Emma Hine

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° 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.

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 Fishers 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 Amemiyas 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 Amemiyas 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 Amemiyas method at subspace recovery.

MULTIVARIATE QUANTITATIVE GENETICS AND THE LEK PARADOX: GENETIC VARIANCE IN MALE SEXUALLY SELECTED TRAITS OF DROSOPHILA SERRATA UNDER FIELD CONDITIONS

Abstract 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.

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.

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.

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.

Comparing G: multivariate analysis of genetic variation in multiple populations.

The additive genetic variance–covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design.

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.

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.