Patrick C. Phillips

Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems

Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.

Visualizing multivariate selection

Recent developments in quantitative‐genetic theory have shown that natural selection can be viewed as the multivariate relationship between fitness and phenotype. This relationship can be described by a multidimensional surface depicting fitness as a function of phenotypic traits. We examine the connection between this surface and the coefficients of phenotypic selection that can be estimated by multiple regression and show how the interpretation of multivariate selection can be facilitated through the use of the method of canonical analysis. The results from this analysis can be used to visualize the surface implied by a set of selection coefficients. Such a visualization provides a compact summary of selection coefficients, can aid in the comparison of selection surfaces, and can help generate testable hypotheses as to the adaptive significance of the traits under study. Further, we discuss traditional definitions of directional, stabilizing, and disruptive selection and conclude that selection may be more usefully classified into two general modes, directional and nonlinear selection, with stabilizing and disruptive selection as special cases of nonlinear selection.

Comparative quantitative genetics: evolution of the G matrix

Abstract Quantitative genetics provides one of the most promising frameworks with which to unify the fields of macroevolution and microevolution. The genetic variance–covariance matrix ( G ) is crucial to quantitative genetic predictions about macroevolution. In spite of years of study, we still know little about how G evolves. Recent studies have been applying an increasingly phylogenetic perspective and more sophisticated statistical techniques to address G matrix evolution. We propose that a new field, comparative quantitative genetics, has emerged. Here we summarize what is known about several key questions in the field and compare the strengths and weaknesses of the many statistical and conceptual approaches now being employed. Past studies have made it clear that the key question is no longer whether G evolves but rather how fast and in what manner. We highlight the most promising future directions for this emerging field.

HIERARCHICAL COMPARISON OF GENETIC VARIANCE-COVARIANCE MATRICES. I. USING THE FLURY HIERARCHY

The comparison of additive genetic variance‐covariance matrices (G‐matrices) is an increasingly popular exercise in evolutionary biology because the evolution of the G‐matrix is central to the issue of persistence of genetic constraints and to the use of dynamic models in an evolutionary time frame. The comparison of G‐matrices is a nontrivial statistical problem because family structure induces nonindependence among the elements in each matrix. Past solutions to the problem of G‐matrix comparison have dealt with this problem, with varying success, but have tested a single null hypothesis (matrix equality or matrix dissimilarity). Because matrices can differ in many ways, several hypotheses are of interest in matrix comparisons. Flury (1988) has provided an approach to matrix comparison in which a variety of hypotheses are tested, including the two extreme hypotheses prevalent in the evolutionary literature. The hypotheses are arranged in a hierarchy and involve comparisons of both the principal components (eigenvectors) and eigenvalues of the matrix. We adapt Flurys hierarchy of tests to the problem of comparing G‐matrices by using randomization testing to account for nonindependence induced by family structure. Software has been developed for carrying out this analysis for both genetic and phenotypic data. The method is illustrated with a garter snake test case.

Genotype to Phenotype: A Complex Problem

In yeast, the impact of gene knockouts depends on genetic background. We generated a high-resolution whole-genome sequence and individually deleted 5100 genes in Σ1278b, a Saccharomyces cerevisiae strain closely related to reference strain S288c. Similar to the variation between human individuals, Σ1278b and S288c average 3.2 single-nucleotide polymorphisms per kilobase. A genome-wide comparison of deletion mutant phenotypes identified a subset of genes that were conditionally essential by strain, including 44 essential genes unique to Σ1278b and 13 unique to S288c. Genetic analysis indicates the conditional phenotype was most often governed by complex genetic interactions, depending on multiple background-specific modifiers. Our comprehensive analysis suggests that the presence of a complex set of modifiers will often underlie the phenotypic differences between individuals.

Mutation load and rapid adaptation favour outcrossing over self-fertilization.

The tendency of organisms to reproduce by cross-fertilization despite numerous disadvantages relative to self-fertilization is one of the oldest puzzles in evolutionary biology. For many species, the primary obstacle to the evolution of outcrossing is the cost of production of males, individuals that do not directly contribute offspring and thus diminish the long-term reproductive output of a lineage. Self-fertilizing (‘selfing’) organisms do not incur the cost of males and therefore should possess at least a twofold numerical advantage over most outcrossing organisms. Two competing explanations for the widespread prevalence of outcrossing in nature despite this inherent disadvantage are the avoidance of inbreeding depression generated by selfing and the ability of outcrossing populations to adapt more rapidly to environmental change. Here we show that outcrossing is favoured in populations of Caenorhabditis elegans subject to experimental evolution both under conditions of increased mutation rate and during adaptation to a novel environment. In general, fitness increased with increasing rates of outcrossing. Thus, each of the standard explanations for the maintenance of outcrossing are correct, and it is likely that outcrossing is the predominant mode of reproduction in most species because it is favoured under ecological conditions that are ubiquitous in natural environments.

Exploring the evolution of environmental sex determination, especially in reptiles

Environmental sex determination has been documented in a variety of organisms for many decades and the adaptive significance of this unusual sex‐determining mechanism has been clarified empirically in most cases. In contrast, temperature‐dependent sex determination (TSD) in amniote vertebrates, first noted 40 years ago in a lizard, has defied a general satisfactory evolutionary explanation despite considerable research effort. After briefly reviewing relevant theory and prior empirical work, we draw attention to recent comparative analyses that illuminate the evolutionary history of TSD in amniote vertebrates and point to clear avenues for future research on this challenging topic. To that end, we then highlight the latest empirical findings in lizards and turtles, as well as promising experimental results from a model organism, that portend an exciting future of progress in finally elucidating the evolutionary cause(s) and significance of TSD.

HIERARCHICAL COMPARISON OF GENETIC VARIANCE-COVARIANCE MATRICES. II. COASTAL-INLAND DIVERGENCE IN THE GARTER SNAKE, THAMNOPHIS ELEGANS

The time‐scale for the evolution of additive genetic variance‐covariance matrices (G‐matrices) is a crucial issue in evolutionary biology. If the evolution of G‐matrices is slow enough, we can use standard multivariate equations to model drift and selection response on evolutionary time scales. We compared the G‐matrices for meristic traits in two populations of gaiter snakes (Thamnophis elegans) with an apparent separation time of 2 million years. Despite considerable divergence in the meristic traits, foraging habits, and diet, these populations show conservation of structure in their G‐matrices. Using Flurys hierarchial approach to matrix comparisons, we found that the populations have retained the principal components (eigenvectors) of their G‐matrices, but their eigenvalues have diverged. In contrast, we were unable to reject the hypothesis of equal environmental matrices (E‐matrices) for these populations. We propose that a conserved pattern of multivariate stabilizing selection may have contributed to conservation of G‐ and E‐matrix structure during the divergence of these populations.

USING POPULATION GENOMICS TO DETECT SELECTION IN NATURAL POPULATIONS: KEY CONCEPTS AND METHODOLOGICAL CONSIDERATIONS

Natural selection shapes patterns of genetic variation among individuals, populations, and species, and it does so differentially across genomes. The field of population genomics provides a comprehensive genome-scale view of the action of selection, even beyond traditional model organisms. However, even with nearly complete genomic sequence information, our ability to detect the signature of selection on specific genomic regions depends on choosing experimental and analytical tools appropriate to the biological situation. For example, processes that occur at different timescales, such as sorting of standing genetic variation, mutation-selection balance, or fixed interspecific divergence, have different consequences for genomic patterns of variation. Inappropriate experimental or analytical approaches may fail to detect even strong selection or falsely identify a signature of selection. Here we outline the conceptual framework of population genomics, relate genomic patterns of variation to evolutionary processes, and identify major biological factors to be considered in studies of selection. As data-gathering technology continues to advance, our ability to understand selection in natural populations will be limited more by conceptual and analytical weaknesses than by the amount of molecular data. Our aim is to bring critical biological considerations to the fore in population genomics research and to spur the development and application of analytical tools appropriate to diverse biological systems.

The Opportunity for Canalization and the Evolution of Genetic Networks

There has been a recent revival of interest in how genetic interactions evolve, spurred on by an increase in our knowledge of genetic interactions at the molecular level. Empirical work on genetic networks has revealed a surprising amount of robustness to perturbations, suggesting that robustness is an evolved feature of genetic networks. Here, we derive a general model for the evolution of canalization that can incorporate any form of perturbation. We establish an upper bound to the strength of selection on canalization that is approximately equal to the fitness load in the system. This method makes it possible to compare different forms of perturbation, including genetic, developmental, and environmental effects. In general, load that arises from mutational processes is low because the mutation rate is itself low. Mutation load can create selection for canalization in a small network that can be achieved through dominance evolution or gene duplication, and in each case selection for canalization is weak at best. In larger genetic networks, selection on genetic canalization can be reasonably strong because larger networks have higher mutational load. Because load induced through migration, segregation, developmental noise, and environmental variance is not mutation limited, each can cause strong selection for canalization.