Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods. © 2021 Society of Biological Psychiatry / National Institutes of Health / Revisión por pares

<p>Complex disorders are caused by multiple genetic, environmental, and lifestyle factors, and their interactions. Most human diseases are complex, including many psychiatric, autoimmune, neurodegenerative, and cardiovascular disorders. Understanding their genetic background is an essential step toward developing effective preventive and therapeutic interventions for these disorders. In this dissertation, we present an overview of state-of-the-art methodology that is used to help elucidate the genetic basis of complex diseases and apply these methods to understand the genetic background of different complex disorders. First, we carried out a GWAS for myasthenia gravis (MG), a rare autoimmune disorder, and detected a novel risk locus, AGRN, which encodes a protein, involved in neuromuscular junction activation. Additionally, we observed significant genetic correlation between MG and ADs, and variants with pleiotropic effects. Second, we explored the genetic and phenotypic relationships among 11 different autoimmune disorders (ADs), using GWAS results o to calculate polygenic risk scores (PRS) and performing a PRS- phenome-wide association study (PheWAS) analysis with 3,281 phenotypes available in the UK Biobank. We observed associations of ADs PRS with phenotypes in multiple categories, including lifestyle, biomarkers, mental and physical health. We also explored the shared genetic components among the ADs, through genetic correlation and cross-disorder meta-analysis approaches, where we</p> <p>identified pleiotropic variants among the correlated ADs. Finally, we performed a meta-analysis GWAS of Tourette Syndrome (TS) followed by post-GWAS analyses including biological annotation of the results, and association tests of TS PRS with brain volumes. We detected a novel locus, NR2F1, associated with TS, supported by eQTL and Hi-C data. TS PRS was significantly associated with right and left thalamus volumes and right putamen volume. Overall, our work demonstrates the power of GWAS and related methods to help disentangle the genetic basis of complex disease and provides important insights into the genetic basis of the specific disorders that are the focus of our studies.</p>

Les thiopurines sont des médicaments cytotoxiques et immunosuppresseurs largement prescrits, notamment dans les maladies inflammatoires chroniques de l’intestin (MICI). Ils représentent l’un des meilleurs exemples d’application clinique de la pharmacogénétique avec le dépistage du déficit en thiopurine S-méthyltransférase (TPMT), enzyme clé du métabolisme des thiopurines. La variabilité interindividuelle de la réponse à ces médicaments rend nécessaire leur optimisation thérapeutique. Ce travail de thèse a d’une part, analysé les relations entre activité TPMT et concentrations des métabolites thiopuriniques, et d’autre part, recherché des facteurs associés à la résistance aux thiopurines. A l’aide d’une base de données pharmacogénétiques hospitalière et d’une étude « PheWAS » à partir d’un entrepôt de données cliniques, nous avons analysé la distribution et la corrélation génotype-phénotype pour la TPMT, en lien avec les concentrations des métabolites thiopuriniques. Nous avons observé qu’une activité TPMT très élevée (phénotype « ultra-rapide ») était associée à des paramètres clinico-biologiques reflétant une maladie évolutive et un traitement inefficace dans les MICI. De plus, une étude clinique rétrospective dans les MICI pédiatriques a permis d’identifier des facteurs associés à la lymphopénie observée sous thiopurines. Enfin, à partir d’un modèle in vitro fondé sur des lignées cellulaires lymphoblastoïdes (LCL) sélectionnées, nous avons établi une signature transcriptomique, incluant 32 gènes, prédictive de la résistance aux thiopurines. Une analyse fonctionnelle bioinformatique a abouti à l’identification de voies métaboliques liées à la protéine p53 et au cycle cellulaire, ainsi que des mécanismes moléculaires associés à la résistance aux thiopurines. En conclusion, ce travail de thèse, qui a exploré la variabilité de réponse aux thiopurines et tout particulièrement la résistance à ces médicaments, propose des hypothèses pour l’individualisation et l’optimisation thérapeutique des thiopurines. / Thiopurines are cytotoxic and immunosuppressive drugs widely prescribed, mainly in inflammatory bowel disease (IBD). They constitute one of the best success story of pharmacogenetic implementation into clinical practice based on the screening of thiopurine S-methyltransferase (TPMT) deficiency, a key enzyme in thiopurine metabolism. Optimization of thiopurine response is challenging because of its large interindividual variability such as inefficacy and toxicities. This thesis has explored, on one hand, the relationships between TPMT activity and metabolite concentrations, and on the other hand, factors associated with thiopurine inefficacy. Using a primary care pharmacogenetic database, we first analyzed TPMT distribution and genotype-phenotype correlation, in relation with thiopurine metabolites in a large population. Using a PheWAS study based on a clinical data warehouse we then reported that a very high TPMT activity (“ultra-rapid” phenotype) was associated with parameters of active IBD and poor response to thiopurines. Furthermore, a retrospective study in pediatric IBD identified factors predicting the occurrence of lymphopenia during thiopurine therapy. Finally, using a lymphoblastoid cell line (LCL) in vitro model, we established a transcriptomic signature, including 32 genes predicting thiopurine cellular resistance. A bioinformatic functional analysis identified metabolic pathways in relation with p53 and cell cycle, as well as molecular mechanisms associated with thiopurine resistance. To conclude, this research work, focusing on the variability of thiopurine response and mainly therapeutic resistance, provides new hypotheses to individualize and optimize therapeutic response to thiopurines.

A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / Genetic association studies simplify genotype‐phenotype relationship investigation by considering only the presence of a given polymorphism and the presence or absence of a given downstream phenotype. Although such associations do not indicate causation, collections of phenotypes sharing association with a single genetic polymorphism may provide valuable mechanistic insights. In this thesis we explore such genetic pleiotropy with Deep Phenotype Association Studies (DeePAS) using data from the Age‐Related Eye Study 2 (AREDS2). We also employ a novel text mining approach to extract pleiotropic associations from the published literature as a hypothesis generation mechanism. Is it possible to identify pleiotropic genetic associations across multiple published abstracts and validate these in data from AREDS2? Data from the AREDS2 trial includes 123 phenotypes including AMD features, other ocular conditions, cognitive function and cardiovascular, neurological, gastrointestinal and endocrine disease. A previously validated relationship extraction algorithm was used to isolate descriptions of genetic associations with these phenotypes in MEDLINE abstracts. Results were filtered to exclude negated findings and normalize variant mentions. Genotype data was available for 1826 AREDS2 participants. A DeePAS was performed by evaluating the association between selected SNPs and all available phenotypes. Associations that remained significant after Bonferroni‐correction were replicated in AREDS. LitWAS analysis identified 9372 SNPs with literature support for at least two distinct phenotypes, with an average of 3.1 phenotypes/SNP. PheWAS analyses revealed that two variants of the ARMS2‐HTRA1 locus at 10q26, rs10490924 and rs3750846, were significantly associated with sub‐retinal hemorrhage in AMD (rs3750846 OR 1.79 (1.41‐2.27), p=1.17*10‐7). This associated remained significant even in populations of participants with neovascular AMD. Furthermore, odds ratios for the development of sub‐retinal hemorrhage in the presence of the rs3750846 SNP were similar between incident and prevalent AREDS2 sub‐populations (OR: 1.94 vs 1.75). This association was also replicated in data from the AREDS trial. No literature‐defined pleiotropic associations tested remained significant after multiple‐testing correction. The rs3750846 variant of the ARMS2‐HTRA1 locus is associated with sub‐retinal hemorrhage. Automatic literature mining, when paired with clinical data, is a promising method for exploring genotype‐phenotype relationships.

En étudiant les variations génétiques au sein d'une population, il est possible d'identifier des polymorphismes génétiques qui confèrent une protection naturelle contre la maladie. Si l'on parvient à comprendre le mécanisme moléculaire qui sous-tend cette protection, par exemple en reliant la variation génétique à la perturbation d'une protéine bien précise, il pourrait être possible de développer des thérapies pharmacologiques qui agissent sur la même cible biologique. Cette relation entre les médicaments et les variations génétiques est une des prémisses centrales de la validation génétique de cibles pharmacologiques qui est un facteur de réussite dans le développement de médicaments. Dans cette thèse, nous utiliserons un modèle génétique pour prédire les effets bénéfiques et indésirables de l'ivabradine, un médicament utilisé afin de réduire la fréquence cardiaque. L'ivabradine est un inhibiteur du canal ionique potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel 4, encodé par le gène HCN4, dont les bénéfices sont hétérogènes chez différentes populations de patients. Ce médicament est efficace pour le traitement de l'angine et de l'insuffisance cardiaque, mais s'est avéré inefficace en prévention secondaire chez des patients coronariens stables sans dysfonction systolique. La caractérisation des effets de l'ivabradine s'est échelonnée sur une période de 6 ans et trois grands essais de phase III ont été menées. Nous étudierons la possibilité d'avoir prédit ou accéléré ce processus à l'aide de modèles génétiques et nous contrasterons les effets spécifiques à l'ivabradine des effets généraux de la réduction de la fréquence cardiaque par une approche de randomisation mendélienne. Deuxièmement, une approche génétique sera utilisée pour évaluer l'effet de l'inhibition de la cholesteryl ester tranfer protein (CETP), une enzyme responsable du transfert des cholestérols estérifiés et des triglycérides entre différentes lipoprotéines ainsi qu'une cible pharmacologique largement étudiée pour le traitement de la maladie coronarienne. Les études génétiques prédisent un bénéfice à l'inhibition de CETP, mais les essais randomisés ont eu des résultats hétérogènes et décevants. Nous utiliserons un modèle génétique d'inhibition de la CETP pour identifier des variables qui peuvent moduler l'effet de l'inhibition de la CETP sur des biomarqueurs et la maladie ischémique. Les biomarqueurs pris en compte comprennent les taux de cholestérol à lipoprotéines de basse et haute densité, mais aussi la capacité du plasma à absorber le cholestérol, une mesure fonctionnelle importante et sous-étudiée. Le sexe et l'indice de masse corporelle se sont avérés être deux variables qui modifient fortement les effets d'une réduction génétiquement prédite de la concentration de CETP sur les paramètres étudiés. Notre modèle prédit un bénéfice plus important de l'inhibition de la CETP pour les femmes et les individus ayant un indice de masse corporelle normal sur le profil lipidique, mais nous n'avons pas pu démontrer une modulation de l'effet sur la maladie ischémique. Cette étude reste importante sur le plan méthodologique, car elle soulève la possibilité d'utiliser des modèles génétiques de cibles pharmacologiques pour prédire l'hétérogénéité dans la réponse au médicament, une lacune des essais randomisés classiques. Enfin, nous avons adopté une approche centrée sur les gènes pour caractériser l'effet de 19 114 protéines humaines sur 1 210 phénotypes de la UK Biobank. Les résultats de cette étude sont accessibles au public (https://exphewas.statgen.org/) et constituent une ressource précieuse pour cerner rapidement les conséquences phénotypiques associées à un locus. Dans le contexte de validation de cibles pharmacologiques, cette plate-forme web peut aider à rapidement identifier les problèmes de sécurité potentiels ou à découvrir des possibilités de repositionnement du médicament. Un exemple d'utilisation de cette plate-forme est présenté où nous identifions le gène de la myotiline comme un nouvel acteur potentiel dans la pathogénèse de la fibrillation auriculaire. / Using population-level data, it is possible to identify genetic polymorphisms that confer natural protection against disease. If the molecular mechanism underlying this protection can be understood, for example by linking variants to the disruption of a particular protein, it may be possible to develop drugs that act on the same biological target. This link between drugs and variants is a central premise of genetic drug target validation. In this work, a genetic model is used to predict the beneficial and adverse effects of ivabradine, a drug used to lower heart rate. Ivabradine is an inhibitor of the ion channel potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel 4, encoded by the HCN4 gene, with heterogeneous benefits in different patient populations. This drug is effective in the treatment of angina and heart failure but it is ineffective in patients with stable coronary artery disease without systolic dysfunction. Characterization of the effect of ivabradine has occurred over a 6-year period and three large phase III trials have been conducted. We will investigate whether this process could have been streamlined using genetic models and contrast the ivabradine-specific effect with the general effect of heart rate reduction using a Mendelian Randomization approach. Second, a genetic approach is used to study the effect of inhibiting cholesteryl ester tranfer protein (CETP), an enzyme responsible for the transfer of cholestery esters and triglycerides between different lipoproteins and a widely studied drug target for the treatment of coronary artery disease. Genetic studies predict a benefit of CETP inhibition, but randomized trials yielded heterogeneous and disappointing results. We will use a genetic model of CETP inhibition to identify variables that may modulate the effect of CETP inhibition on biomarkers and ischemic disease. The biomarkers we considered included low- and high-density lipoprotein cholesterol levels but also the plasma cholesterol efflux capacity, an important and understudied functional measure of high density lipoproteins. Sex and body mass index strongly modulated the effect of a genetically predicted lower CETP concentration on the lipid profile. Our model predicts a greater benefit of CETP inhibition in women and individuals with normal body mass index on the lipid profile, but these observations did not translate to changes in the effect on cardiovascular outcomes. This study remains methodologically important because it demonstrates the possibility of using genetic models of drug targets to predict heterogeneity in drug response, a shortcoming of conventional randomized trials. Finally, we adopted a gene-centric approach to characterize the effect of 19,114 human protein-coding genes on 1,210 UK Biobank phenotypes. The results of this study are publicly available (https://exphewas.statgen.org/) and provide a valuable resource to rapidly screen the phenotypic consequences associated with a gene. In the context of drug target validation, this platform can help quickly identify potential safety issues or discover drug repurposing opportunities. An example of the use of this platform is presented where we identify the myotilin gene as a potential atrial fibrillation gene.