Comparing G: multivariate analysis of genetic variation in multiple populations

The additive genetic variance–covariance matrix (G) summarizes themultivariate genetic relationships among a set of traits. The geometry of Gdescribes the distribution of multivariate genetic variance, and generates geneticconstraints that bias the direction of evolution. Determining if and how the multivariategenetic variance evolves has been limited by a number of analytical challenges incomparing G-matrices. Current methods for the comparison of G typically shareseveral drawbacks: metrics that lack a direct relationship to evolutionary theory, theinability to be applied in conjunction with complex experimental designs, difficultieswith determining statistical confidence in inferred differences and an inherentlypair-wise focus. Here, we present a cohesive and general analytical framework for thecomparative analysis of G that addresses these issues, and that incorporates andextends current methods with a strong geometrical basis. We describe the application ofrandom skewers, common subspace analysis, the 4th-order genetic covariance tensor and thedecomposition of the multivariate breeders equation, all within a Bayesian framework. Weillustrate these methods using data from an artificial selection experiment on eighttraits in Drosophila serrata, where a multi-generational pedigree was availableto estimate G in each of six populations. One method, the tensor, elegantlycaptures all of the variation in genetic variance among populations, and allows theidentification of the trait combinations that differ most in genetic variance. The tensorapproach is likely to be the most generally applicable method to the comparison ofG-matrices from any sampling or experimental design.     

Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates

Bayesian methods have become extremely popular in molecular ecology studies because they allow us to estimate demographic parameters of complex demographic scenarios using genetic data. Articles presenting new methods generally include sensitivity studies that evaluate their performance, but they tend to be limited and need to be followed by a more thorough evaluation. Here we evaluate the performance of a recent method, bayesass , which allows the estimation of recent migration rates among populations, as well as the inbreeding coefficient of each local population. We expand the simulation study of the original publication by considering multi-allelic markers and scenarios with varying number of populations. We also investigate the effect of varying migration rates and F _ST more thoroughly in order to identify the region of parameter space where the method is and is not able to provide accurate estimates of migration rate. Results indicate that if the demographic history of the species being studied fits the assumptions of the inference model, and if genetic differentiation is not too low ( F _ST ≥ 0.05), then the method can give fairly accurate estimates of migration rates even when they are fairly high (about 0.1). However, when the assumptions of the inference model are violated, accurate estimates are obtained only if migration rates are very low ( m  = 0.01) and genetic differentiation is high ( F _ST ≥ 0.10). Our results also show that using posterior assignment probabilities as an indication of how much confidence we can place on the assignments is problematical since the posterior probability of assignment can be very high even when the individual assignments are very inaccurate.     

Genes of the sea urchin embryo: An annotated list as of December 1994

The main literature regarding gene structure and expression in sea urchin embryos is schematically reported and briefly commented upon. Although the subject has expanded particularly over the last 10 years, to which the review mostly refers, some historical reference is also given. More space is reserved to the regulation of the synthesis of histones and cytoskeletal actins, where the attention of various authors has been especially present; the regulation of such a synthesis is described both at a territorial level and a temporal level during the sea urchin development.     

Mitochondrial gene rearrangements: new paradigm in the evolutionary biology and systematics

Mitochondrial (mt) genomic study may reveal significant insight into the molecular evolution and several other aspects of genome evolution such as gene rearrangements evolution, gene regulation, and replication mechanisms. Other questions such as patterns of gene expression mechanism evolution, genomic variation and its correlation with physiology, and other molecular and biochemical mechanisms can be addressed by the mt genomics. Rare genomic changes have attracted evolutionary biology community for providing homoplasy free evidence of phylogenetic relationships. Gene rearrangements are considered to be rare evolutionary events and are being used to reconstruct the phylogeny of diverse group of organisms. Mt gene rearrangements have been established as a hotspot for the phylogenetic and evolutionary analysis of closely as well as distantly related organisms.     

Differential display: Identifying genes involved in cardiomyocyte proliferation

An estimated 15,000 different mRNA species are expressed in a typical mammalian cell. The differential expression of mRNAs in both a temporal and cell-specific manner determines the fate of the cell and creates the organism. Analysis of this differential gene expression has become a central aim of many laboratories attempting to understand the mechanisms underlying various biological processes. Currently, we are using a technique called differential display to analyze the differential expression of genes in cardiomyocytes. Differential display is a rapid and powerful technique that was introduced by Liang and Pardee in 1992. Since that time, it has been successfully applied by several groups, and it is quickly becoming a standard method for studying differential gene expression. Here, we present a detailed article discussing the differential display methodology and how we have utilized it to identify potential genes involved in cardiomyocyte proliferation. Furthermore, we have provided a list of materials and supplied examples of data obtained, in an effort to allow the reader to perform the technique with success in their own laboratory.     

colonise: a computer program to study colonization processes in metapopulations

colonise is a user‐friendly Microsoft® Windows software for the study of colonization events in natural populations. It determines the composition of the newly colonized population and makes inferences about the factors that influenced individuals to establish a new population. colonise uses a hierarchical Bayesian model to combine multilocus genotype data with demographic and environmental data, and a reversible jump Markov chain Monte Carlo (MCMC) algorithm to perform posterior density estimations. The software is written in C and C + + for the graphical user interface and is available at http://www2.ujf‐grenoble.fr/leca/presentation.html . The software integrates a tool to draw posterior density functions (histogram, running mean, traces, etc.) and to estimate parameters from them (mean, mode, variance, HPDI, etc.).