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Thesis (MSc)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Described as one of the costliest and most debilitating disorders, schizophrenia has proven to be among the greatest challenges for medical researchers. The disorder poses difficulties on all levels: from genotype to phenotype. Even though it is known that there is a substantial genetic contribution to schizophrenia susceptibility (~80%), it is unknown whether this is due to common variants, rare variants, epigenetic factors, polymorphisms in regulatory regions of the genome or a combination of all these factors. Over the past few decades, many approaches have been employed to elucidate the genetic architecture of schizophrenia, with the latest and most promising being genome wide association studies (GWAS). However, nearly a decade after the first GWAS, the limitations are increasingly being recognised and new avenues need to be explored. Studies have recently started to focus on the analysis of non-coding regions of the genome since these regions harbour the majority of variants identified in GWAS thus far. This study aimed to use recently developed programs that utilize data from large scale studies such as previous GWAS, the Encyclopaedia of DNA Elements (ENCODE), 1000 Genomes, HapMap and Functional Annotation of the Mammalian Genome (FANTOM) to establish a simple, yet effective bioinformatics pipeline for the identification and assessment of variants in regulatory regions. Using the established workflow, 149 single nucleotide polymorphisms (SNPs) in regulatory regions were implicated in schizophrenia susceptibility, with the most significant SNP being rs200981. Pathway and network analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and GeneMANIA respectively indicated that the most frequently affected genes were involved in immune responses or neurodevelopmental processes, which support previous findings. Yet, novel findings of this study implicated processes crucial for DNA packaging (from DNA level to chromatin level). The second part of the study used restriction fragment length polymorphism analysis of polymerase chain reaction-amplified fragments (PCR-RFLP) to genotype ten of the most significant SNPs (identified by bioinformatic analyses in the first part of the study) in a South African Xhosa cohort of 100 cases and 100 controls, while bi-directional Sanger sequencing was used to confirm the presence of these SNPs. Statistical analyses revealed two haplotypes of regulatory variants, rs200483-rs200485-rs2517611 (p = 0.0385; OR = 1.71; 95% CI = 1.01-2.91) and rs200981-rs2517611-rs3129701 (p = 0.041; OR = 0.51; 95% CI = 0.27-0.98) associated with schizophrenia susceptibility. Bioinformatic analysis indicated that these haplotypes affect DNA packaging, which supported the findings of the first part of the study and could implicate epigenetic processes. The findings of this study support the importance of regulatory variants in schizophrenia susceptibility. This study also showed the importance of combining GWAS data with additional analyses in order to better understand complex diseases. It is hoped that these findings could fuel future research, specifically in genetically unique populations. / AFRIKAANSE OPSOMMING: Skisofrenie kan beskryf word as een van die duurste en mees ernstige siektes en bly steeds een van die grootste uitdagings vir mediese navorsers. Hierdie versteuring behels probleme op alle vlakke: van genotipe tot fenotipe. Alhoewel dit bekend is dat daar 'n aansienlike genetiese bydrae tot skisofrenie vatbaarheid is (~ 80%), is dit onbekend of dit is as gevolg van algemene variasies, skaars variasies, epigenetiese faktore, variasies in regulerende gebiede van die genoom of 'n kombinasie van al hierdie faktore. Oor die afgelope paar dekades is verskeie benaderings gebruik om die genetiese samestelling van skisofrenie te bestudeer, met die nuutste en mees belowende synde genoom-wye assosiasie studies (GWAS). Byna 'n dekade na die eerste GWAS, word die beperkinge egter toenemend erken en nuwe navorsingstrategieë moet gebruik word. Studies het onlangs begin om meer te fokus op die analise van nie-koderende areas van die genoom aangesien hierdie areas die meerderheid van die variasies behels wat tot dusver in GWAS geïdentifiseer is. Hierdie studie het gepoog om onlangs ontwikkelde programme, wat gebruik maak van die data van grootskaalse studies soos vorige GWAS, die “Encyclopaedia of DNA Elements” (ENCODE), “1000 Genomes”, “HapMap” en “Functional Annotation of the Mammalian Genome” (FANTOM), te implementeer om sodoende 'n eenvoudige, maar doeltreffende bioinformatika pyplyn vir die identifisering en evaluering van variante in regulerende gebiede, te vestig. Deur die gebruik van die gevestigde bioinformatika pyplyn, is 149 enkel nukleotied polimorfismes (SNPs) in regulerende gebiede in skisofrenie vatbaarheid betrek, met rs200981 wat die mees betekenisvol was. Pad- en netwerk-analise met die onderskeidelike hulp van die “Database for Annotation, Visualization and Integrated Discovery” (DAVID) en “GeneMANIA”, het aangedui dat die gene wat die meeste geaffekteer was, betrokke is by immuunreaksies en neuro-ontwikkeling. Hierdie bevindinge ondersteun vorige studies. Tog het nuwe bevindinge van hierdie studie prosesse geïmpliseer wat uiters noodsaaklik is vir DNS verpakking (van DNS- tot chromatien-vlak). Die tweede deel van die studie het restriksie fragment lengte polimorfisme analise van polimerase ketting reaksie geamplifiseerde fragmente (PKR-RFLP) gebruik om tien van die belangrikste SNPs (wat geïdentifiseer is deur bioinformatiese ontledings in die eerste deel van die studie) in `n Suid-Afrikaanse Xhosa studiegroep van 100 skisofrenie gevalle en 100 kontroles te genotipeer, terwyl tweerigting Sanger volgordebepaling gebruik is om die teenwoordigheid van hierdie SNPs te bevestig. Statistiese analise het aangedui dat twee / National Research Foundation (DAAD-NRF)

No mundo inteiro, o carcinoma de próstata ocupa o quinto lugar entre as neoplasias malignas de maior mortalidade. No Brasil, estima-se para o ano de 2001 que, entre os tumores malignos no sexo masculino, o carcinoma de próstata terá a segunda maior taxa de mortalidade e a primeira taxa de incidência (Estimativa da incidência e mortalidade por câncer no Brasil – 2001 – INCA). Uma vez que a taxa de mortalidade por carcinoma de próstata na população brasileira tem aumentado significativamente nos últimos anos, a presente tese se propôs a investigar regiões polimórficas em genes conhecidos que poderiam estar associadas a um aumento na predisposição a esta forma de câncer. Assim sendo, estudamos as regiões polimórficas CAG e GGC do gene do receptor de andrógeno; o polimorfismo C1171T do gene do receptor de vitamina D; o polimorfismo D104N do gene da endostatina; o polimorfismo Pro72Arg do gene p53 e a região polimórfica AAAAC localizada na região 3’ não traduzida do gene MXI1. / In the world’s population prostate carcinoma is the fifth most commom male cancer-related death malignancy. In Brazil, among all male invasive cancers it is expected that prostate carcinoma will have the second highest death rate and the highest incidence rate (Estimativa da incidência e mortalidade por câncer no Brasil, 2001). As the prostate carcinoma death rate in brazilian population has been increasing over the last several years we proposed to investigate polymorphic regions of known genes that might be associated with prostate carcinoma predisposition. We studied the androgen receptor CAG and GGC polymorphic regions, the vitamin D receptor C1171T polymorphism, the endostatin D104N polymorphism, the p53 Pro72Arg polymorphism and the MXI1 AAAAC polymorphic region.

The majority of human proteins are post-translationally modified by covalent addition of one or more complex oligosaccharides (glycans). Alterations in glycosylation processing are associated with numerous diseases and glycans are attracting increasing attention both as disease biomarkers and as targets for novel therapeutic approaches. Using a recently developed high performance liquid chromatography (HPLC) method for high-throughput glycan analysis, genome-wide association studies (GWAS) of 33 directly measured and 13 derived N-glycan features were performed in 3533 individuals from four European isolated populations. Polymorphisms at six loci were found to show genome-wide significant association with plasma concentrations of N-glycans. Several of these gene products have well characterised roles in glycosylation, however, SLC9A9 and HNF1A were two of the novel findings. Subsequent work performed by collaborators found HNF1A to be a “master regulator” of genes involved in the fucosylation of plasma N-glycans. Additionally, this work led to the discovery that N-glycans could act as biomarkers to discriminate HNF1A-MODY from type 1 and type 2 diabetes mellitus (T1D, T2D) patients. After the success of the total plasma N-glycan GWAS, it was thought that stronger and more biologically interpretable associations may be found from the investigation of N-glycans isolated from a single protein. Glycosylation of immunoglobulin G (IgG) influences IgG effector function by modulating binding to Fc receptors. To identify genetic networks that govern IgG glycosylation, N-linked IgG glycans were quantitated using ultra performance liquid chromatography (UPLC) in 2247 individuals from the same four European populations from the previous study. GWAS of the 77 N-glycan measures identified 15 loci with a p-value < 5x10-08. Four loci contained genes encoding glycosyltransferases, while the remaining loci contained genes that have not previously been implicated in protein glycosylation. However, most have been associated with autoimmune and inflammatory conditions and/or hematological cancers. Several high-throughput methods for the analysis of N-glycans have been developed in the past few years but thorough validation and standardization of these methods is required before significant resources are invested in large-scale studies. To this end, four of these methods were compared, UPLC, multiplexed capillary gel electrophoresis (xCGE), and two mass spectrometric (MS) methods, for quantitative profiling of N-glycosylation of plasma IgG in a subset of 1201 individuals recruited from two of the cohorts used in the previous GWAS studies. A “minimal” dataset was compiled of N-glycan structures able to be measured by all four methods. To evaluate their accuracy, correlations were calculated for each structure in the minimal dataset. Additionally, GWAS was performed to test if the same associations would be observed across methodologies. Chromatographic methods with either fluorescent or MS-detection yielded slightly stronger associations than MS-only and xCGE, but at the expense of lower levels of throughput. Advantages and disadvantages of each method were identified, which should aid in the selection of the most appropriate method for future studies. This work shows that it is possible to identify new loci that control glycosylation of plasma proteins using GWAS and the potential of N-glycans for biomarker development. It also provides some guidelines for methodology selection for future studies of N-glycans.

The field of nonlinear regression is a long way from reaching a consensus. Once a method decides to explore nonlinear combinations of predictors, a number of questions are raised, such as what nonlinear combinations to permit and how best to search the resulting model space. Genetic Association Studies comprise an area that stands to gain greatly from the development of more sophisticated regression methods. While these studies' ability to interrogate the genome has advanced rapidly over recent years, it is thought that a lack of suitable regression tools prevents them from achieving their full potential. I have tried to investigate the area of regression in a methodical manner. In Chapter 1, I explain the regression problem and outline existing methods. I observe that both linear and nonlinear methods can be categorised according to the restrictions enforced by their underlying model assumptions and speculate that a method with as few restrictions as possible might prove more powerful. In order to design such a method, I begin by assuming each predictor is tertiary (takes no more than three distinct values). In Chapters 2 and 3, I propose the method Sparse Partitioning. Its name derives from the way it searches for high scoring partitions of the predictor set, where each partition defines groups of predictors that jointly contribute towards the response. A sparsity assumption supposes most predictors belong in the 'null group' indicating they have no effect on the outcome. In Chapter 4, I compare the performance of Sparse Partitioning to existing methods using simulated and real data. The results highlight how greatly a method's power depends on the validity of its model assumptions. For this reason, Sparse Partitioning appears to offer a robust alternative to current methods, as its lack of restrictions allows it to maintain power in scenarios where other methods will fail. Sparse Partitioning relies on Markov chain Monte Carlo estimation, which limits the size of problem on which it can be used. Therefore, in Chapter 5, I propose a deterministic version ofthe method which, although less powerful, is not affected by convergence issues. In Chapter 6, I describe Bayesian Projection Pursuit, which adds spline fitting into the method to cope withnon-tertiary predictors.

Genome-wide association studies (GWAS) has played an import role in identifying genetic variants underlying human complex traits. However, its success is hindered by weak effect at causal variants and noise at non-causal variants. Penalized regression can be applied to handle GWAS problems. GWAS data has some specificities. Consecutive genetic markers are usually highly correlated due to linkage disequilibrium. This thesis introduces a moving-window penalized method for GWAS which smooths the effects of consecutive SNPs. Simulation studies indicate that this penalized moving window method provides improved true positive findings. The practical utility of the proposed method is demonstrated by applying it to Genetic Analysis Workshop 16 Rheumatoid Arthritis data. Next, the moving-window penalty is applied on generalized linear model. We call such an approach as smoothed lasso (SLasso). Coordinate descent computing algorithms are proposed in details, for both quadratic and logistic loss. Asymptotic properties are discussed. Then based on SLasso, we discuss a two-stage method called MW-Ridge. Simulation results show that while SLasso can provide more true positive findings than Lasso, it has a side-effect that it includes more unrelated random noises. MW-Ridge can eliminate such a side-effect and result in high true positive rates and low false detective rates. The applicability to real data is illustrated by using GAW 16 Rheumatoid Arthritis data. The SLasso and MW-Ridge approaches are then generalized to multivariate response data. The multivariate response data can be transformed into univariate response data. The causal variants are not required to be the same for different response variables. We found that no matter how the causal variants are matched, being fully matched or 60% matched, MW-Ridge can always over perform Lasso by detecting all true positives with lower false detective rates.

Genetic association analyses have successfully identified thousands of genetic variants contributing to complex disease susceptibility. However, these discoveries do not explain the full heritability of many diseases, due to the limited statistical power to detect loci with small effects, especially in regions with rare variants. The development of new and powerful methods is necessary to fully characterize the underlying genetic basis of complex diseases. Family history (FH) contains information on the disease status of un-genotyped relatives, which is related to the genotypes of probands at disease loci. Exploiting available FH in relatives could potentially enhance the ability to identify associations by increasing sample size. Many studies have very low power for genetic research in late-onset diseases because younger participants do not contribute a sufficient number of cases and older patients are more likely deceased without genotypes. Genetic association studies relying on cases and controls need to progress by incorporating additional information from FH to expand genetic research. This dissertation overcomes these challenges and opens up a new paradigm in genetic research. The first chapter summarizes relevant methods used in this dissertation. In the second chapter, we develop novel methods to exploit the availability of FH in aggregation unit-based test, which have greater power than other existing methods that do not incorporate FH, while maintaining a correct type I error. In the third chapter, we develop methods to exploit FH while adjusting for relatedness using the generalized linear mixed effect models. Such adjustment allows the methods to have well-controlled type I error and maintain the highest sample size because there is no need to restrict the analysis to an unrelated subset in family studies. We demonstrate the flexibility and validity of the methods to incorporate FH from various relatives. The methods presented in the fourth chapter overcome the issue of inflated type I error caused by extremely unbalanced case-control ratio. We propose robust versions of the methods developed in the second and third chapters, which can provide more accurate results for unbalanced study designs. Availability of these novel methods will facilitate the identification of rare variants associated with complex traits.

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Poliomyelitis is a foremost cause of paralysis among preventable diseases among children and adolescents globally. It is caused by persistent infection with poliovirus (PV). The PV infection does not always cause paralysis. A lack of immunization always increases the risk of paralytic polio. Genetic factors also been shown to affect the risk of developing the disease. The aim of this thesis is to investigate whether there are any genetic associations to paralytic poliomyelitis. This is based on a model for understanding its nature as a complex disease, where many genes are involved in contributing to the disease state. This is a population-based case-control study to identify genetic loci that influence disease risk. The study examined the association of genetic variation in single nucleotide polymorphisms (SNPs) across the genome with paralytic poliomyelitis susceptibility in the United States and Canadian survivors of poliomyelitis population, using a genome-wide association study (GWAS) approach. No association was observed. Loci that have been previously implicated were not found to affect the susceptibility to poliomyelitis in this study. The thesis consists of four chapters. Chapter 1 describes the epidemiology, pathogenesis and management of poliomyelitis. Chapter 2 gives an overview of the genomics of infectious diseases in general. Chapter 3 introduces the study population and presents the genome-wide analysis and associations with logistic regression to identify loci explore genes that might be associated with paralytic poliomyelitis and presents results. Chapter 4 discusses the implications of the results and explains future directions. / Thesis / Master of Science (MSc)

Yes / Drug resistance diagnostics that rely on the detection of resistance-related mutations could expedite patient care and TB eradication. We perform minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole-genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We evaluate genome-wide associations between mutations in MTB genes or non-coding regions and resistance, followed by validation in an independent data set of 792 patient isolates. We confirm associations at 13 non-canonical loci, with two involving non-coding regions. Promoter mutations are measured to have smaller average effects on resistance than gene body mutations. We estimate the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causal loci, and emphasizes the contribution of the non-coding portion of the genome. / Biomedical research grant from the American Lung Association (PI MF, RG-270912-N), a K01 award from the BD2K initiative (PI MF, ES026835), and an NIAID U19 CETR grant (P.I. M.M., AI109755), the Belgian Science Policy (Belspo) (L.R., C.J.M.).