scholarly journals Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control

2019 ◽  
Author(s):  
James Boocock ◽  
Megan Leask ◽  
Yukinori Okada ◽  
Hirotaka Matsuo ◽  
Yusuke Kawamura ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Organic Anion ◽  
Serum Urate ◽  
Regulatory Function ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Genes

AbstractSerum urate is the end-product of purine metabolism. Elevated serum urate is causal of gout and a predictor of renal disease, cardiovascular disease and other metabolic conditions. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control, however there has been little progress in understanding the molecular basis of the associated loci. Here we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify ten new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new loci. By cis- and trans-eQTL colocalization analysis we identified 24 and 20 genes respectively where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose-phosphate pathway; and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Trans-ancestral functional fine-mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1, HNF4G) with colocalized eQTL that contained putative causal SNPs (posterior probability of causality > 0.8). This systematic analysis of serum urate GWAS loci has identified candidate causal genes at 19 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.Author SummaryHigh serum urate is a prerequisite for gout and a risk factor for metabolic disease. Previous GWAS have identified numerous loci that are associated with serum urate control, however, only a small handful of these loci have known molecular consequences. The majority of loci are within the non-coding regions of the genome and therefore it is difficult to ascertain how these variants might influence serum urate levels without tangible links to gene expression and / or protein function. We have applied a novel bioinformatic pipeline where we combined population-specific GWAS data with gene expression and genome connectivity information to identify putative causal genes for serum urate associated loci. Overall, we identified 15 novel serum urate loci and show that these loci along with previously identified loci are linked to the expression of 44 genes. We show that some of the variants within these loci have strong predicted regulatory function which can be further tested in functional analyses. This study expands on previous GWAS by identifying further loci implicated in serum urate control and new causal mechanisms supported by gene expression changes.

scholarly journals Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control

2020 ◽  
Vol 29 (6) ◽  
pp. 923-943 ◽  
Author(s):  
James Boocock ◽  
Megan Leask ◽  
Yukinori Okada ◽  
Hirotaka Matsuo ◽  
Yusuke Kawamura ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Association Studies ◽  
Organic Anion ◽  
Serum Urate ◽  
High Serum ◽  
Organic Anion Transporter ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Anion Transporter ◽  
Genome Wide

Abstract High serum urate is a prerequisite for gout and associated with metabolic disease. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control; however, there has been little progress in understanding the molecular basis of the associated loci. Here, we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify 10 new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new candidate loci. By cis- and trans-eQTL colocalization analysis, we identified 34 and 20 genes, respectively, where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose–phosphate pathway and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Functional fine mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1 and HNF4G) with colocalized eQTL containing putative causal SNPs. This systematic analysis of serum urate GWAS loci identified candidate causal genes at 24 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.

scholarly journals A novel quantile regression approach for eQTL discovery

2016 ◽  
Author(s):  
Xiaoyu Song ◽  
Gen Li ◽  
Iuliana Ionita-Laza ◽  
Ying Wei
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Linear Regression ◽  
Genetic Variants ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Expression Level ◽  
Special Focus ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractOver the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Indeed, several studies have already investigated higher order associations with a special focus on detecting heteroskedasticity. In this paper, we develop a Quantile Rank-score Based Test (QRBT) to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRBT to the Genotype-Tissue Expression project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRBT complements the existing methods, and identifies new eQTLs with heterogeneous effects genome-wideacross different quantile levels. Notably, we show that the eQTLs identified by QRBT but missed by linear regression are more likely to be tissue specific, and also associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog. An R package implementing QRBT is available on our website.

scholarly journals PrediXcan: Trait Mapping Using Human Transcriptome Regulation

2015 ◽  
Author(s):  
Eric R Gamazon ◽  
Heather E Wheeler ◽  
Kaanan Shah ◽  
Sahar V Mozaffari ◽  
Keston Aquino-Michaels ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Prediction Models ◽  
Association Studies ◽  
Regulatory Function ◽  
Genetic Profile ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Reference Transcriptome ◽  
Regulated Gene Expression

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene- based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

scholarly journals Untargeted metabolome- and transcriptome-wide association study identifies causal genes modulating metabolite concentrations in urine

2020 ◽  
Author(s):  
Reyhan Sönmez Flitman ◽  
Bita Khalili ◽  
Zoltan Kutalik ◽  
Rico Rueedi ◽  
Sven Bergmann
Keyword(s):  
Gene Expression ◽  
Association Study ◽  
Association Studies ◽  
Urine Concentration ◽  
Resonance Spectroscopy ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Rnaseq Data ◽  
Metabolite Concentrations ◽  
Causal Genes

SummaryIn this study we investigate the results of a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome. We used RNAseq data from lymphoblastoid cell lines (LCLs) derived from 555 Caucasian individuals to characterize their transcriptome. As for the metabolome we took an untargeted approach using binned features from 1H nuclear magnetic resonance spectroscopy (NMR) of urine samples from the same subjects allowing for data-driven discovery of associated compounds (rather than working with a limited set of quantified metabolites).Using pairwise linear regression we identified 21 study-wide significant associations between metabolome features and gene expression levels. We observed the most significant association between the gene ALMS1 and two adjacent metabolome features at 2.0325 and 2.0375 ppm. By using our previously developed metabomatching methodology, we found N-Acetylaspartate (NAA) as the potential underlying metabolite whose urine concentration is correlated with ALMS1 expression. Indeed, a number of metabolome- and genome-wide association studies (mGWAS) had already suggested the locus of this gene to be involved in regulation of N-acetylated compounds, yet were not able to identify unambiguously the exact metabolite, nor to disambiguate between ALMS1 and NAT8, another gene found in the same locus as the mediator gene. The second highest significant association was observed between HPS1 and two metabolome features at 2.8575 and 2.8725 ppm. Metabomatching of the association profile of HPS1 with all metabolite features pointed at trimethylamine (TMA) as the most likely underlying metabolite. mGWAS had previously implicated a locus containing HPS1 to be associated with TMA concentrations in urine but could not disambiguate this association signal from PYROXD2, a gene in the same locus. We used Mendelian randomization to show for both ALMS1 and HPS1 that their expression is causally linked to the respective metabolite concentrations.Our study provides evidence that the integration of metabolomics with gene expression data can support mQTL analysis, helping to identify the most likely gene involved in the modulation of the metabolite concentration.

scholarly journals Common genes underlying asthma and COPD? Genome-wide analysis on the Dutch hypothesis

2014 ◽  
Vol 44 (4) ◽  
pp. 860-872 ◽  
Author(s):  
Joanna Smolonska ◽  
Gerard H. Koppelman ◽  
Cisca Wijmenga ◽  
Judith M. Vonk ◽  
Pieter Zanen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Tissue ◽  
Blood Cells ◽  
Association Studies ◽  
Chronic Obstructive ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Replication Phase ◽  
Genome Wide Significance

Asthma and chronic obstructive pulmonary disease (COPD) are thought to share a genetic background (“Dutch hypothesis”).We investigated whether asthma and COPD have common underlying genetic factors, performing genome-wide association studies for both asthma and COPD and combining the results in meta-analyses.Three loci showed potential involvement in both diseases: chr2p24.3, chr5q23.1 and chr13q14.2, containing DDX1, COMMD10 (both participating in the nuclear factor (NF) κβ pathway) and GNG5P5, respectively. Single nucleotide polymorphisms (SNPs) rs9534578 in GNG5P5 reached genome-wide significance after first replication phase (p=9.96×10−9). The second replication phase, in seven independent cohorts, provided no significant replication. Expression quantitative trait loci (eQTL) analysis in blood cells and lung tissue on the top 20 associated SNPs identified two SNPs in COMMD10 that influenced gene expression.Inflammatory processes differ in asthma and COPD and are mediated by NF-κβ, which could be driven by the same underlying genes, COMMD10 and DDX1. None of the SNPs reached genome-wide significance. Our eQTL studies support a functional role for two COMMD10 SNPs, since they influence gene expression in both blood cells and lung tissue. Our findings suggest that there is either no common genetic component in asthma and COPD or, alternatively, different environmental factors, e.g. lifestyle and occupation in different countries and continents, which may have obscured the genetic common contribution.

Liver and Statins: A Critical Appraisal of the Evidence

2019 ◽  
Vol 25 (42) ◽  
pp. 5835-5846 ◽  
Author(s):  
Anna Licata ◽  
Antonina Giammanco ◽  
Maria Giovanna Minissale ◽  
Salvatore Pagano ◽  
Salvatore Petta ◽  
...  
Keyword(s):  
Adverse Events ◽  
Association Studies ◽  
Organic Anion ◽  
Genome Wide Association Studies ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Organic Anion Transporting Polypeptide ◽  
Treatment And Prevention ◽  
Genome Wide ◽  
Underlying Mechanisms

Adverse drug reactions (ADRs) represent an important cause of morbidity and mortality worldwide. Statins are a class of drugs whose main adverse effects are drug-induced liver injury (DILI) and myopathy. Some of these may be predictable, due to their pharmacokinetic and pharmacodynamic properties, while others, unfortunately, are idiosyncratic. Genetic factors may also influence patient susceptibility to DILI and myopathy in the case of statins. This review will first discuss the role of statins in cardiovascular disease treatment and prevention and the underlying mechanisms of action. Furthermore, to explore the susceptibility of statin-induced adverse events such as myopathy and hepatotoxicity, it will then focus on the recent Genome-Wide Association Studies (GWAS) concerning the transporter genes, Cytochrome P450 (CYP), organic anion-transporting polypeptide (OATP) and ABCB1 and ABCC1, which seem to play a role in the development of clinically relevant adverse events. Finally, we appraise the evidence for and against the use of statins in metabolic syndrome and in HCV-infected patients, in terms of their safety and efficacy in cardiovascular events.

scholarly journals The open targets post-GWAS analysis pipeline

2020 ◽  
Vol 36 (9) ◽  
pp. 2936-2937 ◽  
Author(s):  
Gareth Peat ◽  
William Jones ◽  
Michael Nuhn ◽  
José Carlos Marugán ◽  
William Newell ◽  
...  
Keyword(s):  
Drug Targets ◽  
Gene Expression Regulation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Coding ◽  
Data Resource ◽  
Coding Regions ◽  
Genome Wide ◽  
Causal Genes ◽  
Interactive Data

Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

scholarly journals Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

2021 ◽  
Author(s):  
Robin N Beaumont ◽  
Isabelle K Mayne ◽  
Rachel M Freathy ◽  
Caroline F Wright
Keyword(s):  
Birth Weight ◽  
Statistical Power ◽  
Developmental Disorders ◽  
Association Studies ◽  
Later Life ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Causal Genes

Abstract Birth weight is an important factor in newborn survival; both low and high birth weights are associated with adverse later-life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with maternal or fetal effects on birth weight. Knowledge of the underlying causal genes is crucial to understand how these loci influence birth weight and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme ends of the distribution. Genes implicated in those syndromes may provide valuable information to prioritize candidate genes at the GWAS loci. We examined the proximity of genes implicated in developmental disorders (DDs) to birth weight GWAS loci using simulations to test whether they fall disproportionately close to the GWAS loci. We found birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected both when the DD gene is the nearest gene to the birth weight SNP and also when examining all genes within 258 kb of the SNP. This enrichment was driven by genes causing monogenic DDs with dominant modes of inheritance. We found examples of SNPs in the intron of one gene marking plausible effects via different nearby genes, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight, which has helped identify GWAS loci likely to have direct fetal effects on birth weight, which could not previously be classified as fetal or maternal owing to insufficient statistical power.

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