Abstract
Epigenetics has become one of the major areas of biological research. However, the degree of phenotypic variability that is explained by epigenetic processes still remains unclear. From a quantitative genetics perspective, the estimation of variance components is achieved by means of the information provided by the resemblance between relatives. In a previous study, this resemblance was described as a function of the epigenetic variance component and a reset coefficient that indicates the rate of dissipation of epigenetic marks across generations. Given these assumptions, we propose a Bayesian mixed model methodology that allows the estimation of epigenetic variance from a genealogical and phenotypic database. The methodology is based on the development of a T matrix of epigenetic relationships that depends on the reset coefficient. In addition, we present a simple procedure for the calculation of the inverse of this matrix (T−1) and a Gibbs sampler algorithm that obtains posterior estimates of all the unknowns in the model. The new procedure was used with two simulated data sets and with a beef cattle database. In the simulated populations, the results of the analysis provided marginal posterior distributions that included the population parameters in the regions of highest posterior density. In the case of the beef cattle dataset, the posterior estimate of transgenerational epigenetic variability was very low and a model comparison test indicated that a model that did not included it was the most plausible.
Highlights
Epigenetics has become one of the major areas of biological research
Gene activity modifications may occasionally occur in a sperm or an egg cell and can be transferred to the generation, denoted as transgenerational epigenetic inheritance (Youngson and Whitelaw 2008), a phenomenon that has been reported in a wide range of organisms (Jablonka and Raz 2009)
From the perspective of quantitative genetics, the estimation of variance components is obtained from the statistical information provided by the covariance between the phenotypes of relatives (Falconer and Mckay 1996)
Summary
Epigenetics has become one of the major areas of biological research. the degree of phenotypic variability that is explained by epigenetic processes still remains unclear. This resemblance was described as a function of the epigenetic variance component and a reset coefficient that indicates the rate of dissipation of epigenetic marks across generations. Given these assumptions, we propose a Bayesian mixed model methodology that allows the estimation of epigenetic variance from a genealogical and phenotypic database. Tal et al (2010) developed a model for the calculation of the covariance between relatives for asexual and sexual reproduction as a function of epigenetic heritability (g2), the reset coefficient (v), and its complement, the epigenetic transmission coefficient (1 2 v). Under the Bayesian paradigm, the standard procedure for determining the posterior distribution of the variance components uses the Gibbs Sampler algorithm (Gelfand and Smith 1990) that involves an updated iterative sampling scheme from the full conditional distributions of all the unknowns in the model
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