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