|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.|
|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.|
Tom Britton: Modelling and Analysis of
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
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.
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