics. This approach assumes that a trait is either under the control of some simple mechanism, such as a single locus, or is polygenic, in which case it is modelled using the framework of quan-titative genetics. These two perspectives (phenotypic versus genetic) are complementary approaches, with the phenotypic approach frequently being the simpler analytical method. Although the phenotypic approach can predict the equilibrium optimal combination, it cannot, in general, predict the trajectory that is taken by evolution to reach that optimum. Given the appropriate parameter values, quantitative genetics can in principle predict both the time trace and optimum combination of trait values. At present, quantitative predictions are generally limited to 10–20 generations of selection. Quantitative genetics is primarily a statistical tool that connects genotype with phenotype: one plausible goal of genomics is to ‘fill in’ this gap and produce models that combine statistical and functional components.

Trade-offs in life-history theory are mathematically expressed in two ways: in the phenotypic approach, a trade-off is an equation that relates the negative interac-tion between two traits, whereas in a quantitative genetic perspective, a trade-off enters as a negative genetic and phenotypic correlation between the two traits (a trade-off could plausibly have no negative genetic correlation, but then it would have little or no evolutionary effect). Such formulations might subsume intervening proc-esses, and the likelihood of correctly predicting the outcome of changes in selection regime, such as those that are occurring from global warming, will be greatly enhanced by adding these intervening processes to the life-history model. Depending on the trade-off that is in question, these processes might involve physiologi-cal, behavioural, morphological or ecological factors and, in many instances, a response at the genomic level (for example, changes in transcription rates). Therefore, genomics can address the question of how a trade-off that is expressed at the phenotypic level is manifested or modulated at the level of genetic regulation. In this Review I consider the contribution that genomic studies have made to our understanding of the causal basis of trade-offs, the central pillar of life-history theory. I also briefly consider whether genomic studies indicate that life-history theory should take seriously the possibility that the complexity of interactions among trade-offs generates a fitness surface that consists of several fitness optima of unequal value, and that populations in the vicinity of a suboptimal peak might be driven to this local optimum instead of the global optimum.

The analysis of life-history variationThe analysis of life-history variation (BOX 2) is grounded in an immense body of theory that continues to grow rapidly. Such theory cannot be addressed solely by ref-erence to a few model organisms, and a perusal of the life-history literature shows that the number of species that have been investigated is in the many hundreds; for example, the taxonomic index of The Evolution of Life Histories1 contains over 800 entries, including

Box 1 | An example of the analysis of life-history variation

To determine the optimal combination of life-history traits, we begin by setting an operational definition of fitness. Depending on the particular scenario, several definitions are possible, but one that seems to be applicable to a wide range of circumstances is the expected lifetime fecundity, R0, which is defined by equation 1:

∑R0 = lxmx (1)x = α

x = ∞

where α is the age at first reproduction, lx is the probability of survival to age x, and mx is the number of female births at age x. To show how this equation can be used, consider the problem of predicting the optimal age at first reproduction in ectothermic vertebrates. Fecundity is typically an allometric function of length at age x, Lx, defined by equation 2:

mx = aLx3 (2)

where a is a constant that is specific to a genotype. To obtain mx as a function of age, we use the growth relationship that is typical of ectothermic vertebrates, defined by equation 3:

Lx = L∞ (1–e–kx) (3)

where L∞ is the asymptotic size and k is the growth coefficient. The survival function can be represented as equation 4:

lx = Se–Zx (4)

where S and Z are mortality coefficients (the first approximates egg and larval survival, which occupies a short time period, and the second is the annual survival thereafter).

Two trade-offs are likely: first, allocation to present reproduction reduces future growth and therefore future fecundity, and second, mortality increases as the allocation to reproduction increases. The curves that are shown in the upper panel of the figure illustrate a life-history pattern that is under a particular allocation to reproduction. An increase in reproductive allocation will increase present fecundity, but will reduce future growth and therefore future reproduction, and at the same time will increase the mortality rate. Given particular trade-off functions, it is possible to determine the optimal age and allocation to reproduction80. To apply the above model to variation among species, Roff11 assumed that the trade-offs interact such that fecundity increases that are due to an increase in size are offset by increased mortality, from which the optimal age at first reproduction can be shown by equation 5:

= ln + 1 α 1k

3kZ

(5)

Analysis of the ages at maturity in both fish and reptiles shows this equation to be an accurate predictor of the observed ages in both taxa (bottom panels).

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Homo sapiens. The testing of life-history theory involves not only many species, but several different approaches, which can be conveniently divided into phenotypically and genetically based methods (BOX 2).

Phenotypic approaches. The first level of analysis is the comparison of trait variation in natural populations or among species. Testing hypotheses for the causes of vari-ation in particular traits among individuals and among

species can be confounded by variation in traits that are not included in the theoretical model. Furthermore, comparisons among species might be statistically invalid if the variation among the traits that are under investigation arose in several common ancestors and has not changed among the descendent taxa. Because of this, use of each species as an independent datum might inflate the sample size and produce a spurious correla-tion. Experimental manipulation is commonly used to

Box 2 | Methods for the analysis of life-history traits

Non-genetic approachesThe testing of life-history predictions in the absence of genetic considerations has followed three approaches, which can be illustrated with respect to the question of the existence of a predicted trade-off, such as that between present allocation to reproduction and future survival.

Phenotypic correlation. Phenotypic correlation is the correlation between two traits measured at the level of the phenotype and involving no manipulation of the organism or its environment. There are two potential problems with this approach. First, correlations among species or higher taxa can be misleading because they do not take into account phylogenetic history (for example, all penguins are flightless, but each species cannot be considered a statistically independent datum, because all penguins have evolved from a common, flightless ancestor and so flightlessness in penguins represents only a single evolutionary step). Secondly, phenotypic correlations that are obtained from individuals within a population can be obscured by environmental differences that were experienced by those individuals during an earlier period.

Comparative analysis. To solve the first problem, statistical methods have been developed that allow the estimation of correlations among statistically independent units. Most methods derive from the method of independent contrasts81,82.

Experimental manipulation. To solve the second problem, organisms are subjected to experimental manipulations to expose the presence or absence of a trade-off. For example, the trade-off between reproduction and survival can be tested by experimentally exposing both virgin and gravid females to a predator and measuring the relative survival rate.

Genetic approachesThe above approaches have been criticized as being insufficient, because evolutionary change requires that the traits and the trade-off, as expressed by the negative correlation, must have a genetic basis. To determine the importance of genetic variation in trade-offs, two genetic approaches are frequently adopted.

Pedigree analysis. By rearing organisms of known pedigree, the genetic basis of the trade-off, which is expressed by the genetic correlation (broadly speaking, the correlation due to genes that affect both traits) can be calculated. The two most commonly used methods are parent–offspring relationships or relationships among half siblings (siblings with the same father but different mothers).

Artifical selection. If a trade-off is at least in part genetically determined, then selection on one trait will produce a correlated response in the other trait. To observe a correlated response, selection experiments must typically must run for 5–10 generations.

Molecular genetic approachesMolecular genetic methods to explore life-history variation broadly fall into three categories: mutations and genetic insertions; QTL analysis; and microarray studies.

Mutations and genetic insertions. The effect of individual mutations on phenotypic variation have been used to analyse life-history variation73–84. The insertion of gene copies or other genetic material is used in studies of microorganisms85, and P-element insertions have been used to examine trade-offs between fitness components in Drosophila melanogaster86.

QTL analysis. This approach identifies regions of chromosomes that contribute to variation87,88. The extent to which such regions represent separate genes depends on the density of markers. QTL analysis can be helpful in identifying candidate genes, but large sample sizes (>500 individuals) are required. Because QTL analysis uses a cross between two parent strains, the amount of variation that can be exposed is limited, and might be unrepresentative of the allelic variation in the original sources. Technical difficulties have generally limited QTL analyses to organisms for which there is already extensive molecular information, such as crops of agricultural importance (for example, rice and maize) and model organisms (for example, mice and D. melanogaster).

Microarrays. Developed from the Southern blot method, microarrays allow the examination of hundreds to thousands of genes simultaneously10,89. Although microarray analysis is well developed for model organisms and for economically important species such as salmonids90 and honey bees91, there is limited availability for other species. Consequently, the use of microarrays to explore problems of life-history evolution is still largely inaccessible. Microarrays allow the quantitative measurement of gene expression in different tissues, but are restricted in that alleles that give the same expression cannot be distinguished88. With potentially hundreds of genes showing variation in expression, there are also major statistical problems in the analysis and interpretation of results.

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PleiotropyThe influence of a locus on more than one trait.

overcome the first problem, and statistical methods of combining taxonomic levels are now widely used to correct for phylogenetic history.

Quantitative genetic approaches. The methods discussed above have a basis in variation among phenotypes and do not consider the genetic architecture that underlies life-history traits. Evolutionary change is contingent on the presence of genetic variation and can be predicted, at least in the short term, by the breeder’s equation (BOX 3). As indicated by the questions that are noted in the intro-duction, an important focus of the genetic analysis of life-history variation is the importance of genetic archi-tecture on both the trajectory of evolutionary change and the ultimate end points. Although there is abundant genetic variation for life-history traits8,9, the importance of genetic constraints that result from trade-offs among traits, which is a core assumption of life-history theory, is still poorly understood (BOX 4). The measurement of restrictions on evolutionary changes that are modulated by trade-offs requires the determination of the vari-ances and co-variances of a wide suite of traits. Such an approach is time consuming, potentially costly and is not guaranteed to include the requisite suite of traits. A better approach is to combine a quantitative genetic analysis with a phenotypic analysis that focuses on causal mecha-nisms. It is in addressing these causal mechanisms that genomic methods, particularly the use of microarrays, can make an important contribution.

Genomic approaches. The definition of what constitutes a genomic versus a genetic analysis is vague, and includes analyses that are based on QTLs, microarrays and even single-locus changes (particularly when combined with transgenic methods). QTL analysis detects the presence

and quantitative influence of chromosomal segments that correlate with variation in a trait. With a sufficiently detailed analysis it might be possible to isolate the gene or genes within these segments that are actually involved in determining the trait. QTL analysis has been important in defining the minimum number and action of genes (but as yet not alleles) and is therefore important in an analysis of the genetic basis if life-history traits.

Microarrays enable the determination of which genes are expressed in different tissues during development, and at such crucial periods as the time of maturation, reproduction or response to a physiological challenge. Microarrays can be particularly valuable in that they measure not only whether a gene is turned on or off but also the amount of mRNA that is produced, and can therefore inform on the quantity of end product that is produced10. The challenge is to statistically and functionally interpret the potentially huge amount of data that are produced from a single array. Ratchet this up to questions about variation among individuals in a population and we are faced with a daunting task, not only financially but also intellectually.

Investigations on the genomic basis of trade-offsAs discussed above, trade-offs are a central feature of life-history theory. For example, an increase in fecun-dity might be gained by a decrease in egg size, but the offspring from smaller eggs will have reduced survival; so there is a trade-off between increased fecundity and offspring survival11. Trade-offs between two life-history traits can occur through an intermediate trait; for example, an increase in development time will decrease the chances of survival to maturity in Drosophila melanogaster but will increase body size, which also increases fecundity12. Therefore, in this case, there is a trade-off between survival and fecundity that is mediated by body size. Pleiotropic interactions are particularly well illustrated by the multifaceted effects of hormones on development13,14. Because of the problems of interpreting the extent to which genetic architecture, as measured by genetic variances and co-variances, restricts or modulates evolutionary trajectories, increasing atten-tion is being given to investigating the functional basis of trade-offs.

Genomic analyses of trade-offs in resistance to parasites and pathogens. Animals and plants are continuously subjected to assaults from parasites and pathogens, and therefore the evolution of resistance is to be expected. If such resistance involves trade-offs with life-history traits such as fecundity, survival or growth then we would expect that, in most cases, resistance will not be complete. That organisms are not entirely immune to challenges that are presented by parasites and patho-gens indicates that resistance indeed carries a fitness cost. Such a cost has been demonstrated in Arabadopsis thialana for resistance to the pathogen Pseudomonas syringae. The locus RPM1 codes for a peripheral plasma membrane that enables carriers to recognize P. syringae that carry the virulence genes AvrRpm1 or AvrB15. Susceptible individuals lack the entire coding region of

Box 3 | Predicting evolutionary change using the breeder’s equation

For a single trait, we can predict the evolutionary trajectory using the breeder’s equation, R = h2S, where R is the response to selection, h2 is the heritability of the trait and S is the selection differential (that is, the difference between the population mean and the mean of the parents that contribute to the next generation). The genetic component of this equation is the term ‘heritability’, which technically is the ratio of the additive genetic variance to the phenotypic variance. The simple breeder’s equation can be expanded to include multiple correlated traits. The expanded model takes into account not only the heritabilities of each of the traits, but also the extent to which the genes that control each trait also influence other traits. This overlap of influence is measured by the genetic correlation between traits. We can show this for the simple case of two correlated traits, X and Y. The expanded breeder’s equation becomes equation 6:

RX = XhX2 + YhxhYrGββ (6)

where RX is the response, in phenotypic standard deviation units, in trait X when selection is applied to traits X and Y. The heritabilities of X and Y are hX

2, hY2, and rG is the

genetic correlation between the two traits. The multivariate equivalents of the selection differentials are denoted βX and βY, and are called the selection coefficients (here measured in phenotypic standard deviations). Together, they comprise the ‘selection gradient’ for this suite of two traits. The multivariate breeder’s equation can be written in matrix form, R = Gβ, where R is the vector of responses, G is the genetic variance–co-variance matrix and β is the selection gradient vector. The G matrix is symmetrical, with the diagonal elements being the genetic variances and the off-diagonal elements being the genetic co-variances.

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Trait X

Trai

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Trait X

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RPM1. Resistant and non-resistant forms co-exist within populations, indicating that resistance bears a fitness cost. To test this hypothesis, Tian et al.15 inserted RPM1 into a susceptible strain, thereby creating transgenic lines

that were identical except for the presence or absence of RPM1. Consistent with the trade-off hypothesis, under common garden conditions resistant plants suffered a 9% decrease in seed production15. The causal mechanism for the fitness cost is unknown, but upregulation of resistance-gene expression is one possible mechanism15. Other genes that confer resistance to pests and patho-gens have been identified in Arabidopsis thaliana16–18, but fitness costs have yet to be explored in most cases. An exception is the cost of systemic acquired resist-ance, in which experiments with mutants that lack the resistance response showed an environment-dependent fitness cost16. Infection of susceptible red flour beetles, Tribolium casteneum, by the rat tapeworm, Hymenolepis diminuta, reduces fitness by approximately 30%, but resistant populations show a lower fecundity and egg-to-adult viability. A QTL study showed that three QTLs for beetle fitness were co-localized with the three QTLs that were identified for beetle resistance, indicating that the genomic region that is responsible for resistance was also partially responsible for fitness19.

The importance of gene regulation in the generation of trade-offs. In a population or quantitative genetic context, a trade-off is understood to be the result of either linkage disequilibrium or antagonistic pleiotropy, with antagonistic pleiotropy being the more commonly assumed mode of action for trade-offs between life-history traits. Stearns and Magwene20 suggested that in a genomics context antagonistic pleiotropy can be defined as: “…conflicts between whole-organism function over whole-genome patterns of gene expression,” and described this by con-sidering gene-expression patterns in response to two physiological challenges. In the simplest case, two sets of genes respond independently (that is, with zero genetic correlation) and there is no genomic conflict. When some components of the two gene sets work on the same developmental or regulatory process, there is the potential for genomic conflict in that the upregula-tion of one could be a detrimental response with respect to the other challenge.

Adaptation to a particular environmental challenge, such as a microbe, a pesticide or a territorial intruder, or adaptation by growth of a particular structure, such as a thicker shell to protect against a predator, will probably require the upregulation of genes that control the suite of physiological processes that underlie the response. For example, insecticide resistance is in several instances due to the massive upregulation of enzymes that detoxify the insecticide21–24. However, increased resistance comes at the price of a decrease in fitness-related traits such as growth rate and fecundity25–27, or changes in behav-iour that reduce fitness28,29. Decreased growth rate is presumed to be the consequence of the high metabolic demands of the upregulation of detoxifying enzymes, which results in slow growth and a small adult size, leading to reduced fecundity. A genomic analysis of male reproductive success in D. melanogaster showed that the most significant, but not only, effect was due to a downregulation of cytochrome P450 (Cyp6g1), which confers insecticide resistance when upregulated30.

Box 4 | A quantitative genetics perspective on trade-offs

A trade-off is specified in quantitative genetics by a negative co-variance or correlation between two traits; in this context it is possible to precisely define the circumstances under which some evolutionary trajectories are not allowed. According to standard quantitative genetic theory, evolutionary trajectories can, at least in the short term, be predicted using the matrix of genetic variances and co-variances, known as the G matrix (BOX 3). As shown in the figure, for two traits, the genetic and phenotypic distributions are bivariate normal, a negative correlation being specified by the negative slope of the main axis. The G matrix can be reduced, using principal components analysis, to a set of orthogonal axes that are designated by the eigenvectors, corresponding to the major and minor axes (shown in white in the figure for the case of two traits). The variance in each principal component is given by the eigenvalue. In the figure, there is variation along both axes and so evolution can proceed in any direction. If an eigenvalue is zero (for two traits, this means a genetic correlation of –1) there is no variance in the respective direction, and therefore evolution cannot proceed in that direction (so, as shown in the lower left plot, for two traits with a genetic correlation of –1 selection cannot produce combinations of trait values that do not fall exactly on the single trade-off line). The diagram in the lower right corner shows three traits that are constrained to lie on the plain, as indicated by the yellow dots. In this case, all pair-wise combinations are possible, but not all three-way combinations. Mathematically, this is shown by a zero eigenvalue in the third axis (that is, the axis that is orthogonal to the plane). Calculating the set of eigenvalues and testing for deviation from zero is extremely difficult, requiring large sample sizes, and to date no analysis has convincingly demonstrated that evolution in any particular direction is not possible.

Even if there are no eigenvalues that are exactly zero, movement along a particular evolutionary trajectory can be slow if the eigenvalue in that direction is small relative to the other eigenvalues; that is, selection can proceed more rapidly in the direction of the major axis than the minor axis92,93. Therefore, if we wish to make statements about the importance of a particular trade-off in modulating and directing evolutionary change, it is necessary to know how that trade-off is integrated with other traits.

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EigenvectorsThe set of coefficients that define each principal component.

EigenvalueThe variance of each principal component.

Linkage disequilibriumThe non-random association between two loci. This can be caused by physical linkage due to the two loci being on the same chromosome, or to disassortative mating.

Antagonistic pleiotropyA negative genetic correlation between traits such that selection on one trait is opposed by the consequent selection on the second trait.

Dimorphic variationVariation in which two distinct morphs can be identified (for example, wing dimorphism in insects).

Threshold modelThe threshold model assumes that there is a normally distributed underlying trait, called the liability, plus a threshold: individuals above the threshold follow one developmental pathway, whereas individuals below the threshold follow the alternative pathway.

HolometabolousA mode of development in insects in which there are discrete larval and pupal stages (as in Diptera and Lepidoptera).

HemimetabolousInsects in which development proceeds without a distinct pupal stage, with the nymphal stages moulting directly into the adult form (as in Hemiptera and Orthoptera).

Trade-offs can result from ‘gene competition’ for limited resources. In many cases, the functional basis of trade-offs can be understood as a competition for limited resources within the organism. Herbicide resistance in several plant species has been shown to be associ-ated with a reduction in resource acquisition31. Using microarrays, Bochdanovits and de Jong32 identified 34 candidate genes that might be responsible for the trade-off between larval survival and adult weight in male D. melanogaster. On the basis of the known molecu-lar function of some of the genes, they concluded that adaptive resource allocation underlies the trade-off, and is caused by a trade-off between protein biosynthesis (growth) and energy metabolism (survival). Although the proposed mechanism for this trade-off is intuitively attractive, it must be viewed as a working hypothesis until the precise details have been demonstrated con-vincingly. Nevertheless, this study stands as arguably the best study to date on the genomic basis of a life-history trade-off using microarray technology, and will be a guide to future studies.

Genomic differences that underlie trade-offs in dimor-phic species. A well studied class of trade-offs are those that maintain dimorphic variation in traits that themselves might not be life-history traits but which impinge directly on fitness. For example, in many salmon spe-cies there are two morphs, one large (up to a metre in length) and one small (less than 30 cm in length). One morph migrates to the ocean, grows to a large size and holds a territory on return to its natal river, whereas the other matures without a marine migration, is small and attempts to sneak copulations. There is both a genetic and environmental component to this dimorphism, and its quantitative genetic basis can be modelled using the threshold model33,34. For any given genotype, fast growth in early life favours development into a sneaker form, although the threshold at which this occurs is geneti-cally variable. Examination of over 3,000 genes in the brains of the two types of male showed that 15% were differentially expressed, with some being characteristic of the morph and some of the rearing environment34,35. A similar circumstance is found in the African cichlid fish, Haplochromis burtoni, in which growth rate is correlated with social status as indicated by territorial behaviour or rank within non-territorial individu-als36. A comparison of genes that are expressed in the pre-optic areas of territorial and non-territorial indi-viduals identified 59 differentially expressed genes37. The number of genes that were differentially expressed in these studies show the potentially large degree of epistatic interactions underlying life-history variation, although details of which interactions are necessary for the expression of particular life histories are far from being resolved.

Another example of dimorphic variation that has been well studied from a life-history perspective is wing dimorphism in many species of insects38. In wing-dimorphic insects, one morph is long-winged and flight capable, and has reduced fecundity or male mating success relative to the other morph, which lacks

flight capability38. Although the reproductive fitness of the long-winged morph is reduced locally it has a fitness advantage at the metapopulation scale, as it is the only morph that can successfully migrate to newly arising habitats39. It is well known that the determination of wing morph occurs at different points in the ontogeny in different insect species, and that even within the same species there are ontogenetic windows during which particular environmental cues can influence future wing development. Given these observations, it comes as no surprise to find that different molecular mechanisms have evolved to inhibit wing formation, thereby producing the short-winged morph40 (FIG. 1). In this study, the production of wings was related to castes, but in many insect species, including the queens of some ant species, wing production is dichotomous within castes, with some individuals producing a fully functional flight apparatus (long wings, functional flight muscles) and others developing only a non-functional apparatus (short wings, insignificant flight muscles). In almost all insects that show holometabolous development, the genetic control of the dimorphism is under the control of a single locus, with brachyptery (short wings) being dominant. On the other hand, in hemimetabolous insects, the control is almost universally polygenic, which, as previously discussed, can be mod-elled using the threshold model of quantitative genetics. Furthermore, wing dimorphism with polygenic control always shows phenotypic plasticity. Microarray analy-sis of variation in the gene products that are associated with wing polymorphism among different insect taxa could be highly informative. The important question from the point of view of life-history theory is whether these two forms of control represent constraints on phenotypic expression, which would limit the range of adaptive responses. For example, phenotypic plasticity in wing-morph determination could be highly advanta-geous, as it could eliminate the costs of producing and maintaining wings, wing muscles, flight fuels and so on when environmental conditions were favourable for immediate reproduction. Single locus control of wing morph, as is seen in holometabolous insects, would be unable to facilitate such an adaptive response.

Selection experimentsTrade-offs can be uncovered using either artificial selection or experimental evolution (also known as laboratory evolution). In artificial selection, a single trait is typically selected and trade-offs assessed by the correlated response in those fitness traits that are not under direct selection. This approach can be restrictive in that fitness is defined by the experimenter and might bear little relationship to a real situation; for example, selection for low fecundity might simply select for a physiologically inferior organism. One way to avoid this problem is to always select in the direction of increased fitness (that is, in the aforementioned example, to select for high fecundity). A good example of the power and the problems that are associated with the use of artificial selection experiments to demonstrate the presence of a trade-off and its genomic basis is given by the numerous

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Drosophila Precis Neoformica

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Origin of wingpolyphenism

Presence of wing-patterning network

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Growth, differentiation and morphogenesis

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Late embryo

Late larva

Mutation accumulationOne hypothesis to account for senescence. Mutations accumulate during life as a result of errors that are incurred during sequential somatic cell divisions. Such mutations have deleterious effects on survival.

attempts to select for increased longevity. QTL analysis of D. melanogaster lines that were selected for either short or long generation times identified similar QTLs to those that were previously identified by composite interval mapping41. Also, many marker loci responded in oppo-site directions to selection, indicating negative genetic correlations as predicted by the trade-off hypothesis41. Negative correlations between early and late mortality QTL have been reported in other experiments42, but age-specific fecundity QTLs seem to be independent43. On the other hand, negative correlations between indices of female fecundity and male longevity have been found in D. melanogaster43,44 and a QTL for ovariole number has been shown to be co-localized with one for longevity44, but whether this corresponds to the same gene or genes has yet to be established. Further QTL analyses that use lines that have been selected for differences in longevity or age-specific fecundity are required to determine

the relative position of fecundity and longevity genes. DNA repair capacity has been shown to be correlated with mammalian lifespan45, and genes that are known to affect lifespan in model organisms are linked with energy metabolism through the insulin signalling pathway46,47–49. Although selection experiments and genomic analyses of these have generally been successful, and a trade-off between reproduction and ageing has been established50,46, it has proved difficult to establish the genomic basis of this trade-off, most particularly to distinguish between antagonistic pleiotropy and mutation accumulation.

The evidence from either genomic analyses or selection experiments indicates that senescence is determined in part by both phenomena, and that trade-offs have an important role46,51.

An alternative approach to artificial selection, known as experimental evolution, is to define a particular environ-ment and allow organisms to adapt to that environment

Figure 1 | Different molecular mechanisms can produce the same phenotype. A single wing-patterning network (shown as a green bar in the phylogeny) evolved approximately 325 million years (Myr) ago and has been largely conserved in holometabolus insects, as exemplified by the network in Drosophila melanogaster (shown on lower right), for which the separations indicate mid-embryonic, late-embryonic and late-larval stages. Wing polyphenism in ants (shown as a blue bar) evolved only once, approximately 125 Myr ago. Network diagrams for the winged reproductive castes are shown in the row that is labelled ‘wings’, and the network diagrams for the wingless sterile castes are shown in the row that is labelled ‘no wings’. Conserved gene expression is indicated in green, interrupted expression is indicated in red and genes that were not examined are indicated in grey. Modified with permission from REF. 40 © (2002) American Association for the Advancement of Science.

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without outside intervention. This approach has been used extensively to study adaptation in organisms such as viruses52, bacteriophage53, Escherichia coli54, yeast55,56, Caenhorhabditis elegans57,58, Chlamydomonas reinhardtii59, D. melanogaster61–63, Gryllus firmus64 and Brassica rapa65. Adaptation in clonally reproducing organisms, such as the first six in the preceding list, occurs primarily by accumulation of new mutations, whereas adaptation in diploid, sexually reproducing species occurs by changes in gene frequency and pos-sibly the accumulation of new mutations. Both artificial selection and experimental evolution have demon-strated the presence of genetically based trade-offs, consistent with the central assumption of life-history theory, but relatively few have been able to isolate the molecular mechanisms that are involved. Stocks of D. melanogaster that are maintained under different thermal regimes varied in the copy number of the heat shock gene hsp70 in a manner that is consistent with adaptation to that regime61. Previous experiments have shown that overexpression of the heat shock protein inhibits growth and reduces survival66, which explains why the gene is found at an intermediate frequency in the wild, variable environment. Another example in which the molecular mechanism has been investigated is the evolution of E. coli in a lactose-limited environment, in which the mutations that enhance the uptake of lac-tose also increase susceptibility to certain antibiotics67. It is unlikely that trade-offs will be a consequence of a single locus in most multicellular organisms, and micro-array analyses are likely to yield important information in these organisms.

Trade-offs and paths of adaptationSelection approaches. In most life-history models, there is a single optimal combination of parameter values at which fitness is globally maximized. However, there might be combinations of parameters at which fitness is not maximized but from which any small deviation reduces fitness, thereby ‘trapping’ the population on a suboptimal fitness ‘peak’. At this time, it is not clear if life-history theory should be concerned with the possibility of multiple evolutionary trajectories or end points. Certainly, if the prediction of the evolutionary trajectory is important, then these are crucial ques-tions. One way to address this problem is by means of selection experiments. Starting from the same base population, multiple replicate lines can be subjected to the same selection pressures and, provided a significant response is observed, the lines can be assayed to exam-ine whether the same end point has been reached by the same mechanism. Even if the same mechanism in terms of physiology, morphology or behaviour occurs in all replicate lines, it is still possible, and likely, that the underlying gene frequencies will differ among lines; that is, different genetic architectures can produce the same phenotype.

Differences among selected lines can be assessed by analysis of crosses between the lines or by QTL analysis of the separate lines. Line-cross analysis of mice that were selected for growth rate and nest-building behaviour

produced highly significant statistical differences in genetic architecture68,69, showing that the same selection regime operating on the same initial genetic make-up can result in changes in genetic composition. On the other hand, variation in genetic background does not necessarily mean that response to selection will be different. QTL analysis of eight lines of A. thaliana following three generations of selection on viability and fertility indicated that variation in genetic back-ground was not important in determining the response to selection, although one QTL showed a variable response70,71.

In none of the above studies was the mechanism of the response studied in sufficient detail to ascertain whether there was phenotypic variation in the suite of changes; that is, although there might have been variation in the suite of genes that were altered by selection, these could have resulted in the same biochemical, physiological or behavioural mechanisms. Experimental evolution of competitive ability in D. melanogaster and Drosophila simulans showed that populations that are descended from a common base can evolve increased competitive ability by different routes when subjected to interspecific competition72. On the other hand, laboratory evolution in multiple lines of two bacteriophages occurred by the same amino-acid changes, indicating that the same molecular mechanism was being invoked73. Antifungal drug resistance of Saccharomyces cerevisiae can be attained by several distinct mutations, which, if occur-ring together, reduce fitness56, indicating that multiple fitness peaks might exist.

Comparing different species or populations. An alterna-tive approach to artificial selection is to examine different populations or species that show a common adaptive response. Because of its effect on development time and fecundity, variation in body size in D. melanogaster has important fitness consequences12. Studies of clinal variation in wing-size divergence in three parallel body-size clines of D. melanogaster have shown that different genetic architectures are involved74. QTL analysis of populations in South America and Australia implicated the same QTL in the variation, although sample sizes were too small to determine whether there are also biologically significant, but statistically undetectable, dif-ferences75. Clinal variation is also observed in Drosophila subobscura, and, in this case, although the clines from different continents mirror each other with respect to overall size, the components of the wing show striking differences76. In both D. melanogaster and D. subobscura, clinal variation in wing size results from changes in cell number, whereas thermal selection produces differences in wing size due to changes in cell size77.

Multiple genetic pathways to the same trait are clearly evident in studies among species, and it is likely that selection in several lines to a given combination of phe-notypic values will entail variation in gene frequencies. What is still obscure is whether this frequently results in variation in the phenotypic basis of the trait, major or minor changes in levels of gene regulation, or differences in structural genes.

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Conclusion Although genomics has so far made only a modest contribution to life-history theory, the results to date indicate that a genomics approach can and will be important in future development of this field. It is in the area of explaining the causal mechanism that underlies the numerous observed trade-offs that genomics is making its greatest impact to date. Such data can contribute to answering the question of how immutable particular trade-offs might be. This could lead to life-history theories that consider not just equi-librium conditions, but sequential equilibria that arise as new combinations of traits arise, either through a shuffling of the genes or new mutations. A good empirical example of this is given by the evolution of insecticide resistance in the Australian sheep blowfly. Initially, resistance carried a high fitness cost, but a muta-tion at a modifier locus eliminated this cost78. So, the ini-tial ‘equilibrium’ trait combinations were shaped by the costs and benefits of insecticide resistance, but the final equilibrium was independent of costs. This result might have been predictable from an understanding of the causal mechanism of resistance, in which case

life-history theory would predict multiple equilibria, with one being contingent on the arisal of the appro-priate mutation. These equilibria might be expressed temporally or spatially, or both.

Both selection experiments and comparative analy-ses show that the same end points can be achieved by different genetic architectures, which might or might not result in detectable differences in the intermediate processes that underlie the trait in question. There is variation in life-history traits both within and among populations, but we need more studies on the extent to which variation can be ascribed to variation in structural versus regulatory genes, although the regulatory genes are likely to be the most important. Of considerable inter-est would be experiments that examine how quantitative variation in gene expression fits into phenotypic and quantitative genetic variation in life-history traits. One thing is evident: although genomics will make a signifi-cant contribution to life-history theory, other techniques such as quantitative genetics will remain a fundamen-tal component of the analysis79, and progress will require the joint input from mathematical, organismal and molecular approaches.

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AcknowledgementsThis work was supported by the US National Science Foundation.

Competing interests statementThe authors declare no competing financial interests.

DATABASESThe following terms in this article are linked online to:Entrez Gene: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=geneAvrRpm1 | Cyp6g1 | RPM1

FURTHER INFORMATIONDerek A. Roff’s web site: http://www.biology.ucr.edu/people/faculty/Roff.htmlAccess to this links box is available online.

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contributions of genomics to life-history theory

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