Functional genomics and molecular networks Gene expression regulations in complex diseases: Down syndrome as a case study

Marie-Claude Potier (1) , Isabelle Rivals (2)

1 CRICM - Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière
2 ESA - Equipe de Statistique Appliquée (UMRS 1158)

The goal of functional genomics is to understand the relationship be-tween whole genomes and phenotypes through a dynamic approach. It requires high throughput technologies such as microarrays and data analysis. The power of this approach allowed to study complex biological functions as well as diseases. In this chapter, we introduce functional genomics and describe the statistical methods that are used to find differentially expressed genes. We analyze a large num-ber of data sets produced on a complex disease, namely Down syn-drome, in different models. We show that, whatever the model, genes that are in three copies are globally overexpressed. However, we failed to identify a set of two-copy genes that would be dysregu-lated in all studies. It either suggests that studies are incomplete, or that this set of genes does not exist and that overexpression of the three-copy genes impacts on the whole transcriptome in a "stochas-tic" way.

Keywords: DNA microarray; Down syndrom; differentially expressed genes; statistical tests; statistical tests.

Domains: Methodology [stat.ME]; Quantitative Methods [q-bio.QM]; Bioinformatics [q-bio.QM]; Neurobiology

Marie-Claude Potier, Isabelle Rivals. Functional genomics and molecular networks Gene expression regulations in complex diseases: Down syndrome as a case study. N. Le Novère. Computational Systems Neurobiology Textbook, Part I : Functional Genomics and Molecular Networks, Springer, pp.1-22, 2012. ⟨hal-00801558⟩