scholarly journals Transethnic meta-analysis of genome-wide association studies identifies three new loci and characterizes population-specific differences for coronary artery disease

2019 ◽  
Author(s):  
Hiroshi Matsunaga ◽  
Kaoru Ito ◽  
Masato Akiyama ◽  
Atsushi Takahashi ◽  
Satoshi Koyama ◽  
...  
Keyword(s):  
Adrenal Glands ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Tissue Cell ◽  
Genome Wide Association Studies ◽  
Diverse Populations ◽  
Cell Type ◽  
Genome Wide ◽  
Artery Disease

AbstractBackgroundGenome-wide association studies (GWAS) provided many biological insights into coronary artery disease (CAD), but these studies were mainly performed in Europeans. GWAS in diverse populations have the potential to advance our understanding of CAD.Methods and ResultsWe conducted two GWAS for CAD in the Japanese population, which included 12,494 cases and 28,879 controls, and 2,808 cases and 7,261 controls, respectively. Then, we performed transethnic meta-analysis using the results of the CARDIoGRAMplusC4D 1000 Genomes meta-analysis with UK Biobank. We identified 3 new loci on chromosome 1q21 (CTSS), 10q26 (WDR11-FGFR2), and 11q22 (RDX-FDX1). Quantitative trait locus analyses suggested the association of CTSS and RDX-FDX1 with atherosclerotic immune cells. Tissue/cell type enrichment analysis showed the involvement of arteries, adrenal glands and fat tissues in the development of CAD. Finally, we performed tissue/cell type enrichment analysis using East Asian-frequent and European-frequent variants according to the risk allele frequencies, and identified significant enrichment of adrenal glands in the East Asian-frequent group while the enrichment of arteries and fat tissues was found in the European-frequent group. These findings indicate biological differences in CAD susceptibility between Japanese and Europeans.ConclusionsWe identified 3 new loci for CAD and highlighted the genetic differences between the Japanese and European populations. Moreover, our transethnic analyses showed both shared and unique genetic architectures between the Japanese and Europeans. While most of the underlying genetic bases for CAD are shared, further analyses in diverse populations will be needed to elucidate variations fully.

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

Identification of Critical Core Genes of Sarcoma Based on Centrality Analysis of Networks Nodes

2020 ◽  
Vol 10 (7) ◽  
pp. 1776-1784
Author(s):  
Shudong Wang ◽  
Jixiao Wang ◽  
Xinzeng Wang ◽  
Yuanyuan Zhang ◽  
Tao Yi
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Complex Diseases ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Core Gene ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Genome Wide

Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.

scholarly journals Identifying insomnia-related chemicals through integrative analysis of genome-wide association studies and chemical–genes interaction information

SLEEP ◽  
2020 ◽  
Vol 43 (9) ◽  
Author(s):  
Om Prakash Kafle ◽  
Shiqiang Cheng ◽  
Mei Ma ◽  
Ping Li ◽  
Bolun Cheng ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Modern Society ◽  
Environmental Chemicals ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Study Results

Abstract Study Objectives Insomnia is a common sleep disorder and constitutes a major issue in modern society. We provide new clues for revealing the association between environmental chemicals and insomnia. Methods Three genome-wide association studies (GWAS) summary datasets of insomnia (n = 113,006, n = 1,331,010, and n = 453,379, respectively) were driven from the UK Biobank, 23andMe, and deCODE. The chemical–gene interaction dataset was downloaded from the Comparative Toxicogenomics Database. First, we conducted a meta-analysis of the three datasets of insomnia using the METAL software. Using the result of meta-analysis, transcriptome-wide association studies were performed to calculate the expression association testing statistics of insomnia. Then chemical-related gene set enrichment analysis (GSEA) was used to explore the association between chemicals and insomnia. Results For GWAS meta-analysis dataset of insomnia, we identified 42 chemicals associated with insomnia in brain tissue (p < 0.05) by GSEA. We detected five important chemicals such as pinosylvin (p = 0.0128), bromobenzene (p = 0.0134), clonidine (p = 0.0372), gabapentin (p = 0.0372), and melatonin (p = 0.0404) which are directly associated with insomnia. Conclusion Our study results provide new clues for revealing the roles of environmental chemicals in the development of insomnia.

vSampler: fast and annotation-based matched variant sampling tool

2020 ◽  
Author(s):  
Dandan Huang ◽  
Zhao Wang ◽  
Yao Zhou ◽  
Qian Liang ◽  
Pak Chung Sham ◽  
...  
Keyword(s):  
Association Studies ◽  
Statistical Significance ◽  
Enrichment Analysis ◽  
Supplementary Information ◽  
Tissue Cell ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Regulatory Variant ◽  
Local Program ◽  
Sampling Algorithms

Abstract Summary Sampling of control variants having matched properties with input variants is widely used in enrichment analysis of genome-wide association studies/quantitative trait loci and negative data construction for pathogenic/regulatory variant prediction methods. Spurious enrichment results because of confounding factors, such as minor allele frequency and linkage disequilibrium pattern, can be avoided by calibration of statistical significance based on matched controls. Here, we presented vSampler which can generate sets of randomly drawn variants with comprehensive choices of matching properties, such as tissue/cell type-specific epigenomic features. Importantly, the development of a novel data structure and sampling algorithms for vSampler makes it significantly fast than existing tools. Availability and implementation vSampler web server and local program are available at http://mulinlab.org/vsampler. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Transethnic meta-analysis identifies GSDMA and PRDM1 as susceptibility genes to systemic sclerosis

2017 ◽  
Vol 76 (6) ◽  
pp. 1150-1158 ◽  
Author(s):  
Chikashi Terao ◽  
Takahisa Kawaguchi ◽  
Philippe Dieude ◽  
John Varga ◽  
Masataka Kuwana ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
T Cell ◽  
Autoimmune Diseases ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Hla Association ◽  
Genome Wide

ObjectivesSystemic sclerosis (SSc) is an autoimmune disease characterised by skin and systemic fibrosis culminating in organ damage. Previous genetic studies including genome-wide association studies (GWAS) have identified 12 susceptibility loci satisfying genome-wide significance. Transethnic meta-analyses have successfully expanded the list of susceptibility genes and deepened biological insights for other autoimmune diseases.MethodsWe performed transethnic meta-analysis of GWAS in the Japanese and European populations, followed by a two-staged replication study comprising a total of 4436 cases and 14 751 controls. Associations between significant single nuclear polymorphisms (SNPs) and neighbouring genes were evaluated. Enrichment analysis of H3K4Me3, a representative histone mark for active promoter was conducted with an expanded list of SSc susceptibility genes.ResultsWe identified two significant SNP in two loci, GSDMA and PRDM1, both of which are related to immune functions and associated with other autoimmune diseases (p=1.4×10−10 and 6.6×10−10, respectively). GSDMA also showed a significant association with limited cutaneous SSc. We also replicated the associations of previously reported loci including a non-GWAS locus, TNFAIP3. PRDM1 encodes BLIMP1, a transcription factor regulating T-cell proliferation and plasma cell differentiation. The top SNP in GSDMA was a missense variant and correlated with gene expression of neighbouring genes, and this could explain the association in this locus. We found different human leukocyte antigen (HLA) association patterns between the two populations. Enrichment analysis suggested the importance of CD4-naïve primary T cell.ConclusionsGSDMA and PRDM1 are associated with SSc. These findings provide enhanced insight into the genetic and biological basis of SSc.

scholarly journals A large-scale genome-wide enrichment analysis identifies new trait-associated genes, pathways and tissues across 31 human phenotypes*

2017 ◽  
Author(s):  
Xiang Zhu ◽  
Matthew Stephens
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Late Onset ◽  
Association Studies ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Artery Disease

Genome-wide association studies (GWAS) aim to identify genetic factors that are associated with complex traits. Standard analyses test individual genetic variants, one at a time, for association with a trait. However, variant-level associations are hard to identify (because of small effects) and can be difficult to interpret biologically. “Enrichment analyses” help address both these problems by focusing on sets of biologically-related variants. Here we introduce a new model-based enrichment analysis method that requires only GWAS summary statistics, and has several advantages over existing methods. Applying this method to interrogate 3,913 biological pathways and 113 tissue-based gene sets in 31 human phenotypes identifies many previously-unreported enrichments. These include enrichments of the endochondral ossification pathway for adult height, the NFAT-dependent transcription pathway for rheumatoid arthritis, brain-related genes for coronary artery disease, and liver-related genes for late-onset Alzheimer’s disease. A key feature of our method is that inferred enrichments automatically help identify new trait-associated genes. For example, accounting for enrichment in lipid transport genes yields strong evidence for association between MTTP and low-density lipoprotein levels, whereas conventional analyses of the same data found no significant variants near this gene.

scholarly journals Integrative Prioritization of Causal Genes for Coronary Artery Disease

2021 ◽  
Author(s):  
Ke Hao ◽  
Raili Ermel ◽  
Katyayani Sukhavasi ◽  
Haoxiang Cheng ◽  
Lijiang Ma ◽  
...  
Keyword(s):  
Causal Effect ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association ◽  
Tissue Cell ◽  
Genome Wide Association Studies ◽  
Integrative Genomics ◽  
Genome Wide ◽  
Causal Genes ◽  
Artery Disease

Background: Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association studies data sets. Methods: We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). Results: We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1 ; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. Conclusions: We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.

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