Hybridization refers to mating between species or between genetically differentiated populations of the same species. Although hybrid offspring may exhibit sterility and/or inviability, hybridization can generate novel genotypic and phenotypic diversities, leading to the origin of new traits and new species, the expansion into ecological niches outside of the parental range (e.g., host range), and altered virulence properties in pathogens. However, the relationship between parental genetic divergence and hybrid performance remains largely unknown. The human pathogenic Cryptococcus (HPC) is an ideal model to study the impacts of parental divergence on the genetic and phenotypic consequences of hybridization. HPC consists of a group of divergent lineages with various degrees of interfertility. These yeasts are the etiologic agents of cryptococcosis, a potentially lethal disease in humans and animals. In this thesis, I examined the effects of parental divergence on cryptococcal hybrids from multiple aspects. I conducted genetic crosses between different lineages to evaluate the mating success and the germination of sexual spores (i.e., basidiospores) under various environmental conditions. Then, I investigated the genotypic and phenotypic diversities among the hybrids under different environmental conditions. Furthermore, I examined the genome stability of diploid inter-lineage hybrids through laboratory experimental evolution and the effect of antifungal drug stress on the loss of heterozygosity (LOH) in these hybrids. We found that parental genetic divergence plays an important role in genotypic and phenotypic diversities among hybrid progeny in HPC. However, our results indicate that parental genetic di-vergence alone can’t explain most of the observed variations. Instead, genetic divergence along with specific parental strains, environmental factors, and their interactions all contributed to hybridization success and to hybrid genotypic and phenotypic variations. My findings will broaden the current understanding of the phenotypic and genotypic consequences of hybridization and explore the connection between genetic architecture and hybrid speciation in the human pathogenic Cryptococcus. / Thesis / Doctor of Science (PhD) / The role of hybridization in evolution can vary widely, giving rise to hybrid vigor and hybrid weakness. Hybridization plays an important role in plants and animals, especially crops, with advantages of increased yield and quality of products. However, the emergence of hybrid vigor in pathogens with increased virulence is an increasing threat to plant, animal, and human healths. My PhD thesis aimed at understanding the effects of parental divergence on hybridization and hybrids in the human pathogenic Cryptococcus. Here, I investigated basidiospore germination rate and hybrid progeny genotypes and phenotypes from diverse genetic crosses in this group of pathogens. My findings contribute towards understanding cryptococcal hybrids and establishing treatment plans against infections by these hybrids.

Assigning human genes to diseases and related phenotypes is an important topic in modern genomics. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities that occur in human diseases. Computational methods such as label-propagation and supervised-learning address challenges posed by traditional approaches such as manual curation to link genes to phenotypes in the HPO. It is only in recent years that computational methods have been applied in a network-based approach for predicting genes to disease-related phenotypes. In this thesis, we present an extensive benchmarking of various computational methods for the task of network-based gene classification. These methods are evaluated on multiple protein interaction networks and feature representations. We empirically evaluate the performance of multiple prediction tasks using two evaluation experiments: cross-fold validation and the more stringent temporal holdout. We demonstrate that all of the prediction methods considered in our benchmarking analysis have similar performance, with each of the methods outperforming a random predictor. / Master of Science / For many years biologists have been working towards studying diseases, characterizing dis- ease history and identifying what factors and genetic variants lead to diseases. Such studies are critical to working towards the advanced prognosis of diseases and being able to iden- tify targeted treatment plans to cure diseases. An important characteristic of diseases is that they can be expressed by a set of phenotypes. Phenotypes are defined as observable characteristics or traits of an organism, such as height and the color of the eyes and hair. In the context of diseases, the phenotypes that describe diseases are referred to as clinical phenotypes, with some examples being short stature, abnormal hair pattern, etc. Biologists have identified the importance of deep phenotyping, which is defined as a concise analysis that gathers information about diseases and their observed traits in humans, in finding genetic variants underlying human diseases. We make use of the Human Phenotype Ontology (HPO), a standardized vocabulary of phenotypic abnormalities that occur in human diseases. The HPO provides relationships between phenotypes as well as associations between phenotypes and genes. In our study, we perform a systematic benchmarking to evaluate different types of computational approaches for the task of phenotype-gene prediction, across multiple molecular networks using various feature representations and for multiple evaluation strategies.

As enzimas CYP450 são as principais enzimas metabolizadoras de fármacos. Elas são codificadas por genes que apresentam polimorfismos gênicos que lhes confere características fenotípicas diversas. Estes fenótipos são: Metabolizadores Lentos ou PM, Metabolizadores Normais ou EM, Metabolizadores Intermediários ou IM e Metabolizadores Ultra-rápidos ou UM. A Farmacogenética é a ciência que estuda estas variações gênicas e sua relação com a resposta terapêutica no organismo. Entre as enzimas CYP450 se encontram as enzimas CYP2D6, que são responsáveis pelo metabolismo de 25% dos fármacos clinicamente prescritos. O objetivo principal deste estudo foi a identificação dos polimorfismos mais importantes deste gene: CYP2D6*1, CYP2D6*2, CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, CYP2D6*10, CYP2D6*17 e CYP2D6*41 pelos métodos de Genotipagem (PCR Tetra Primer e Seqüenciamento) e Fenotipagem (analisado pelo Índice Metabólico) em 75 voluntários sadios da região de Campinas. Para a caracterização da Fenotipagem foi usada a substância teste Dextrometorfano (DM). Esta foi monitorada por espectrometria de massa mediante a determinação da concentração do seu principal metabólito o Dextrorfano (DX), que foi extraída das amostras de urina. Os resultados foram comparados entre estas duas metodologias e apresentaram alta correlação. Os resultados obtidos são a identificação das freqüências dos alelos *1, *3 e *4 pelo método PCR Tetra Primer (30.66%, 1.3% e 14%, respectivamente). O método de seqüenciamento detectou também outros alelos que não foram detectados pela PCR Tetra Primer. A avaliação do número de cópias do gene CYP2D6 também foi avaliada, detectando em um voluntário 3 cópias do gene CYP2D6, característica de metabolizadores Ultra-rápidos. Podemos afirmar que os métodos usados forneceram perfis dos polimorfismos de maneira rápida e prática. / The CYP450 enzymes are the major drug metabolizing enzymes. They are encoded by genes that show genetic polymorphisms which gives them several phenotypic characteristics. These phenotypes are Poor Metabolizers or PM, or Extensive Metabolizers or EM, Intermediate Metabolizers or IM, and finally, Ultra-rapid Metabolizers or UM. Pharmacogenetics is the science that studies these genetic variations and its relationship to therapeutic response in the body. One of CYP450 enzymes is CYP2D6 enzyme, which are responsible for the metabolism of 25% of clinically prescribed drugs. The main objective of this study was to identify the most important polymorphisms of this gene: CYP2D6 * 1, CYP2D6 * 2, CYP2D6 * 3, CYP2D6 * 4, CYP2D6 * 5, CYP2D6 * 6, CYP2D6 * 10, CYP2D6 * 17 and CYP2D6 * 41 by genotyping methods (PCR Tetra Primer and Sequencing) and phenotyping (by metabolic rate monitoring) in 75 healthy volunteers in Campinas region.To characterize the phenotyping was used to test substance Dextromethorphan (DM). This was monitored by mass spectrometry by determining the concentration of its major metabolite the Dextrorphan (DX), which was extracted from urine samples. The results were compared between these two methods and showed high correlation. We can obtain the identification of allelic frequencies of alleles * 1, * 3 and * 4 by Tetra Primer PCR (30.66%, 1.3% and 14% respectively). The sequencing method has also detected other alleles that were not detected by PCR Tetra Primer. The assessment of the number of copies of the CYP2D6 gene was also assessed. This method detected a volunteer which carrying three copies of CYP2D6 gene, characteristic of Ultra-rapid metabolizers. We can say that the methods used in this study provide polymorphism profiles quickly and conveniently.

During early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether the developmental programme is the result of a dynamic driven by a fixed architecture of regulatory interactions, or alternatively, the result of waves of regulatory reorganization is not known. Here we contrast these two alternative models by examining existing expression data derived from the developing human brain in prenatal and postnatal stages. We reveal a sharp change in gene expression profiles at birth across brain areas. This sharp division between foetal and postnatal profiles is not the result of sudden changes in level of expression of existing gene networks. Instead we demonstrate that the perinatal transition is marked by the widespread regulatory rearrangement within and across existing gene clusters, leading to the emergence of new functional groups. This rearrangement is itself organized into discrete blocks of genes, each associated with a particular set of biological functions. Our results provide evidence of an acute modular reorganization of the regulatory architecture of the brain transcriptome occurring at birth, reflecting the reassembly of new functional associations required for the normal transition from prenatal to postnatal brain development.

Background: Psoriatic arthritis (PsA) is a chronic heterogenous inflammatory arthritis with five phenotypes. The two least studied phenotypes are investigated in this thesis, including: psoriatic spondyloarthropathy (PsSpA) and psoriatic arthritis mutilans (PAM). The aims of this thesis were to determine the prevalence, clinical characteristics and radiographic characteristics of PsSpA and PAM in a cohort of PsA patients, and serum-soluble bone- turnover biomarkers of these phenotypes. Aims: Comparisons were made with PsA patients without axial disease (pPsA), and ankylosing spondylitis (AS) patients. Methods: A prospective single-centre cross-sectional study was conducted of PsA and AS patients. Serum on psoriasis-only patients (PsC) and healthy controls (HC) were also obtained. Multivariate clinical, radiographic, genetic and serum biomarker comparisons were made between these five groups of subjects. Results: The study enrolled 201 PsA and 201 AS patients, who were then reclassified as 118 PsSpA, 127 pPsA and 157 AS cases, alongside 200 PsC and 50 HC subjects. Several clinical biomarkers, imaging biomarkers, serum-soluble biomarkers and genetic biomarkers were identified that differentiate PsSpA from pPsA and AS. PsSpA affected a significant proportion of PsA patients, and was not a milder version of AS. PsSpA involvement was as disabling and clinically impactful as AS. PAM was found to be associated with PsSpA, and clinical biomarkers of PAM occurrence and radiographic progression were identified. Conclusions: In conclusion, this thesis indicates that PsSpA is on a spectrum of musculoskeletal disease, in between pPsA and AS; with PsSpA comprising a continuum itself, and with a phenotype expression related to disease duration. These findings may prompt the inception of an international-consensus classification system for PsSpA, for which there is a great clinical need. Given that PsSpA has its own discrete clinical and biomarker signature, its clinical management and research should be tailored from that of pPsA and AS. Ultimately this may further the effort for stratified and personalised medicine.

Genome-wide association (GWA) studies have been successfully applied in detection of susceptibility loci for complex diseases, but most of the identified variants have a large to moderate effect, and explain only a limited proportion of the heritability of the diseases. It is believed that the majority of the latent risk alleles have very small risk effects that are difficult to be identified and GWA study may have inadequate power in dealing with those small effect variants. Researchers will often collect other phenotypic information in addition to disease status to maximize the output from the study. Some of the phenotypes can be on the pathway to the disease, i.e., intermediate phenotype. Statistical methods based on both the disease status and intermediate phenotype should be more powerful than a case-control study as it incorporates more information. Meta-analysis has been used in genetic association analysis for many years to combine information from multiple populations, but never been used in a single population GWA study. In this study, simulations were conducted and the results show that when an intermediate phenotype is available, the meta-analysis incorporating the disease status and intermediate phenotype information from a single population has more power than a case-control study only in GWA study of complex diseases, especially for identification of those loci that have a very small effect. And compared with Fisher's method, the modified inverse variance weighted meta-analysis method is more robust as it is more powerful and has a lower type I error rate at the same time, which provides a potent approach in detecting the susceptibility loci associated with complex diseases, especially for those latent loci whose effect are very small. In the meta-analysis of lung cancer with smoking data, the results replicate the signal in \emph{CHRNA3} and \emph{CHRNA5} genes on chromosome 15q25. Some new signals in \emph{CYP2F1} on chromosome 19, \emph{SUMF1} on chromosome 3, and \emph{ARHGAP10} on chromosome 4 are also detected. And the \emph{CYP2F1} gene, close to the already known cigarette-induced lung cancer gene \emph{CYP2A6}, is highly possible another cytochrome P450 (CYP) gene that is related to the smoking-involved lung cancer. The meta-analysis of rheumatoid arthritis with anti-cyclic citrullinated peptide (anti-CCP) data identified new signals on 9q24 and 16q12. There are evidences these two regions are involved in other autoimmune diseases and different autoimmune/inflammatory diseases may share same genetic susceptibility loci. Both the theoretical and empirical studies show that the modified variance weighted meta-analysis method is a robust method and is a potent approach in detecting the susceptibility loci associated with complex diseases when an intermediate phenotype is available.

Many important scientific questions can not be studied properly using a single measurement as a response. For example, many phenotypes of interest in recent clinical research may be difficult to characterize due to their inherent complexity. It may be difficult to determine the presence or absence of disease based on a single measurement, or even a few measurements, or the phenotype may only be defined based on a series of symptoms. Similarly, a set of related phenotypes or measurements may be studied together in order to detect a shared etiology. In this work, we propose methods for studying complex phenotypes of these types, where the phenotype may be characterized either longitudinally or by a diverse set of continuous, discrete, or not fully observed components. In chapter 1, we seek to identify predictors that are related to multiple components of diverse outcomes. We take up specifically the question of identifying a multiple regulator, where we seek a genetic marker that is associated with multiple biomarkers for autoimmune disease. To do this, we propose sparse multiple regulation testing (SMRT) both to estimate the relationship between a set of predictors and diverse outcomes and to provide a testing framework in which to identify which predictors are associated with multiple elements of the outcomes, while controlling error rates. In chapter 2, we seek to identify risk profiles or risk scores for diverse outcomes, where a risk profile is a linear combination of predictors. The risk profiles will be chosen to be highly correlated to latent traits underlying the outcomes. To do this, we propose semiparametric canonical correlation analysis (sCCA), an updated version of the classical canonical correlation analysis. In chapter 3, the scientific question of interest pertains directly to the progression of disease over time. We provide a testing framework in which to detect the association between a set of genetic markers and the progression of disease in the context of a GWAS. To test for this association while allowing for highly nonlinear longitudinal progression of disease, we propose functional principal variance component (FPVC) testing.

Licentiatavhandling (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2008. / Härtill 2 uppsatser. I boken felaktigt tryckår 2006.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2009. / Härtill 4 uppsatser.

Idiopathic Parkinson’s disease (IPD) is the second most common neurodegenerative disease, yet effective disease modifying treatments are still lacking. Neurodegeneration involves multiple interacting pathological pathways. The extent to which neurovascular mechanisms are involved in IPD is not well defined. Indeed within the umbrella term of IPD great heterogeneity of motor (and non-motor) features exists, suggesting that different phenotypes may have differing underlying pathophysiologies. We aimed to determine whether novel magnetic resonance imaging (MRI) techniques can reveal changes in structural or physiological neurovascular measures, herein also referred to as ‘altered neurovascular status (NVS)’, in IPD.Based on preliminary data from our initial exploratory study in a small IPD cohort, phenotypic differences in structural and physiological MRI measures of NVS were investigated in a larger study. The 3 Tesla (3T) MRI protocol included T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging to assess white matter lesion (WML) burden, arterial spin labelling (ASL) measurements of cerebral blood flow (CBF) and arterial arrival time (AAT) and dynamic contrast enhanced (DCE) measures of blood-brain barrier (BBB) integrity. Analysis was undertaken of IPD clinical phenotypes, by comparison with two control groups. In total, fifty-one patients with IPD (mean age 69.0 ± 7.7 years) (21 tremor dominant [TD], 24 postural instability and gait disorder [PIGD] and 6 intermediates) were compared with 2 control groups, the first comprising 18 control positive (CP) subjects with a history of clinical cerebrovascular disease (CVD) (mean age 70.1 ± 8.0 years) and the second comprising 34 control negative (CN) subjects without a history of clinical CVD (mean age 67.4 ± 7.6 years). IPD patients showed diffuse regions of significantly prolonged AAT and lower CBF by comparison with CN subjects, and a few regions of prolonged AAT by comparison with CP subjects, despite significantly fewer vascular risk factors. TD patients showed regions of significantly prolonged AAT and lower WML volume by comparison with PIGD patients. IPD patients also showed increased leakiness of the BBB in basal ganglia regions compared to the CN group, with a similar pattern in both IPD phenotypes. These data provide evidence of altered NVS in IPD, with IPD phenotype specific differences.