ArticleHeredity2014
Comparing G: multivariate analysis of genetic variation in multiple populations.
Article in Heredity, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Predictive evolutionary genomics: principles, validation, and practice.The New phytologist · 2026Review
- Apparent stabilizing selection explains micro- and macroevolution of Drosophila wings.Nature ecology & evolution · 2026Article
- Environmental effects on genetic variance are likely to constrain adaptation in novel environments.Evolution letters · 2024Article
- Consequences of the cost of living: is variation in metabolic rate evolutionarily significant?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Divergence in evolutionary potential of life history traits among wild populations is predicted by differences in climatic conditions.Evolution letters · 2024Article
- Article
- Using inbreeding to test the contribution of non-additive genetic effects to additive genetic variance: a case study inProceedings. Biological sciences · 2023Article
- Variation in mutational (co)variances.G3 (Bethesda, Md.) · 2023Article
- Phenotypic stasis with genetic divergence.Peer community journal · 2023Article
- A method to predict the response to directional selection using a Kalman filter.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Analysis of genotype-by-environment interactions in a maize mapping population.G3 (Bethesda, Md.) · 2022Article
- Multivariate analysis of morphology, behaviour, growth and developmental timing in hybrids brings new insights into the divergence of sympatric Arctic charr morphs.BMC ecology and evolution · 2021Article
- The evolution of trait correlations constrains phenotypic adaptation to high COProceedings. Biological sciences · 2021Article
- Introduced populations of ragweed show as much evolutionary potential as native populations.Evolutionary applications · 2021Article
- The genetic architecture of sexual dimorphism in the mossProceedings. Biological sciences · 2021Article
- Loss of ecologically important genetic variation in late generation hybrids reveals links between adaptation and speciation.Evolution letters · 2020Article
- Behavioural syndromes shape evolutionary trajectories via conserved genetic architecture.Proceedings. Biological sciences · 2020Article
- Environmentally triggered variability in the genetic variance-covariance of herbivory resistance of an exotic plantEcology and evolution · 2020Article
- Comparative analysis of the multivariate genetic architecture of morphological traits in three species of Gomphocerine grasshoppers.Heredity · 2020Article
- Mouse Skull Mean Shape and Shape Robustness Rely on Different Genetic Architectures and Different Loci.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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