Unorthodox Humeanism

The thesis is situated in the domain of contemporary metaphysics of science. The question is which ontology fits best with our knowledge of the world. The method chosen is the one of evaluating the consequences of different ontological frameworks against the background of our scientific knowledge of the world. The thesis analyses the two main frameworks in today's metaphysics of science, Humeanism and dispositionalism. It advocates that only an unorthodox version of Humeanism and only an unorthodox version of dispositionalism can be defended, the unorthodox character of these versions consisting in taking the fundamental properties to be relations rather than intrinsic properties. The thesis then sets out in detail what such an unorthodox version of Humeanism amounts to. Chapters 1 and 2 introduce the standard versions of Humeanism and dispositionalism, focussing on the accounts of laws of nature and causation. Chapter 3 compares both these positions and concludes that as far as the orthodox versions are concerned, dispositionalism fares better than Humeanism, since it can avoid Humeanism's commitments to quidditism and humility. However, as is argued in chapter 4, instead of replying to the objections from quidditism and humility by switching to dispositionalism, there is an unorthodox version of Humeanism available that does not run into these problematic consequences and that is supported by science: if one takes the fundamental physical properties to be relations instead of intrinsic properties, the objection from quidditism is avoided, since there is no hidden intrinsic essence of relations. As regards the objection from humility, one can maintain that science is in principle able to provide knowledge of the fundamental relations that there are in the world so that there is no principled ignorance. Consequently, the thesis concludes that Humeanism and dispositionalism are on a par as regards the remaining charge of humility. Unorthodox Humeanism provides a competitive and adequate ontology in the light of contemporary science.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.