42 research outputs found

    Liver and Adipose Expression Associated SNPs Are Enriched for Association to Type 2 Diabetes

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    Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS

    Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects and tissue-specific enrichment of eQTLs

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    We performed genome-wide meta-analysis of lipid traits on three samples of Mexican and Mexican American ancestry comprising 4,383 individuals and followed up significant and highly suggestive associations in three additional Hispanic samples comprising 7,876 individuals. Genome-wide significant signals were observed in or near CELSR2, ZNF259/APOA5, KANK2/DOCK6 and NCAN/MAU2 for total cholesterol, LPL, ABCA1, ZNF259/APOA5, LIPC and CETP for HDL cholesterol, CELSR2, APOB and NCAN/MAU2 for LDL cholesterol and GCKR, TRIB1, ZNF259/APOA5 and NCAN/MAU2 for triglycerides. Linkage disequilibrium and conditional analyses indicate that signals observed at ABCA1 and LIPC for HDL cholesterol and NCAN/MAU2 for triglycerides are independent of previously reported lead SNP associations. Analyses of lead SNPs from the European Global Lipids Genetics Consortium (GLGC) dataset in our Hispanic samples show remarkable concordance of direction of effects as well as strong correlation in effect sizes. A meta-analysis of the European GLGC and our Hispanic datasets identified five novel regions reaching genome-wide significance: two for total cholesterol (FN1 and SAMM50), two for HDL cholesterol (LOC100996634 and COPB1) and one for LDL cholesterol (LINC00324/CTC1/PFAS). The top meta-analysis signals were found to be enriched for SNPs associated with gene expression in a tissue-specific fashion, suggesting an enrichment of tissue-specific function in lipid-associated loci

    Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

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    Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.Peer reviewe

    Genomic architecture of sickle cell disease clinical variation in children from West Africa : a case-control study design

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    Contexte : L’anémie falciforme ou drépanocytose est un problème de santé important, particulièrement pour les patients d’origine africaine. La variation phénotypique de l’anémie falciforme est problématique pour le suivi et le traitement des patients. L’architecture génomique responsable de cette variabilité est peu connue. Principe : Mieux saisir la contribution génétique de la variation clinique de cette maladie facilitera l’identification des patients à risque de développer des phénotypes sévères, ainsi que l’adaptation des soins. Objectifs : L’objectif général de cette thèse est de combler les lacunes relatives aux connaissances sur l’épidémiologie génomique de l’anémie falciforme à l’aide d’une cohorte issue au Bénin. Les objectifs spécifiques sont les suivants : 1) caractériser les profils d’expressions génomiques associés à la sévérité de l’anémie falciforme ; 2) identifier des biomarqueurs de la sévérité de l’anémie falciforme ; 3) identifier la régulation génétique des variations transcriptionelles ; 4) identifier des interactions statistiques entre le génotype et le niveau de sévérité associé à l’expression ; 5) identifier des cibles de médicaments pour améliorer l’état des patients atteints d’anémie falciforme. Méthode : Une étude cas-témoins de 250 patients et 61 frères et soeurs non-atteints a été menée au Centre de Prise en charge Médical Intégré du Nourrisson et de la Femme Enceinte atteints de Drépanocytose, au Bénin entre février et décembre 2010. Résultats : Notre analyse a montré que des profils d’expressions sont associés avec la sévérité de l’anémie falciforme. Ces profils sont enrichis de génes des voies biologiques qui contribuent à la progression de la maladie : l’activation plaquettaire, les lymphocytes B, le stress, l’inflammation et la prolifération cellulaire. Des biomarqueurs transcriptionnels ont permis de distinguer les patients ayant des niveaux de sévérité clinique différents. La régulation génétique de la variation de l’expression des gènes a été démontrée et des interactions ont été identifiées. Sur la base de ces résultats génétiques, des cibles de médicaments sont proposées. Conclusion: Ce travail de thèse permet de mieux comprendre l’impact de la génomique sur la sévérité de l’anémie falciforme et ouvre des perspectives de développement de traitements ciblés pour améliorer les soins offerts aux patients.Background: Sickle Cell Disease (SCD) is an important public health issue, particularly in Africa. Phenotypic heterogeneity of SCD is problematic for follow-up and treatment of patients. Little is known about the underlying genomic architecture responsible for this variation. Rationale: Understanding the genetic contribution to the inter-patient variability will help in identifying patients at risk of developing more severe clinical outcomes, as well as help guide future developments for treatment options. Objectives: To characterize genome-wide gene expression patterns associated with SCD clinical severities and to identify genetic regulators of this variation. More specifically, our objectives were to associate gene expression profiles with SCD severity, identify transciptional biomarkers, characterise the genetic control of gene expression variation, and propose drug targets. Methods: A case-control population of 250 SCD patients and 61 unaffected siblings from the National SCD Center in Benin were recruited. Genome-wide gene expression profiles and genotypic data were generated. Results: Genome-wide gene expression patterns associated with SCD clinical variation were enriched in B-lymphocyte development, platelet activation, stress, inflammation and cell proliferation pathways. Transcriptional biomarkers that can discriminate SCD patients with respect to clinical severities were identified. Hundreds of genetic regulators were significantly associated with gene expression variation and potential drug targets are suggested. Conclusion: This work improves our understanding of the biological basis of SCD clinical variation and has the potential to guide development of targeted treatments for SCD patients

    A Combinatorial Approach of Ionomics, Quantitative Trait Locus Mapping, and Transcriptome Analysis to Characterize Element Homeostasis in Maize

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    In plant systems, genetic and biochemical pathways impact uptake of elements from the soil. These environment-sensitive pathways often act in the root tissue to impact element concentrations throughout the plant. In order to characterize element regulation as well as apply ionomics to understand plant adaptation, perspectives are needed from multiple tissues and environments and from approaches that take interactions between elements into account. The work described in this thesis includes multi-environment and multi-tissue experiments that connect variation in genetic sequence, and in gene expression, with variation in element accumulation. The associations found here include those that are sensitive to environment, reflecting the complex environmental influence on the ionome, as well as those that exhibit consistent effects across different environments. A variety of statistical tools were employed to model genetic by environment interactions and test methodologies that can be applied to future studies of the ionome with more in-depth environmental data. Genetic loci with strong effects on elements across environments were further explored using root-based gene expression data, which identified candidate genes and gene networks underlying element accumulation. Additional research on these candidate genes has the potential to improve our understanding of the genetic basis of homeostatic processes that involve the ionome, as well as isolate targets for genetic modification or selective breeding that can enhance nutritional content and adaptive capacity of crops

    Context matters:the power of single-cell analyses in identifying context-dependent effects on gene expression in blood immune cells

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    The human immune system is a complex system that we still do not fully understand. No two humans react in the same way to attacks by bacteria, viruses or fungi. Factors such as genetics, the type of pathogen or previous exposure to the pathogen may explain this diversity in response. Single-cell RNA sequencing (scRNA-seq) is a new technique that enables us to study the gene expression of each cell individually, allowing us to study immune diversity in much greater detail. This increased resolution helps us discern how disease-associated genetic variants actually contribute to disease. In this thesis, I studied the relation between disease-associated genetic variants and gene expression levels in the context of different cell types and pathogen exposures in order to gain insight into the working mechanisms of these variants. For many variants we learnt in which cell types and under which pathogen exposures they affect gene expression, and we were even able to identify changes in gene co-expression, suggesting that disease-associated variants change how our genes interact with each other. With the single-cell field being so new, much of my work was showing the feasibility of using scRNA-seq to study the interplay between genetics and gene expression. To set up future research, we created guidelines for these analyses and established a consortium that brings together many major scientists in the field to enable large-scale studies across an even wider variety of contexts. This final work helps inform current and future large-scale scRNA-seq research

    Effects of Genetic Variants on Gene Expression Variability

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    Variation and variability of gene expression are central concepts in biology. Variation refers to differences among individuals, whereas variability refers to the potential of a population to vary. The advent of next-generation sequencing technology has led to the accumulation of an ever-increasing number of population level, largescale genotype and gene expression data sets, which provide excellent opportunities to identify the genetic loci that potentially affect gene expression variation and variability. Over the last several years, much effort has been made to identify genetic loci that affect the mean differences in phenotypic expression between genotypes, but these studies have largely ignored loci that affect the variance of phenotypic expression within individual genotypes. Although studies of expression quantitative trait loci (eQTL) have established a convincing relationship between genotype and levels of gene expression, the impact of genetic variants on gene expression variance remains unclear. In addition, the analytical frameworks adopted by most eQTL studies have been based on population-level test statistics, which are powerful for assessing the effects of common genetic variants, but not rare or private genetic variants. Few frameworks or statistics are available for assessing the impacts of rare genetic mutations on gene expression. Thus, a new statistical method is required to address this issue. In this dissertation, I aim to address these questions in humans using publically available large-scale, Next-generation RNA sequencing datasets and new experimental data from my own work. I first adopted a new statistical method called double generalized linear model (DGLM) to study the effect of common genetic variants on gene expression variability, which I define as expression variability QTL (evQTL), using data from the TwinsUK study. I searched the whole genome to identify common genetic variants associated with variable expression at cis-acting genes and showed the contribution of both genetic and nongenetic factors to variable gene expression. I next examined two distinct modes of action of evQTLs: GxG interaction (the interaction between genotypes at different loci) and GxE interaction (the interaction between genotype and environment), which showed that common genetic variants work interactively or independently to influence gene expression variance. Lastly, I established a novel analytical framework to evaluate the effects of rare or private variants on gene expression variability. This method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers
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