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.

Morph-specific genetic and environmental variation in innate and acquired immune response in a color polymorphic raptor.

Genetic color polymorphism is widespread in nature. There is an increasing interest in understanding the adaptive value of heritable color variation and trade-off resolution by differently colored individuals. Melanin-based pigmentation is often associated with variation in many different life history traits. These associations have recently been suggested to be the outcome of pleiotropic effects of the melanocortin system. Although pharmacological research supports that MC1R, a gene with a major role in vertebrate pigmentation, has important immunomodulatory effects, evidence regarding pleiotropy at MC1R in natural populations is still under debate. We experimentally assessed whether MC1R-based pigmentation covaries with both inflammatory and humoral immune responses in the color polymorphic Eleonora's falcon. By means of a cross-fostering experiment, we disentangled potential genetic effects from environmental effects on the covariation between coloration and immunity. Variation in both immune responses was primarily due to genetic factors via the nestlings' MC1R-related color genotype/phenotype, although environmental effects via the color morph of the foster father also had an influence. Overall, dark nestlings had lower immune responses than pale ones. The effect of the color morph of the foster father was also high, but in the opposite direction, and nestlings raised by dark eumelanic foster fathers had higher immune responses than those raised by pale foster fathers. Although we cannot completely discard alternative explanations, our results suggest that MC1R might influence immunity in this species. Morph-specific variation in immunity as well as pathogen pressure may therefore contribute to the long-term maintenance of genetic color polymorphism in natural populations.