Biomedical subjects

G Nuel

Publications and source records attributed to G Nuel.

4 recordsLinked to original sources

PubMed2006

Computing power in case-control association studies through the use of quadratic approximations: application to meta-statistics.

In the framework of case-control studies many different test statistics are available to measure the association of a marker with a given disease. Nevertheless, choosing one particular statistic can lead to very different conclusions. In the absence of a consensus for this choice, a tempting option is to evaluate the power of these different statistics prior to make any decision. We review the available methods dedicated to power computation and assess their respective reliability in treating a wide range of tests on a wide range of alternative models. Considering Monte-Carlo, non-central chi-square and Delta-Method estimates, we evaluate empirical, asymptotic and numerical approaches. Additionally we introduce the use of the Delta-Method, extended to order 2, intended to provide better results than the traditional order-1 Delta-Method. Supplementary data can be found at: http://stat.genopole.cnrs.fr/software/dm2.

Alleles↗

PubMed2006

A fast, unbiased and exact allelic test for case-control association studies.

Association studies are traditionally performed in the case-control framework. As a first step in the analysis process, comparing allele frequencies using the Pearson's chi-square statistic is often invoked. However such an approach assumes the independence of alleles under the hypothesis of no association, which may not always be the case. Consequently this method introduces a bias that deviates the expected type I error-rate. In this article we first propose an unbiased and exact test as an alternative to the biased allelic test. Available data require to perform thousands of such tests so we focused on its fast execution. Since the biased allelic test is still widely used in the community, we illustrate its pitfalls in the context of genome-wide association studies and particularly in the case of low-level tests. Finally, we compare the unbiased and exact test with the Cochran-Armitage test for trend and show it perfoms similarly in terms of power. The fast, unbiased and exact allelic test code is available in R, C++ and Perl at: http://stat.genopole.cnrs.fr/software/fueatest.

Alleles↗

PubMed2004

LD-SPatt: large deviations statistics for patterns on Markov chains.

Statistics on Markov chains are widely used for the study of patterns in biological sequences. Statistics on these models can be done through several approaches. Central limit theorem (CLT) producing Gaussian approximations are one of the most popular ones. Unfortunately, in order to find a pattern of interest, these methods have to deal with tail distribution events where CLT is especially bad. In this paper, we propose a new approach based on the large deviations theory to assess pattern statistics. We first recall theoretical results for empiric mean (level 1) as well as empiric distribution (level 2) large deviations on Markov chains. Then, we present the applications of these results focusing on numerical issues. LD-SPatt is the name of GPL software implementing these algorithms. We compare this approach to several existing ones in terms of complexity and reliability and show that the large deviations are more reliable than the Gaussian approximations in absolute values as well as in terms of ranking and are at least as reliable as compound Poisson approximations. We then finally discuss some further possible improvements and applications of this new method.

Computational Biology↗

PubMed2003

SPA: Simple web tool to assess statistical significance of DNA patterns.

Many statistical methods and programs are available to compute the significance of a given DNA pattern in a genome sequence. In this paper, after outlining the mathematical background of this problem, we present SPA (Statistic for PAtterns), an expert system with a simple web interface designed to be applied to two of these methods (large deviation approximations and exact computations using simple recurrences). A few results are presented, leading to a comparison between the two methods and to a simple decision rule in the choice of that to be used. Finally, future developments of SPA are discussed. This tool is available at the following address: http://stat.genopole.cnrs.fr/SPA/.

Data Interpretation, Statistical↗