BIOINFORMATICS, GENETICAL STATISTICS, EPIDEMIC MODELLING

Time | Speaker | Title |
---|---|---|

13.30-14.10 | Arndt van Haeseler, Max-Planck-Institute for Evolutionary Anthropology, Leipzig, Germany | Comparative Sequence Biology. |

14.20-14.50 | Tom Britton, Uppsala University | Modelling and Analysis of Epidemics. |

14.50-15.10 | Coffebreak | |

15.10-15.50 | Bernard Prum, La Genopole, Evry, France | Using Hidden Markov Models in the Analysis of Biological Sequences. |

16.00-16.30 | Mikael Knutsson, Chalmers | Power Studies in Nonparametric Linkage Analysis, Combining Simulations and Multivariate Normal Approximations. |

16.30-17.00 | Staffan Nilsson, Chalmers | Model Based Sampling and Weights in Affected Sib Pair Methods. |

Abstract: We have studied the complex pattern of nucleotide substitution in the control region of human mtDNA and of chimpanzee mtDNA hypervariable regions I. In order to elucidate the model of nucleotide substitutions that describes the evolution in an apropriate manner, a database was developed that allows an easy retrieval of the data.

We will briefly describe the set-up of the database and then explain the results obtained for humans and chimpanzees separately. Finally, the models of sequence evolution inferred for both species are compared. Thus, giving us insights, whether both species evolve according to the same model for the region studied.

Tom Britton: Modelling and Analysis of
Epidemics.

Abstract: In the talk we will aim at giving a survey
of the mathematical modelling, and its statistical analysis, for the
spread of infectious diseases. In particular we will derive certain
equations determining whether a major epidemic outbreak is possible or
not, and in case of a long term outbreak what the endemic equlibrium
will be. We will also discuss how model parameters may be estimated
from data and how these estimates can be used in determining the
necessary fraction to vaccinate in order to have the disease go
extinct.

Bernard Prum: Using Hidden
Markov Models in the Analysis of Biological Sequences.

Abstract: A way to
look for information contained in DNA sequences (apart from the well
known "genetic code") is to search "words" with a number of occurences
higher (or smaller) than what can be predict.ed This can not be done
without taking into account the number of sub-words of a word; this
leads to work in Markov chain models (MC). Estimating the parameters
of the MC shows differences between coding or non coding parts, as
well as differences from one organism to another. Hence it is
possible to suppose that a long sequence corresponds (in an unknow
way) to successive (unknown) models. This is a Hidden Markov Model
(HMM). Some example of the use of this tool will be shown, in
particular concerning the search for horizontal transfers.

Mikael Knutsson: Power Studies in Nonparametric Linkage Analysis,
Combining Simulations and Multivariate Normal Approximations.

Abstract: I will present a simulation method for power studies in
linkage analysis using the NPL-score statistic calculated by the
GENEHUNTER software. The method involves 3 steps; (1) Simulation of
marker data given a specified genetic model, (2) NPL calculations
using GENEHUNTER, and (3) Sampling from a multivariate normal
distribution. Here, I will consider ASP families only and the method
is illustrated and tested by a short example.

Staffan Nilsson: Model Based Sampling and Weights in Affected Sib Pair
Methods.

Abstract: When running a genome scan with affected sib pairs and
a nonparametric statistic in the form of sums of IBD counts, it turns
out that, depending on the genetic model, some families are better than
other if we consider the phenotypes of all family members. This can be
utilized by performing selective sampling and/or putting weights on
the IBD counts. Some different approaches are introduced and compared
on simulated examples.

If you have questions, please contact nerman@math.chalmers.se

Stochastic Centre, Gothenburg

Last modified: Tue Aug 31 10:30:22 MET DST 1999