Kosiol Group Research




Group Members

Carolin Kosiol




Modelling Evolutionary Trajectories from Experimental Evolution Studies of Drosophila Species
(with Antti Honkela and Christian Schloetterer)

This project combines NGS technologies with experimental evolution studies. The substantial decrease in costs has made it feasible to not only sequence the last generation of a population at the end of a long-term artificial selection experiment but to sequence intermediate generations. Using NGS technologies we monitor the allele frequency changes in populations that undergo a selection experiment for temperature adaptation. The resulting data represents evolutionary trajectories, time-series data, that we analyse using Gaussian Process models. A Gaussian process is a probability distribution over functions that can represent changes in concentration. Analogous to the Gaussian distribution which is fully characterised by a mean function and a covariance function. To simplify the inference we will use population genetic models describing changes in allele frequencies such that they will allow us to simplify and reduce the number of parameters of the covariance function. A further advantage of the Gaussian process approach is that it can handle replicate population to identify trends across populations.

Polymorphism-Aware Phylogenetic Models (with Nicola De Maio)
Comparative analysis of genomes of related species, and of different individuals of the same species, can reveal adaptive trends in the history of the considered taxa, as well as show intensity and genomic variation of evolutionary patterns of species that undergo speciation. However, these intra and interspecific data also bring new challenges, such as the presence of incomplete lineage sorting and ancestral shared polymorphisms.
We have developed a new method called POlymorphisms-aware phylogenetic MOdel (PoMo). It is a phylogenetic Markov model with states representing fixed alleles as well as polymorphisms at different allele frequencies. A substitution is hereby modeled through a mutational event followed by a gradual fixation. Polymorphisms can either be observed in the present (tips of the phylogeny) or be ancestral (present at inner nodes). With this approach, we naturally account for incomplete lineage sorting and shared ancestral polymorphisms. Our method can accurately and time-efficiently estimate the parameters describing evolutionary patterns for phylogenetic trees of any shape (species trees, population trees, or any combination of those). Furthermore, we are able to disentangle the contribution of mutation rates and fixation biases in shaping substitution patterns.

Estimation of Population Genetic Parameters from Pooled Next Generation Sequencing Data
(with Andreas Futschik, Robert Kofler and Christian Schloetterer)

Pooled sequencing at population level is often cheaper and more accurate than individual sequencing. We are developing software which implements the our unbiased estimates of Tajima’s pi and Watersons theta . We not only account for the bias derived by pooled sequencing, but also for the one generated by sequencing errors, that are higher in next generation sequencing.

Empirical Codon Models
(with Maria Anisimova, Nick Goldman and Ian Holmes)

We have estimated a first empirical codon model using maximum likelihood methods. We show that modelling the evolutionary process is improv ed by allowing for single, double and triple nucleotide changes and that the affiliation between DNA triplets and the amino acid they encode is a main factor driving evolution. We plan to extend this approach to the estimation of a codon model assuming rate heterogeneity among codon sites caused by the influence of natural selection. Web page

Patterns of Positive Selection on the Mammalian Tree
(with Tomas Vinar, Rute Da Fonseca, Rasmus Nielsen and Adam Siepel)

We have performed the most comprehensive examination of positive selection on the mammalian tree to date, using the six high-coverage genome assemblies now available for eutherian mammals (human, chimpanzee, macaque, mouse, rat, dog). The increased phylogenetic depth of this data set results in substantially improved statistical power, and permits several new lineage- and clade-specific tests to be applied. Webpage