Patients with schizophrenia and their siblings typically show subtle changes of brain structures, such as a reduction of hippocampal volume. Hippocampal volume is heritable, may explain a variety of cognitive symptoms of schizophrenia and is thus considered an intermediate phenotype for this mental illness. The aim of our analyses was to identify single-nucleotide polymorphisms (SNP) related to hippocampal volume without making prior assumptions about possible candidate genes. In this study, we combined genetics, imaging and neuropsychological data obtained from the Mind Clinical Imaging Consortium study of schizophrenia (n = 328). A total of 743,591 SNPs were tested for association with hippocampal volume in a genome-wide association study. Gene expression profiles of human hippocampal tissue were investigated for gene regions of significantly associated SNPs. None of the genetic markers reached genome-wide significance. However, six highly correlated SNPs (rs4808611, rs35686037, rs12982178, rs1042178, rs10406920, rs8170) on chromosome 19p13.11, located within or in close proximity to the genes NR2F6, USHBP1, and BABAM1, as well as four SNPs in three other genomic regions (chromosome 1, 2 and 10) had p-values between 6.75×10−6 and 8.3×10−7. Using existing data of a very recently published GWAS of hippocampal volume and additional data of a multicentre study in a large cohort of adolescents of European ancestry, we found supporting evidence for our results. Furthermore, allelic differences in rs4808611 and rs8170 were highly associated with differential mRNA expression in the cis-acting region. Associations with memory functioning indicate a possible functional importance of the identified risk variants. Our findings provide new insights into the genetic architecture of a brain structure closely linked to schizophrenia. In silico replication, mRNA expression and cognitive data provide additional support for the relevance of our findings. Identification of causal variants and their functional effects may unveil yet unknown players in the neurodevelopment and the pathogenesis of neuropsychiatric disorders.

Le matériel génétique (l’ADN) d’un organisme contient l’information nécessaire à sa survie, sa croissance et sa reproduction. La perte de cette information affecte grandement la santé de l’organisme et cette altération est l’un des facteurs les plus courants dans le vieillissement ou le cancer. Quasiment toutes les cellules d’un organisme contiennent une copie de ce matériel génétique, communément appelé le génome, et font usage de plusieurs mécanismes pour en réparer les sections endommagées ainsi que pour le copier avec précision lors de la division cellulaire. Nous avons cherché à étudier les processus cellulaires qui maintiennent la stabilité génomique en inactivant systématiquement chacun des gènes avec la technique de criblage par CRISPR afin d’en étudier les rôles. Nous avons effectué ces criblages à l’échelle du génome dans des lignées cellulaires humaines en combinaison avec des perturbations chimiques dans le but d’identifier l’effet du traitement chimique ou le rôle de gènes qui exacerbent ou atténuent la perturbation. Nous nous sommes d’abord concentrés sur le resvératrol, une molécule initialement extraite de plantes qui a démontré des propriétés antivieillissement dans certains organismes modèles ainsi que la capacité d’inhiber la prolifération cellulaire. Notre criblage génétique a révélé que le resvératrol inhibait la réplication de l’ADN. En comparant les effets cellulaires du resvératrol à l’hydroxyurée, un agent connu pour causer du stress réplicatif, nous avons montré que ces deux traitements menaient à une diminution similaire de la progression de la fourche de réplication ainsi qu’à une activation de la signalisation en réponse au stress réplicatif. Nous avons également démontré que l’inhibition de la réplication de l’ADN dans les cellules humaines par le resvératrol est l’un des effets principaux de la molécule sur la prolifération cellulaire et ne requiert pas la présence de la déacétylase d’histone Sirtuin-1, protéine qui a été suggérée comme étant la cible principale du resvératrol pour son effet antivieillissement. Nous avons également étudié la perturbation d’un second processus cellulaire, soit le maintien des télomères. Ces séquences spéciales aux extrémités des chromosomes sont indispensables à la protection du génome et leur érosion graduelle est contrebalancée par l’activité enzymatique de la télomérase. Nous avons effectué un crible génétique par CRISPR à l’échelle du génome dans une lignée cellulaire dont nous avons inhibé la télomérase en utilisant BIBR1532, un inhibiteur spécifique de la télomérase. Nous avons découvert une forte interaction génétique entre la télomérase et C16orf72, un gène non-annoté que nous avons nommé TAPR1. Nous avons montré que les cellules déficientes en TAPR1 possèdent des niveaux élevés de la protéine p53, un facteur de transcription central à la réponse cellulaire aux dommages télomériques et aux dommages à l’ADN. Nous suggérons que TAPR1 agit comme un inhibiteur de la stabilité protéique de p53. En somme, ces travaux mettent en évidence la capacité des cribles génétiques CRISPR à approfondir nos connaissances sur le fonctionnement des processus de maintien de la stabilité génomique chez l’humain. / The genetic material (DNA) of an organism contains the necessary information for survival, growth and reproduction. Loss of this information strongly impacts the health of the organism and is the leading factor in aging and cancer. Almost all cells in an organism contain a copy of said genetic material (genome) and employ several mechanisms to repair any damaged section of the genome and to accurately copy it during cell division. We sought to understand the cellular processes by which cells maintain genome stability by systematically inactivating individual genes to uncover their role using pooled CRISPR-Cas9 screening. We employed genome-wide CRISPR screening in human cell lines in combination with specific chemical perturbations to identify gene deletions that enhance or suppress the phenotype of the chemical treatment, thereby shedding light on the effect of the treatment or the role of said enhancer/suppressor genes. We first focused on resveratrol; a small molecule first discovered in plants that has been suggested to extend lifespan in model organisms while also inhibiting cell proliferation ex vivo. Chemical-genetic screening pinpointed a role of resveratrol in inhibition of DNA replication. When we compared the cellular effects of resveratrol to hydroxyurea, a known inducer of replicative stress, we found that both treatments led to slower replication fork progression and activation of signaling in response to replicative stress. Importantly, we showed that the inhibition of DNA replication by resveratrol in human cells is a primary effect on cell proliferation and independent of the histone deacetylase Sirtuin-1, which has been implicated as the primary target in lifespan extension by resveratrol. We then studied the perturbation of a second cellular process, namely telomere maintenance. These specialized sequences at the termini of chromosomes are critical for the protection of chromosome ends and their erosion is counteracted by the enzymatic activity of telomerase. We performed a genome-wide CRISPR screen in cells that were concomitantly treated with a specific telomerase inhibitor, BIBR1532. We uncovered a strong genetic interaction between telomerase and a previously unannotated gene, C16orf72, which we named TAPR1. We found that TAPR1-depleted cells led to elevated p53 levels, a transcription factor central for the cellular response to telomeric and global DNA damage. We propose that TAPR1 is a negative regulator of p53 protein levels by promoting its turnover. Altogether, these studies highlight the power of CRISPR-Cas9 in genetic screening to uncover novel insight into the human genome stability maintenance network.

Le mélanome cutané est un cancer des cellules de la peau (mélanocytes) qui se situe, en France, au 11e rang des cancers les plus fréquents. Sa mortalité reste élevée lorsqu’il est diagnostiqué à un stade tardif. Ce cancer résulte de nombreux facteurs génétiques, environnementaux et des interactions entre ces facteurs. La susceptibilité génétique à ce cancer recouvre un large spectre de variabilité génétique, depuis des mutations rares conférant un risque élevé jusqu’à des variants fréquents conférant un risque modeste. C’est dans le cadre de l’identification de variants fréquents liés à l’apparition du mélanome et à son pronostic que se situe mon travail de thèse. À ce jour, les études d’associations pangénomiques du mélanome ont identifié des variants fréquents à effets relativement modestes qui expliquent seulement une part de la composante génétique. Les variants fonctionnels au sein des régions identifiées sont le plus souvent inconnus. Les études pangénomiques ont eu principalement recours à des analyses simple-marqueur qui peuvent manquer de puissance pour détecter des variants ayant un effet individuel faible ou interagissant avec d’autres variants. L’objectif principal de ce travail de thèse a été de proposer des stratégies d’analyse multi-marqueurs pour identifier de nouveaux gènes impliqués dans le mélanome et pour caractériser des variants potentiellement fonctionnels au sein des régions du génome associées au mélanome.Pour identifier de nouveaux gènes associés au risque de mélanome et à un facteur pronostique de ce cancer (l’indice de Breslow), nous avons proposé une stratégie d’analyse multi-marqueurs qui intègre une analyse de pathways biologiques basée sur la méthode GSEA (Gene Set Enrichment Analysis) et une analyse d’interactions entre gènes au sein des pathways associés au mélanome. Ces analyses ont été menées dans deux études : l’étude française MELARISK et l’étude américaine du MD Anderson Cancer Center (MDACC), totalisant 2 980 cas et 3 823 témoins. Nous avons identifié une interaction entre les gènes, TERF1 et AFAP1L2, pour le risque de mélanome et une interaction entre les gènes, CDC42 et SCIN, pour l’indice de Breslow. Ces gènes sont particulièrement pertinents sur le plan biologique du fait de leur rôle dans la biologie des télomères pour la première paire de gènes et dans la dynamique des filaments d’actine pour la seconde paire. Afin d’identifier les variants potentiellement fonctionnels au sein des régions du génome mises en évidence par études pangénomiques, nous avons proposé une stratégie de cartographie fine qui repose principalement sur une méthode de régression pénalisée (méthode HyperLasso) appliquée à tous les variants de la région étudiée. Par l’analyse de la région 16q24 qui contient le gène MC1R dont les variants fonctionnels sont connus, nous avons montré que cette stratégie était capable d’identifier ces variants parmi de nombreux variants associés au mélanome dans cette région. Nous avons contribué à identifier cinq nouvelles régions du génome associées au mélanome par méta-analyse d’études pangénomiques réalisées au niveau mondial (43 000 sujets) puis mené une étude de cartographie fine de toutes les régions associées au mélanome, en se basant sur la stratégie proposée et validée dans la région 16q24. Les stratégies d’analyses multi-marqueurs proposées dans le cadre de ce travail de thèse ont permis d’identifier de nouveaux gènes associés au risque de mélanome et à un facteur pronostique de ce cancer et de caractériser les variants génétiques potentiellement fonctionnels au sein des régions du génome identifiées par études pangénomiques. / Cutaneous melanoma is a skin cancer developed from melanocytes. It is the 11th most common cancers in France. Mortality due to melanoma remains high when diagnosed at a late stage. This cancer results from many genetic, environmental factors and interactions between these factors. The genetic susceptibility to melanoma covers a broad spectrum of genetic variation, from rare mutations conferring high risk to common variants conferring low risk. My thesis was conducted in the framework of low-risk variants associated with melanoma occurrence and prognosis. To date, genome-wide association studies (GWAS) of melanoma have identified common variants with relatively modest effects which only explain a part of the genetic component of this cancer. Functional variants at the identified loci are mostly unknown. GWASs have been mainly conducted using single-marker analysis which may be underpowered to detect variants with small effect or interacting with each other. The main objective of this thesis was to propose multi-marker analysis strategies to identify novel genes involved in melanoma and to characterize potentially functional variants in chromosomal regions found associated with melanoma. To identify new genes associated with melanoma risk and a prognostic factor for this cancer (Breslow thickness), we proposed a multi-marker analysis strategy which integrates pathway analysis based on the GSEA (Gene Set Enrichment Analysis) method and gene-gene interaction analysis within melanoma-associated pathways. These analyses were conducted in two studies: the French MELARISK study and the North-American MD Anderson Cancer Center (MDACC) study, with a total of 2,980 cases and 3,823 controls. We identified gene-gene interactions between TERF1 and AFAP1L2 genes for melanoma risk and between CDC42 and SCIN genes for Breslow thickness. These genes are biologically relevant because of their role in telomere biology for the former gene pair and in actin dynamics for the latter pair. To identify potentially functional variants at loci identified by GWAS, we proposed a fine mapping strategy which is mainly based on a penalized regression approach (HyperLasso method) that can be applied to all variants of the region under study. By studying the 16q24 region which harbors the MC1R gene whose functional variants are known, we showed this strategy was able to identify those variants among many variants associated with melanoma in this region. We contributed to the identification of five novel regions associated with melanoma through a worldwide meta-analysis of melanoma GWASs (43,000 subjects) and conducted fine mapping of all melanoma-associated loci using the strategy we proposed and validated in the 16q24 region. The multi-marker strategies proposed in this work have allowed identifying new biologically relevant genes associated with risk of melanoma and a major melanoma prognostic factor and characterizing potentially functional genetic variants within regions identified by GWAS.

Background: Imputation of partially missing or unobserved genotypes is an indispensable tool for SNP data analyses. However, research and understanding of the impact of initial SNP-data quality control on imputation results is still limited. In this paper, we aim to evaluate the effect of different strategies of pre-imputation quality filtering on the performance of the widely used imputation algorithms MaCH and IMPUTE. Results: We considered three scenarios: imputation of partially missing genotypes with usage of an external reference panel, without usage of an external reference panel, as well as imputation of ompletely un-typed SNPs using an external reference panel. We first created various datasets applying different SNP quality filters and masking certain percentages of randomly selected high-quality SNPs. We imputed these SNPs and compared the results between the different filtering scenarios by using established and newly proposed measures of imputation quality. While the established measures assess certainty of imputation results, our newly proposed measures focus on the agreement with true genotypes. These measures showed that pre-imputation SNP-filtering might be detrimental regarding imputation quality. Moreover, the strongest drivers of imputation quality were in general the burden of missingness and the number of SNPs used for imputation. We also found that using a reference panel always improves imputation quality of partially missing genotypes. MaCH performed slightly better than IMPUTE2 in most of our scenarios. Again, these results were more pronounced when using our newly defined measures of imputation quality. Conclusion: Even a moderate filtering has a detrimental effect on the imputation quality. Therefore little or no SNP filtering prior to imputation appears to be the best strategy for imputing small to moderately sized datasets. Our results also showed that for these datasets, MaCH performs slightly better than IMPUTE2 in most scenarios at the cost of increased computing time.

Worldwide, alcohol is the third leading risk factor for disease burden, while its harmful use leads to 2.5 million deaths every year. Alcohol dependence (AD) is a complex disease, with devastating effects on individuals, families, and society. It is estimated that 76.3 million people worldwide have suffered from alcohol use disorders (AUD), including alcohol abuse and AD. Alcohol withdrawal or alcohol withdrawal symptom (AWS) refers to a cluster of symptoms that may occur when a heavy drinker suddenly stops or significantly reduces their alcohol intake. These symptoms can start as early as 2 h after the last drink, persist for weeks, and range from mild anxiety and shakiness to severe complications, such as seizures and delirium tremens. Family, twin, and adoption studies have indicated that genetic and environmental factors and their interactions contribute to the development of AD and related phenotypes, with a heritability coefficient of more than 0.5 for AD. Whole-genome linkage and candidate gene association studies have successfully identified several chromosome regions and genes that are related to AD and AWS. Furthermore, gene expression analysis, epigenetic studies, and genome-wide association studies (GWAS) have provided regions and loci for AWS. This chapter reviews the recent findings in genetic studies of AWS.

Over the last decade, development and application of a set of molecular genomic approaches based on the chromosome conformation capture method (3C), combined with increasingly powerful imaging approaches have enabled high resolution and genome-wide analysis of the spatial organization of chromosomes. The aim of this thesis is two-fold; 1), to provide guidelines for analyzing and interpreting data obtained from genome-wide 3C methods such as Hi-C and 3C-seq and 2), to leverage the 3C technology to solve genome function, structure, assembly, development and dosage problems across a broad range of organisms and disease models. First, through the introduction of cWorld, a toolkit for manipulating genome structure data, I accelerate the pace at which *C experiments can be performed, analyzed and biological insights inferred. Next I discuss a set of practical guidelines one should consider while planning an experiment to study the structure of the genome, a simple workflow for data processing unique to *C data and a set of considerations one should be aware of while attempting to gain insights from the data. Next, I apply these guidelines and leverage the cWorld toolkit in the context of two dosage compensation systems. The first is a worm condensin mutant which shows a reduction in dosage compensation in the hermaphrodite X chromosomes. The second is an allele-specific study consisting of genome wide Hi-C, RNA-Seq and ATAC-Seq which can measure the state of the active (Xa) and inactive (Xi) X chromosome. Finally I turn to studying specific gene – enhancer looping interactions across a panel of ENCODE cell-lines. These studies, when taken together, further our understanding of how genome structure relates to genome function.

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Introduction/Background: Guidelines from the World Health Organization currently recommend targeting a body mass index (BMI) between 18.5 and 24.9 kg/m2 based on the lowest risk of mortality observed in epidemiological studies. However, these recommendations are based on population observations and do not take into account potential inter-individual differences. We hypothesized that genetic and non-genetic differences in adiposity, anthropometric, and metabolic measures result in inter-individual variation in the optimal BMI. Methods: Genetic variants associated with BMI as well as related adiposity, anthropometric, and metabolic phenotypes (e.g. triglyceride (TG)) were combined into polygenic risk scores (PRS), cumulative risk scores derived from the weighted contributions of each variant. 387,692 participants in the UK Biobank were split by quantiles of PRS or clinical biomarkers such as C-reactive protein (CRP), and alanine aminotransferase (ALT). The BMI linked with the lowest risk of all-cause and cause-specific mortality outcomes (“nadir value”) was then compared across quantiles (“Cox meta-regression model”). Our results were replicated using the non-linear mendelian randomization (NLMR) model to assess causality. Results: The nadir value for the BMI–all-cause mortality relationship differed across percentiles of BMI PRS, suggesting inter-individual variation in optimal BMI based on genetics (p = 0.005). There was a difference of 1.90 kg/m2 in predicted optimal BMI between individuals in the top and bottom 5th BMI PRS percentile. Individuals having above and below median TG (p = 1.29×10-4), CRP (p = 7.92 × 10-5), and ALT (p = 2.70 × 10-8) levels differed in nadir for this relationship. There was no difference in the computed nadir between the Cox meta-regression or NLMR models (p = 0.102). Conclusions: The impact of BMI on mortality is heterogenous due to individual genetic and clinical biomarker level differences. Although we cannot confirm that are results are causal, genetics and clinical biomarkers have potential use for making more tailored BMI recommendations for patients. / Thesis / Master of Science (MSc) / The World Health Organization (WHO) recommends targeting a body mass index (BMI) between 18.5 - 24.9 kg/m2 for optimal health. However, this recommendation does not take into account individual differences in genetics or biology. Our project aimed to determine whether the optimal BMI, or the BMI associated with the lowest risk of mortality, varies due to genetic or biological variation. Analyses were conducted across 387,692 individuals. We divided participants into groups according to genetic risk for obesity or clinical biomarker profile. Our results show that the optimal BMI varies according to genetic or biomarker profile. WHO recommendations do not account for this variation, as the optimal BMI can fall under the normal 18.5 - 24.9 kg/m2 or overweight 25.0 – 29.0 kg/m2 WHO BMI categories depending on individual genetic or biomarker profile. Thus, there is potential for using genetic and/or biomarker profiles to make more precise BMI recommendations for patients.

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