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We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.

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.

We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.

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

A pigmentação humana é uma característica variável e complexa determinada por fatores genéticos e hormonais, exposição à radiação ultravioleta, idade, doenças, entre outros. Alguns polimorfismos em genes de pigmentação têm sido associados com a diversidade fenotípica de cor da pele, cabelo e olhos e em populações homogêneas. A técnica denominada Fenotipagem Forense pelo DNA (FDP) vem beneficiando a ciência forense em vários países e auxiliando investigações criminais por ser capaz de sugerir, com boa precisão, os possíveis fenótipos para as características externamente visíveis (EVCs) em amostras de origem desconhecida. No presente trabalho foram avaliadas as associações entre os SNPs presentes nos genes SLC24A5 (rs1426654; rs16960620; rs2555364), TYR (rs1126809) e ASIP (rs6058017) com cor de pele, cabelo e olhos em indivíduos da população brasileira para apontar o possível uso desses marcadores na prática forense em populações miscigenadas. Os voluntários responderam um questionário no qual fizeram a autodeclaração dessas características e estes dados foram usados para as comparações entre genótipos e fenótipos. Os resultados mostraram que para os SNPs rs2555364 e rs1426654 o alelo ancestral esteve associado com as características cor de pele negra, cabelos castanhos ou pretos e olhos castanhos. Além disso, o alelo ancestral do SNP rs6058017 foi significativamente associado com cor de pele negra e olhos castanhos. Inversamente, os alelos variantes destes SNPs são correlacionados com características de pigmentação clara para as EVCs avaliadas, corroborando os estudos prévios realizados em diferentes populações. Esses resultados mostram que a informação molecular pode ser útil para a inferência de EVCs, e a técnica de FDP é uma importante ferramenta para estudos forenses em amostra brasileira / Human pigmentation is a variable and complex trait determined by genetic and hormonal factors, exposure to ultraviolet radiation, age, diseases, among others. Some polymorphisms in pigmentation genes have been associated with the phenotypic diversity of skin, hair and eyes color in homogeneous populations. Forensic DNA Phenotyping (FDP) is benefiting forensic science in several countries, helping in criminal investigations due to its ability to suggest, with good accuracy, the possible phenotypes for externally visible characteristics (EVCs) in samples of unknown origin. Herein, we evaluated the associations between the SNPs present in the genes SLC24A5 (rs1426654; rs16960620; rs2555364), TYR (rs1126809) and ASIP (rs6058017) with skin, hair and eyes color in individuals of the Brazilian population in order to point out the possible use of these markers in forensic practice in admixed populations. The volunteers answered a questionnaire in which they self reported these characteristics for comparison between genotypes and phenotypes. The results showed that for the SNPs rs2555364 and rs1426654 the ancestral allele was associated with characteristics of black skin color, brown or black hair and brown eyes. In addition, the ancestral allele of the SNP rs6058017 was significantly associated with black skin color and brown eyes. Inversely, the variant alleles of these SNPs are correlated with fair pigmentation characteristics for the evaluated EVCs, corroborating the previous studies performed in different populations. These results show that molecular information may be useful for the inference of EVCs, and the FDP technique is an important tool for forensic studies in a Brazilian sample

Anorexia Nervosa (AN) and Bulimia Nervosa (BN) are diseases with considerable individual variation. Genetic background plays an important role in disease susceptibility and severity. To evaluate the relationship between certain genetic loci and diseases subtypes we genotyped and analysed evolution of selected clinical parameters. We investigated a group of 75 pacients with AN (1. study), 127 DSM-4 and ICD-10 diagnosed patients with AN and BN (2. study), and contributed to sample of 2907 AN patients in large GWAS study. Results from the 1st study support association of polymorphism -1438G/A in serotonine receptor 5-HT2A with AN and compare the results from other studies with metaanalyses. In next, polymorphism responsible for the serotonine neurotransmission (serotonine transporter 5-HTT, polymorphisms LPR and VNTR) the study shows different association trend of LPR with AN in Czech population compared to other studies. 5-HTT VNTR polymorphism had no observed association. The second study investigated the role of hemeoxygenase 1 (plays a pivotal role in metabolic stress protecting cells) in eating disorders, in interaction with enviromental stress. We investigated the usefulness of an aggregate measure of the risks of AN and BN that is based on genetic susceptibility loci and the added effect of...

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...