No description available.

La génétique, et par extension le séquençage de l’ADN, sont des outils qui ont transformé la compréhension des mécanismes impliqués dans la survenue de nombreuses pathologies, dont l’obésité. Les technologies aujourd’hui à notre disposition nous permettent de déterminer rapidement si un patient est ou non porteur d’un évènement génétique pouvant expliquer sa pathologie. L’une des techniques les plus utilisées en diagnostic aujourd’hui est le séquençage d’exome, ou WES, qui permet une excellente détection des mutations ponctuelles dans les régions codantes du génome. Mais d’autres évènements comme les copy number variants, ou CNV, peuvent également expliquer certaines pathologies, dont l’obésité, via entre autres les CNV de la région 16p11.2. Actuellement, la technique de référence pour la détection de ces copy number variants est l’analyse de puces CGH (Comparative Genomic Hybridization), mais celles-ci ne permettent pas de détecter des mutations non répertoriées au préalable lors de la création de la puce. Sur le principe, le séquençage d’exome peut lui aussi être utilisé pour détecter les CNV, mais son absence de couverture des régions non codantes du génome ne permet pas une détection efficace de ces CNV, car ceux-ci peuvent survenir sur l’ensemble du génome, en englobant des régions codantes et non codantes au sein d’un seul évènement. Le séquençage génome complet peut détecter ces deux types d’évènement, mais son cout est encore élevé ce qui freine sa démocratisation, et l’analyse de données associées nécessite d’importantes ressources informatiques, et le rend difficilement utilisable en diagnostic de routine en l’état actuel des choses. Il est donc pour l’instant nécessaire d’avoir recours à deux techniques différentes pour couvrir ces deux types d’évènements génétiques. Cela implique d’utiliser des échantillons parfois très précieux à deux reprises, de supporter les couts liés à deux techniques diagnostiques (d’environ 450 euros pour le séquençage d’exome au laboratoire et un cout un peu plus élevé pour une puce à ADN dans un laboratoire clinique), et d’allonger les temps de rendu de résultats et donc la durée d’établissement du diagnostic du patient. Cet état de fait nous a conduit à développer une technique de séquençage, que nous avons nommé CoDE-seq (Copy number variation Detection and Exome sequencing), et qui permettra la détection simultanée de ces deux types d’évènements, pour diminuer les temps d’établissement de diagnostics, leurs couts, et la quantité d’échantillon nécessaire. Ce travail a nécessité deux aspects : la mise au point technique et la mise au point analytique. La mise au point technique est passée par la création d’une nouvelle « capture », permettant une détection correcte des mutations ponctuelles de l’exome et des CNV de tout le génome. La mise au point analytique a consisté à définir la méthode à employer, et à permettre d’arriver à une détection fiable, à la fois sensible et spécifique, des CNV sur l’ensemble du génome. Une fois ces CNV identifiés, la question de leur signification fonctionnelle se pose également, et une seconde partie de ma thèse porte sur l’étude de cette signification fonctionnelle, via l’étude de la conformation spaciale de la chromatine et de l’influence des CNV sur celle-ci. / Genetics, and by extention DNA sequencing, are tools that have modified the understanding of the mechanisms involved in genetic diseases, like obesity. Today’s technology has allowed us to rapidly find if a patient carries a genetic event that may explain his/her pathology. One of the most used technology for diagnostic is exome sequencing, or WES, which enables an excellent detection of point mutations in coding regions of the genome. However other events, such as copy number variations, or CNV, can also explain some pathologies, like a severe form of obesity due to CNV in the chr16p11.2 region. Actually, the gold standard method for an accurate detection of CNV is array CGH, but this technology cannot detect new point mutations. Exome sequencing can be used to detect CNV, but the lack of coverage in non-coding regions limits CNV detection sensitivity. Of note, whole genome sequencing can detect both CNVs and point mutations, but it is still very expensive and needs huge informatics capacities, which is an obvious limitation for a routine diagnostic use.For now, we have had to use two different methods in order to accurately detect both CNVs and point mutations. In other words, we have had to use precious samples two times, to assume the cost of two different methods (which is nearly 450 euros in the laboratory for exome sequencing, and a bit more for array CGH in a clinical laboratory), and to consider the time of the realization of two different methods in order to achieve a complete diagnostic.In this context, we aimed to develop an innovative sequencing method, named CoDE-seq (Copy number variation Detection and Exome sequencing), which would allow us to simultaneously detect both CNVs and point mutations, in order to reduce the time of diagnostic, the cost, and the needed quantity of sample.This work included the method conception, and the data analysis steps. The method conception has been done through the creation of a new capture enabling the detection of point mutations in the exome, and CNVs all along the genome. Furthermore, the data analysis step included the choice of the bioinformatics methods to be used, in order to get a specific and sensitive CNV detection, all along the genome.We were also interested in the fonctional significance of identified CNV, and tried to decipher it by the study of chromatine spacial conformation and the influence of these CNV.

Evolution frequently plays out over ecological timescales. Local adaptation under the joint action of evolutionary and ecological processes frequently leads to novel outcomes, as is evidenced by the theoretical work on adaptation at species' borders. However, to date this body of work does not have a theory for the effect of stochastic processes on local adaptation. The primary goal of this thesis is to show that demographic and genetic fluctuations can significantly impact upon local adaptation. In addition, the effect of polygenic evolution is also analysed. Specifically, three types of models are considered. First a deterministic mainland-island, subject to hard directional selection, maladaptive gene flow and density regulation is solved for two different trait architectures: an explicit multilocus trait and a quantitative trait. The maladaptive and adaptive steady states can be bistable. This depends on the underlying architecture of the trait, as well as locus number and ploidy. Sourcesink structure can emerge, accompanied by a novel, upper critical threshold above which maladaptation occurs. The most favourable condition for local adaptation occurs for few loci and low migration. Second, a stochastic version of the mainland-island model is analysed as a diffusion process. This is the central premise of the thesis and is explored by examining properties of the stationary distributions of both trait architectures, and the first-passage properties of the single locus case. It is found that across a range of migration rates that depend on locus number and migrant polymorphism, local adaptation may be reversed or escape from maladaptation becomes possible at varying transition rates. The diffusion model is compared to a similar discrete model. The continuous model is in good qualitative agreement with the discrete model. Third, the stochastic model is generalised to the infinite island model, which evolves deterministically. Under deterministic dynamics a range of equilibria are possible, depending on whether habitat size varies or is fixed. Multilocus dynamics restrict the conditions for polymorphism. Stochastic dynamics can have potentially detrimental consequences for the persistence of the island population when drift is strong. The relevance of the stochastic model to border populations is discussed. Although the diffusion process imposes severe constraints on the permissible parameter ranges, it is still able to provide a good qualitative understanding of the impact demographic and genetic fluctuations have on local adaptation.

No description available.

Breast cancer is a heterogeneous, polygenic disease and is influenced by genetic, environmental and life-style factors. Many single nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified in genome-wide association studies (GWASs) by several research groups for different populations. However, the variants identified so far contribute to a small proportion of disease risk. The objectives of the work described in this thesis were (i) to seek relevance/replicability of reported risk alleles from SNP scans to our study population; and (ii) to perform an independent GWAS for identification of additional/novel polymorphisms in the Albertan population. We approached these two end points by using cases and controls recruited in Alberta (total sample size, n=3064) in a two-stage association study (discovery study followed by replication study). We reproduced 14 of the 28 variants reported by others and also identified seven novel variants associated with breast cancer risk in our study population.

Deficits in a number of cognitive domains have been associated with core symptoms of schizophrenia, including working memory, attention, motor skills, reaction time, episodic memory and executive function. Bipolar Disorder is also associated with cognitive impairment; however the level of impairment appears to be less severe than that seen in schizophrenia. A translocation (t(1;11)) containing the Disrupted-in-Schizophrenia 1 (DISC1) gene has been found to be highly associated with schizophrenia, bipolar disorder and major depressive disorder. As such, this gene has been the focus of much research and to date DISC1 has been found to be associated with brain development, brain structure and the glutamate system - all key factors in current models of schizophrenia and affective disorders. The aim of this PhD is to identify cognitive domains that are differentially impaired or unimpaired in a large Scottish family, some of whom carry this rare DISC1 variant, a balanced translocation (t (1;11) (q 42; q14.3)), that segregates with schizophrenia and affective disorders, as well as psychiatric patients with schizophrenia and bipolar disorder and healthy control subjects. All participants have undergone standardised cognitive assessments to measure premorbid I.Q. (NART), current I.Q. (WASI) verbal memory, working memory, verbal fluency, processing speed, motor skills, executive function (BACS) and selected CANTAB tasks to assess simple and five-choice reaction time. Polygenic risk profile scores and self-report questionnaire data have also been investigated. Results indicate an impact of the DISC1 t(1;11) translocation on general intelligence and attention and processing speed. Significant differences were also identified between DISC1 t(1;11) carriers and non-carriers on self-report questionnaire data. Mean scores for polygenic risk for bipolar disorder were significantly different between DISC1 t(1;11) carriers and non-carriers and polygenic risk for schizophrenia was significantly associated with symptom severity, as measured by the Positive and Negative Symptom Scale (PANSS). Within the patient groups, a measure of processing speed (the token motor task) was found to be significantly different between those with schizophrenia and bipolar disorder and there was also a trend for attention and processing speed. As expected, I.Q. was significantly different between patients and control participants. Clinical ratings were significantly associated with neuropsychological and self-report measures. Polygenic risk for major depressive disorder was found to be significantly associated with impaired general intelligence (current IQ) and slowed reaction time in patients who were not currently depressed, suggesting there may be genetic risk markers in this population which impact on cognition. This is a novel finding and further suggests the possibility of a biological component related to the genetics of depression. In conclusion, and in line with the literature, psychosis has a negative impact on cognition with reduced performance across several neuropsychological tasks between patient groups, with schizophrenia patients performing worse than patients with bipolar disorder and both patient groups performing worse than healthy control participants. Cognition is markedly more impaired in DISC1 t(1;11) translocation carriers and especially in those with psychosis. The DISC1 t(1;11) translocation and psychosis may therefore confer a “double hit” on cognition - in addition to psychosis itself - which is known to impair cognitive function, significantly increasing the level of cognitive impairment and increasing the risk for psychosis in general.

Alcohol use (AU) and alcohol use disorder (AUD) are leading causes of morbidity, premature death, and economic burden. They are also associated with high levels of disability and many other negative outcomes. Twin and family studies have consistently shown that AU and AUD are complex traits influenced by both genetic and environmental factors. Although much has been learned about the genetic and environmental etiology of AU and AUD, significant gaps remain. These include the need for a more comprehensive understanding of the roles of risk and protective factors, and the nature of developmental trajectories underpinning the progression from AU to AUD. The aims of this dissertation are: (1) to examine the roles of resilience and personality disorders in the etiology of AU and AUD; (2) to investigate the nature of longitudinal changes in genetic and environmental risk factors responsible for individual differences in AU; and (3) to determine the moderating roles of key environmental risk factors on the impact of aggregate molecular, or polygenic, risk for AU during adolescence. Using both biometrical behavioral genetic and molecular genetic methodologies, five key findings were observed: (1) Resilience is strongly associated with a reduction in risk for AUD, and this relationship appears to be the result of overlapping genetic and shared environmental influences; (2) Borderline and antisocial personality disorders are the strongest and most stable personality pathology predictors of the phenotypic and genotypic liability to AU and AUD across time; (3) Genetic influences on the development of AUD from early adulthood to mid-adulthood are dynamic, whereby two sets of genetic risk factors contribute to AUD risk; (4) The specific genetic influences on AU follow an unfolding pattern of growth over time, whereas unique environmental risk factors are consistent with an accumulation of environmental impacts and risks across time; and (5) High peer group deviance and low parental monitoring are associated with increased AU, while early parental monitoring moderates the polygenic risk for AU at age 20. The implications of these results with regard to prevention and intervention efforts are discussed.

By controlling the inheritance of sex, the sex determination mechanism constrains sex allocation strategies and sex ratio adaptation; however, sex ratio selection also influences the evolution of sex determination mechanisms. Much of the sex determination literature focuses on how sex determination mechanisms transition between genetic and environmental factors (i.e. GSD vs. ESD), and if genetic sex factors are involved, how many (e.g. chromosomal vs polygenic systems). The study of sex allocation largely focuses on deviations in sex ratio from a theoretically 1:1 evolutionarily stable strategy, such as when sex ratios reflect ‘cost’ differences between the sexes. Tigriopus californicus is a tidepool copepod with polygenic sex determination, and shows wide variability in sex ratios in the field and lab that cannot be explained by genetic and stochastic processes alone, which suggests that an environmental variable might influence sex ratio. Females and their offspring were fed diets of different nutritional quality in a crossed design, and the sex ratio of each clutch was recorded for up to 8 clutches from a given female: this design allowed the influence of female diet vs. that of her offspring to be distinguished. The clutch sex ratio changed over the laying order according to the offspring’s diet, which is evidence for environmental sex determination in this species. Sex ratio also showed the influence of maternal diet, consistent with sex allocation theory. While dietary carotenoids showed no association with sex ratio or clutch size, long chain polyunsaturated fatty acids (particularly EPA and DHA) were implicated as the agent of sex ratio effect, providing a direction for future studies. The situation of T. californicus at the intersection of major themes in sex evolution makes this system an ideal model for selection studies. / Graduate / 2018-09-13

Indiana University-Purdue University Indianapolis (IUPUI) / Determining an individual’s genetic susceptibility in complex diseases like Alzheimer’s disease (AD) is challenging as multiple variants each contribute a small portion of the overall risk. Polygenic Risk Scores (PRS) are a mathematical construct or composite that aggregates the small effects of multiple variants into a single score. Potential applications of PRS include risk stratification, biomarker discovery and increased prognostic accuracy. A systematic review demonstrated that methodological refinement of PRS is an active research area, mostly focused on large case-control genome-wide association studies (GWAS). In AD, where there is considerable phenotypic and genetic heterogeneity, we hypothesized that PRS based on endophenotypes, and pathway-relevant genetic information would be particularly informative. In the first study, data from the NIA Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to develop endophenotype-based PRS based on amyloid (A), tau (T), neurodegeneration (N) and cerebrovascular (V) biomarkers, as well as an overall/combined endophenotype-PRS. Results indicated that combined phenotype-PRS predicted neurodegeneration biomarkers and overall AD risk. By contrast, amyloid and tau-PRSs were strongly linked to the corresponding biomarkers. Finally, extrinsic significance of the PRS approach was demonstrated by application of AD biological pathway-informed PRS to prediction of cognitive changes among older women with breast cancer (BC). Results from PRS analysis of the multicenter Thinking and Living with Cancer (TLC) study indicated that older BC patients with high AD genetic susceptibility within the immune-response and endocytosis pathways have worse cognition following chemotherapy±hormonal therapy rather than hormonal-only therapy. In conclusion, PRSs based on biomarker- or pathway- specific genetic information may provide mechanistic insights beyond disease susceptibility, supporting development of precision medicine with potential application to AD and other age-associated cognitive disorders.

Genome-wide Association Studies (GWAS) is the study used to associate common variants and phenotypes and has uncovered thousands of disease-associated variants. However, there is limited research on the contribution of a rare variant. The UK Biobank (UKB) contains detailed medical records and genetic information for nearly 500,000 individuals and offers a great opportunity for genetic association studies on rare variants. Here we focused on the role of rare protein-coding variants on UKB phenotypes. We selected three diseases for analysis: breast cancer, hypothyroidism and type II diabetes. We defined criteria for qualifying variants and pruned the control group to reduce interference signals from similar phenotypes. We identified the most known biomarkers for those diseases, such as BRCA1 and BRCA2 gene for breast cancer, TG and TSHR gene for hypothyroidism and GCK for type II diabetes. This result supports the model validity and clarifies the contribution of rare variants to diseases. Moreover, we also tried the geneset based collapsing method to aggregate information across genes to strengthen the signal from rare variants and build a diagnosis model that only relies on the genetic information. Our model could achieve great performance with an AUC of more than 20% improvement for type II diabetes and breast cancer and more than 90% accuracy for hypothyroidism.