Mark. W. Blows

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.

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 modelling to 3,385 gene expression traits from Drosophila melanogaster and from D. serrata to explore how genetic variance is distributed across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme (>3 IQR from the median) values. This observation, in the two independently sampled species, suggests that the House of Cards (HoC) model might apply not only to individual expression traits, but also to emergent co-expression phenotypes. 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 outliers for multivariate factors but not for any individual traits. We observed other consistent differences between heritable multivariate factors with outlier lines versus those factors that conformed to a Gaussian distribution of genetic effects, 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.

Characteristics of the new phenotypic variation introduced via mutation have broad implications in evolutionary and medical genetics. Standardized estimates of this mutational variance, V_M, span 2 orders of magnitude, but the causes of this remain poorly resolved. We investigated estimate heterogeneity using 2 approaches. First, meta-analyses of ∼150 estimates of standardized V_M from 37 mutation accumulation studies did not support a difference among taxa (which differ in mutation rate) but provided equivocal support for differences among trait types (life history vs morphology, predicted to differ in mutation rate). Notably, several experimental factors were confounded with taxon and trait, and further empirical data are required to resolve their influences. Second, we analyzed morphological data from an experiment in Drosophila serrata to determine the potential for unintentional heterogeneity among environments in which phenotypes were measured (i.e. among laboratories or time points) or transient segregation of mutations within mutation accumulation lines to affect standardized V_M. Approximating the size of an average mutation accumulation experiment, variability among repeated estimates of (accumulated) mutational variance was comparable to variation among published estimates of standardized V_M. This heterogeneity was (partially) attributable to unintended environmental variation or within line segregation of mutations only for wing size, not wing shape traits. We conclude that sampling error contributed substantial variation within this experiment, and infer that it will also contribute substantially to differences among published estimates. We suggest a logistically permissive approach to improve the precision of estimates, and consequently our understanding of the dynamics of mutational variance of quantitative traits.

Testing whether local adaptation and intrinsic reproductive isolation share a genetic basis can reveal important connections between adaptation and speciation. Local adaptation arises as advantageous alleles spread through a population, but whether these same advantageous alleles fail on the genetic backgrounds of other populations remains largely unknown. We used a quantitative genetic breeding design to produce a late generation (F4) recombinant hybrid population by equally mating amongst four contrasting ecotypes of a native Australian daisy for four generations. We tracked fitness across generations and measured morphological traits in the glasshouse, and used a reciprocal transplant to quantify fitness in all four parental habitats. In the glasshouse, plants in the second generation showed a reduction in fitness as a loss of fertility, but this was fully recovered in the following generation. The F4 hybrid lacked extreme phenotypes present in the parental ecotypes, suggesting that genes reducing hybrid fitness were linked to traits specific to each ecotype. In the natural habitats, additive genetic variance for fitness was greatest for habitats that showed stronger native-ecotype advantage, suggesting that a loss of genetic variation present in the parental ecotypes were adaptive in the natural habitats. Reductions in genetic variance for fitness were associated with a loss of ecological trade-offs previously described in the parental ecotypes. Furthermore, natural selection on morphological traits differed amongst the parental habitats, but was not predicted to occur towards the morphology of the parental ecotypes. Together, these results suggest that intrinsic reproductive isolation removed adaptive genetic variation present in the parental ecotypes. The evolution of these distinct ecotypes was likely governed by genetic variation that resulted in both ecological trade-offs and intrinsic reproductive isolation among populations adapted to contrasting environments.

Genetic correlations between traits can bias adaptation away from optimal phenotypes and constrain the rate of evolution. If genetic correlations between traits limit adaptation to contrasting environments, rapid adaptive divergence across a heterogeneous landscape may be difficult. However, if genetic variance can evolve and align with the direction of natural selection, then abundant allelic variation can promote rapid divergence during adaptive radiation. Here, we explored adaptive divergence among ecotypes of an Australian native wildflower by quantifying divergence in multivariate phenotypes of populations that occupy four contrasting environments. We investigated differences in multivariate genetic variance underlying morphological traits and examined the alignment between divergence in phenotype and divergence in genetic variance. We found that divergence in mean multivariate phenotype has occurred along two major axes represented by different combinations of plant architecture and leaf traits. Ecotypes also showed divergence in the level of genetic variance in individual traits, and the multivariate distribution of genetic variance among traits. Divergence in multivariate phenotypic mean aligned with divergence in genetic variance, with most of the divergence in phenotype among ecotypes associated with a change in trait combinations that had substantial levels of genetic variance in each ecotype. Overall, our results suggest that divergent natural selection acting on high levels of standing genetic variation might fuel ecotypic differentiation during the early stages of adaptive radiation.

Sexual selection on males is predicted to have widespread effects on genetic variation as a consequence of the pleiotropic allelic effects on sexual and nonsexual traits. We manipulated the opportunity for sexual selection on males during 27 generations of mutation accumulation in inbred lines of Drosophila serrata, and used a microarray platform to investigate the effect of sexual selection on the expression of 2689 genes. While gene expression signal was, on average, higher in the absence of sexual selection, this difference was small (0.1%). In contrast, sexual selection impacted substantially on the mutational variance in gene expression. Over all genes, mutational variance in gene expression was, on average, 42% higher when sexual selection operated than when it was absent. Our results indicate that sexual selection on males can generate widespread effects across the genome. An increase in mutational variance without a corresponding change in mean suggested that most expression traits were unlikely to be under direct sexual selection. Instead, the mutational variance in gene expression traits is consistent with divergence generated by widespread pleiotropic associations with traits affecting male mating success.

How new mutations contribute to genetic variation is a key question in biology. Although the evolutionary fate of an allele is largely determined by its heterozygous effect, most estimates of mutational variance and mutational effects derive from highly inbred lines, where new mutations are present in homozygous form. In an attempt to overcome this limitation, middle-class neighborhood (MCN) experiments have been used to assess the fitness effect of new mutations in heterozygous form. However, because MCN populations harbor substantial standing genetic variance, estimates of mutational variance have not typically been available from such experiments. Here we employ a modification of the animal model to analyze data from 22 generations of Drosophila serrata bred in an MCN design. Mutational heritability, measured for eight cuticular hydrocarbons, 10 wing-shape traits, and wing size in this outbred genetic background, ranged from 0.0006 to 0.006 (with one exception), a similar range to that reported from studies employing inbred lines. Simultaneously partitioning the additive and mutational variance in the same outbred population allowed us to quantitatively test the ability of mutation-selection balance models to explain the observed levels of additive and mutational genetic variance. The Gaussian allelic approximation and house-of-cards models, which assume real stabilizing selection on single traits, both overestimated the genetic variance maintained at equilibrium, but the house-of-cards model was a closer fit to the data. This analytical approach has the potential to be broadly applied, expanding our understanding of the dynamics of genetic variance in natural populations.

A general observation emerging from estimates of additive genetic variance in sets of functionally or developmentally related traits is that much of the genetic variance is restricted to few trait combinations as a consequence of genetic covariance among traits. While this biased distribution of genetic variance among functionally related traits is now well documented, how it translates to the broader phenome and therefore any trait combination under selection in a given environment is unknown. We show that 8,750 gene expression traits measured in adult male Drosophila serrata exhibit widespread genetic covariance among random sets of five traits, implying that pleiotropy is common. Ultimately, to understand the phenome-wide distribution of genetic variance, very large additive genetic variance-covariance matrices (G) are required to be estimated. We draw upon recent advances in matrix theory for completing high-dimensional matrices to estimate the 8,750-trait G and show that large numbers of gene expression traits genetically covary as a consequence of a single genetic factor. Using gene ontology term enrichment analysis, we show that the major axis of genetic variance among expression traits successfully identified genetic covariance among genes involved in multiple modes of transcriptional regulation. Our approach provides a practical empirical framework for the genetic analysis of high-dimensional phenome-wide trait sets and for the investigation of the extent of high-dimensional genetic constraint.

In contrast to our growing understanding for patterns of additive genetic variance in single and multi-trait combinations, the relative contribution of non-additive genetic variance, particularly dominance variance, to multivariate phenotypes is largely unknown. While mechanisms for the evolution of dominance genetic variance have been, and to some degree remain, subject to debate, the pervasiveness of dominance is widely recognized, and may play a key role in several evolutionary processes. Theoretical and empirical evidence suggests that the contribution of dominance variance to phenotypic variance may increase with the correlation between a trait and fitness; however, direct tests of this hypothesis are few. Using a multigenerational breeding design in an unmanipulated population of Drosophila serrata, we estimated additive and dominance genetic covariance matrices for multivariate wing shape phenotypes, together with a comprehensive measure of fitness, to determine whether there is an association between directional selection and dominance variance. Fitness, a trait unequivocally under directional selection, had no detectable additive genetic variance, but significant dominance genetic variance contributing 32% of the phenotypic variance. For single and multivariate morphological traits, however, no relationship was observed between trait-fitness correlations and dominance variance. A similar proportion of additive and dominance variance was found to contribute to phenotypic variance for single traits, and double the amount of additive compared to dominance variance was found for the multivariate trait combination under directional selection. These data suggest that for many fitness components a positive association between directional selection and dominance genetic variance may not be expected.

The role of adaptation in biological invasions will depend on the availability of genetic variation for traits under selection in the new environment. Although genetic variation is present for most traits in most populations, selection is expected to act on combinations of traits, not individual traits in isolation. The distribution of genetic variance across trait combinations can be characterized by the empirical spectral distribution of the genetic variance–covariance (G) matrix. Empirical spectral distributions of G from a range of trait types and taxa all exhibit a characteristic shape; some trait combinations have large levels of genetic variance, while others have very little genetic variance. In this study, we review what is known about the empirical spectral distribution of G and show how it predicts the response to selection across phenotypic space. In particular, trait combinations that form a nearly null genetic subspace with little genetic variance respond only inconsistently to selection. We go on to set out a framework for understanding how the empirical spectral distribution of G may differ from the random expectations that have been developed under random matrix theory (RMT). Using a data set containing a large number of gene expression traits, we illustrate how hypotheses concerning the distribution of multivariate genetic variance can be tested using RMT methods. We suggest that the relative alignment between novel selection pressures during invasion and the nearly null genetic subspace is likely to be an important component of the success or failure of invasion, and for the likelihood of rapid adaptation in small populations in general.