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Content archived on 2024-04-30

Statistical methods and software tools for the genetic analysis of monogenic and multifactorial diseases

Objective

- To develop parametric and non parametric methods of linkage analysis to handle more complex situations with the aim of elucidating the genetic mechanisms and gene-environment interactions in the etiology of multifactorial diseases.
- To develop faster computing algorithms to facilitate the rapid fine location of genes causing monogenic disorders and to accelerate analyses with the complex methods developed for the genetic analysis of multifactorial diseases.
- To validate the methods and algorithms through computer simulations.
- To implement these methods and algorithms in user-friendly computer programs to be ultimately merged into a unique modular package.
- To make these techniques and computer programs easily available to the scientific community.

Many disease genes have been identified via linkage analysis approaches which test for co-segregation within families of the disease trait with a random marker locus. At least 400 disease genes have been mapped by linkage approaches already, but the majority of these are genes for monogenic Mendelian diseases with simple patterns of inheritance. Now human geneticists are beginning to study the genetics of multifactorial diseases such as hypertension, diabetes, heart disease, multiple sclerosis, arthritis, and obesity. Multifactorial diseases are caused by multiple genes interacting with each other and with environmental factors. Identifying and characterising the genes involved in these disorders is a difficult task because it requires substantial resources including very large collections of family data, highly informative genetic markers which span the genome, and specifically developed statistical approaches which deal with multifactorial disease. As data sets on multifactorial disease have been generated, it has become clear that there is an urgent need for better methods and programs, which, for example, handle multilocus disease models, environmental effects, and covariates which may interact with the genetic factors. This project will meet this need via a European collaboration between three groups of statistical geneticists (Oxford, Paris, and Berlin), who will be aided by talented computer scientists and post-doctoral fellows. This collaboration will pursue the objectives described above.

Identification of genetic variants contributing to susceptibility to multifactorial diseases will have important consequences for public health, especially since multifactorial diseases are quite common in European countries. Many multifactorial diseases are degenerative and are becoming increasingly common as the European population ages. Greater understanding of the susceptibility genes will enable the development of preventive and therapeutic strategies as well as contribute to the knowledge of the aetiology of complex disease.

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Coordinator

Institut National de la Santé et de la Recherche Médicale
EU contribution
No data
Address
1,Avenue Claude Vellefaux
75475 Paris
France

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Total cost

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Participants (2)

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