Genome-wide association studies (GWAS) exploit the correlation in ge- netic diversity along chromosomes in order to detect effects on disease risk without having to type causal loci directly. The inevitable downside of this approach is that, when the correlation between the marker and the causal variant is imperfect, the risk associated with carrying the predisposing allele is diluted and its effect is underestimated. This thesis explores four different facets of this risk dilution: (1) estimating true effect sizes from those observed in GWAS; (2) asking how the context of a GWAS, including the population studied, the genotyping chip employed, and the use of im- putation, affects risk estimates; (3) assessing how often the best-associated SNP in a GWAS coincides with the causal variant; and (4) quantifying how departures from the simplest disease risk model at a causal variant distort the observed disease risk model. Using simulations, where we have information about the true risk at the causal locus, we show that the correlation between the marker and the causal variant is the primary driver of effect size underestimation. The extent of the underestimation depends on a number of factors, including the population in which the study is conducted, the genotyping chip employed, whether imputation is used, and the strength, frequency, and disease model of the risk allele. Suppose that a GWAS study is conducted in a European population, with an Affymetrix 6.0 genotyping chip, without imputation, and that the causal loci have a modest effect on disease risk, are common in the population, and follow an additive disease risk model. In such a study, we show that the risk estimated from the most associated SNP is very close to the truth approximately two-thirds of the time (although we predict that fine mapping of GWAS loci will infrequently identify causal variants with considerably higher risk), and that the best-associated variant is very often perfectly or nearly-perfectly correlated with, and almost always within 0.1cM of, the causal variant. However, the strong correlations among nearby loci mean that the causal and best-associated variants coincide infrequently, less than one-fifth of the time, even if the causal variant is genotyped. We explore ways in which these results change quantitatively depending on the parameters of the GWAS study. Additionally, we demonstrate that we expect to identify substantial deviations from the additive disease risk model among loci where association is detected, even though power to detect departures from the model drops off very quickly as the correlation between the marker and causal loci decreases. Finally, we discuss the implications of our results for the design and interpretation of future GWAS studies.

A complex trait is a trait or disease that is controlled by both genetic and environmental factors, along with their interactions. Trait architecture encompasses the genetic variants and environmental causes of variation in the trait or disease, their effects on the trait or disease and the mechanism by which these factors interact at molecular and organism levels. It is important to understand trait architecture both from a biological viewpoint and a health perspective. In this thesis, I laid emphasis on exploring the influence of familial environmental factors on complex trait architecture alongside the genetic components. I performed a variety of studies to explore the architecture of anthropometric and cardio-metabolic traits, such as height, body mass index, high density lipoprotein content of blood and blood pressure, using a cohort of 20,000 individuals of recent Scottish descent and their phenotype measurements, Single Nucleotide Polymorphism (SNP) data and genealogical information. I extended a method of variance component analysis that could simultaneously estimate SNP-associated heritability and total heritability whilst considering familial environmental effects shared among siblings, couples and nuclear family members. I found that most missing heritability could be explained by including closely related individuals in the analysis and accounting for these close relationships; and that, on top of genetics, couple and sibling environmental effects are additional significant contributors to the complex trait variation investigated. Subsequently, I accounted for couple and sibling environmental effects in Genome- Wide Association Study (GWAS) and prediction models. Results demonstrated that by adding additional couple and sibling information, both GWAS performance and prediction accuracy were boosted for most traits investigated, especially for traits related to obesity. Since couple environmental effects as modelled in my study might, in fact, reflect the combined effect of assortative mating and shared couple environment, I explored further the dissection of couple effects according to their origin. I extended assortative mating theory by deriving the expected resemblance between an individual and in-laws of his first-degree relatives. Using the expected resemblance derived, I developed a novel pedigree study which could jointly estimate the heritability and the degree of assortative mating. I have shown in this thesis that, for anthropometric and cardio-metabolic traits, environmental factors shared by siblings and couples seem to have important effects on trait variation and that appropriate modelling of such effects may improve the outcome of genetic analyses and our understanding of the causes of trait variation. My thesis also points out that future studies on exploring trait architecture should not be limited to genetics because environment, as well as mate choice, might be a major contributor to trait variation, although trait architecture varies from trait to trait.

Dans un contexte de changement climatique, la caractérisation des variétés de blé tendre en réponse à des évènements de sécheresse et de stress thermique est un des défis de l’agriculture. Cette thèse, issue d’un partenariat -public entre Arvalis-Institut du Végétal, Biogemma et l’INRA (Institut National de la Recherche Agronomique), avait pour but de développer des connaissances et des outils nécessaires à l’identification de variétés tolérantes à la sécheresse et au stress thermique et à la création de variétés répondant à cette exigence. Pour ce faire, nous avons analysé un panel de 220 variétés commerciales, génotypées avec 280K SNP et testées dans 35 environnements variés (combinaison d’année, lieu et régime hydrique), plus une expérimentation en conditions contrôlées où un stress thermique a été appliqué pendant le remplissage du grain. La complexité de l’étude de la tolérance à la sécheresse nous a conduit à présenter cette thèse en séparant, dans un premier temps, l’étude des stress hydriques et thermiques, puis de prospecter une méthode d’analyse multi-stress. Nous avons montré que même si la sélection a amélioré la performance des variétés en condition hydrique optimale, le progrès génétique doit être accéléré et mieux réparti en fonction des différents types de stress. Nous proposons pour cela plusieurs déterminants génétiques qui pourraient permettre un gain dans des environnements stressants. Nos résultats et méthodes sont discutés au regard des besoins en préconisation et amélioration variétale. Des pistes de recherche complémentaires et des améliorations ont aussi été suggérées. / In a context of climate change, the characterization of wheat varieties in response to drought and heat stress events is one of the major challenges of agriculture. This PhD thesis, resulting from a private-public partnership between Arvalis ‘Institut du Végétal’, Biogemma and INRA (“Institut National de la Recherche Agronomique”), aimed at providing necessary knowledge and tools to identify drought or heat-tolerant varieties and breed for varieties that meet these requirements. Analyses were conducted using a panel of 220 commercial varieties, genotyped with 280K SNP and tested in 35 environments (combination of year, location and water regime) and an experiment under controlled conditions where heat stress was applied during grain filling. The complexity of the study of drought and heat tolerance led us to present this thesis by first separating hydric and thermal stresses, and then to explore a multi-stress analysis method. Even if breeding has improved the performance of varieties under optimal water conditions, we showed that genetic progress must be accelerated and better distributed according to different stress scenarios. We propose several genetic determinants that could allow genetic gain in stressful environments. Our results and methods are discussed in view of the needs for varietal recommendation and improvement. Additional research strategies and methods improvements were also suggested.

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The genes that regulate pubertal timing in the general population are not well understood. Recently, genome-wide association studies have demonstrated that genetic variants near LIN28B associate with variation in pubertal timing in humans. To investigate where within the hypothalamic-pituitary-ovarian (HPO) axis Lin28b, and its homologue Lin28a, regulate pubertal timing, expression of these genes was assessed across the pubertal transition. The finding that Lin28a/b expression decreases only in the ovary suggests that the Lin28 pathway may exert its regulatory effects with respect to puberty in the ovary. Another aim of this thesis was to examine the effect of estrogen on Lin28b expression in immortalized GnRH neuronal cells, but the data remains equivocal and detailed future studies are needed to make definitive conclusions. The ovarian expression data lay the foundation for further studies using conditional knockout mice to verify the importance of the tissue and age specific developmental pattern that was identified.

The genes that regulate pubertal timing in the general population are not well understood. Recently, genome-wide association studies have demonstrated that genetic variants near LIN28B associate with variation in pubertal timing in humans. To investigate where within the hypothalamic-pituitary-ovarian (HPO) axis Lin28b, and its homologue Lin28a, regulate pubertal timing, expression of these genes was assessed across the pubertal transition. The finding that Lin28a/b expression decreases only in the ovary suggests that the Lin28 pathway may exert its regulatory effects with respect to puberty in the ovary. Another aim of this thesis was to examine the effect of estrogen on Lin28b expression in immortalized GnRH neuronal cells, but the data remains equivocal and detailed future studies are needed to make definitive conclusions. The ovarian expression data lay the foundation for further studies using conditional knockout mice to verify the importance of the tissue and age specific developmental pattern that was identified.

The existence of new technologies, implemented in efficient platforms and workflows has made massive genotyping available to all fields of biology and medicine. Genetic analyses are no longer dominated by experimental work in laboratories, but rather the interpretation of the resulting data. When billions of data points representing thousands of individuals are available, efficient computational tools are required. The focus of this thesis is on developing models, methods and implementations for such tools. The first theme of the thesis is multi-dimensional scans for quantitative trait loci (QTL) in experimental crosses. By mating individuals from different lines, it is possible to gather data that can be used to pinpoint the genetic variation that influences specific traits to specific genome loci. However, it is natural to expect multiple genes influencing a single trait to interact. The thesis discusses model structure and model selection, giving new insight regarding under what conditions orthogonal models can be devised. The thesis also presents a new optimization method for efficiently and accurately locating QTL, and performing the permuted data searches needed for significance testing. This method has been implemented in a software package that can seamlessly perform the searches on grid computing infrastructures. The other theme in the thesis is the development of adapted optimization schemes for using hidden Markov models in tracing allele inheritance pathways, and specifically inferring haplotypes. The advances presented form the basis for more accurate and non-biased line origin probabilities in experimental crosses, especially multi-generational ones. We show that the new tools are able to reconstruct haplotypes and even genotypes in founder individuals and offspring alike, based on only unordered offspring genotypes. The tools can also handle larger populations than competing methods, resolving inheritance pathways and phase in much larger and more complex populations. Finally, the methods presented are also applicable to datasets where individual relationships are not known, which is frequently the case in human genetics studies. One immediate application for this would be improved accuracy for imputation of SNP markers within genome-wide association studies (GWAS). / eSSENCE

Les études d'association sur un génome complet (GWAS) sont conçues pour découvrir les combinaisons de points de polymorphisme (SNP) associées à des maladies. La découverte de ces associations permet d'élaborer de meilleures stratégies pour détecter, traiter ou prévenir les maladies. Récemment, l'utilisation de techniques d'extraction de patterns discriminatif a été investiguée dans le cadre de problématiques GWAS. Toutefois, la découverte de combinaisons de SNP dans de grands jeux de données GWAS est encore difficile à cause de la complexité des algorithmes utilisés. La thèse se propose donc d'améliorer l'état de l'art des approches d'extraction de motifs discriminants, dans le cadre d'extraction de combinaisons de SNP corrélées à un phénotype d'intérêt. Plusieurs solutions ont été proposées, s'attaquant aux problèmes majeurs en GWAS : évaluation de la force d'association, découverte efficace de combinaisons de SNP et visualisation de ces combinaisons. Les approches proposées sont également prometteuses pour d'autres tâches de bioinformatique comme la découverte d'expressions génique, la détection de motifs de phosphorylation et la détection de motifs de régulation. / Discovering high-order SNP combinations associated with diseases is an important task of bioinformatics. Once new genetic associations are identified, they can be used to develop better trategies to detect, treat and prevent the diseases. Recently, this issue has been effectively tackled with discriminative pattern mining algorithms. However, the number of SNPs is often very large, discovering of SNP combinations remains many challenges. To address these challenges this thesis has been advanced the state-of-the-art discriminative pattern mining techniques to discover SNP combinations associated with interesting phenotype. Different solutions have been proposed in this thesis to tackle GWAS analysis. These solutions focus on efficient association strength evaluation, statistically significant discriminative SNP combinations discovery and interesting SNP combinations visualization. The solutions proposed in this thesis are also promising for other tasks of bioinformatics such as differential gene expression discovery, phosphorylation motifs detection and regulatory motif combination mining.

4 Abstract The prevalence of obesity and its related cardiometabolic complications in children remains high across the world. Obesity is a multifactorial disease caused by interaction between genes and environmental factors. Genome-wide association studies have discovered several single nucleotide polymorphisms associated with obesity. A causal role of infection in the pathogenesis of obesity has also been considered, particularly the role of adenovirus 36 (Adv36). The aim of the Ph.D. thesis was to investigate the associations of obesity susceptibility loci (TMEM18, SH2B1, KCTD15, PCSK1, BDNF, SEC16B, MC4R, FTO) and Adv36 infection with obesity-related characteristics and complications in the Czech adolescent population. The results are described in eight publications, of which six are original papers and two are reviews. Studies were performed on a cohort of Czech adolescents recruited either from the general population (1,533 individuals from the epidemiological study) and from in-patient or outpatient weight management clinics (562 overweight/obese individuals underwent an intervention). The results demonstrated an association of TMEM18, SEC16B and FTO gene variants with obesity. Some variants of the genes involved in hypothalamic regulation of energy homeostasis − MC4R, BDNF, PCSK1 − were related to...

Over the last ten years, genome-wide association studies (GWAS) have been used to identify genetic variants associated with many diseases as well as quantitative phenotypes, by exploiting naturally occurring genetic variation in large cohorts of individuals. More recently, the GWAS approach has also been applied to highthroughput RNA sequencing (RNA-seq) data in order to find loci associated with different levels of gene expression, called expression quantitative trait loci (eQTL). Because of the large amount of data that is required for such high-resolution eQTL studies, most of them have so far been carried out in humans, where the cost of data collection could be justified by a possible future impact in human health. However, due to the rapidly falling price of high-throughput sequencing it is now also becoming feasible to perform high-resolution eQTL studies in higher model organisms. This enables the study of gene regulation in biological contexts that have so far been beyond our reach for practical or ethical reasons, such as early embryonic development. Taking advantage of these new possibilities, we performed a high-resolution eQTL study on 80 inbred fruit fly lines from the Drosophila Genetic Reference Panel, which represent naturally occurring genetic variation in a wild population of Drosophila melanogaster. Using a 3′ Tag RNA-sequencing protocol we were able to estimate the level of expression both of genes as well as of different 3′ isoforms of the same gene. We estimated these expression levels for each line at three different stages of embryonic development, allowing us to not only improve our understanding of D. melanogaster gene regulation in general, but also investigate how gene regulation changes during development. In this thesis, I describe the processing of 3′ Tag-Seq data into both 3′ isoform expression levels and overall gene expression levels. Using these expression levels I call proximal eQTLs both common and specific to a single developmental stage with a multivariate linear mixed model approach while accounting for various confounding factors. I then investigate the properties of these eQTLs, such as their location or the gene categories enriched or depleted in eQTLs. Finally, I extend the proximal eQTL calling approach to distal variants to find gene regulatory mechanisms acting in trans. Taken together, this thesis describes the design, challenges and results of performing a multivariate eQTL study in a higher model organism and provides new insights into gene regulation in D. melanogaster during embryonic development.