scholarly journals Mendelian Randomization Integrating GWAS, eQTL, and mQTL Data Identified Genes Pleiotropically Associated With Atrial Fibrillation

2021 ◽  
Vol 8 ◽  
Author(s):  
Yaozhong Liu ◽  
Biao Li ◽  
Yingxu Ma ◽  
Yunying Huang ◽  
Feifan Ouyang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Regulation Of Transcription ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Trait Loci

Background: Atrial fibrillation (AF) is the most common arrhythmia. Genome-wide association studies (GWAS) have identified more than 100 loci associated with AF, but the underlying biological interpretation remains largely unknown. The goal of this study is to identify gene expression and DNA methylation (DNAm) that are pleiotropically or potentially causally associated with AF, and to integrate results from transcriptome and methylome.Methods: We used the summary data-based Mendelian randomization (SMR) to integrate GWAS with expression quantitative trait loci (eQTL) studies and methylation quantitative trait loci (mQTL) studies. The HEIDI (heterogeneity in dependent instruments) test was introduced to test against the null hypothesis that there is a single causal variant underlying the association.Results: We prioritized 22 genes by eQTL analysis and 50 genes by mQTL analysis that passed the SMR & HEIDI test. Among them, 6 genes were overlapped. By incorporating consistent SMR associations between DNAm and AF, between gene expression and AF, and between DNAm and gene expression, we identified several mediation models at which a genetic variant exerted an effect on AF by altering the DNAm level, which regulated the expression level of a functional gene. One example was the genetic variant-cg18693985-CPEB4-AF axis.Conclusion: In conclusion, our integrative analysis identified multiple genes and DNAm sites that had potentially causal effects on AF. We also pinpointed plausible mechanisms in which the effect of a genetic variant on AF was mediated by genetic regulation of transcription through DNAm. Further experimental validation is necessary to translate the identified genes and possible mechanisms into clinical practice.

scholarly journals Gene Expression and RNA Splicing Imputation Identifies Novel Candidate Genes Associated with Osteoporosis

2020 ◽  
Vol 105 (12) ◽  
pp. e4742-e4757
Author(s):  
Yong Liu ◽  
Hui Shen ◽  
Jonathan Greenbaum ◽  
Anqi Liu ◽  
Kuan-Jui Su ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Mineral Density ◽  
Mendelian Randomization Analysis ◽  
Trait Loci ◽  
Randomization Analysis

Abstract Context Though genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with osteoporosis related traits, such as bone mineral density (BMD) and fracture, it remains a challenge to interpret their biological functions and underlying biological mechanisms. Objective Integrate diverse expression quantitative trait loci and splicing quantitative trait loci data with several powerful GWAS datasets to identify novel candidate genes associated with osteoporosis. Design, Setting, and Participants Here, we conducted a transcriptome-wide association study (TWAS) for total body BMD (TB-BMD) (n = 66 628 for discovery and 7697 for validation) and fracture (53 184 fracture cases and 373 611 controls for discovery and 37 857 cases and 227 116 controls for validation), respectively. We also conducted multi-SNP-based summarized mendelian randomization analysis to further validate our findings. Results In total, we detected 88 genes significantly associated with TB-BMD or fracture through expression or ribonucleic acid splicing. Summarized mendelian randomization analysis revealed that 78 of the significant genes may have potential causal effects on TB-BMD or fracture in at least 1 specific tissue. Among them, 64 genes have been reported in previous GWASs or TWASs for osteoporosis, such as ING3, CPED1, and WNT16, as well as 14 novel genes, such as DBF4B, GRN, TMUB2, and UNC93B1. Conclusions Overall, our findings provide novel insights into the pathogenesis mechanisms of osteoporosis and highlight the power of a TWAS to identify and prioritize potential causal genes.

scholarly journals Epigenomes of Human Hearts Reveal New Genetic Variants Relevant for Cardiac Disease and Phenotype

2020 ◽  
Vol 127 (6) ◽  
pp. 761-777 ◽  
Author(s):  
Wilson Lek Wen Tan ◽  
Chukwuemeka George Anene-Nzelu ◽  
Eleanor Wong ◽  
Chang Jie Mick Lee ◽  
Hui San Tan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Quantitative Trait Loci ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci

Rationale: Identifying genetic markers for heterogeneous complex diseases such as heart failure is challenging and requires prohibitively large cohort sizes in genome-wide association studies to meet the stringent threshold of genome-wide statistical significance. On the other hand, chromatin quantitative trait loci, elucidated by direct epigenetic profiling of specific human tissues, may contribute toward prioritizing subthreshold variants for disease association. Objective: Here, we captured noncoding genetic variants by performing epigenetic profiling for enhancer H3K27ac chromatin immunoprecipitation followed by sequencing in 70 human control and end-stage failing hearts. Methods and Results: We have mapped a comprehensive catalog of 47 321 putative human heart enhancers and promoters. Three thousand eight hundred ninety-seven differential acetylation peaks (FDR [false discovery rate], 5%) pointed to pathways altered in heart failure. To identify cardiac histone acetylation quantitative trait loci (haQTLs), we regressed out confounding factors including heart failure disease status and used the G-SCI (Genotype-independent Signal Correlation and Imbalance) test 1 to call out 1680 haQTLs (FDR, 10%). RNA sequencing performed on the same heart samples proved a subset of haQTLs to have significant association also to gene expression (expression quantitative trait loci), either in cis (180) or through long-range interactions (81), identified by Hi-C (high-throughput chromatin conformation assay) and HiChIP (high-throughput protein centric chromatin) performed on a subset of hearts. Furthermore, a concordant relationship between the gain or disruption of TF (transcription factor)-binding motifs, inferred from alternative alleles at the haQTLs, implied a surprising direct association between these specific TF and local histone acetylation in human hearts. Finally, 62 unique loci were identified by colocalization of haQTLs with the subthreshold loci of heart-related genome-wide association studies datasets. Conclusions: Disease and phenotype association for 62 unique loci are now implicated. These loci may indeed mediate their effect through modification of enhancer H3K27 acetylation enrichment and their corresponding gene expression differences (bioRxiv: https://doi.org/10.1101/536763 ). Graphical Abstract: A graphical abstract is available for this article.

Expression Quantitative Trait Loci Reveal Regulatory Regions Important In The Pathogenesis of Multiple Myeloma

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1847-1847
Author(s):  
David C Johnson ◽  
Niels Weinhold ◽  
Tobias Meissner ◽  
Brian A Walker ◽  
Peter Broderick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Plasma Cells ◽  
Eqtl Analysis ◽  
Expression Quantitative Trait Loci ◽  
Expression Data ◽  
Regulatory Regions ◽  
Trait Loci

Abstract Genomewide association studies (GWAS) have identified seven independent regions associated with Multiple Myeloma (MM) risk with an additional locus linked to the t11;14 subtype. Inherited variation is an important determinant of gene expression, such that the majority of published GWAS risk loci across all diseases can be linked to gene regulation. To understand whether the functional mechanisms that confer MM risk are related to allelic differences within regulatory regions, we sought to identify expression quantitative trait loci (eQTLs) in MM plasma cells. Recent studies have also suggested that eQTLs can be specific to cell type. In a heterogeneous disease such as MM, we might expect there to be variation in eQTLs between cytogenetic subgroups. To address this, we performed a genomewide analysis to identify MM related eQTLs, as well as potential subgroup specific MM eQTLs. The identification of eQTLs specific to MM plasma cells provides a means to link regulatory function to the powerful hypothesis-free tool of GWAS. To generate MM related eQTLs, we combined orthogonal mRNA expression data (Affymetrix U133+2 arrays) from CD138+ selected plasma cells with genotyping data (Illumina Omni Express BeadChips) from germline DNA in the same individual. Two independent datasets comprising 183 MM patients from UK and 662 from Germany were analysed in parallel. Genotype data was filtered by standard quality control parameters. Single nucleotide polymorphisms (SNPs) showing deviation from Hardy-Weinberg equilibrium with P <1 × 10−6, having a call rate <95% or a minor allele frequency <1% were excluded. Samples were removed if closely related or if they had a non-Northern and Western European descent (CEU) ancestry. German and UK expression data were normalised independently using GC-RMA and a custom chip definition file (v17) mapping to Entrez genes. Genes showing a variance of less than 0.1 in expression between the analysed cases or a log2expression value of less than 5 in at least 95% of cases or genes located on the X or Y chromosome were excluded from the analysis. Known batch effects and hidden co-founders due to experimental and tumour-related factors were accounted for using a Bayesian Framework model. eQTLs were identified by performing a linear regression between residual expression levels and genotypes. A cis-eQTL analysis was performed that included SNPs located within 1 Mb of the transcript start site of the proximal gene. Results from the two independent studies were combined by meta-analysis. We report in a cis-eQTL analysis, that there is evidence that 6 out of the 8 MM risk alleles have an impact on regulation of a proximal gene. We also show that the eQTLs can be replicated on contrasting technologies i.e. competitive allele-specific PCR (genotyping) and real-time PCR (expression). In a global cis-eQTL analysis, we found that the expression of >600 genes was significantly influenced by proximal SNPs (P < 5 x 10-8). By comparing these results with previous described eQTLs in other tissue types, we estimate some 10% of these eQTLs to be specific to MM plasma cells. We conclude that informative regulatory regions important to myeloma biology can be identified by the combination of global gene expression and genomewide genotyping data. A number of these eQTLs can be shown to be MM specific and even specific to a cytogenetic subgroup. This can give us a greater understanding of the regulatory mechanisms underpinning genetic associations linked to MM risk and clinical outcomes following MM treatments. Disclosures: No relevant conflicts of interest to declare.

Abstract 12905: Gene Expression and Expression Quantifying Trait Loci Analysis of Left Atrium of Patients With Post-operative Atrial Fibrillation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Martin I Sigurdsson ◽  
Mahyar Heydarpour ◽  
Louis Saddic ◽  
Tzuu-Wang Chang ◽  
Stanton K Shernan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Left Atrium ◽  
High Throughput ◽  
Specific Gene ◽  
Eqtl Analysis ◽  
Gene Variants ◽  
Nucleotide Polymorphisms ◽  
Novel Genes ◽  
Trait Loci

Introduction: The majority of information on the genetic background of atrial fibrillation (AF) results from genomic DNA variant analysis without consideration of tissue expression. Hypothesis: Analysis of tissue-specific gene expression in left atrium (LA) can further understanding of the molecular mechanism of identified AF risk variants, and identify novel genes and gene variants associated with AF. Methods: We isolated mRNA from samples of the LA free wall taken during mitral valve surgery in 62 Caucasian individuals. Gene expression in the LA was compared between patients who did and did not have post-operative AF (poAF) using high-throughput RNA expression. Using genotypes of 1.4 million single nucleotide polymorphisms (SNP) we performed cis expression quantifying trait loci (eQTL) analysis, correlating gene expression of each gene with the genotypes of adjacent (<1Mbp) SNPs. Results: We identified 23 differentially expressed genes in the LA of patients with poAF, including three potassium channel genes (KCNA7, KCNH8 and KCNK17). The largest expression difference was in LOC645323, a long non-coding RNA. The expression of PITX2, ZFHX3 and KCNN3, previously shown to be associated with AF, did not differ between patients with and without poAF. We identified 12,476 cis eQTL relationships in the LA, several of those included genetic regions and genes previously associated with AF. We confirmed an eQTL relationship between rs3744029 genotype and the expression of MYOZ1. Furthermore we describe a novel eQTL relationship between rs6795970 genotype and the expression of the SCN10A gene. Conclusions: We have analysed the human LA expression via high-throughput RNA sequencing, and identified novel genes and gene variants likely involved in the molecular pathophysiology of AF.

scholarly journals Identification, deployment, and transferability of quantitative trait loci from genome-wide association studies in plants

2020 ◽  
Vol 24 ◽  
pp. 100145 ◽  
Author(s):  
Mohsen Mohammadi ◽  
Alencar Xavier ◽  
Travis Beckett ◽  
Savannah Beyer ◽  
Liyang Chen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci

ADDO: a comprehensive toolkit to detect, classify and visualize additive and non-additive quantitative trait loci

2019 ◽  
Vol 36 (5) ◽  
pp. 1517-1521
Author(s):  
Leilei Cui ◽  
Bin Yang ◽  
Nikolas Pontikos ◽  
Richard Mott ◽  
Lusheng Huang
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genetic Effects ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Additive Effects ◽  
A Genome ◽  
Trait Loci ◽  
Additive Genetic Effects

Abstract Motivation During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. Results We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. Availability and implementation ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DNMIVD: DNA methylation interactive visualization database

2019 ◽  
Vol 48 (D1) ◽  
pp. D856-D862 ◽  
Author(s):  
Wubin Ding ◽  
Jiwei Chen ◽  
Guoshuang Feng ◽  
Geng Chen ◽  
Jun Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Interactive Visualization ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Differentially Methylated Genes ◽  
Trait Loci ◽  
Methylation Quantitative Trait Loci

Abstract Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression–methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.

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