On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo.

For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis challenging, as methods need not only to be accurate, but also time efficient given the tremendous amount of data to process. In this article, we introduce an efficient method to infer the evolutionary history of individuals under the multispecies coalescent model in networks (MSNC).

Training set optimization of genomic prediction by means of EthAcc.

Genomic prediction is a useful tool for plant and animal breeding programs and is starting to be used to predict human diseases as well. A shortcoming that slows down the genomic selection deployment is that the accuracy of the prediction is not known a priori. We propose EthAcc (Estimated THeoretical ACCuracy) as a method for estimating the accuracy given a training set that is genotyped and phenotyped.

On the Accuracy of Genomic Selection.

Genomic selection is focused on prediction of breeding values of selection candidates by means of high density of markers. It relies on the assumption that all quantitative trait loci (QTLs) tend to be in strong linkage disequilibrium (LD) with at least one marker. In this context, we present theoretical results regarding the accuracy of genomic selection, i.

Detecting and locating whole genome duplications on a phylogeny: a probabilistic approach.

Whole genome duplications (WGDs) followed by massive gene loss occurred in the evolutionary history of many groups. WGDs are usually inferred from the age distribution of paralogs (Ks-based methods) or from gene collinearity data (synteny). However, Ks-based methods are restricted to detect the recent WGDs due to saturation effects and the difficulty to date old duplicates, and synteny is difficult to reconstruct for distantly related species.