For complex traits, population genetic studies ask: to what extent do genetic variation and environmental variation influence, determine and predict phenotypic variation? More specifically, researchers ask two questions. First, how much of the phenotypic variation is genetic in origin? Second, if the genetic component of a trait has been ascertained, then by what mechanisms do the causal variants contribute to the genetic variation that impacts on the phenotype? Previous studies have indicated a polygenic structure for many complex traits, which means that the genetic variation in those traits is the result of the cumulative effect from hundreds or even thousands of genetic variants. To further decipher the polygenic genetic architecture of a complex trait, genetic studies aim to identify the number, the location in the genome, and the distribution of the effect sizes of causal variants, as well as their individual and interacting effects. Linkage analysis and genome-wide association studies (GWAS), either based on single variants or sets of variants categorized by functional annotations, can be applied to map the potentially causal variants in the genome. The identification of disease-associated loci, however, is only the starting point in identifying causal variants. Causal variants are usually difficult to distinguish from the large number of variants in linkage disequilibrium (LD) within the associated loci, and may be in incomplete LD with genotyped variants. Computational prediction integrated with multi-level ‘Omic’ data will help the prioritization of candidate causal variants, which then become important targets for experimental validation (Chapter 1). Major depressive disorder (MDD) is a complex trait, contributes the second most important burden to global disease. Both genetic and environmental components have been suggested for this disorder in previous studies, although a clear partitioning of the contribution of each component and the identification of major contributing components is yet to be achieved. In efforts to map causal genetic variants, genome-wide association studies of MDD have identified few significant associations so far. The polygenic architecture combined with the widespread clinical and genetic heterogeneity of MDD between populations may impede the identification of causal variants (Chapter 2). In this thesis, I will present three studies; the first study estimated the proportions of the phenotypic variation that are genetic or familial environmental in origin in two depression definitions(chapter 3), followed by two studies where distinct (non- GWAS) methods were used to identify candidate causal genetic variants for MDD (chapter 4,5). In detail, in chapter 3, a variance component analysis was applied to GS:SFHS (Generation Scotland: Scottish Family Health Study) to investigate the relative genetic and environmental contributions to diagnosed major depressive disorder (MDD) and self-declared depression (SDD). Models for MDD and SDD that simultaneously included genetic and environmental effects suggested that narrow-sense heritability could be inflated by the environments shared by nuclear family members. The most parsimonious models selected for both MDD and SDD included SNP and pedigree-associated genetic effects and the effect of the common environment of couples. In chapter 4, I integrated pathway analysis and multi-level regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS studies (GS:SFHS and PGC1-MDD). The NETRIN1 signalling pathway showed the most consistent association with MDD across the two samples. Polygenic risk scores (PRSs) from this pathway showed predictive accuracy better than whole-genome PRSs when using AUC statistics, logistic regression and the linear mixed model. In chapter 5, genome-wide Haplotype-block-based regional heritability mapping (HRHM) was applied to identify haplotype blocks significantly contributing to MDD. A haplotype block across a 24kb region within the TOX2 gene reached genotype-wide significance in GS:SFHS. Single-SNP and haplotype based association tests were used to localize the association signal within the region identified by HRHM, and demonstrated that five out of nine genotyped SNPs and two haplotypes were significantly associated with MDD. The results were replicated in the UK-Ireland group in PGC2-MDD. The brain expression of TOX2 and brain-specific LncRNA RP1-269M15.3 were also significantly regulated by MDD-associated SNPs within the identified haplotype block. The three studies highlight the value of the application of multiple population genetics and bioinformatics methods to multiple family-based and population-based cohorts in identification of risk factors for MDD.

<p>Systemic inflammation is one potential mechanism underlying the depression to cardiovascular disease (CVD) relationship. In addition, somatic rather than cognitive/affective symptoms of depression may be more predictive of poorer CVD outcomes due to systemic inflammation. However, the small existing literature in this area has yielded mixed results. Therefore, the present study aimed to examine longitudinal associations between depressive symptom clusters and inflammatory biomarkers implicated in CVD (i.e., interleukin-6, IL-6; and C-reactive protein, CRP) using data from the eIMPACT trial.<b> </b>In addition, race was examined as a moderator given findings from two previous studies. </p> <p>The eIMPACT trial was a phase II, single-center randomized controlled trial comparing 12 months of the eIMPACT intervention to usual primary care for depression. Participants were 216 primary care patients aged ≥ 50 years with a depressive disorder and CVD risk factors but no clinical CVD from a safety net healthcare system (<i>M_age</i> = 58.7 years, 78% female, 50% Black, <i>M</i><i>_education</i> = 12.8 years). Depressive symptoms clusters (i.e., somatic and cognitive/affective clusters) were assessed using the Patient Health Questionnaire-9 (PHQ-9). IL-6 and high-sensitivity CRP were assessed by the local clinical research laboratory using R&D Systems ELISA kits. Change variables were modeled in MPlus using a latent difference score approach. </p> <p>The results of this study were largely null. Very few associations between depressive symptom clusters and inflammatory biomarkers implicated in CVD were observed, and the detected relationships may be due to type I error. Similarly, only one association was observed for race as a moderator, and the detected relationship may be due to type I error. The present findings do not provide strong support for the longitudinal associations between depressive symptom clusters and inflammatory biomarkers implicated in CVD nor the moderating effects of race. However, the present findings do not rule out the possibility of these relationships given important study limitations, such as study design and power. Future prospective cohort studies with multiple waves of data collection are needed to determine the longitudinal associations between depression facets and various inflammatory biomarkers implicated in CVD. In addition, a biologically-based approach to identifying facets of depression – e.g., the endophenotype model – may provide a clearer understanding of the depression-inflammation relationship.</p>

No description available.

This thesis comprises research investigating explicit EM biases in MDD during acute depression and euthymia (i.e., clinical remission). First, a systematic review was conducted to investigate whether acutely depressed and euthymic MDD participants display an explicit EM bias. An ‘explicit EM bias’ was operationally defined to denote enhanced memory for negative or positive stimuli compared to matched healthy controls (HCs). Studies that were included in this systematic review investigated explicit EM using free recall and recognition memory paradigms. The main finding from this investigation was that acutely depressed MDD participants do not display an explicit EM bias. An unintended consequence of this investigation was the identification that research on explicit EM in MDD during euthymia is surprisingly sparse. Next, building upon the findings from our systematic review, we conducted an empirical investigation of explicit EM within a sample of well-characterized euthymic MDD participants compared to age/sex/gender/IQ-matched HCs. In this study, participants performed incidental encoding (i.e., emotional reactivity) and recognition memory tasks (separated by one week). These tasks employed emotionally-valent picture stimuli obtained from the International Affective Picture System. Results from this study revealed that, compared to matched HCs, euthymic MDD participants do not display an emotional reactivity or explicit EM bias. Taken together, the findings from this thesis suggest that explicit EM represents a sub-domain of cognition that may be unaffected in individuals with MDD. Our findings have important implications for the unified model of depression and may represent a basis upon which future research can build in an attempt to understand the nuanced cognitive phenotypes associated with MDD. / Thesis / Master of Science (MSc) / Major depressive disorder (MDD) is one of the most common mental disorders worldwide. It is estimated that over 10% of Canadians will experience MDD at some point in their lifetime. The symptoms of MDD include, among other things: depressed mood, loss of interest in regular daily activities, and impairments in cognition (e.g., attention, emotion, memory, etc.). Clinicians and researchers have argued for years that MDD is associated with negative cognitive biases, including increased attention to, and more accurate memory for, negative information; however, attention, emotion and memory are general forms of cognition, and the existence of cognitive biases for specific sub-domains of cognition in MDD are largely unknown. Given that MDD has a negative effect on emotion and memory, one potentially important sub-domain of cognition is explicit emotional memory (EM; i.e., conscious memory for emotionally-stimulating information). The purpose of the current thesis was to investigate whether MDD, during both the active (i.e., acute) and euthymic (i.e., clinically-remitted) stages, is associated with explicit EM biases compared to healthy volunteers. This thesis discusses how patterns of explicit EM may be important for our understanding of the development of MDD.

O surgimento e o uso crescente de novas tecnologias têm levado à produção e armazenamento de grandes volumes de dados biomédicos. Tais dados são provenientes de diferentes técnicas, armazenados em formatos de representação diversos e utilizados por diferentes ferramentas. Esta heterogeneidade representa um empecilho ao maior uso desses dados em abordagens integrativas de pesquisa como, por exemplo, a biologia sistêmica. Neste cenário, artefatos de modelagem conceitual, tais como ontologias, têm sido utilizados para organizar e integrar dados heterogêneos de uma forma coerente. A OBO Foundry representa, atualmente, o maior esforço no desenvolvimento de ontologias biomédicas de forma colaborativa. Dentre as ontologias desenvolvidas pela OBO Foundry, destaca-se Ontologia de Relacionamentos (RO-OBO). A RO-OBO provê definições formais para um conjunto de relacionamentos de propósito geral utilizados nas ontologias biomédicas e busca promover a criação de ontologias mais corretas e integráveis. Um perfil UML foi proposto para representar formalmente o conjunto de conceitos e relacionamentos existentes na RO-OBO. Este perfil permite desenvolver modelos UML utilizando os conceitos presentes nesta ontologia, bem como torna possível o desenvolvimento de suporte à validação sintática dos modelos criados em relação a um conjunto de restrições formalmente definidas. Adicionalmente, percebe-se na literatura que o suporte à integração de modelos UML e ontologias OBO, em particular as ontologias representadas na linguagem OBO File Format, é limitado. Neste sentido, este trabalho teve como objetivo geral investigar o suporte ao desenvolvimento de ontologias biomédicas na linguagem UML. De forma específica, investigou-se o desenvolvimento de um editor gráfico, chamado OBO-RO Editor, para o suporte à construção de ontologias utilizando o perfil UML proposto, bem como a integração de ontologias desenvolvidas utilizando UML e ontologias desenvolvidas na linguagem OBO File Format. De forma a atingir nossos objetivos, uma arquitetura de referência foi definida e um processo de desenvolvimento orientado a modelos foi utilizado. A arquitetura definida é composta por uma série de artefatos inter-relacionados os quais são transformados (semi) automaticamente em código de aplicação, possibilitando a obtenção de ciclos de desenvolvimento mais rápidos e confiáveis. O OBO-RO Editor disponibiliza um conjunto de elementos gráficos de modelagem definidos a partir do perfil UML proposto, bem como provê mecanismos para a validação sintática (semi) automática de uma ontologia desenvolvida segundo as restrições definidas neste perfil. Adicionalmente, o OBO-RO Editor também provê suporte à integração de modelos UML a outras ontologias da OBO Foundry, permitindo o reuso e o desenvolvimento menos propenso a erros de ontologias no domínio biomédico. / The development and increasing use of new technologies has resulted in the production and storage of a huge amount of biomedical data. These data are produced using different techniques, stored in different formats and consumed by different (software) tools. This heterogeneity hinders effective data usage in integrative research approaches, including systems biology. In this scenario, conceptual modeling artifacts, such as ontologies, have been used to organize and integrate heterogeneous data in a coherent manner. Nowadays, the OBO Foundry represents the most important effort for the collaborative development of ontologies in the biomedical domain. The OBO Relation Ontology (OBO-RO) can be considered one of the most relevant ontologies in the domain. This ontology provides formal definitions for a number of general purpose relationships used in biomedical ontologies, thus facilitating the integration of existing ontologies and the development of new ontologies in the domain. An UML profile has been proposed to formally define the different types of concepts and relationships provided by the OBO-RO. This profile enables the creation of UML models using such concepts and allows the development of support for the automatic validation of these models based on formal constraints. Additionally, the support for the integration between UML models and OBO ontologies, particularly ontologies represented using the OBO File Format, is limited. In this sense, this project aimed at investigating the support for the development of biomedical ontologies using UML. In particular, we investigated the development of a graphical editor, named OBO-RO Editor, to support ontology development using the proposed UML profile. Additionally, we also investigated the integration of ontologies developed using UML and ontologies developed using the OBO File Format. In order to achieve our goals, we have defined a reference architecture and a model-driven development process. The reference architecture consists of a number of related artifacts that are transformed to application code (semi) automatically. Such characteristic allowed us to obtain faster and more reliable development cycles. The OBO-RO Editor provides a number of graphical elements defined in the proposed UML profile for the modeling of biomedical ontologies and support the (semi) automatic syntactic validation of such ontologies against the contraints defined in the profile. Additionally, OBO-RO Editor also provides support for the integration of developed UML models and other OBO ontologies, allowing the reuse and the accurate development of biomedical ontologies.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-01T11:58:46Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_Jones_final.pdf: 3458864 bytes, checksum: 62e534b2f1fc2266364c4ce46ccf020a (MD5) / Made available in DSpace on 2016-07-01T11:58:46Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_Jones_final.pdf: 3458864 bytes, checksum: 62e534b2f1fc2266364c4ce46ccf020a (MD5) Previous issue date: 2015-08-28 / FACEPE / Model-Driven Development (MDD) é um paradigma que usa modelos como o principal artefato no processo de desenvolvimento de sistemas. Isto é, neste paradigma, modelos não são apenas artefatos de documentação, pois devem corresponder a códigos executáveis. Em projetos de Banco de Dados Espaciais (BDE), existem várias linguagens de modelagens (e.g., OMT-G, MADS, GeoProfile e UML-GeoFrame), as quais permitem representar características espaciais (e.g., Múltiplas Representações e Relacionamento Espacial) por meio de uma ferramenta do tipo Computer-Aided Software Engineering (CASE). Embora essas linguagens sejam bem exploradas e difundidas na literatura, constatou-se que estas têm deficiências para modelar e implementar integridade espacial a partir de entidades com múltiplas representações. Assim, de modo a avançar o estado da arte sobre projeto de BDE, este trabalho faz uma análise dos principais trabalhos relacionados, e, de modo a contribuir para superar as deficiências encontradas, propõe uma extensão espacial para a linguagem de modelagem Enhanced Entity Relationship (EER). Essa extensão é implementada na ferramenta EERCASE e avalidada por meio de uma análise comparativa com os principais trabalhos relacionados, evidenciando seus pontos fortes e fracos. / Model-Driven Development (MDD) is a paradigm that uses models as the primary artifact in the systems development process. That is, in this paradigm, models are not only documentation artifacts, since these should be the executable code. In Spatial Databases projects (SDB), there are several modeling languages (e.g., OMT-G, MADS, GeoProfile and UML-GeoFrame), which allow to represent spatial characteristics (e.g., Multiple Representations and Spatial Relationship) by means of a tool type Computer-Aided Software Engineering (CASE). Although these languages are better exploited and widespread in the literature, it was found that they have deficiencies in modeling and implement spatial integrity from entities with multiple representations. Thus, in order to advance the state of the art in SDB project, this paper analyzes the main works related and, in order to contribute to overcome the deficiencies, proposes a spatial extension for modeling language Enhanced Entity Relationship (EER). This extension is implemented in tool EERCASE and evaluated through a comparison with the main work related, highlighting their strengths and weaknesses.

O surgimento e o uso crescente de novas tecnologias têm levado à produção e armazenamento de grandes volumes de dados biomédicos. Tais dados são provenientes de diferentes técnicas, armazenados em formatos de representação diversos e utilizados por diferentes ferramentas. Esta heterogeneidade representa um empecilho ao maior uso desses dados em abordagens integrativas de pesquisa como, por exemplo, a biologia sistêmica. Neste cenário, artefatos de modelagem conceitual, tais como ontologias, têm sido utilizados para organizar e integrar dados heterogêneos de uma forma coerente. A OBO Foundry representa, atualmente, o maior esforço no desenvolvimento de ontologias biomédicas de forma colaborativa. Dentre as ontologias desenvolvidas pela OBO Foundry, destaca-se Ontologia de Relacionamentos (RO-OBO). A RO-OBO provê definições formais para um conjunto de relacionamentos de propósito geral utilizados nas ontologias biomédicas e busca promover a criação de ontologias mais corretas e integráveis. Um perfil UML foi proposto para representar formalmente o conjunto de conceitos e relacionamentos existentes na RO-OBO. Este perfil permite desenvolver modelos UML utilizando os conceitos presentes nesta ontologia, bem como torna possível o desenvolvimento de suporte à validação sintática dos modelos criados em relação a um conjunto de restrições formalmente definidas. Adicionalmente, percebe-se na literatura que o suporte à integração de modelos UML e ontologias OBO, em particular as ontologias representadas na linguagem OBO File Format, é limitado. Neste sentido, este trabalho teve como objetivo geral investigar o suporte ao desenvolvimento de ontologias biomédicas na linguagem UML. De forma específica, investigou-se o desenvolvimento de um editor gráfico, chamado OBO-RO Editor, para o suporte à construção de ontologias utilizando o perfil UML proposto, bem como a integração de ontologias desenvolvidas utilizando UML e ontologias desenvolvidas na linguagem OBO File Format. De forma a atingir nossos objetivos, uma arquitetura de referência foi definida e um processo de desenvolvimento orientado a modelos foi utilizado. A arquitetura definida é composta por uma série de artefatos inter-relacionados os quais são transformados (semi) automaticamente em código de aplicação, possibilitando a obtenção de ciclos de desenvolvimento mais rápidos e confiáveis. O OBO-RO Editor disponibiliza um conjunto de elementos gráficos de modelagem definidos a partir do perfil UML proposto, bem como provê mecanismos para a validação sintática (semi) automática de uma ontologia desenvolvida segundo as restrições definidas neste perfil. Adicionalmente, o OBO-RO Editor também provê suporte à integração de modelos UML a outras ontologias da OBO Foundry, permitindo o reuso e o desenvolvimento menos propenso a erros de ontologias no domínio biomédico. / The development and increasing use of new technologies has resulted in the production and storage of a huge amount of biomedical data. These data are produced using different techniques, stored in different formats and consumed by different (software) tools. This heterogeneity hinders effective data usage in integrative research approaches, including systems biology. In this scenario, conceptual modeling artifacts, such as ontologies, have been used to organize and integrate heterogeneous data in a coherent manner. Nowadays, the OBO Foundry represents the most important effort for the collaborative development of ontologies in the biomedical domain. The OBO Relation Ontology (OBO-RO) can be considered one of the most relevant ontologies in the domain. This ontology provides formal definitions for a number of general purpose relationships used in biomedical ontologies, thus facilitating the integration of existing ontologies and the development of new ontologies in the domain. An UML profile has been proposed to formally define the different types of concepts and relationships provided by the OBO-RO. This profile enables the creation of UML models using such concepts and allows the development of support for the automatic validation of these models based on formal constraints. Additionally, the support for the integration between UML models and OBO ontologies, particularly ontologies represented using the OBO File Format, is limited. In this sense, this project aimed at investigating the support for the development of biomedical ontologies using UML. In particular, we investigated the development of a graphical editor, named OBO-RO Editor, to support ontology development using the proposed UML profile. Additionally, we also investigated the integration of ontologies developed using UML and ontologies developed using the OBO File Format. In order to achieve our goals, we have defined a reference architecture and a model-driven development process. The reference architecture consists of a number of related artifacts that are transformed to application code (semi) automatically. Such characteristic allowed us to obtain faster and more reliable development cycles. The OBO-RO Editor provides a number of graphical elements defined in the proposed UML profile for the modeling of biomedical ontologies and support the (semi) automatic syntactic validation of such ontologies against the contraints defined in the profile. Additionally, OBO-RO Editor also provides support for the integration of developed UML models and other OBO ontologies, allowing the reuse and the accurate development of biomedical ontologies.

Test Driven Development (TDD), Model-Driven Development (MDD), and Test Case Generation with their associated practices and tools each in their own right promise to deliver robust higher quality code more economically then other approaches. These process are not mutually exclusive but are not typically used together. This thesis develops a combined approach using complimentary aspects of each of the above three process. Test cases are described, generated, and then injected back into the model, which is then used to produce the test and production code. We have enhanced a model-driven tool to support the approach, adding a test case generator, capable of understanding augmented MDD software model and utilizing the constraints captured in our test-centric language to generate model-level test cases back into the model. Our results show that, with a reduction in overall effort one can produce a tested model-based system in which its test and implementation for multiple platforms such as C and Java, using one of multiple test xUnit frameworks.

Major Depressive Disorder is a devastating psychiatric illness with a complex genetic and environmental component that affects 10% of the UK population. Previous studies have shown that that individuals with depression show poorer performance on measures of cognitive domains such as memory, attention, language and executive functioning. A major risk factor for depression is a higher level of neuroticism, which has been shown to be associated with depression throughout life. Understanding cognitive performance in depression and neuroticism could lead to a better understanding of the aetiology of depression. The first aim of this thesis focused on assessing phenotypic and genetic differences in cognitive performance between healthy controls and depressed individuals and also between single episode and recurrent depression. A second aim was determining the capability of two decision-tree based methods to detect simulated gene-gene interactions. The third aim was to develop a novel statistical methodology for simultaneously analysing single SNP, additive and interacting genetic components associated with neuroticism using machine leaning. To assess the phenotypic and genetic differences in depression, 7,012 unrelated Generation Scotland participants (of which 1,042 were clinically diagnosed with depression) were analysed. Significant differences in cognitive performance were observed in two domains: processing speed and vocabulary. Individuals with recurrent depression showed lower processing speed scores compared to both controls and individuals with single episode depression. Higher vocabulary scores were observed in depressed individuals compared to controls and in individuals with recurrent depression compared to controls. These significant differences could not be tied to significant single locus associations. Derived polygenic scores using the large CHARGE processing speed GWAS explained up to 1% of variation in processing speed performance among individuals with single episode and recurrent depression. Two greedy non-parametric decision-tree based methods - C5.0 and logic regression - were applied to simulated gene-gene interaction data from Generation Scotland. Several gene-gene interactions were simulated under multiple scenarios (e.g. size, strength of association levels and the presence of a polygenic component) to assess the power and type I error. C5.0 was found to have an increased power with a conservative type I error using simulated data. C5.0 was applied to years of education as a proxy of educational attainment in 6,765 Generation Scotland participants. Multiple interacting loci were detected that were associated with years of education, some most notably located in genes known to be associated with reading and spelling (RCAN3) and neurodevelopmental traits (NPAS3). C5.0 was incorporated in a novel methodology called Machine-learning for Additive and Interaction Combined Analysis (MAICA). MAICA allows for a simultaneous analysis of single locus, polygenic components, and gene-gene interaction risk factors by means of a machine learning implementation. MAICA was applied on neuroticism scores in both Generation Scotland and UK Biobank. The MAICA model in Generation Scotland included 151 single loci and 11 gene-gene interaction sets, and explained ~6.5% of variation in neuroticism scores. Applying the same model to UK Biobank did not lead to a statistically significant prediction of neuroticism scores. The results presented in this thesis showed that individuals with depression performed significantly lower on the processing speed tests but higher on vocabulary test and that 1% of variation in processing speed can be explained by using a large processing speed GWAS. Evidence was provided that C5.0 had increased power and acceptable type I error rates versus logic regression when epistatic models exist - even with a strong underlying polygenic component, and that MAICA is an efficient tool to assess single locus, polygenic and epistatic components simultaneously. MAICA is open-source, and will provide a useful tool for other researchers of complex human traits who are interested in exploring the relative contributions of these different genomic architectures.

The feasibility of using a thyroid uptake counter, normally used to measure the uptake of radioactive iodine in thyroid treatments, to assay radioactivity deposited in a persons lungs has been investigated. Variations in radioactive material distributions in the lungs, the response of the detector system to radionuclides of interest to homeland security, and the change in detection efficiency due to the varying thicknesses of intervening tissue of the victims have been simulated using the Monte Carlo N-Particle transport code (MCNP5) developed by Los Alamos National Laboratory. Point source and homogenously distributed models were created for Co-60, I-131, Cs-137, Ir-192, and Am-241 sources to simulate radiation transport between the lungs of multiple phantom models representing children and adults and the radiation detection system. To validate the simulations undertaken, the response of the counter to radiation sources in air and behind layers of Lucite have been modeled and compared to measured results.