SEX-SPECIFIC GENETIC VARIANCE AND THE EVOLUTION OF SEXUAL DIMORPHISM: A SYSTEMATIC REVIEW OF CROSS-SEX GENETIC CORRELATIONS

The independent evolution of the sexes may often be constrained if male and female homologous traits share a similar genetic architecture. Thus, cross-sex genetic covariance is assumed to play a key role in the evolution of sexual dimorphism (SD) with consequent impacts on sexual selection, population dynamics, and speciation processes. We compiled cross-sex genetic correlations ( r _MF) estimates from 114 sources to assess the extent to which the evolution of SD is typically constrained and test several specific hypotheses. First, we tested if r _MF differed among trait types and especially between fitness components and other traits. We also tested the theoretical prediction of a negative relationship between r _MF and SD based on the expectation that increases in SD should be facilitated by sex-specific genetic variance. We show that r _MF is usually large and positive but that it is typically smaller for fitness components. This demonstrates that the evolution of SD is typically genetically constrained and that sex-specific selection coefficients may often be opposite in sign due to sub-optimal levels of SD. Most importantly, we confirm that sex-specific genetic variance is an important contributor to the evolution of SD by validating the prediction of a negative correlation between r _MF and SD.     

TYPE I ERROR RATES FOR TESTING GENETIC DRIFT WITH PHENOTYPIC COVARIANCE MATRICES: A SIMULATION STUDY

Studies of evolutionary divergence using quantitative genetic methods are centered on the additive genetic variance–covariance matrix ( G ) of correlated traits. However, estimating G properly requires large samples and complicated experimental designs. Multivariate tests for neutral evolution commonly replace average G by the pooled phenotypic within‐group variance–covariance matrix ( W ) for evolutionary inferences, but this approach has been criticized due to the lack of exact proportionality between genetic and phenotypic matrices. In this study, we examined the consequence, in terms of type I error rates, of replacing average G by W in a test of neutral evolution that measures the regression slope between among‐population variances and within‐population eigenvalues (the Ackermann and Cheverud [AC] test) using a simulation approach to generate random observations under genetic drift. Our results indicate that the type I error rates for the genetic drift test are acceptable when using W instead of average G when the matrix correlation between the ancestral G and P is higher than 0.6, the average character heritability is above 0.7, and the matrices share principal components. For less‐similar G and P matrices, the type I error rates would still be acceptable if the ratio between the number of generations since divergence and the effective population size (t/N_e) is smaller than 0.01 (large populations that diverged recently). When G is not known in real data, a simulation approach to estimate expected slopes for the AC test under genetic drift is discussed.     

QUANTITATIVE GENETIC VARIANCE MAINTAINED BY FLUCTUATING SELECTION WITH OVERLAPPING GENERATIONS: VARIANCE COMPONENTS AND COVARIANCES

The quantitative genetic variance-covariance that can be maintained in a random environment is studied, assuming overlapping generations and Gaussian stabilizing selection with a fluctuating optimum. The phenotype of an individual is assumed to be determined by additive contributions from each locus on paternal and maternal gametes (i.e., no epistasis and no dominance). Recurrent mutation is ignored, but linkage between loci is arbitrary. The genotype distribution in the evolutionarily stable population is generically discrete: only a finite number of polymorphic alleles with distinctly different effects are maintained, even though we allow a continuum of alleles with arbitrary phenotypic contributions to invade. Fluctuating selection maintains nonzero genetic variance in the evolutionarily stable population if the environmental heterogeneity is larger than a certain threshold. Explicit asymptotic expressions for the standing variance-covariance components are derived for the population near the threshold, or for large generational overlap, as a function of environmental variability and genetic parameters (i.e., number of loci, recombination rate, etc.), using the fact that the genotype distribution is discrete. Above the threshold, the population maintains considerable genetic variance in the form of positive linkage disequilibrium and positive gamete covariance (Hardy-Weinberg disequilibrium) as well as allelic variance. The relative proportion of these disequilibrium variances in the total genetic variance increases with the environmental variability.     

COMPARISON OF NON‐GAUSSIAN QUANTITATIVE GENETIC MODELS FOR MIGRATION AND STABILIZING SELECTION

The balance between stabilizing selection and migration of maladapted individuals has formerly been modeled using a variety of quantitative genetic models of increasing complexity, including models based on a constant expressed genetic variance and models based on normality. The infinitesimal model can accommodate nonnormality and a nonconstant genetic variance as a result of linkage disequilibrium. It can be seen as a parsimonious one‐parameter model that approximates the underlying genetic details well when a large number of loci are involved. Here, the performance of this model is compared to several more realistic explicit multilocus models, with either two, several or a large number of alleles per locus with unequal effect sizes. Predictions for the deviation of the population mean from the optimum are highly similar across the different models, so that the non‐Gaussian infinitesimal model forms a good approximation. It does, however, generally estimate a higher genetic variance than the multilocus models, with the difference decreasing with an increasing number of loci. The difference between multilocus models depends more strongly on the effective number of loci, accounting for relative contributions of loci to the variance, than on the number of alleles per locus.     

THE EVOLUTIONARY STABILITY OF CROSS‐SEX,CROSS‐TRAIT GENETIC COVARIANCES

Although knowledge of the selective agents behind the evolution of sexual dimorphism has advanced considerably in recent years, we still lack a clear understanding of the evolutionary durability of cross‐sex genetic covariances that often constrain its evolution. We tested the relative stability of cross‐sex genetic covariances for a suite of homologous contact pheromones of the fruit fly Drosophila serrata, along a latitudinal gradient where these traits have diverged in mean. Using a Bayesian framework, which allowed us to account for uncertainty in all parameter estimates, we compared divergence in the total amount and orientation of genetic variance across populations, finding divergence in orientation but not total variance. We then statistically compared orientation divergence of within‐sex ( G ) to cross‐sex ( B ) covariance matrices. In line with a previous theoretical prediction, we find that the cross‐sex covariance matrix, B , is more variable than either within‐sex G matrix. Decomposition of B matrices into their symmetrical and nonsymmetrical components revealed that instability is linked to the degree of asymmetry. We also find that the degree of asymmetry correlates with latitude suggesting a role for spatially varying natural selection in shaping genetic constraints on the evolution of sexual dimorphism.     

MODELING THE GENETIC BASIS OF HETEROSIS: TESTS OF ALTERNATIVE HYPOTHESES

Houle (1994) showed that marker-associated heterosis due to general inbreeding depression could not be distinguished from direct overdominance at the marker locus by examining mean genotypic fitnesses, in the one-locus case. Indeed, both hypotheses equally fit the same regression model, referred to as the “adaptive distance model” (Smouse 1986). I here extend the analysis to several loci and to the relationship between marker genotype and variance in fitness. Several predictions differ between the overdominance and inbreeding hypotheses: (1) all locus-specific effects are equal under inbreeding, whereas they are not under overdominance; (2) the adaptive distance model has an increasingly low fit when the number of loci increases, under inbreeding, whereas it always explains the whole variance in fitness under overdominance; (3) a negative relationship is predicted between mean fitness and the variance in fitness, under inbreeding, which is not predicted under overdominance. Some statistical tests are derived from these predictions, that help to identify the genetic basis of heterosis. Simulations show that the power of these tests allows their application to real datasets.     

THE COMPARISON OF QUANTITATIVE GENETIC PARAMETERS BETWEEN POPULATIONS

A statistical method for comparing matrices of genetic variation and covariation between groups (e.g., species, populations, a single population grown in distinct environments) is proposed. This maximum-likelihood method provides a test of the overall null hypothesis that two covariance component matrices are identical. Moreover, when the overall null hypothesis is rejected, the method provides a framework for isolating the particular components that differ significantly between the groups. Simulation studies reveal that discouragingly large experiments are necessary to obtain acceptable power for comparing genetic covariance component matrices. For example, even in cases of a single trait measured on 900 individuals in a nested design of 100 sires and three dams per sire in each population, the power was only about 0.5 when additive genetic variance differed by a factor of 2.5. Nevertheless, this flexible method makes valid comparison of covariance component matrices possible.     

EPISTASIS AND THE INCREASE IN ADDITIVE GENETIC VARIANCE: IMPLICATIONS FOR PHASE 1 OF WRIGHT'S SHIFTING-BALANCE PROCESS

Central to Wright's shifting-balance theory is the idea that genetic drift and selection in systems with gene interaction can lead to the formation of “adaptive gene complexes.” The theory of genetic drift has been well developed over the last 60 years; however, nearly all of this theory is based on the assumption that only additive gene effects are acting. Wright's theory was developed recognizing that there was a “universality of interaction effects,” which implies that additive theory may not be adequate to describe the process of differentiation that Wright was considering. The concept of an adaptive gene complex implies that an allele that is favored by individual selection in one deme may be removed by selection in another deme. In quantitative genetic terms, the average effects of an allele relative to other alleles changes from deme to deme. The model presented here examines the variance in local breeding values (LBVs) of a single individual and the covariance in the LBVs of a pair of individuals mated in the same deme relative to when they are mated in different demes. Local breeding value is a measure of the average effects of the alleles that make up that individual in a particular deme. I show that when there are only additive effects the covariance between the LBVs of individuals equals the variance in the LBV of an individual. As the amount of epistasis in the ancestral population increases, the variance in the LBV of an individual increases and the covariance between the LBVs of a pair of individuals decreases. The divergence in these two values is a measure of the extent to which the LBV of an individual varies independently of the LBVs of other individuals. When this value is large, it means that the relative ordering of the average effects of alleles will change from deme to deme. These results confirm an important component of Wright's shifting-balance theory: When there is gene interaction, genetic drift can lead to the reordering of the average effects of alleles and when coupled with selection this will lead to the formation of the adaptive gene complexes.     

THE DETERMINATION OF GENETIC COVARIANCES AND PREDICTION OF EVOLUTIONARY TRAJECTORIES BASED ON A GENETIC CORRELATION MATRIX

There is much interest in measuring selection, quantifying evolutionary constraints, and predicting evolutionary trajectories in natural populations. For these studies, genetic (co)variances among fitness traits play a central role. We explore the conditions that determine the sign of genetic covariances and demonstrate a critical role of selection in shaping genetic covariances. In addition, we show that genetic covariance matrices rather than genetic correlation matrices should be characterized and studied in order to infer genetic basis of population differentiation and/or to predict evolutionary trajectories.     

QUANTITATIVE GENETICS OF MORPHOLOGICAL DIFFERENTIATION IN PEROMYSCUS. I. TESTS OF THE HOMOGENEITY OF GENETIC COVARIANCE STRUCTURE AMONG SPECIES AND SUBSPECIES

Phenotypic and additive genetic covariance matrices were estimated for 15 morphometric characters in three species and subspecies of Peromyscus. Univariate and multivariate ANOVAs indicate these groups are highly diverged in all characters, P. leucopus having the largest body size, P. maniculatus bairdii the smallest, and P. maniculatus nebrascensis being intermediate. Comparing the structure of P and G within each taxon revealed significant similarities in all three cases. This proportionality was strong enough to justify using P in the place of G to analyze evolutionary processes using quantitative genetic models when G can not be estimated, as in fossil material. However, the similarity between genetic and phenotypic covariance structures is sufficiently low that estimates of the genetic parameters should be used when possible. The additive genetic covariance matrices were compared to examine the assumption that they remain constant during evolution, an assumption which underlies many applications of quantitative-genetic models. While matrix permutation tests indicated statistically significant proportionality between the genetic covariance structures of the two P. maniculatus subspecies, there is no evidence of significant genetic structural similarity between species. This result suggests that the assumption of constant genetic covariance structure may be valid only within species. (It does not, however, necessarily imply a causal relationship between speciation and heterogeneity of genetic covariance structures.) The low matrix correlation for the two P. maniculatus subspecies' genetic covariance matrices indicates G may not be functionally constant, even within species. The lack of similarity observed here may be due partly to sampling variation.     

A MULTIVARIATE ANALYSIS OF GENETIC VARIATION IN THE ADVERTISEMENT CALL OF THE GRAY TREEFROG,HYLA VERSICOLOR

Genetic variation in sexual displays is crucial for an evolutionary response to sexual selection, but can be eroded by strong selection. Identifying the magnitude and sources of additive genetic variance underlying sexually selected traits is thus an important issue in evolutionary biology. We conducted a quantitative genetics experiment with gray treefrogs (Hyla versicolor) to investigate genetic variances and covariances among features of the male advertisement call. Two energetically expensive traits showed significant genetic variation: call duration, expressed as number of pulses per call, and call rate, represented by its inverse, call period. These two properties also showed significant genetic covariance, consistent with an energetic constraint to call production. Combining the genetic variance–covariance matrix with previous estimates of directional sexual selection imposed by female preferences predicts a limited increase in call duration but no change in call rate despite significant selection on both traits. In addition to constraints imposed by the genetic covariance structure, an evolutionary response to sexual selection may also be limited by high energetic costs of long‐duration calls and by preferences that act most strongly against very short‐duration calls. Meanwhile, the persistence of these preferences could be explained by costs of mating with males with especially unattractive calls.     

QUANTITATIVE GENETIC APPROACHES TO EVOLUTIONARY CONSTRAINT: HOW USEFUL?

The study of evolutionary constraint is an active and important area, and genetic correlations and quantitative genetic techniques more generally have been the dominant approach to constraint. Here, I argue that genetic correlations are not very useful for studying constraint, review recent alternative approaches, and briefly discuss the state of our knowledge of the evolutionary importance of constraints caused by genetic variance and covariance.     

EVOLUTION OF TRADE-OFFS BETWEEN SEXUAL AND ASEXUAL PHASES AND THE ROLE OF REPRODUCTIVE PLASTICITY IN THE GENETIC ARCHITECTURE OF APHID LIFE HISTORIES

Life‐history theory postulates that evolution is constrained by trade‐offs (i.e., negative genetic correlations) among traits that contribute to fitness. However, in organisms with complex life cycles, trade‐offs may drastically differ between phases, putatively leading to different evolutionary trajectories. Here, we tested this possibility by examining changes in life‐history traits in an aphid species that alternates asexual and sexual reproduction in its life cycle. The quantitative genetics of reproductive and dispersal traits was studied in 23 lineages (genotypes) of the bird cherry‐oat aphid Rhopalosiphum padi, during both the sexual and asexual phases, which were induced experimentally under specific environmental conditions. We found large and significant heritabilities (broad‐sense) for all traits and several negative genetic correlations between traits (trade‐offs), which are related to reproduction (i.e., numbers of the various sexual or asexual morphs) or dispersal (i.e., numbers of winged or wingless morphs). These results suggest that R. padi exhibits lineage specialization both in reproductive and dispersal strategies. In addition, we found important differences in the structure of genetic variance–covariance matrices ( G ) between phases. These differences were due to two large, negative genetic correlations detected during the asexual phase only: (1) between fecundity and age at maturity and (2) between the production of wingless and winged parthenogenetic females. We propose that this differential expression in genetic architecture results from a reallocation scheme during the asexual phase, when sexual morphs are not produced. We also found significant G × E interaction and nonsignificant genetic correlations across phases, indicating that genotypes could respond independently to selection in each phase. Our results reveal a rather unique situation in which the same population and even the same genotypes express different genetic (co)variation under different environmental conditions, driven by optimal resource allocation criteria.     

GENETIC CONSTRAINTS ON THE INDEPENDENT EVOLUTION OF MALE AND FEMALE REPRODUCTIVE CHARACTERS IN THE TRISTYLOUS PLANT LYTHRUM SALICARIA

Here we test whether the potential exists for the independent evolution of allocation to male, female, and attractive functions within a flower. We employed half-sib and parent-offspring regression methods in the tristylous plant Lythrum salicaria to determine whether there is additive genetic variation for characters important to male and female reproductive success and whether genetic correlations could constrain the independent evolution of male and female function. Although significance levels were not consistent among morph types or between populations, there were significant narrow-sense heritabilities for several traits including stamen mass, pistil mass, perianth mass, petal length, and calyx length. Traits that might be under strong stabilizing selection to promote specific pollen transfer, such as stamen and style lengths, had little heritable variation. In the majority of cases in which heritable variation was present, there were positive genetic correlations among floral traits. A strong positive genetic correlation appeared between stamen and pistil mass in the short-styled morph from one of the populations studied. This suggests that selection might not be able to act independently on biomass allocation to male and female flower parts. No evidence of negative genetic correlations appeared that would suggest trade-offs and that could augment a selection response towards sexual specialization. The observed positive correlations could be explained if we consider the “functional architecture” that underlies the covariance structure. If there is more covariance generated by pleiotropic loci controlling overall flower size than at loci controlling male versus female allocation, it could result in the observed positive covariance. At the phenotypic level, we did find significant negative partial correlations between male and female traits when flower size was controlled, but these trade-offs were among rather than within morphs.     

应用微卫星标记对稀有(鱼句)鲫封闭群建群过程的遗传监测

以采自四川省汉源县的野生稀有鮈鲫(Gobiocypris rarus)50对为建群原代(P0),采用最佳避免近交法建立了封闭群Ihb:IHB。应用11对微卫星引物对原代、F1—F4代传代亲鱼进行了遗传多样性监测。11个微卫星位点在稀有鮈鲫野生型封闭群共发现等位基因57个,各位点平均等位基因数5.2个、平均有效等位基因数3.3个、平均多态信息含量0.5282。封闭群各代平均期望杂合度为0.5553—0.5742,平均多态信息含量范围为0.5060—0.5318,平均观察等位基因数、平均有效等位基因数、平均观察杂合度、平均期望杂合度、平均多态信息含量在各代之间均无显著性差异(P>0.05)。各代间的遗传相似性在0.99以上,遗传分化系数FST为0.0008(P>0.05),基因频率也无显著性差异(P>0.05)。监测结果表明,Ihb:IHB保持了野生种群的遗传多样性,建群传代过程中未出现显著的遗传分化,符合实验动物封闭群的要求,可以作为一个实验动物的新品系。     

ACROSS‐ENVIRONMENT GENETIC CORRELATIONS AND THE FREQUENCY OF SELECTIVE ENVIRONMENTS SHAPE THE EVOLUTIONARY DYNAMICS OF GROWTH RATE IN IMPATIENS CAPENSIS

Trade‐offs can exist within and across environments, and constrain evolutionary trajectories. To examine the effects of competition and resource availability on trade‐offs, we grew individuals of recombinant inbred lines of Impatiens capensis in a factorial combination of five densities with two light environments (full light and neutral shade) and used a Bayesian logistic growth analysis to estimate intrinsic growth rates. To estimate across‐environment constraints, we developed a variance decomposition approach to principal components analysis, which accounted for sample size, model‐fitting, and within‐RIL variation prior to eigenanalysis. We detected negative across‐environment genetic covariances in intrinsic growth rates, although only under full‐light. To evaluate the potential importance of these covariances, we surveyed natural populations of I. capensis to measure the frequency of different density environments across space and time. We combined our empirical estimates of across‐environment genetic variance–covariance matrices and frequency of selective environments with hypothetical (yet realistic) selection gradients to project evolutionary responses in multiple density environments. Selection in common environments can lead to correlated responses to selection in rare environments that oppose and counteract direct selection in those rare environments. Our results highlight the importance of considering both the frequency of selective environments and the across‐environment genetic covariances in traits simultaneously.     

RESISTANCE OF GENETIC CORRELATION STRUCTURE TO DIRECTIONAL SELECTION IN DROSOPHILA MELANOGASTER

The genetic covariance and correlation matrices for five morphological traits were estimated from four populations of fruit flies, Drosophila melanogaster, to measure the extent of change in genetic covariances as a result of directional selection. Two of the populations were derived from lines that had undergone selection for large or small thorax length over the preceding 23 generations. A third population was constituted using flies from control lines that were maintained with equivalent population sizes as the selected lines. The fourth population contained flies from the original cage population from which the selected and control lines had been started. Tests of the homogeneity of covariance matrices using maximum likelihood techniques revealed significant changes in covariance structure among the selected lines. Prediction of base population trait means from selected line means under the assumption of constant genetic covariances indicated that genetic covariances for the small population differed more from the base population than did the covariances for the large population. The predicted small population means diverged farther from the expected means because the additive genetic variance associated with several traits increased in value and most of the genetic covariances associated with one trait changed in sign. These results illustrate that genetic covariances may remain nearly constant in some situations while changing markedly in others. Possible developmental reasons for the genetic changes are discussed.     

MIXED MODEL APPROACHES FOR ESTIMATING GENETIC VARIANCES AND COVARIANCES

The limitations of methods for analysis of variance(ANOVA)in estimating genetic variances are discussed. Among the three methods(maximum likelihood ML, restricted maximum likelihood REML, and minimum norm quadratic unbiased estimation MINQUE)for mixed linear models, MINQUE method is presented with formulae for estimating variance components and covariances components and for predicting genetic effects. Several genetic models, which cannot be appropriately analyzed by ANOVA methods, are introduced in forms of mixed linear models. Genetic models with independent random effects can be analyzed by MINQUE(1)method whieh is a MINQUE method with all prior values setting 1. MINQUE(1)method can give unbiased estimation for variance components and covariance components, and linear unbiased prediction (LUP) for genetic effects. There are more complicate genetic models for plant seeds which involve correlated random effects. MINQUE(0/1)method, which is a MINQUE method with all prior covariances setting 0 and all prior variances setting 1, is suitable for estimating variance and covariance components in these models. Mixed model approaches have advantage over ANOVA methods for the capacity of analyzing unbalanced data and complicated models. Some problems about estimation and hypothesis test by MINQUE method are discussed.     

利用SRAP标记研究四个暗纹东方鲀群体的遗传多样性

利用相关序列扩增多态性SRAP(Sequence-related amplified polymorphism)分子标记首次对4个暗纹东方鲀(Takifugu obscurus)群体(1个长江捕捞群体、1个放流群体和2个养殖群体)进行了遗传多样性分析。从49对引物组合中筛选出18对扩增条带清晰、稳定的引物组合对4个群体进行扩增,共获得231个位点,其中多态位点数为156个,总多态位点百分率为67.53%。4个群体内多态位点比例为54.98%—58.87%,群体的Nei’s基因多样性指数(H)为0.1992—0.2005,群体Shannon多样性指数(I)为0.2953—0.3016,群体间基因流(Nm)为4.1291。长江捕捞群体的多态位点比例、基因多样性指数、Shannon多样性指数均略高于其他三个群体。采用UPGMA法对4个群体进行聚类分析显示,上海养殖群体单独聚为一类,其余3个群体聚为另一类。AMOVA结果表明遗传变异主要来源于群体内,占总遗传变异的87.40%。以上结果表明,4个暗纹东方鲀群体具有较高的遗传多样性,群体间相似性较大,并且存在一定的基因交流。     

GENETIC AND MATERNAL INFLUENCES ON BRAIN AND BODY SIZE IN RANDOMBRED HOUSE MICE

Genetic and environmental (chiefly maternal) variance and covariance components were estimated for brain and body size in randombred house mice of three different ages (one, three, and five months). Heritabilities estimated from regressions of offspring on their five-month-old male parents were fairly low over all three ages, averaging about 0.2 for brain size and about 0.3 for body size. Heritability estimates from female parents were higher, however, presumably because of the influence of maternal-environmental components of variance. The total maternal impact was estimated from full-sib analyses and, for the more reliable three- and five-month ages, averaged 23% for brain size and 20% for body size. Phenotypic, genetic, and environmental correlations and regressions of brain and body size also were calculated by parent-offspring and sib-correlation techniques, the phenotypic correlations generally decreasing from about 0.4 at one month to 0.2 at three and five months. Genetic correlations of brain and body size estimated from covariances of offspring on male parents were negative whereas those from female parents were positive in sign, and this as well as positive maternal correlations was taken as evidence of the influence of maternal sources of covariance for these traits. It was concluded that, in addition to direct genetic effects, indirect genetic sources of variance and covariance mediated through the prenatal maternal environment are quite important in the determination of brain size and its association with body size.