Computational Population Genetics - Part II, WS 2010/2011

Lecture with Exercises (Vorlesung mit Übung):

Note: this is part II of a course. To attend it, it is necessary to finish part I.

Instructor: Prof. Dr. Dirk Metzler

Time: EES Block II from 8.11.2010 to 26.11.2010
Lecture: Each Tuesday and each Thursday from 9 to 11 a.m. in room C00.013
Exercises: Each Tuesday from 11 a.m. to 12 p.m. and on Thursday from 3 p.m. to 5 p.m. in computer room C00.005

Important for students in the EES Master's program: For the EES Module the lecture has to be combined with the second part of Wolfgang Stephan's lecture Population Genetics II: Theoretical Population Genetics

Contents of Lecture by Dirk Metzler

Data sets of DNA, RNA or protein sequences contain a lot of hidden informations about the history of evolution, about evolutionary processes and about the roles of particular genes in evolutionary adaptation. It is a challenge to develop methods to uncover these informations. Methods that are based on explicit models for evolutionary processes and on the application of statistical principles (like likelihood-maximization or Bayesian inferrence) are most promising. Some of these methods, however, can be very demanding - computationally and intellectually. A thorough understanding of the models and methods is crucial, not only for those who aim to contribute to the further development of such methods but also for those who want to apply these methods to their datasets and have to decide which method to choose, how to set their optional parameters and how to interprete the outcome.
Given population genetic data, how can we infer evolutionary and ecological features like population substructure, change of population size, recent speciation, natural selection and adaptation? Many computational methods for this purpose have been proposed and most of them are freely available in software packages. In this course we will discuss the theoretical and practical aspects of these methods. The theoretical aspects are the underlying models, statistical principles and computational strategies. In the practical part we will analyze these methods. We will also try out various software packages and explore under which circumstances they are appropriate. Among the models that we discuss are the coalescent process and its variants with structure and demography, the ancestral selection graph, and the ancestral recombination graph. Among the parameter estimation strategies are full-likelihood and full-Bayesian methods, methods based on summary statistics, and Approximate-Bayesian Computation. These methods use computational strategies like importance sampling and variants of MCMC. Software: LAMARC, GENETREE, Hudson's MS, IM/IMa, MIMAR, STRUCTURE, etc...

Language: English


web page last updated: Dirk Metzler, October 5, 2010