Orientation of the Genetic Variance‐Covariance Matrix and the Fitness Surface for Multiple Male Sexually Selected Traits

Blows, Mark W.; Chenoweth, Stephen F.; Hine, Emma

doi:10.1086/381941

Cited by 247 publications

(411 citation statements)

References 57 publications

(90 reference statements)

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“…Other studies, however, suggest that the number of dimensions can be reasonably high (Mezey and Houle, 2005). It seems to me that the number of dimensions usually will be very high, certainly much higher than suggested by the current analyses of suites of very similar traits, such as cuticular hydrocarbons (Blows et al, 2004) or wing shape (Mezey and Houle, 2005;McGuigan and Blows, 2007). The reason is that overall adaptation to a given environment will inevitably involve a host of morphological, life history, physiological and behavioral changes, which will not collapse down to only a few dimensions.…”

Section: Ecosystem Functionmentioning

confidence: 53%

Key questions in the genetics and genomics of eco-evolutionary dynamics

Hendry

2013

Heredity

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Increasing acceptance that evolution can be 'rapid' (or 'contemporary') has generated growing interest in the consequences for ecology. The genetics and genomics of these 'eco-evolutionary dynamics' will be-to a large extent-the genetics and genomics of organismal phenotypes. In the hope of stimulating research in this area, I review empirical data from natural populations and draw the following conclusions. (1) Considerable additive genetic variance is present for most traits in most populations. (2) Trait correlations do not consistently oppose selection. (3) Adaptive differences between populations often involve dominance and epistasis. (4) Most adaptation is the result of genes of small-to-modest effect, although (5) some genes certainly have larger effects than the others. (6) Adaptation by independent lineages to similar environments is mostly driven by different alleles/genes. (7) Adaptation to new environments is mostly driven by standing genetic variation, although new mutations can be important in some instances. (8) Adaptation is driven by both structural and regulatory genetic variation, with recent studies emphasizing the latter. (9) The ecological effects of organisms, considered as extended phenotypes, are often heritable. Overall, the study of eco-evolutionary dynamics will benefit from perspectives and approaches that emphasize standing genetic variation in many genes of small-to-modest effect acting across multiple traits and that analyze overall adaptation or 'fitness'. In addition, increasing attention should be paid to dominance, epistasis and regulatory variation.

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Section: Ecosystem Functionmentioning

confidence: 53%

Key questions in the genetics and genomics of eco-evolutionary dynamics

Hendry

2013

Heredity

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“…They found that the divergence of traits that were likely to be under strong selection showed less evidence of being affected by correlations than traits that were less likely to be targets of strong selection. Blows et al (2004) examined the relationship between G and selection by measuring the angle between b and the projection of the 'major' subspace of G onto b, and by comparing the angles between the axes of major subspaces of G and g. As with our metric, this approach is intended to give a sense of the extent to which patterns of genetic variation constrain or facilitate evolution. This approach can be useful when applied to systems where the biology is well understood and the results are carefully interpreted.…”

Section: Discussionmentioning

confidence: 99%

“…First, the resulting metrics are not simple to understand or interpret. Second, the choice of principal component axes used to define the subspaces is somewhat arbitrary, and not all of the principal component axes can be used (Blows et al 2004). Third, it is possible for this approach to give misleading results when only one or a few of the correlated traits are under selection unless results are interpreted with great caution.…”

Section: Discussionmentioning

confidence: 99%

How much do genetic covariances alter the rate of adaptation?

2008

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Genetically correlated traits do not evolve independently, and the covariances between traits affect the rate at which a population adapts to a specified selection regime. To measure the impact of genetic covariances on the rate of adaptation, we compare the rate fitness increases given the observed G matrix to the expected rate if all the covariances in the G matrix are set to zero. Using data from the literature, we estimate the effect of genetic covariances in real populations. We find no net tendency for covariances to constrain the rate of adaptation, though the quality and heterogeneity of the data limit the certainty of this result. There are some examples in which covariances strongly constrain the rate of adaptation but these are balanced by counter examples in which covariances facilitate the rate of adaptation; in many cases, covariances have little or no effect. We also discuss how our metric can be used to identify traits or suites of traits whose genetic covariances to other traits have a particularly large impact on the rate of adaptation.

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“…The sum of the eigenvalues of S gives a bounded statistic, which ranges between complete orthogonality (0) and complete overlap (k) of the two subspaces, respectively. Blows et al (2004) give further details on the use of this approach for comparing G-matrices and second-order fitness surfaces.…”

Section: Methods 2 Krzanowski's Common Subspacesmentioning

confidence: 99%

Comparing G: multivariate analysis of genetic variation in multiple populations

et al. 2013

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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.

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Orientation of the Genetic Variance‐Covariance Matrix and the Fitness Surface for Multiple Male Sexually Selected Traits

Cited by 247 publications

References 57 publications

Key questions in the genetics and genomics of eco-evolutionary dynamics

Key questions in the genetics and genomics of eco-evolutionary dynamics

How much do genetic covariances alter the rate of adaptation?

Comparing G: multivariate analysis of genetic variation in multiple populations

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