Maps as risk mitigation tools. Adaptation of the Swiss hazard assessment and mapping methodology to a Moroccan site: Beni Mellal

Dans le contexte climatique actuel, les régions méditerranéennes connaissent une intensification des phénomènes hydrométéorologiques extrêmes. Au Maroc, le risque lié aux inondations est devenu problématique, les communautés étant vulnérables aux événements extrêmes. En effet, le développement économique et urbain rapide et mal maîtrisé augmente l'exposition aux phénomènes extrêmes. La Direction du Développement et de la Coopération suisse (DDC) s'implique activement dans la réduction des risques naturels au Maroc. La cartographie des dangers et son intégration dans l'aménagement du territoire représentent une méthode efficace afin de réduire la vulnérabilité spatiale. Ainsi, la DDC a mandaté ce projet d'adaptation de la méthode suisse de cartographie des dangers à un cas d'étude marocain (la ville de Beni Mellal, région de Tadla-Azilal, Maroc). La méthode suisse a été adaptée aux contraintes spécifiques du terrain (environnement semi-aride, morphologie de piémont) et au contexte de transfert de connaissances (caractéristiques socio-économiques et pratiques). Une carte des phénomènes d'inondations a été produite. Elle contient les témoins morphologiques et les éléments anthropiques pertinents pour le développement et l'aggravation des inondations. La modélisation de la relation pluie-débit pour des événements de référence, et le routage des hydrogrammes de crue ainsi obtenus ont permis d'estimer quantitativement l'aléa inondation. Des données obtenues sur le terrain (estimations de débit, extension de crues connues) ont permis de vérifier les résultats des modèles. Des cartes d'intensité et de probabilité ont été obtenues. Enfin, une carte indicative du danger d'inondation a été produite sur la base de la matrice suisse du danger qui croise l'intensité et la probabilité d'occurrence d'un événement pour obtenir des degrés de danger assignables au territoire étudié. En vue de l'implémentation des cartes de danger dans les documents de l'aménagement du territoire, nous nous intéressons au fonctionnement actuel de la gestion institutionnelle du risque à Beni Mellal, en étudiant le degré d'intégration de la gestion et la manière dont les connaissances sur les risques influencent le processus de gestion. L'analyse montre que la gestion est marquée par une logique de gestion hiérarchique et la priorité des mesures de protection par rapport aux mesures passives d'aménagement du territoire. Les connaissances sur le risque restent sectorielles, souvent déconnectées. L'innovation dans le domaine de la gestion du risque résulte de collaborations horizontales entre les acteurs ou avec des sources de connaissances externes (par exemple les universités). Des recommandations méthodologiques et institutionnelles issues de cette étude ont été adressées aux gestionnaires en vue de l'implémentation des cartes de danger. Plus que des outils de réduction du risque, les cartes de danger aident à transmettre des connaissances vers le public et contribuent ainsi à établir une culture du risque. - Severe rainfall events are thought to be occurring more frequently in semi-arid areas. In Morocco, flood hazard has become an important topic, notably as rapid economic development and high urbanization rates have increased the exposure of people and assets in hazard-prone areas. The Swiss Agency for Development and Cooperation (SADC) is active in natural hazard mitigation in Morocco. As hazard mapping for urban planning is thought to be a sound tool for vulnerability reduction, the SADC has financed a project aimed at adapting the Swiss approach for hazard assessment and mapping to the case of Morocco. In a knowledge transfer context, the Swiss method was adapted to the semi-arid environment, the specific piedmont morphology and to socio-economic constraints particular to the study site. Following the Swiss guidelines, a hydro-geomorphological map was established, containing all geomorphic elements related to known past floods. Next, rainfall / runoff modeling for reference events and hydraulic routing of the obtained hydrographs were carried out in order to assess hazard quantitatively. Field-collected discharge estimations and flood extent for known floods were used to verify the model results. Flood hazard intensity and probability maps were obtained. Finally, an indicative danger map as defined within the Swiss hazard assessment terminology was calculated using the Swiss hazard matrix that convolves flood intensity with its recurrence probability in order to assign flood danger degrees to the concerned territory. Danger maps become effective, as risk mitigation tools, when implemented in urban planning. We focus on how local authorities are involved in the risk management process and how knowledge about risk impacts the management. An institutional vulnerability "map" was established based on individual interviews held with the main institutional actors in flood management. Results show that flood hazard management is defined by uneven actions and relationships, it is based on top-down decision-making patterns, and focus is maintained on active mitigation measures. The institutional actors embody sectorial, often disconnected risk knowledge pools, whose relationships are dictated by the institutional hierarchy. Results show that innovation in the risk management process emerges when actors collaborate despite the established hierarchy or when they open to outer knowledge pools (e.g. the academia). Several methodological and institutional recommendations were addressed to risk management stakeholders in view of potential map implementation to planning. Hazard assessment and mapping is essential to an integrated risk management approach: more than a mitigation tool, danger maps represent tools that allow communicating on hazards and establishing a risk culture.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

cDNA cloning and mapping of a novel islet-brain/JNK-interacting protein.

IB1/JIP-1 is a scaffold protein that regulates the c-Jun NH(2)-terminal kinase (JNK) signaling pathway, which is activated by environmental stresses and/or by treatment with proinflammatory cytokines including IL-1beta and TNF-alpha. The JNKs play an essential role in many biological processes, including the maturation and differentiation of immune cells and the apoptosis of cell targets of the immune system. IB1 is expressed predominantly in brain and pancreatic beta-cells where it protects cells from proapoptotic programs. Recently, a mutation in the amino-terminus of IB1 was associated with diabetes. A novel isoform, IB2, was cloned and characterized. Overall, both IB1 and IB2 proteins share a very similar organization, with a JNK-binding domain, a Src homology 3 domain, a phosphotyrosine-interacting domain, and polyacidic and polyproline stretches located at similar positions. The IB2 gene (HGMW-approved symbol MAPK8IP2) maps to human chromosome 22q13 and contains 10 coding exons. Northern and RT-PCR analyses indicate that IB2 is expressed in brain and in pancreatic cells, including insulin-secreting cells. IB2 interacts with both JNK and the JNK-kinase MKK7. In addition, ectopic expression of the JNK-binding domain of IB2 decreases IL-1beta-induced pancreatic beta-cell death. These data establish IB2 as a novel scaffold protein that regulates the JNK signaling pathway in brain and pancreatic beta-cells and indicate that IB2 represents a novel candidate gene for diabetes.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

An integrated genetic and functional analysis of the role of type II transmembrane serine proteases (TMPRSSs) in hearing loss.

Building on our discovery that mutations in the transmembrane serine protease, TMPRSS3, cause nonsyndromic deafness, we have investigated the contribution of other TMPRSS family members to the auditory function. To identify which of the 16 known TMPRSS genes had a strong likelihood of involvement in hearing function, three types of biological evidence were examined: 1) expression in inner ear tissues; 2) location in a genomic interval that contains a yet unidentified gene for deafness; and 3) evaluation of hearing status of any available Tmprss knockout mouse strains. This analysis demonstrated that, besides TMPRSS3, another TMPRSS gene was essential for hearing and, indeed, mice deficient for Hepsin (Hpn) also known as Tmprss1 exhibited profound hearing loss. In addition, TMPRSS2, TMPRSS5, and CORIN, also named TMPRSS10, showed strong likelihood of involvement based on their inner ear expression and mapping position within deafness loci PKSR7, DFNB24, and DFNB25, respectively. These four TMPRSS genes were then screened for mutations in affected members of the DFNB24 and DFNB25 deafness families, and in a cohort of 362 sporadic deaf cases. This large mutation screen revealed numerous novel sequence variations including three potential pathogenic mutations in the TMPRSS5 gene. The mutant forms of TMPRSS5 showed reduced or absent proteolytic activity. Subsequently, TMPRSS genes with evidence of involvement in deafness were further characterized, and their sites of expression were determined. Tmprss1, 3, and 5 proteins were detected in spiral ganglion neurons. Tmprss3 was also present in the organ of Corti. TMPRSS1 and 3 proteins appeared stably anchored to the endoplasmic reticulum membranes, whereas TMPRSS5 was also detected at the plasma membrane. Collectively, these results provide evidence that TMPRSS1 and TMPRSS3 play and TMPRSS5 may play important and specific roles in hearing.

A new locus for congenital cataract, microcornea, microphthalmia, and atypical iris coloboma maps to chromosome 2.

OBJECTIVE: To report a novel phenotype of autosomal dominant atypical congenital cataract associated with variable expression of microcornea, microphthalmia, and iris coloboma linked to chromosome 2. Molecular analysis of this phenotype may improve our understanding of anterior segment development. DESIGN: Observational case study, genome linkage analysis, and gene mutation screening. PARTICIPANTS: Three families, 1 Egyptian and 2 Belgians, with a total of 31 affected were studied. METHODS: Twenty-one affected subjects and 9 first-degree relatives underwent complete ophthalmic examination. In the Egyptian family, exclusion of PAX6, CRYAA, and MAF genes was demonstrated by haplotype analysis using microsatellite markers on chromosomes 11, 16, and 21. Genome-wide linkage analysis was then performed using 385 microsatellite markers on this family. In the 2 Belgian families, the PAX6 gene was screened for mutations by direct sequencing of all exons. MAIN OUTCOME MEASURES: Phenotype description, genome-wide linkage of the phenotype, linkage to the PAX6, CRYAA, and MAF genes, and mutation detection in the PAX6 gene. RESULTS: Affected members of the 3 families had bilateral congenital cataracts inherited in an autosomal dominant pattern. A novel form of hexagonal nuclear cataract with cortical riders was expressed. Among affected subjects with available data, 95% had microcornea, 39% had microphthalmia, and 38% had iris coloboma. Seventy-five percent of the colobomata were atypical, showing a nasal superior location in 56%. A positive lod score of 4.86 was obtained at theta = 0 for D2S2309 on chromosome 2, a 4.9-Mb common haplotype flanked by D2S2309 and D2S2358 was obtained in the Egyptian family, and linkage to the PAX6, CRYAA, or MAF gene was excluded. In the 2 Belgian families, sequencing of the junctions and all coding exons of PAX6 did not reveal any molecular change. CONCLUSIONS: We describe a novel phenotype that includes the combination of a novel form of congenital hexagonal cataract, with variably expressed microcornea, microphthalmia, and atypical iris coloboma, not caused by PAX6 and mapping to chromosome 2. FINANCIAL DISCLOSURE(S): The authors have no proprietary or commercial interest in any materials discussed in this article.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Copy number variants, diseases and gene expression.

Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation likely to play a role in phenotypic diversity and evolution. Much effort has been put into the identification and mapping of regions that vary in copy number among seemingly normal individuals in humans and a number of model organisms, using bioinformatics or hybridization-based methods. These have allowed uncovering associations between copy number changes and complex diseases in whole-genome association studies, as well as identify new genomic disorders. At the genome-wide scale, however, the functional impact of CNV remains poorly studied. Here we review the current catalogs of CNVs, their association with diseases and how they link genotype and phenotype. We describe initial evidence which revealed that genes in CNV regions are expressed at lower and more variable levels than genes mapping elsewhere, and also that CNV not only affects the expression of genes varying in copy number, but also have a global influence on the transcriptome. Further studies are warranted for complete cataloguing and fine mapping of CNVs, as well as to elucidate the different mechanisms by which they influence gene expression.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Segmental copy number variation shapes tissue transcriptomes

Abstract : Copy number variation (CNV) of DNA segments has recently gained considerable interest as a source of genetic variation likely to play a role in phenotypic diversity and evolution. Much effort has been put into the identification and mapping of regions that vary in copy number among seemingly normal individuals, both in humans and in a number of model organisms, using both bioinformatic and hybridization-based methods. Synteny studies suggest the existence of CNV hotspots in mammalian genomes, often in connection with regions of segmental duplication. CNV alleles can be in equilibrium within a population, but can also arise de novo between generations, illustrating the highly dynamic nature of these regions. A small number of studies have assessed the effect of CNV on single loci, however, at the genome-wide scale, the functional impact of CNV remains poorly studied. We have explored the influence of CNV on gene expression, first using the Williams-Beuren syndrome (WBS) associated deletion as a model, and second at the genome-wide scale in inbred mouse strains. We found that the WBS deletion influences the expression levels not only of the hemizygous genes, but also affects the euploid genes mapping nearby. Consistently, on a genome wide scale we observe that CNV genes are expressed at more variable levels than genes that do not vary in copy number. Likewise, CNVs influence the relative expression levels of genes that map to the flank of the genome rearrangements, thus globally influencing tissue transcriptomes. Further studies are warranted to complete cataloguing and fine mapping of CNV regions, as well as to elucidate the different mechanisms by which CNVs influence gene expression. Résumé : La variation en nombre de copies (copy number variation ou CNV) de segments d'ADN suscite un intérêt en tant que variation génétique susceptible de jouer un r81e dans la diversité phénotypique et l'évolution. Les régions variables en nombre de copies parmi des individus apparemment normaux ont été cartographiées et cataloguées au moyen de puces à ADN et d'analyse bioinformatique. L'étude de la synténie entre plusieurs espèces de mammifères laisse supposer l'existence de régions à haut taux de variation, souvent liées à des duplications segmentaires. Les allèles CNV peuvent être en équilibre au sein d'une population ou peuvent apparaître de novo. Ces faits illustrent la nature hautement dynamique de ces régions. Quelques études se sont penchées sur l'effet de la variation en nombre de copies de loci isolés, cependant l'impact de ce phénomène n'a pas été étudié à l'échelle génomique. Nous avons examiné l'influence des CNV sur l'expression des gènes. Dans un premier temps nous avons utilisé la délétion associée au syndrome de Williams-Beuren (WBS), puis, dans un second temps, nous avons poursuivi notre étude à l'échelle du génome, dans des lignées consanguines de souris. Nous avons établi que la délétion WBS influence l'expression non seulement des gènes hémizygotes, mais également celle des gènes euploïdes voisins. A l'échelle génomique, nous observons des phénomènes concordants. En effet, l'expression des gènes variant en nombre de copies est plus variable que celles des gènes ne variant pas. De plus, à l'instar de la délétion WBS, les CNV influencent l'expression des gènes adjacents, exerçant ainsi un impact global sur les profils d'expression dans les tissus. Résumé pour un large public : De nombreuses maladies ont pour cause un défaut génétique. Parmi les types de mutations, on compte la disparition (délétion) d'une partie de notre génome ou sa duplication. Bien que l'on connaisse les anomalies associées à certaines maladies, les mécanismes moléculaires par lesquels ces réarrangements de notre matériel génétique induisent les maladies sont encore méconnus. C'est pourquoi nous nous sommes intéressés à la régulation des gènes dans les régions susceptibles à délétion ou duplication. Dans ce travail, nous avons démontré que les délétions et les duplications influencent la régulation des gènes situés à proximité, et que ces changements interviennent dans plusieurs organes.