Network-based analysis of genome-wide scans of natural selection

The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Análise exômica em pacientes portadores de cardiomiopatia hipertrófica

A cardiomiopatia hipertrófica (CMH) é uma doença geneticamente determinada, caracterizada por hipertrofia ventricular primária, com prevalência estimada de 0.2% na população geral. Qualquer portador tem 50% de chance de transmitir esta doença para seus filhos, o que torna cada vez mais relevante a importância do estudo genético dos indivíduos acometidos e de seus familiares. Já foram descritas diversas mutações genéticas causadoras de CMH, a maioria em genes que codificam proteínas do sarcômero, e algumas mutações mais raras em genes não sarcoméricos. O objetivo desse estudo é sequenciar as regiões exônicas de genes candidatos, incluindo os principais envolvidos na hipertrofia miocárdica, utilizando o sequenciamento de nova geração (Generation Sequencing); testar a aplicabilidade e viabilidade deste sistema para identificar mutações já confirmadas e propor as prováveis novas mutações causadoras de CMH. Métodos e resultados: 66 pacientes não aparentados portadores de CMH foram estudados e submetidos à coleta de sangue para obtenção do DNA para analisar as regiões exômicas de 82 genes candidatos, utilizando a plataforma MiSeq (Illumina). Identificou-se 99 mutações provavelmente patogênicas em 54 pacientes incluídos no estudo (81,8%) relacionadas ou não a CMH, e distribuídas em 42 genes diferentes. Destas mutações 27 já haviam sido publicadas, sendo que 17 delas descritas como causadoras de CMH. Em 28 pacientes (42,4%) identificou-se mutação nos três principais genes sarcoméricos relacionados à CMH (MYH7, MYBPC3, TNNT2). Encontrou-se também um grande número de variantes não sonôminas de efeito clínico incerto e algumas mutações relacionadas a outras enfermidades. Conclusão: a análise da sequencia dos exônos de genes candidatos, demonstrou ser uma técnica promissora para o diagnóstico genético de CMH de forma mais rápida e sensível. A quantidade de dados gerados é o um fator limitante até o momento, principalmente em doenças geneticamente complexas com envolvimento de diversos genes e com sistema de bioinformática limitado.

Determinantes genéticos e ambientais das doenças hipertensivas da gravidez

The development of complex diseases such as preeclampsia are determined by both environmental and genetic factors, but there is also interaction among these factors. Preeclampsia is a pregnancy-specific disorder characterized by de-novo hypertension and proteinuria after 20th week of gestation. There is a broad spectrum of clinical presentations related to hypertensive disorders of pregnancy (HDP) that can range from mild preeclampsia to eclampsia (seizures) or HELLP syndrome (Hemolysis, Elevation of Liver enzymes, Low Platelets). Those clinical outcomes might be linked to different pathological mechanisms. Our work aims to identify factors (i.e. genes and environmental) associated with the HDP’s clinical spectrum. Using a case-control approach, we selected a total of 1498 pregnant women for epidemiological and genetic studies, encompassing 755 normotensive (control); 518 preeclampsia; 84 eclampsia; and 141 HELLP. Women were genotyped for 18 SNPs across 5 candidate genes (FLT1, ACVR2A, ERAP1, ERAP2 and LNPEP). For the environmental factors, we found maternal age, parity status and pre-gestational body mass index as important risk factors associated with disease. Genes were associated in a phenotype-specific manner: ACVR2A with early preeclampsia (rs1424954, p=0.002); FLT1 with HELLP syndrome (rs9513095, p=0.003); and ERAP1 with eclampsia (rs30187, p=0.03). Our results suggest that different genetic mechanisms along with specific environmental factors might determine the clinical spectrum of HDP. In addition, phenotype refinement seems to be an essential step in the search for complex disease genes

Genetic insights into common pathways and complex relationships among iImmune-mediated diseases

Shared aetiopathogenic factors among immune-mediated diseases have long been suggested by their co-familiality and co-occurrence, and molecular support has been provided by analysis of human leukocyte antigen (HLA) haplotypes and genome-wide association studies. The interrelationships can now be better appreciated following the genotyping of large immune disease sample sets on a shared SNP array: the 'Immunochip'. Here, we systematically analyse loci shared among major immune-mediated diseases. This reveals that several diseases share multiple susceptibility loci, but there are many nuances. The most associated variant at a given locus frequently differs and, even when shared, the same allele often has opposite associations. Interestingly, risk alleles conferring the largest effect sizes are usually disease-specific. These factors help to explain why early evidence of extensive 'sharing' is not always reflected in epidemiological overlap. © 2013 Macmillan Publishers Limited. All rights reserved.

A complex system perspective on the emergence and spread of infectious diseases: integrating economic and ecological aspects

The emergence and spread of infectious diseases reflects the interaction of ecological and economic factors within an adaptive complex system. We review studies that address the role of economic factors in the emergence and spread of infectious diseases and identify three broad themes. First, the process of macro-economic growth leads to environmental encroaching, which is related to the emergence of infectious diseases. Second, there are a number of mutually reinforcing processes associated with the emergence/spread of infectious diseases. For example, the emergence and spread of infectious diseases can cause significant economic damages, which in turn may create the conditions for further disease spread. Also, the existence of a mutually reinforcing relationship between global trade and macroeconomic growth amplifies the emergence/spread of infectious diseases. Third, microeconomic approaches to infectious disease point to the adaptivity of human behavior, which simultaneously shapes the course of epidemics and responds to it. Most of the applied research has been focused on the first two aspects, and to a lesser extent on the third aspect. With respect to the latter, there is a lack of empirical research aimed at characterizing the behavioral component following a disease outbreak. Future research should seek to fill this gap and develop hierarchical econometric models capable of integrating both macro and micro-economic processes into disease ecology.

Gene targeting approaches to complex genetic diseases: atherosclerosis and essential hypertension.

Gene targeting allows precise, predetermined changes to be made in a chosen gene in the mouse genome. To date, targeting has been used most often for generation of animals completely lacking the product of a gene of interest. The resulting "knockout" mice have confirmed some hypotheses, have upset others, but have rarely been uninformative. Models of several human genetic diseases have been produced by targeting--including Gaucher disease, cystic fibrosis, and the fragile X syndrome. These diseases are primarily determined by defects in single genes, and their modes of inheritance are well understood. When the disease under study has a complex etiology with multiple genetic and environmental components, the generation of animal models becomes more difficult but no less valuable. The problems associated with dissecting out the individual genetic factors also increases substantially and the distinction between causation and correlation is often difficult. To prove causation in a complex system requires rigorous adherence to the principle that the experiments must allow detection of the effects of changing only a single variable at one time. Gene targeting experiments, when properly designed, can test the effects of a precise genetic change completely free from the effects of differences in any other genes (linked or unlinked to the test gene). They therefore allow proofs of causation.

Development of the Andalusian Registry of Patients Receiving Community Case Management, for the follow-up of people with complex chronic diseases.

Background: Complex chronic diseases are a challenge for the current configuration of Health services. Case management is a service frequently provided for people with chronic conditions and despite its effectiveness in many outcomes, such as mortality or readmissions, uncertainty remains about the most effective form of team organization, structures, and the nature of the interventions. Many processes and outcomes of case management for people with complex chronic conditions cannot be addressed with the information provided by electronic clinical records. Registries are frequently used to deal with this weakness. The aim of this study was to generate a registry-based information system of patients receiving case management to identify their clinical characteristics, their context of care, events identified during their follow-up, interventions developed by case managers, and services used. Methods and design: The study was divided into three phases, covering the detection of information needs, the design and its implementation in the healthcare system, using literature review and expert consensus methods to select variables that would be included in the registry. Objective: To describe the essential characteristics of the provision of ca re lo people who receive case management (structure, process and outcomes), with special emphasis on those with complex chronic diseases. Study population: Patients from any District of Primary Care, who initiate the utilization of case management services, to avoid information bias that may occur when including subjects who have already been received the service, and whose outcomes and characteristics could not be properly collected. Results: A total of 102 variables representing structure, processes and outcomes of case management were selected for their inclusion in the registry after the consensus phase. Total sample was composed of 427 patients, of which 211 (49.4%) were women and 216 (50.6%) were men. The average functional level (Barthel lndex) was 36.18 (SD 29.02), cognitive function (Pfeiffer) showed an average of 4.37 {SD 6.57), Chat1son Comorbidity lndex, obtained a mean of 3.03 (SD 2.7) and Social Support (Duke lndex) was 34.2 % (SD 17.57). More than half of patients include in the Registry, correspond lo immobilized or transitional care for patients discharged from hospital (66.5 %). The patient's educational level was low or very low (50.4%). Caregivers overstrain (Caregiver stress index), obtained an average value of 6.09% (SD 3.53). Only 1.2 % of patients had declared their advanced directives, 58.6 had not defined the tutelage and the vast majority lived at home 98.8 %. Regarding the major events recorded at RANGE Registry, 25.8 % of the selected patients died in the first three months, 8.2 % suffered a hospital admission at least once time, 2.3%, two times, and 1.2% three times, 7.5% suffered a fall, 8.7% had pressure ulcer, 4.7% had problems with medication, and 3.3 % were institutionalized. Stroke is the more prevalent health problem recorded (25.1%), followed by hypertension (11.1%) and COPD (11.1%). Patients registered by NCMs had as main processes diabetes (16.8%) and dementia (11.3 %). The most frequent nursing diagnoses referred to the self-care deficit in various activities of daily living. Regarding to nursing interventions, described by the Nursing Intervention Classification (NIC), dementia management is the most used intervention, followed by mutual goal setting, caregiver and emotional support. Conclusions: The patient profile who receive case management services is a chronic complex patient with severe dependence, cognitive impairment, normal social support, low educational level, health problems such as stroke, hypertension or COPD, diabetes or dementia, and has an informal caregiver. At the first follow up, mortality was 19.2%, and a discrete rate of readmissions and falls.

Internet-based surveillance systems for monitoring emerging infectious diseases

Emerging infectious diseases present a complex challenge to public health officials and governments; these challenges have been compounded by rapidly shifting patterns of human behaviour and globalisation. The increase in emerging infectious diseases has led to calls for new technologies and approaches for detection, tracking, reporting, and response. Internet-based surveillance systems offer a novel and developing means of monitoring conditions of public health concern, including emerging infectious diseases. We review studies that have exploited internet use and search trends to monitor two such diseases: influenza and dengue. Internet-based surveillance systems have good congruence with traditional surveillance approaches. Additionally, internet-based approaches are logistically and economically appealing. However, they do not have the capacity to replace traditional surveillance systems; they should not be viewed as an alternative, but rather an extension. Future research should focus on using data generated through internet-based surveillance and response systems to bolster the capacity of traditional surveillance systems for emerging infectious diseases.

Spatial-temporal epidemiological analyses of two sympatric, co-endemic alphaviral diseases in Queensland, Australia

Background: The two most reported mosquito-borne diseases in Queensland, a northern state of Australia, are Ross River virus (RRV) disease and Barmah Forest virus (BFV) disease. Both diseases are endemic in Queensland and have similar clinical symptoms and comparable transmission cycles involving a complex inter-relationship between human hosts, various mosquito vectors, and a range of nonhuman vertebrate hosts, including marsupial mammals that are unique to the Australasian region. Although these viruses are thought to share similar vectors and vertebrate hosts, RRV is four times more prevalent than BFV in Queensland. Methods: We performed a retrospective analysis of BFV and RRV human disease notification data collected from 1995 to 2007 in Queensland to ascertain whether there were differences in the incidence patterns of RRV and BFV disease. In particular, we compared the temporal incidence and spatial distribution of both diseases and considered the relationship between their disease dynamics. We also investigated whether a peak in BFV incidence during spring was indicative of the following RRV and BFV transmission season incidence levels. Results: Although there were large differences in the notification rates of the two diseases, they had similar annual temporal patterns, but there were regional variations between the length and magnitude of the transmission seasons. During periods of increased disease activity, however, there was no association between the dynamics of the two diseases. Conclusions: The results from this study suggest that while RRV and BFV share similar mosquito vectors, there are significant differences in the ecology of these viruses that result in different epidemic patterns of disease incidence. Further investigation is required into the ecology of each virus to determine which factors are important in promoting RRV and BFV disease outbreaks.

Replication of association of IL1 gene complex members with ankylosing spondylitis in Taiwanese Chinese

Objective: To test the association of interleukin 1 (IL1) gene family members with ankylosing spondylitis (AS), previously reported in Europid subjects, in an ethnically remote population. Methods: 200 Taiwanese Chinese AS patients and 200 ethnically matched healthy controls were genotyped for five single nucleotide polymorphisms (SNPs) and the IL1RN.VNTR, markers previously associated with AS. Allele, genotype, and haplotype frequencies were compared between cases and controls. Results: Association of alleles and genotypes of the markers IL1F10.3, IL1RN.4, and IL1RN.VNTR was observed with AS (p<0.05). Haplotypes of pairs of these markers and of the markers IL1RN.6/1 and IL1RN.6/2 were also significantly associated with AS. The strongest associations observed were with the marker IL1RN.4, and with the two-marker haplotype IL1RN.4-IL1RN.VNTR (both p = 0.004). Strong linkage disequilibrium was observed between all marker pairs except those involving IL1B-511 (D′ 0.4 to 0.9, p<0.01). Conclusions: The IL1 gene cluster is associated with AS in Taiwanese Chinese. This finding provides strong statistical support that the previously observed association of this gene cluster with AS is a true positive finding.

Prospective meta-analysis of interleukin 1 gene complex polymorphisms confirms associations with ankylosing spondylitis

Objectives: The aim of the current study was to determine the contribution of interleukin (IL) 1 gene cluster polymorphisms previously implicated in susceptibility for ankylosing spondylitis (AS) to AS susceptibility in different populations worldwide. Methods: Nine polymorphisms in the IL1 gene cluster members IL1A (rs2856836, rs17561 and rs1894399), IL1B (rs16944), IL1F10 (rs3811058) and IL1RN (rs419598, the IL1RA VNTR, rs315952 and rs315951) were genotyped in 2675 AS cases and 2592 healthy controls recruited in 12 different centres in 10 countries. Association of variants with AS was tested by Mantel-Haenszel random effects analysis. Results: Strong association was observed with three single nucleotide polymorphisms (SNPs) in the IL1A gene (rs2856836, rs17561, rs1894399, p = 0.0036, 0.000019 and 0.0003, respectively). There was no evidence of significant heterogeneity of effects between centres, and no evidence of non-combinability of findings. The population attributable risk fraction of these variants in Caucasians is estimated at 4-6%. Conclusions: This study confirms that IL1A is associated with susceptibility to AS. Association of the other IL1 gene complex members could not be excluded in specific populations. Prospective meta-analysis is a useful tool in confirmation studies of genes associated with complex genetic disorders such as AS, providing sufficiently large sample sizes to produce robust findings often not achieved in smaller individual cohorts.

Non-major-histocompatibility-complex genetics of ankylosing spondylitis

There is strong evidence from twin and family studies indicating that a substantial proportion of the heritability of susceptibility to ankylosing spondylitis (AS) and its clinical manifestations is encoded by non-major-histocompatibility-complex genes. Efforts to identify these genes have included genomewide linkage studies and candidate gene association studies. One region, the interleukin (IL)-1 gene complex on chromosome 2, has been repeatedly associated with AS in both Caucasians and Asians. It is likely that more than one gene in this complex is involved in AS, with the strongest evidence to date implicating IL-1A. Identifying the genes underlying other linkage regions has been difficult due to the lack of obvious candidates and the low power of most studies to date to identify genes of the small to moderate magnitude that are likely to be involved. The field is moving towards genomewide association analysis, involving much larger datasets of unrelated cases and controls. Early successes using this approach in other diseases indicates that it is likely to identify genes in common diseases like AS, but there remains the risk that the common-variant, common-disease hypothesis will not hold true in AS. Nonetheless, it is appropriate for the field to be cautiously optimistic that the next few years will bring great advances in our understanding of the genetics of this condition.

Allergen capture by IgE and IgG enhanced cell-binding by allergen multimers: How complex is it?

Allergic diseases are the most common chronic disease of the western world, costing $7.8 billion per year in lost productivity and medical care in Australia alone.1 IgE is central to the immunopathogenesis of allergic diseases and important advances are now being made on multiple fronts of IgE research. In particular, two groups independently invested in the generation of IgE reporter mice to address the vexing question of the route of development of the elusive IgE+ B cell.2, 3 Two new anti-IgE mAb targeting membrane IgE and cell-bound IgE have the potential to deplete the cellular source of IgE.4, 5 These could be candidates for alternative anti-IgE treatment options with advantages over current anti-IgE therapy (OmalizumAb), which depletes free serum IgE. Researchers are still intrigued by the modes of interaction of IgE with allergen, and with both its receptors; the high affinity FcεR1 on mast cells and basophils, and the low affinity, C-type lectin, IgE receptor, CD23,6 on B cells and monocytes (Figure 1a and b). A new approach to the study of the complexity of these interactions was recently reported by Reginald et al.7 on page 167 of this issue.