Pharmacogénomique de la sclérose en plaques : méthodes et applications

L'expansion ainsi que l'évolution du domaine de la génétique au cours de ces dernières années a été fulgurante. Cela s'accompagne par la génération d'une masse importante d'information génétique sur les traits complexes chez l'homme. Une question naturelle est de savoir comment utiliser cette information dans la pratique médicale quotidienne. Il y a dix ans à peine le séquençage du génome humain nécessitait une collaboration scientifique d'envergure internationale entre les différents acteurs de la recherche biomédicale. Aujourd'hui, il n'est pas exclu à ce que, dans un avenir proche, on puisse obtenir le profil génétique de chaque patient dans la pratique médicale courante. La pharmacogénomique, une fusion de la pharmacologie et de la génomique, vise à déterminer le traitement le plus approprié à chaque patient en fonction de son patrimoine génétique. En effet, plusieurs études pharmacogénomiques ont pu démontrer l'intérêt d'intégrer l'information génétique du patient pour déterminer son traitement optimal. Le cas de la warfarine, un anticoagulant, a souvent été considéré comme l'un des succès les plus motivants pour poursuivre ce type d'études. Cependant, le succès ainsi que le besoin de ces études dépendent de multiples facteurs et varient considérablement selon les traits étudiés. L'objectif de ce travail est d'évaluer l'état actuel des connaissances pour la sclérose en plaques (SEP), une maladie neurologique invalidante touchant principalement les jeunes adultes. À ce jour, il n'existe aucun remède à la SEP, mais il existe des traitements modificateurs de la maladie avec des degrés d'efficacité et de toxicité variable. Les facteurs génétiques qui influencent la réponse au traitement chez les patients atteints de SEP sont à ce jour mal connus. Même si ces facteurs peuvent être mis en évidence dans le futur, il n'en demeure pas moins que leur utilisation en routine clinique n'est pas aussi simple que supposée. Dans ce travail, nous avons essayé de mettre en évidence la complexité du passage de l'utilisation de données génétiques à grande échelle à la pratique médicale pour les traits complexes. Nous avons mené des études d'association et de prédiction. Tout d'abord, nous exposons leurs concepts et revisitons les différences dans leurs objectifs. Plus précisément, nous avons effectué une analyse d'association simple-marqueur de la réponse à l'interféron-bêta chez les patients atteint de SEP. Ensuite, nous avons comparé les modèles simple-marqueur et multi-marqueur dans le contexte de la recherche d'association puis dans celui de la prédiction en utilisant des données réelles et des données simulées. Différentes approches de modélisation multi-marqueur existent. Nous nous sommes basés sur l'analyse des scores polygéniques et des méthodes d'estimation bayésienne en évaluant plusieurs des propriétés de ces approches de modélisation. Nos résultats montrent que, dans la cadre d'une étude d'association pangénomique, les modèles multi-marqueurs, récemment préconisés, ne sont pas forcément plus puissants que les modèles classiques simple-marqueur. En revanche, les modèles multi-marqueurs qui prennent en compte l'effet de plusieurs marqueurs simultanément apparaissent clairement mieux adaptés pour prédire le risque génétique. Néanmoins, en se concentrant sur l'analyse des scores polygéniques, nous montrons que de nombreux facteurs comme la taille de l'échantillon de l'étude et l'héritabilité du trait influencent la performance prédictive d'un modèle. Les études pharmacogénomiques peuvent révolutionner les soins aux patients. Cependant, en dehors de l'enthousiasme qu'elles peuvent susciter, nous discutons dans la dernière partie de cette thèse les questions sociales, éthiques et économiques qu'elles soulèvent.The field of genetics is rapidly expanding and evolving. As more and more is understood on the genetics of complex human traits, a natural question arises as to how these findings can be translated to the everyday medical practice. While a little more than a decade ago sequencing the entire human genome was achieved by the largest international scientific collaboration ever undertaken in biology, today it is not farfetched to expect that in the near future obtaining the genetic profile of each patient may become routine medical practice. Pharmacogenomics, a blend of pharmacology and genomics, aims to determine the most suitable treatment for each patient as a function of his or her genetic makeup. Pharmacogenomic studies have increasingly provided evidence that there are gains to be achieved by incorporating genetic information when determining the optimal treatment choice for a patient. The case of warfarin, an anticoagulant, has often been considered as one of the most motivating success stories to pursue such type of studies. The success as well as the need of such studies, however, depend on a multitude of factors and vary greatly across traits. The objective of this thesis is to evaluate the current state of the art for Multiple Sclerosis (MS), a debilitating neurological disorder affecting primarily young adults. To date, no cure exists for MS but a number of disease-modifying therapies have been approved with varying degree of efficacy and toxicity. So far, little is known on the genetic factors that influence response to treatment in MS patients. Moreover, even if such factors are known apriori, evaluating and proving their utility at the clinical level is not as straightforward as one may be inclined to think. In this thesis, we highlight why the road to translate such findings to medical practice remains rough and challenging. In particular, relying on the association and prediction studies that we have conducted, we expose the design and limitations of each and discuss model choice in each context. Specifically, we conducted single-marker association analysis of response to interferon-bêta in MS patients. We compared single-marker to multi-marker models in the context of association and also in that of prediction using both real and simulated datasets. Different approaches to multi-marker modeling exist. We focused on polygenic score analyses and Bayesian estimation methods and evaluated several of the properties of these modeling approaches. Our findings showed that, in the context of association, the use of more complex and computationally heavy multi-marker models that has been recently advocated may lead to little, if any, benefit over the classical single-marker association analysis. On the other hand, multi-marker models that take into account the effect of many markers simultaneously clearly appear better suited to predict genetic risk. Nevertheless, focusing on polygenic score analyses, we demonstrated that many factors such as the study sample size and the heritability of the trait influence the predictive performance of a model. Pharmacogenomic studies may revolutionize patient care. However, in all the excitement of the promise that they hold, in the concluding part of this thesis we also address the social, ethical and economic issues that they raise

Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data

Genome-wide association studies for complex traits are based on the common disease/common variant (CDCV) and common disease/rare variant (CDRV) assumptions. Under the CDCV hypothesis, classical genome-wide association studies using single-marker tests are powerful in detecting common susceptibility variants, but under the CDRV hypothesis they are not as powerful. Several methods have been recently proposed to detect association with multiple rare variants collectively in a functional unit such as a gene. In this paper, we compare the relative performance of several of these methods on the Genetic Analysis Workshop 17 data. We evaluate these methods using the unrelated individual and family data sets. Association was tested using 200 replicates for the quantitative trait Q1. Although in these data the power to detect association is often low, our results show that collapsing methods are promising tools. However, we faced the challenge of assessing the proper type I error to validate our power comparisons. We observed that the type I error rate was not well controlled; however, we did not find a general trend specific to each method. Each method can be conservative or nonconservative depending on the studied gene. Our results also suggest that collapsing and the single-locus association approaches may not be affected to the same extent by population stratification. This deserves further investigation

A case of scientific fraud? : a statistical approach

In 1986 Thereza Imanishi-Kari, then an assistant professor at the Massachusetts Institute of Technology, was at the peak of her career. She had just coauthored a paper in the prestigious journal Cell with David Baltimore, a Nobel laureate. Their research was exciting and their findings promising.Margot O'Toole, Imanishi-Kari's postdoctoral fellow at the time, was unable to reproduce some of the experimental results published in the paper and could not resolve this with her postdoctoral supervisor. Subsequently, O'Toole became convinced that there were serious errors in the paper and, shortly afterwards, the National Institutes of Health began officially investigating the questions she raised about it.It may have been simply a character clash between Imanishi-Kari and O'Toole but partly due to the involvement of a figure such as Baltimore, this clash possibly ruined their careers, took 10 years to settle down, cost millions of dollars of public money, polarized the scientific community, and went down in history as one of the most widely followed cases of scientific fraud.Based on statistical, forensic and other evidence, Imanishi-Kari was found guilty of scientific misconduct and banned from receiving public funding for 10 years. This was not the end of the matter, however, because Imanishi-Kari appealed the decision and was later exonerated.In this thesis, we tell the statistical story by putting forward the statistical arguments that were used against Imanishi-Kari and the counterarguments to them

Pharmacogenomics of multiple sclerosis (methods and applications)

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF