Gene regulatory networks refer to the interactions that occur among genes and other cellular products. The topology of these networks can be inferred from measurements of changes in gene expression over time. However, because the measurement device (i.e., microarrays) typically yields information on thousands of genes over few biological replicates, these systems are quite difficult to elucidate. An approach with proven effectiveness for inferring networks is the Dynamic Bayesian Network. We have developed an iterative empirical Bayesian procedure with a Kalman filter that estimates the posterior distributions of network parameters. We compare our method to similar existing methods on simulated data and real microarray time series data. We find that the proposed method performs comparably on both model-based and data-based simulations in considerably less computational time. The R and C code used to implement the proposed method are publicly available in the R package ebdbNet.

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Conceptual aspects of estimation of genetic components of variance and covariance under selection are discussed, with special attention to likelihood methods. Certain selection processes are described and alternative likelihoods that can be used for analysis are specified. There is a mathematical relationship between the likelihoods that permits comparing the relative amount of information contained in them. Theoretical arguments and evidence indicate that point inferences made from likelihood functions are not affected by some forms of selection.

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SummaryA sire evaluation procedure is proposed for situations in which there is uncertainty with respect to the assignment of progeny to sires. The method requires the specification of the prior probabilities P;j that progeny i is out of sire j. Inferences