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.

No description available.

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.

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.

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.