Accounting for Mutation Effects in the Additive Genetic Variance-Covariance Matrix and Its Inverse

Biometrics ◽  
1990 ◽  
Vol 46 (1) ◽  
pp. 177 ◽  
Author(s):  
Naomi R. Wray
Keyword(s):  
Covariance Matrix ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Variance Covariance Matrix

scholarly journals Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

2015 ◽  
Vol 282 (1819) ◽  
pp. 20151119 ◽  
Author(s):  
Vincent Careau ◽  
Matthew E. Wolak ◽  
Patrick A. Carter ◽  
Theodore Garland
Keyword(s):  
Covariance Matrix ◽  
Adaptive Response ◽  
Genetic Variance ◽  
Environmental Changes ◽  
Wheel Running ◽  
Additive Genetic Variance ◽  
Phenotypic Selection ◽  
Directional Selection ◽  
Genetic Covariance ◽  
Variance Covariance Matrix

Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix ( G ). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset ( n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change.

Genetic correlations and maternal effect coefficients obtained from offspring-parent regression.

Genetics ◽  
1989 ◽  
Vol 122 (4) ◽  
pp. 915-922 ◽  
Author(s):  
R Lande ◽  
T Price
Keyword(s):  
Covariance Matrix ◽  
Maternal Effects ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Phenotypic Variance ◽  
Weak Selection ◽  
Quantitative Characters ◽  
Evolutionary Response ◽  
The Mean ◽  
Variance Covariance Matrix

Abstract Additive genetic variances and covariances of quantitative characters are necessary to predict the evolutionary response of the mean phenotype vector in a population to natural or artificial selection. Standard formulas for estimating these parameters, from the resemblance between relatives in one or two characters at a time, are biased by natural selection on the parents and by maternal effects. We show how these biases can be removed using a multivariate analysis of offspring-parent regressions. A dynamic model of maternal effects demonstrates that, in addition to the phenotypic variance-covariance matrix of the characters, sufficient parameters for predicting the response of the mean phenotype vector to weak selection are the additive genetic variance-covariance matrix and a set of causal coefficients for maternal effects. These can be simultaneously estimated from offspring-parent regressions alone, in some cases just from the daughter-mother regressions, if all of the important selected and maternal characters have been measured and included in the analysis.

Rapid Divergence of Genetic Variance‐Covariance Matrix within a Natural Population

2008 ◽  
Vol 171 (3) ◽  
pp. 291-304 ◽  
Author(s):  
Agnieszka Doroszuk ◽  
Marcin W. Wojewodzic ◽  
Gerrit Gort ◽  
Jan E. Kammenga
Keyword(s):  
Covariance Matrix ◽  
Natural Population ◽  
Genetic Variance ◽  
Rapid Divergence ◽  
Variance Covariance Matrix

A case study of extant and extinct Xenarthra cranium covariance structure: implications and applications to paleontology

Paleobiology ◽  
2016 ◽  
Vol 42 (3) ◽  
pp. 465-488 ◽  
Author(s):  
Alex Hubbe ◽  
Diogo Melo ◽  
Gabriel Marroig
Keyword(s):  
Covariance Matrix ◽  
Additive Genetic Variance ◽  
Covariance Structure ◽  
Phenotypic Variance ◽  
Phenotypic Evolution ◽  
Sample Sizes ◽  
Selected Traits ◽  
Larger Sample ◽  
Variance Covariance Matrix

AbstractMost of the mammalian diversity is known only from fossils, and only a few of these fossils are well preserved or abundant. This undersampling poses serious problems for understanding mammalian phenotypic evolution under a quantitative genetics framework, since this framework requires estimation of a group’s additive genetic variance–covariance matrix (G matrix), which is impossible, and estimating a phenotypic variance–covariance matrix (P matrix) requires larger sample sizes than what is often available for extinct species. One alternative is to use G or P matrices from extant taxa as surrogates for the extinct ones. Although there are reasons to believe this approach is usually safe, it has not been fully explored. By thoroughly determining the extant and some extinct Xenarthra (Mammalia) cranium P matrices, this study aims to explore the feasibility of using extant G or P matrices as surrogates for the extinct ones and to provide guidelines regarding the reliability of this strategy and the necessary sample sizes. Variance–covariance and correlation P matrices for 35 cranium traits from 16 xenarthran genera (12 extant and 4 extinct) were estimated and compared between genera. Results show xenarthran P-matrix structures are usually very similar if sample sizes are reasonable. This study and others developed with extant therian mammals suggest, in general, that using extant G or P matrices as an approximation to extinct ones is a valid approach. Nevertheless, the accuracy of this approach depends on sample size, selected traits, and the type of matrix being considered.

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