Bayesian analysis of genetic change due  to selection using Gibbs sampling

-Gompertz growth curves were fitted to the data of 137 rabbits from control (C) and selected (S) lines. The animals came from a synthetic rabbit line selected for an increased growth rate. The embryos from generations 3 and 4 were frozen and thawed to be contemporary of rabbits born in generation 10. Group C was the offspring of generations 3 and 4, and group S was the contemporary offspring of generation 10. The animals were weighed individually twice a week during the first four weeks of life, and once a week thereafter, until 20 weeks of age. Subsequently, the males were weighed weekly until 40 weeks of age. The random samples of the posterior distributions of the growth curve parameters were drawn by using Markov Chain Monte Carlo (MCMC) methods. As a consequence of selection, the selected animals were heavier than the C animals throughout the entire growth curve. Adult body weight, estimated as a parameter of the Gompertz curve, was 7% higher in the selected line. The other parameters of the Gompertz curve were scarcely affected by selection. When selected and control growth curves are represented in a metabolic scale, all differences disappear. growth curves / selection / rabbits / Bayesian analysis

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“…We propose, according to Sorensen et al [23], an inverted Wishart distribution for prior distributions of R:…”

Section: Third Stage Of the Model: Uncertainty About The Second Stagementioning

confidence: 99%

A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits

2003

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“…The results reported below indicate that similar qualitative conclusions are drawn from both methods of inference. The Bayesian analysis reported below is based on the method described in Sorensen et al (1994). Essentially, it consists of computing the posterior distribution of the average genetic values (across individuals) over generations, accounting for all other sources of variation included in the model.…”

Section: Data Analysesmentioning

confidence: 99%

A strong response to selection on mass-independent maximal metabolic rate without a correlated response in basal metabolic rate

et al. 2015

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Metabolic rates are correlated with many aspects of ecology, but how selection on different aspects of metabolic rates affects their mutual evolution is poorly understood. Using laboratory mice, we artificially selected for high maximal mass-independent metabolic rate (MMR) without direct selection on mass-independent basal metabolic rate (BMR). Then we tested for responses to selection in MMR and correlated responses to selection in BMR. In other lines, we antagonistically selected for mice with a combination of high mass-independent MMR and low mass-independent BMR. All selection protocols and data analyses included body mass as a covariate, so effects of selection on the metabolic rates are mass adjusted (that is, independent of effects of body mass). The selection lasted eight generations. Compared with controls, MMR was significantly higher (11.2%) in lines selected for increased MMR, and BMR was slightly, but not significantly, higher (2.5%). Compared with controls, MMR was significantly higher (5.3%) in antagonistically selected lines, and BMR was slightly, but not significantly, lower (4.2%). Analysis of breeding values revealed no positive genetic trend for elevated BMR in high-MMR lines. A weak positive genetic correlation was detected between MMR and BMR. That weak positive genetic correlation supports the aerobic capacity model for the evolution of endothermy in the sense that it fails to falsify a key model assumption. Overall, the results suggest that at least in these mice there is significant capacity for independent evolution of metabolic traits. Whether that is true in the ancestral animals that evolved endothermy remains an important but unanswered question. INTRODUCTIONEnergy metabolism is one of the most fundamental aspects of biology, and it is key to understanding life histories of living organisms. Its central importance is reflected in the thousands of studies published on energy metabolism (Houston et al., 1993;Hayes and O'Connor, 1999;Speakman, 2008; Burton et al., 2011, Konarzewski andKsiążek, 2013;White and Kearney, 2013). Despite these studies, many questions about metabolic rates and energy metabolism remain unanswered. For example, is there a universal metabolic scaling law, why is resting metabolism correlated with daily energy use in mammals but not birds, how did the diverse resting and maximal metabolic rates of animals evolve and is there a necessary correlation between resting and maximal aerobic metabolism in vertebrates (Ricklefs et al., 1996;Clavijo-Baque and Bozinovic, 2012)? Some of these questions are very difficult to answer but ecological and evolutionary physiologists have recently made increasing use of artificial selection experiments to test hypotheses about the phenotypic and genetic integration of energy metabolism (Swallow et al

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“…It requires including all data used for the selection (Im et al, 1989) and to know the true variance components (Thompson, 1986). The latter requirement can be alleviated by using the bayesian inference (Sorensen et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Partition of the genetic trend to validate multiple selection decisions

García-Cortés

Martínez-Ávila

Toro

2008

Animal

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In this note, a procedure to partition the genetic trend of a selected population is presented. Each part of the genetic gain accounts for the Mendelian sampling terms of different groups of animals, which can be sometimes assigned to different selection policies. The method is based on a simple transformation of the predicted breeding values. The procedure was illustrated with two simulated examples. In the first example, the genetic trend is partitioned into two pieces, one coming from the selection on sires and the other coming from the selection on dams. The second example shows how the impact of an artificial insemination center in the genetic gain of the whole population can be evaluated.

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Bayesian analysis of genetic change due to selection using Gibbs sampling

Abstract: Summary -A method of analysing response to selection using a Bayesian perspective is presented. The

Cited by 54 publications

References 7 publications

A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits

A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits

A strong response to selection on mass-independent maximal metabolic rate without a correlated response in basal metabolic rate

Partition of the genetic trend to validate multiple selection decisions

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