The contribution of mutation and selection to multivariate quantitative genetic variance in an outbred population of Drosophila serrata.

Genetic variance is not equal for all multivariate combinations of traits. This inequality, in which some combinations of traits have abundant genetic variation while others have very little, biases the rate and direction of multivariate phenotypic evolution. However, we still understand little about what causes genetic variance to differ among trait combinations. Here, we investigate the relative roles of mutation and selection in determining the genetic variance of multivariate phenotypes. We accumulated mutations in an outbred population of Drosophila serrata and analyzed wing shape and size traits for over 35,000 flies to simultaneously estimate the additive genetic and additive mutational (co)variances. This experimental design allowed us to gain insight into the phenotypic effects of mutation as they arise and come under selection in naturally outbred populations. Multivariate phenotypes associated with more (less) genetic variance were also associated with more (less) mutational variance, suggesting that differences in mutational input contribute to differences in genetic variance. However, mutational correlations between traits were stronger than genetic correlations, and most mutational variance was associated with only one multivariate trait combination, while genetic variance was relatively more equal across multivariate traits. Therefore, selection is implicated in breaking down trait covariance and resulting in a different pattern of genetic variance among multivariate combinations of traits than that predicted by mutation and drift. Overall, while low mutational input might slow evolution of some multivariate phenotypes, stabilizing selection appears to reduce the strength of evolutionary bias introduced by pleiotropic mutation.

Evolutionary constraints in high-dimensional trait sets.

Genetic variation for individual traits is typically abundant, but for some multivariate combinations it is very low, suggesting that evolutionary limits might be generated by the geometric distribution of genetic variance. To test this prediction, we artificially selected along all eight genetic eigenvectors of a set of eight quantitative traits in Drosophila serrata. After six generations of 50% truncation selection, at least one replicate population of all treatments responded to selection, allowing us to reject a null genetic subspace as a cause of evolutionary constraint in this system. However, while all three replicate populations of the first five selection treatments displayed a significant response, the remaining three, characterized by low genetic variance in their selection indexes in the base population, displayed inconsistent responses to selection. The observation that only four of the nine replicate populations evolved in response to the direct selection applied to them in these low genetic variance treatments, led us to conclude that a nearly null subspace did limit evolution. Dimensions associated with low genetic variance are often found in multivariate analyses of standing genetic variance in morphological traits, suggesting that the nearly null genetic subspace may be a common mechanism of evolutionary constraint in nature.

Natural selection stops the evolution of male attractiveness.

Sexual selection in natural populations acts on highly heritable traits and tends to be relatively strong, implicating sexual selection as a causal agent in many phenotypic radiations. Sexual selection appears to be ineffectual in promoting phenotypic divergence among contemporary natural populations, however, and there is little evidence from artificial selection experiments that sexual fitness can evolve. Here, we demonstrate that a multivariate male trait preferred by Drosophila serrata females can respond to selection and results in the maintenance of male mating success. The response to selection was associated with a gene of major effect increasing in frequency from 12 to 35% in seven generations. No further response to selection, or increase in frequency of the major gene, was observed between generations 7 and 11, indicating an evolutionary limit had been reached. Genetic analyses excluded both depletion of genetic variation and overdominance as causes of the evolutionary limit. Relaxing artificial selection resulted in the loss of 52% of the selection response after a further five generations, demonstrating that the response under artificial sexual selection was opposed by antagonistic natural selection. We conclude that male D. serrata sexually selected traits, and attractiveness to D. serrata females conferred by these traits, were held at an evolutionary limit by the lack of genetic variation that would allow an increase in sexual fitness while simultaneously maintaining nonsexual fitness. Our results suggest that sexual selection is unlikely to cause divergence among natural populations without a concomitant change in natural selection, a conclusion consistent with observational evidence from natural populations.

Maintenance of quantitative genetic variance in complex, multitrait phenotypes: the contribution of rare, large effect variants in 2 Drosophila species.

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

Uneven Distribution of Mutational Variance Across the Transcriptome of Drosophila serrata Revealed by High-Dimensional Analysis of Gene Expression.

There are essentially an infinite number of traits that could be measured on any organism, and almost all individual traits display genetic variation, yet substantial genetic variance in a large number of independent traits is not plausible under basic models of selection and mutation. One mechanism that may be invoked to explain the observed levels of genetic variance in individual traits is that pleiotropy results in fewer dimensions of phenotypic space with substantial genetic variance. Multivariate genetic analyses of small sets of functionally related traits have shown that standing genetic variance is often concentrated in relatively few dimensions. It is unknown if a similar concentration of genetic variance occurs at a phenome-wide scale when many traits of disparate function are considered, or if the genetic variance generated by new mutations is also unevenly distributed across phenotypic space. Here, we used a Bayesian sparse factor model to characterize the distribution of mutational variance of 3385 gene expression traits of Drosophila serrata after 27 generations of mutation accumulation, and found that 46% of the estimated mutational variance was concentrated in just 21 dimensions with significant mutational heritability. We show that the extent of concentration of mutational variance into such a small subspace has the potential to substantially bias the response to selection of these traits.

Determining the effective dimensionality of the genetic variance-covariance matrix.

Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by Amemiya (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.

Multivariate quantitative genetics and the lek paradox: genetic variance in male sexually selected traits of Drosophila serrata under field conditions.

Single male sexually selected traits have been found to exhibit substantial genetic variance, even though natural and sexual selection are predicted to deplete genetic variance in these traits. We tested whether genetic variance in multiple male display traits of Drosophila serrata was maintained under field conditions. A breeding design involving 300 field-reared males and their laboratory-reared offspring allowed the estimation of the genetic variance-covariance matrix for six male cuticular hydrocarbons (CHCs) under field conditions. Despite individual CHCs displaying substantial genetic variance under field conditions, the vast majority of genetic variance in CHCs was not closely associated with the direction of sexual selection measured on field phenotypes. Relative concentrations of three CHCs correlated positively with body size in the field, but not under laboratory conditions, suggesting condition-dependent expression of CHCs under field conditions. Therefore condition dependence may not maintain genetic variance in preferred combinations of male CHCs under field conditions, suggesting that the large mutational target supplied by the evolution of condition dependence may not provide a solution to the lek paradox in this species. Sustained sexual selection may be adequate to deplete genetic variance in the direction of selection, perhaps as a consequence of the low rate of favorable mutations expected in multiple trait systems.

Orientation of the genetic variance-covariance matrix and the fitness surface for multiple male sexually selected traits.

Stabilizing selection has been predicted to change genetic variances and covariances so that the orientation of the genetic variance-covariance matrix (G) becomes aligned with the orientation of the fitness surface, but it is less clear how directional selection may change G. Here we develop statistical approaches to the comparison of G with vectors of linear and nonlinear selection. We apply these approaches to a set of male sexually selected cuticular hydrocarbons (CHCs) of Drosophila serrata. Even though male CHCs displayed substantial additive genetic variance, more than 99% of the genetic variance was orientated 74.9 degrees away from the vector of linear sexual selection, suggesting that open-ended female preferences may greatly reduce genetic variation in male display traits. Although the orientation of G and the fitness surface were found to differ significantly, the similarity present in eigenstructure was a consequence of traits under weak linear selection and strong nonlinear (convex) selection. Associating the eigenstructure of G with vectors of linear and nonlinear selection may provide a way of determining what long-term changes in G may be generated by the processes of natural and sexual selection.

Positive genetic correlation between female preference and offspring fitness.

In many species, females display preferences for extreme male signal traits, but it has not been determined if such preferences evolve as a consequence of females gaining genetic benefits from exercising choice. If females prefer extreme male traits because they indicate male genetic quality that will enhance the fitness of offspring, a genetic correlation will evolve between female preference genes and genes that confer offspring fitness. We show that females of Drosophila serrata prefer extreme male cuticular hydrocarbon (CHC) blends, and that this preference affects offspring fitness. Female preference is positively genetically correlated with offspring fitness, indicating that females have gained genetic benefits from their choice of males. Despite male CHCs experiencing strong sexual selection, the genes underlying attractive CHCs also conferred lower offspring fitness, suggesting a balance between sexual selection and natural selection may have been reached in this population.

Adult survivorship of the dengue mosquito Aedes aegypti varies seasonally in central Vietnam.

The survival characteristics of the mosquito Aedes aegypti affect transmission rates of dengue because transmission requires infected mosquitoes to survive long enough for the virus to infect the salivary glands. Mosquito survival is assumed to be high in tropical, dengue endemic, countries like Vietnam. However, the survival rates of wild populations of mosquitoes are seldom measured due the difficulty of predicting mosquito age. Hon Mieu Island in central Vietnam is the site of a pilot release of Ae. aegypti infected with a strain of Wolbachia pipientis bacteria (wMelPop) that induces virus interference and mosquito life-shortening. We used the most accurate mosquito age grading approach, transcriptional profiling, to establish the survival patterns of the mosquito population from the population age structure. Furthermore, estimations were validated on mosquitoes released into a large semi-field environment consisting of an enclosed house, garden and yard to incorporate natural environmental variability. Mosquito survival was highest during the dry/cool (January-April) and dry/hot (May-August) seasons, when 92 and 64% of Hon Mieu mosquitoes had survived to an age that they were able to transmit dengue (12 d), respectively. This was reduced to 29% during the wet/cool season from September to December. The presence of Ae. aegypti older than 12 d during each season is likely to facilitate the observed continuity of dengue transmission in the region. We provide season specific Ae. aegypti survival models for improved dengue epidemiology and evaluation of mosquito control strategies that aim to reduce mosquito survival to break the dengue transmission cycle.

High-dimensional variance partitioning reveals the modular genetic basis of adaptive divergence in gene expression during reproductive character displacement.

Although adaptive change is usually associated with complex changes in phenotype, few genetic investigations have been conducted on adaptations that involve sets of high-dimensional traits. Microarrays have supplied high-dimensional descriptions of gene expression, and phenotypic change resulting from adaptation often results in large-scale changes in gene expression. We demonstrate how genetic analysis of large-scale changes in gene expression generated during adaptation can be accomplished by determining high-dimensional variance partitioning within classical genetic experimental designs. A microarray experiment conducted on a panel of recombinant inbred lines (RILs) generated from two populations of Drosophila serrata that have diverged in response to natural selection, revealed genetic divergence in 10.6% of 3762 gene products examined. Over 97% of the genetic divergence in transcript abundance was explained by only 12 genetic modules. The two most important modules, explaining 50% of the genetic variance in transcript abundance, were genetically correlated with the morphological traits that are known to be under selection. The expression of three candidate genes from these two important genetic modules was assessed in an independent experiment using qRT-PCR on 430 individuals from the panel of RILs, and confirmed the genetic association between transcript abundance and morphological traits under selection.

Characterizing the evolution of genetic variance using genetic covariance tensors.

Determining how genetic variance changes under selection in natural populations has proved to be a very resilient problem in evolutionary genetics. In the same way that understanding the availability of genetic variance within populations requires the simultaneous consideration of genetic variance in sets of functionally related traits, determining how genetic variance changes under selection in natural populations will require ascertaining how genetic variance-covariance (G) matrices evolve. Here, we develop a geometric framework using higher-order tensors, which enables the empirical characterization of how G matrices have diverged among populations. We then show how divergence among populations in genetic covariance structure can then be associated with divergence in selection acting on those traits using key equations from evolutionary theory. Using estimates of G matrices of eight male sexually selected traits from nine geographical populations of Drosophila serrata, we show that much of the divergence in genetic variance occurred in a single trait combination, a conclusion that could not have been reached by examining variation among the individual elements of the nine G matrices. Divergence in G was primarily in the direction of the major axes of genetic variance within populations, suggesting that genetic drift may be a major cause of divergence in genetic variance among these populations.