Introduction The present study evaluated reported methodological characteristics of GWAS, investigating relationships between reported methodological characteristics and outcomes observed. Methods GWAS were identified from NHGRI’s catalogue of GWAS (2005 to 2009). Multivariate meta-regression models (random effects) were produced to identify the impact of reported study characteristics and the strength of relationships between the variables and outcomes. Results The summary odds ratios for replication components of GWAS in cancer was 1.34 (95% CI 1.25, 1.43) and neuropsychiatric disorders was 1.43 (95% CI 1.30, 1.57). Heterogeneity was accounted for by nature of the control group, relationship between case/control groups, whether cases/controls were drawn from the same population, if data was a primary collection or a build on pre-existing data, if quality assurance was reported, and if the study reported power/sample size. Conclusion Evidence supports the existence of variability in reporting, with index components demonstrating less variability than replication components in the GWAS.

Introduction The present study evaluated reported methodological characteristics of GWAS, investigating relationships between reported methodological characteristics and outcomes observed. Methods GWAS were identified from NHGRI’s catalogue of GWAS (2005 to 2009). Multivariate meta-regression models (random effects) were produced to identify the impact of reported study characteristics and the strength of relationships between the variables and outcomes. Results The summary odds ratios for replication components of GWAS in cancer was 1.34 (95% CI 1.25, 1.43) and neuropsychiatric disorders was 1.43 (95% CI 1.30, 1.57). Heterogeneity was accounted for by nature of the control group, relationship between case/control groups, whether cases/controls were drawn from the same population, if data was a primary collection or a build on pre-existing data, if quality assurance was reported, and if the study reported power/sample size. Conclusion Evidence supports the existence of variability in reporting, with index components demonstrating less variability than replication components in the GWAS.

Introduction The present study evaluated reported methodological characteristics of GWAS, investigating relationships between reported methodological characteristics and outcomes observed. Methods GWAS were identified from NHGRI’s catalogue of GWAS (2005 to 2009). Multivariate meta-regression models (random effects) were produced to identify the impact of reported study characteristics and the strength of relationships between the variables and outcomes. Results The summary odds ratios for replication components of GWAS in cancer was 1.34 (95% CI 1.25, 1.43) and neuropsychiatric disorders was 1.43 (95% CI 1.30, 1.57). Heterogeneity was accounted for by nature of the control group, relationship between case/control groups, whether cases/controls were drawn from the same population, if data was a primary collection or a build on pre-existing data, if quality assurance was reported, and if the study reported power/sample size. Conclusion Evidence supports the existence of variability in reporting, with index components demonstrating less variability than replication components in the GWAS.

Introduction The present study evaluated reported methodological characteristics of GWAS, investigating relationships between reported methodological characteristics and outcomes observed. Methods GWAS were identified from NHGRI’s catalogue of GWAS (2005 to 2009). Multivariate meta-regression models (random effects) were produced to identify the impact of reported study characteristics and the strength of relationships between the variables and outcomes. Results The summary odds ratios for replication components of GWAS in cancer was 1.34 (95% CI 1.25, 1.43) and neuropsychiatric disorders was 1.43 (95% CI 1.30, 1.57). Heterogeneity was accounted for by nature of the control group, relationship between case/control groups, whether cases/controls were drawn from the same population, if data was a primary collection or a build on pre-existing data, if quality assurance was reported, and if the study reported power/sample size. Conclusion Evidence supports the existence of variability in reporting, with index components demonstrating less variability than replication components in the GWAS.

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

Autism Spectrum Conditions (henceforth, autism) refers to a group of neurodevelopmental conditions characterized by difficulties in social interaction and communication, difficulties in adjusting to unexpected change, alongside unusually narrow interests and repetitive behaviour, and sensory hyper-sensitivity. Twin and family-based studies have consistently identified high heritabilities for autism and autistic traits, with recent studies converging at 60 – 90% heritability. Common genetic variants are thought to additively contribute to as much as 50% of the total risk for autism. In this thesis, I investigate the contribution of common genetics variants (including SNPs, and InDels) to autism and related traits. In Chapter 1, I discuss the recent advances in the field of autism genetics, focussing on the contribution of common genetic variants to the risk for autism. Chapters 2 – 7 report the results of various studies investigating the genetic correlates of autism and related traits. In Chapter 2, I surveyed the evidence for 552 candidate genes associated with autism, and conducted a meta-analysis for 58 common variants in 27 genes, investigated in at least 3 independent cohorts. Meta-analysis did not identify any SNPs that were replicably associated with autism in the Psychiatric Genetics Consortium genome-wide association study (PGC-GWAS) dataset after Bonferroni correction, suggesting that candidate gene association studies are not statistically well-powered. In Chapters 3 – 7, I conducted genome-wide association studies (GWAS) for 6 traits associated with autism: self-reported empathy (N = 46,861, Chapter 3), cognitive empathy (N = 89,553, Chapter 4), theory of mind in adolescents (N = 4,577, Chapter 5), friendship satisfaction (Neffective = 158,116) and family relationship satisfaction (Neffective = 164,112, both Chapter 6), and systemizing (N = 51,564, Chapter 7). GWAS identified significant loci for self-reported empathy, systemizing, friendship and family relationship satisfaction, and cognitive empathy. Genetic correlation analyses replicably identified a significant negative genetic correlation between autism and family relationship satisfaction and friendship satisfaction, and a significant positive genetic correlation between autism and systemizing. In addition, there was a negative genetic correlation between autism and self-reported empathy. Chapter 8 draws all of these studies together, concluding that there may be at least two independent sources of genetic risk for autism: one stemming from social traits and another from non-social traits. I discuss some future directions about how this can be leveraged using polygenic scores from multiple phenotypes to potentially stratify individuals within the autism spectrum, and both the strengths and limitations of the reported studies.

No description available.

Diet and lifestyle have changed dramatically in the last few decades, leading to an increase in prevalence of obesity, defined as a body mass index >30Kg/m2, dyslipidaemias (defined as abnormal lipid profiles) and type 2 diabetes (T2D). Together, these cardiometabolic traits and diseases, have contributed to the increased burden of cardiovascular disease, the leading cause of death in Western societies. Complex traits and diseases, such as cardiometabolic traits, arise as a result of the interaction between an individual's predisposing genetic makeup and a permissive environment. Since 2007, genome-wide association studies (GWAS) have been successfully applied to complex traits leading to the discovery of thousands of trait-associated variants. Nonetheless, much is still to be understood regarding the genetic architecture of these traits, as well as their underlying biology. This thesis aims to further explore the genetic architecture of cardiometabolic traits by using complementary approaches with greater genetic and phenotype resolution, ranging from studying clinically ascertained extreme phenotypes, deep molecular profiling, or sequence level data. In chapter 2, I investigated the genetic architecture of healthy human thinness (N=1,471) and contrasted it to that of severe early onset childhood obesity (N=1,456). I demonstrated that healthy human thinness, like severe obesity, is a heritable trait, with a polygenic component. I identified a novel BMI-associated locus at PKHD1, and found evidence of association at several loci that had only been discovered using large cohorts with >40,000 individuals demonstrating the power gains in studying clinical extreme phenotypes. In chapter 3, I coupled high-resolution nuclear magnetic resonance (NMR) measurements in healthy blood donors, with next-generation sequencing to establish the role of rare coding variation in circulating metabolic biomarker biology. In gene-based analysis, I identified ACSL1, MYCN, FBXO36 and B4GALNT3 as novel gene-trait associations (P < 2.5x10-6). I also found a novel link between loss-of-function mutations in the "regulation of the pyruvate dehydrogenase (PDH) complex" pathway and intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL) and circulating cholesterol measurements. In addition, I demonstrated that rare "protective" variation in lipoprotein metabolism genes was present in the lower tails of four measurements which are CVD risk factors in this healthy population, demonstrating a role for rare coding variation and the extremes of healthy phenotypes. In chapter 4, I performed a genome-wide association study of fructosamine, a measurement of total serum protein glycation which is useful to monitor rapid changes in glycaemic levels after treatment, as it reflects average glycaemia over 2-3 weeks. In contrast to HbA1c, which reflects average glucose concentration over the life-span of the erythrocyte (~3 months), fructosamine levels are not predicted to be influenced by factors affecting the erythrocyte. Surprisingly, I found that in this dataset fructosamine had low heritability (2% vs 20% for HbA1c), and was poorly correlated with HbA1c and other glycaemic traits. Despite this, I found two loci previously associated with glycaemic or albumin traits, G6PC2 and FCGRT respectively (P < 5x10-8), associated with fructosamine suggesting shared genetic influence. Altogether my results demonstrate the utility of higher resolution genotype and phenotype data in further elucidating the genetic architecture of a range of cardiometabolic traits, and the power advantages of study designs that focus on individuals at the extremes of phenotype distribution. As large cohorts and national biobanks with sequencing and deep multi-dimensional phenotyping become more prevalent, we will be moving closer to understanding the multiple aetiological mechanisms leading to CVD, and subsequently improve diagnosis and treatment of these conditions.

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)

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.